by Mihir Patel, Senior Vice President – Performance and Technology, AlixPartners
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Supply Chains in the Line of Fire
Dear Readers,
War no longer disrupts supply chains at the margins—it rewires them at the core. What unfolds on battlefields today is quickly felt across ports, pipelines, factories, and freight corridors, altering the rhythm of global trade.
In our Focus section, Mihir Patel, Senior Vice President – Performance and Technology at the AlixPartners, underscores this reality with clarity: conflicts now ripple silently but powerfully through supply networks, reshaping how goods move and where risks concentrate. The implications are structural, not temporary.
In this environment, stability has become an outdated assumption. Demand planning is being fundamentally redefined—no longer a periodic forecasting exercise, but a dynamic, enterprise-wide discipline. It is increasingly central to how organisations drive growth, respond to volatility, and build competitive advantage. Our Cover Story brings together sharp, experience-backed perspectives from industry leaders who are navigating this shift in real time—balancing uncertainty with precision and agility.
We also revisit a theme that continues to gain momentum: The Rise of Intelligent Logistics Hubs. Drawing from our archives, this curated piece traces how logistics infrastructure is evolving into integrated, tech-enabled ecosystems—where data, connectivity, and automation converge to unlock efficiency and responsiveness at scale.
The message across this issue is clear. Supply chains are no longer designed for stability—they are engineered for disruption. The organisations that will lead are those that can sense faster, decide smarter, and execute with discipline in an increasingly unpredictable world.
Bansal on behalf of Celerity India Marketing Services
12 COVER STORY
Demand Planning at the Edge: Orchestrating Precision in a Volatile World
Stability has quietly exited the demand landscape—replaced by constant flux, fractured signals, and decisions that can no longer wait for certainty. In this environment, demand planning is being redefined in real time. No longer confined to forecasts and monthly cycles, it is emerging as a high-stakes, enterprise-wide discipline that directly shapes growth, agility, and competitive advantage. This cover story brings together incisive perspectives from industry leaders who are navigating this shift firsthand. Through their insights, we examine how organizations are rebuilding planning frameworks for continuous uncertainty, embedding AI-driven sensing to respond at speed, and breaking down structural silos that limit true Integrated Business Planning.
The War of Supply Chains
The nature of conflict has changed—and so has its battlefield. Today, wars are no longer confined to borders; they ripple through ports, pipelines, factories, and freight corridors, quietly reshaping the flow of global commerce. As Mihir Patel, Senior Vice President – Performance and Technology, AlixPartners, highlights, in this interconnected landscape, the real challenge is not just managing supply chains—but truly understanding them.
26 | INTERVIEW
From Banking Discipline to Supply Chain Strategy: Redefining Liquidity in Motion
In an increasingly volatile business environment, Supply Chain Finance (SCF) is evolving from a transactional working capital tool into a strategic enabler of ecosystem resilience and growth. For Megha Kaushik, GM & Head – Supply Chain Finance, Patanjali Foods Ltd., this transformation is deeply shaped by her dual grounding in banking and corporate finance.
Warehousing Reimagined: The Rise of Intelligent Logistics Hubs
As demand patterns grow more complex and delivery expectations tighten, businesses are rethinking warehouse design, technology adoption, and operational processes. Investments in automation, data visibility, and modern infrastructure are gradually transforming facilities into high-performance logistics hubs. This feature revisits industry perspectives that helped shape the conversation around next-generation warehousing—insights that remain highly relevant as the sector continues its shift toward smarter logistics ecosystems.
THE WAR OF SUPPLY CHAINS
The nature of conflict has changed—and so has its battlefield. Today, wars are no longer confined to borders; they ripple through ports, pipelines, factories, and freight corridors, quietly reshaping the flow of global commerce. For India, this shift carries immediate and tangible implications. As a rapidly growing economy deeply integrated into global trade networks, India is highly sensitive to disruptions in energy supplies, critical imports, and export corridors. From oil volatility in the Middle East to supply shocks triggered by the Russia–Ukraine conflict, distant geopolitical events are increasingly influencing domestic costs, production stability, and consumer prices. As Mihir Patel, Senior Vice President – Performance and Technology, AlixPartners, highlights, in this interconnected landscape, the real challenge is not just managing supply chains—but truly understanding them. Visibility and traceability are emerging as essential capabilities, helping Indian businesses navigate uncertainty, respond faster, and build resilience in an era where disruption is constant.
THE War Is No Longer Just Physical… Recent geopolitical conflicts including the Russia–Ukraine war and the escalation of tensions between the United States and Iran, have demonstrated how quickly global supply chains can be disrupted by regional instability. What were once considered localized conflicts are now having far-reaching effects on trade routes, energy flows, agricultural exports, and the availability of critical industrial and household inputs.
These disruptions are not limited to governments or large multinational corporations. They increasingly affect dayto-day business operations and, in many cases, the everyday lives of consumers. Fluctuations in fuel prices, increases in food costs, delays in the availability of goods, and rising transportation expenses are often downstream effects of disruptions that originate thousands of miles away.
At the same time, global supply chains have become more interconnected and specialized. Companies rely on complex networks of suppliers, manufacturers, and logistics providers that span multiple regions and countries. While this has enabled efficiency and scale, it has also
increased exposure to geopolitical events that can disrupt even a small but critical part of the network.
In this environment, companies need a clearer understanding of where their materials originate, how products move across borders, and which parts of their supply networks may be exposed to geopolitical risk. Without this visibility, disruptions are often identified too late, leaving limited options to respond effectively.
Traceability, which is the ability to track the origin and movement of materials and products across the supply chain, is therefore becoming an important capability for managing disruption and supporting more informed sourcing and logistics decisions. It provides organizations with a structured way to understand supply chain dependencies and respond more effectively when external events impact supply continuity.
For countries like India, which are deeply integrated into global manufacturing, energy, and agricultural trade networks, these challenges are particularly relevant. Indian industries depend on both global sourcing and export markets, making them sensitive to disruptions in key trade corridors
Mihir Patel is a supply chain and operations professional with over 15 years of international experience advising organizations across manufacturing, retail, and consumer goods. His work focuses on strengthening supply chain traceability, improving operational resilience, and helping companies enhance overall supply chain performance. He holds an MBA from the Kelley School of Business at Indiana University and a degree in Engineering. His experience includes work in manufacturing operations, strategic sourcing, and supply chain transformation initiatives across global organizations.
A significant challenge in today’s supply chains is not disruption itself, but the inability to see it coming. Most organizations have clear visibility only up to their Tier 1 suppliers, leaving deeper, multi-tier dependencies largely uncharted. This becomes a critical blind spot during geopolitical disruptions, where exposure often lies several layers upstream. Indirect dependencies—on regions, raw materials, or logistics routes—remain hidden until they trigger delays or shortages. Combined with fragmented data across systems, this visibility deficit forces companies into reactive decision-making, limiting their ability to anticipate risks, assess impact, and respond with speed and precision.
and commodity flows. Strengthening traceability capabilities can help businesses better assess exposure, respond to disruptions more quickly, and operate with greater confidence in an increasingly uncertain environment.
HOW GEOPOLITICAL CONFLICTS DISRUPT SUPPLY CHAINS
Geopolitical conflicts affect supply chains through a set of interconnected mechanisms that, while varying in origin, tend to follow similar patterns in their impact on global trade and production systems. These disruptions typically manifest through three primary
pathways: interference with trade routes and logistics networks, constraints on the availability of critical commodities and inputs, and cascading effects that propagate across industries and ultimately reach end consumers.
One of the most immediate consequences of geopolitical conflict is the disruption of key trade routes and logistics infrastructure. Global supply chains rely heavily on a relatively small number of strategic maritime corridors, and instability in these regions can quickly impede the movement of goods. The escalation of tensions involving Iran has brought renewed attention
to the Strait of Hormuz, a narrow but highly critical passage through which a significant share of global oil and liquefied natural gas shipments transit. Any disruption in this region whether due to military activity, restricted passage, or heightened security risks can delay shipments, force rerouting of vessels, and increase transportation costs. Similarly, instability in the Red Sea region has led shipping companies to divert vessels around the Cape of Good Hope, significantly extending transit times between Asia and Europe. These rerouting decisions increase fuel consumption, reduce effective shipping
capacity, and create congestion at alternative ports. For businesses, such disruptions translate into longer lead times, higher freight costs, and reduced reliability of delivery schedules, all of which complicate planning and execution.
Beyond logistics, geopolitical conflicts often disrupt the availability of key commodities that serve as the foundation of global supply chains. The Russia–Ukraine war provides a clear example of this dynamic. Both countries are major exporters of wheat, fertilizers, and energy products, and disruptions to production and export infrastructure in the region have constrained global supply. This has led to price volatility and uncertainty, affecting downstream industries such as food processing, agriculture, and manufacturing. Tensions in the Middle East similarly influence global energy markets. Oil and gas are essential inputs across nearly all sectors of the economy, affecting transportation, production, and energy generation. When supply is disrupted or perceived to be at risk, prices tend to fluctuate, increasing cost pressures for businesses that rely on these inputs. In many cases, these inputs are sourced from geographically concentrated regions, making it difficult to quickly identify or transition to alternative sources when disruptions occur.
The broader impact of these disruptions is often felt through ripple effects that extend well beyond the initial point of disruption. An increase in oil prices, for instance, raises transportation and logistics costs, which in turn increases the cost of raw materials and finished goods. Manufacturers may absorb these costs temporarily, but over time they
are often passed through to customers in the form of higher prices. Similarly, disruptions in agricultural inputs such as fertilizers can reduce crop yields, leading to increases in food prices that may only become apparent months later. In tightly integrated manufacturing environments, delays in the availability of even a single critical component can halt production lines, particularly in industries that operate with lean inventory models. These cascading effects are what ultimately connect distant geopolitical events to everyday experiences, such as higher fuel costs, cooking gas scarcity, increased grocery prices, or delays in the availability of consumer and industrial goods.
Across different conflicts and regions, a consistent pattern emerges. Disruptions typically begin at a specific node within the supply chain, such as a supplier region, production facility, or transportation corridor. These disruptions then constrain the movement or availability of goods, which in turn propagate through the supply chain in the form of delays, shortages, or increased costs. Over time, these effects reach businesses and consumers, often in ways that are not immediately linked to the original source of disruption. This recurring pattern underscores a fundamental reality: supply chains are highly interconnected systems, and their resilience is often determined by their least visible or least understood points of vulnerability.
Despite the frequency and predictability of these disruptions, many organizations still lack a clear understanding of how such risks affect their own supply chains. In many cases,
companies are unable to identify their exposure until disruptions have already begun to impact operations. This gap between external events and internal visibility presents a significant challenge and it is precisely this gap that traceability is increasingly expected to address.
THE VISIBILITY GAP IN TODAY’S SUPPLY CHAINS
While geopolitical disruptions are becoming more frequent and their impact patterns are increasingly well understood, many organizations still struggle to assess how these events affect their own supply chains. The primary reason is a persistent gap in visibility. Most companies lack a clear, end-to-end understanding of their supply networks beyond immediate suppliers.
Over the past two decades, supply chains have evolved to become more global, specialized, and interconnected. Companies have optimized sourcing strategies to improve cost efficiency, often relying on multi-tier supplier networks that span several countries and regions. While these structures have delivered scale and efficiency, they have also introduced layers of complexity that are not always fully visible. In many cases, organizations have strong relationships and visibility with their direct, or Tier 1, suppliers, but limited insight into upstream suppliers, sub-tier dependencies, and the origin of critical materials.
This lack of visibility becomes particularly challenging in the context of geopolitical disruptions. When a conflict affects a specific region, companies often find it difficult to quickly determine whether their supply chains are exposed.
Traceability is no longer about tracking products—it is about enabling better decisions. By connecting supplier networks, material origins, and logistics flows into a unified view, traceability transforms fragmented data into actionable intelligence. This allows organizations to quickly identify which parts of their supply chain are exposed to disruption and evaluate alternatives with greater confidence. In dynamic environments, where conditions shift rapidly, such clarity becomes a competitive advantage. Companies can prioritize critical shipments, adjust sourcing strategies, and rebalance inventory in near real time, moving from reactive firefighting to proactive, informed supply chain management.
For example, a manufacturer may not directly source materials from a conflictaffected country, but one of its Tier 2 or Tier 3 suppliers may depend on inputs from that region. Similarly, logistics disruptions in a key shipping corridor may impact suppliers located elsewhere, creating indirect exposure that is not immediately apparent.
The result is that many organizations are forced into a reactive mode of operation. Disruptions are often identified only after they begin to affect production schedules, inventory levels, or customer deliveries. At that point, response options are limited. Alternative suppliers may not be readily available, logistics routes may already be constrained, and decisions must be made with incomplete information. This reactive approach can lead to increased costs, operational inefficiencies, and reduced service levels.
Another dimension of the visibility gap lies in the movement of goods across the supply chain. Even when companies have a general understanding of their supplier base, they often lack real-time or near real-time visibility into how materials and products move through logistics networks. This includes limited insight into shipment routes, transit delays, and potential points of disruption along the way. As a result, companies
may be aware of a disruption at a macro level such as congestion in a major shipping lane but unable to determine which specific shipments or products are affected.
In addition, data related to supply chains is often fragmented across systems and organizations. Supplier information, logistics data, inventory levels, and production plans are typically managed in separate systems, making it difficult to create a unified view of the supply chain. This fragmentation further limits the ability to quickly assess exposure and respond to disruptions in a coordinated manner.
The implications of this visibility gap are significant. Without a clear understanding of supply chain dependencies and flows, companies are unable to proactively identify risks, evaluate alternative scenarios, or make timely decisions when disruptions occur. In an environment where geopolitical events can quickly alter supply and logistics conditions, this lack of visibility becomes a critical constraint.
Addressing this challenge requires more than incremental improvements in reporting or data sharing. It requires a more structured approach to understanding supply chain relationships and material flows one that enables organizations to track not just who they
buy from, but where materials originate and how they move through the network. This is where traceability begins to play a central role.
TRACEABILITY AS A PRACTICAL RESPONSE TO DISRUPTION
As organizations look to address the visibility gap in their supply chains, traceability is increasingly emerging as a practical and scalable capability. While often associated with quality assurance or regulatory requirements, traceability in this context serves a broader operational purpose: it provides a structured way to understand supply chain dependencies and respond more effectively to disruption.
At its core, traceability enables organizations to build a more complete picture of their supply networks. This begins with extending visibility beyond direct suppliers to map multi-tier relationships and identify where critical materials originate. In a geopolitically uncertain environment, this level of understanding becomes particularly important. It allows companies to assess whether key inputs are sourced from regions that may be exposed to conflict, instability, or logistical disruption. Without this insight, risks often remain hidden until they materialize in the form of supply shortages or delays.
India stands at a critical intersection of risk and opportunity in the evolving global supply chain landscape. Heavy reliance on imported energy and critical inputs exposes industries to external shocks, while growing global efforts to diversify sourcing are opening new avenues for manufacturing expansion. The ability to balance these dynamics will define India’s competitiveness. Strengthening supply chain transparency and responsiveness is no longer optional—it is strategic. Organizations that invest in traceability and visibility will not only manage disruptions more effectively but also position themselves as reliable partners in a world increasingly prioritizing resilient and transparent supply networks.
In addition to understanding supplier dependencies, traceability provides visibility into the movement of materials and products across the supply chain. This includes tracking how goods flow through manufacturing sites, distribution networks, and transportation corridors. When disruptions occur whether due to port congestion, shipping delays, or route closures this level of visibility allows organizations to identify which shipments are affected and take targeted action. For example, companies can prioritize critical orders, adjust transportation routes, or reallocate inventory across locations to maintain service levels.
Another important benefit of traceability is its ability to support faster and more informed decision-making. In
the absence of clear data, organizations often rely on assumptions or incomplete information when responding to disruptions. Traceability provides a more reliable foundation for evaluating options, such as switching suppliers, adjusting production schedules, or modifying inventory strategies. While it does not eliminate the impact of disruption, it reduces uncertainty and enables more coordinated and timely responses.
It is important to note that traceability does not require a complete overhaul of existing systems. In many cases, it can be developed incrementally by improving the way data is captured, connected, and shared across the supply chain. This may include enhancing supplier data, integrating logistics
information, and establishing clearer links between sourcing, production, and distribution activities. Over time, these capabilities can be expanded to create a more comprehensive and dynamic view of the supply network.
In the context of geopolitical disruption, the value of traceability lies not in preventing disruption altogether, but in improving an organization’s ability to navigate it. Companies that have a clearer understanding of their supply chain structure and flows are better positioned to anticipate potential issues, evaluate alternatives, and respond with greater speed and precision.
As supply chains continue to operate in an environment characterized by uncertainty and external shocks, traceability is becoming less of a
specialized capability and more of a foundational element of effective supply chain management.
IMPLICATIONS FOR INDIA: EXPOSURE AND OPPORTUNITY
India’s position within global supply chains makes it both highly exposed to geopolitical disruptions and uniquely positioned to benefit from shifts in global trade patterns. As one of the world’s fastest-growing major economies, India is deeply integrated into international supply networks through both imports and exports. This integration spans a wide range of sectors, including energy, pharmaceuticals, automotive components, electronics, and agriculture.
A significant portion of India’s exposure stems from its dependence on imported inputs that are sensitive to geopolitical developments. Energy is a primary example. India relies heavily on crude oil imports, a large share of which originates from the Middle East. Disruptions in key shipping routes or supply constraints driven by regional tensions can have immediate implications for fuel prices, transportation costs, and broader industrial activity. Similarly, India imports critical components and raw materials such as electronic parts, specialty chemicals, and fertilizers that are often sourced through complex global networks. Disruptions in any part of these networks can affect production schedules and cost structures across multiple industries.
At the same time, India’s manufacturing and export sectors are closely tied to global demand and logistics flows. Industries such as pharmaceuticals, textiles, and automotive components depend on stable supply chains to meet international customer requirements. Delays in shipping routes, increased freight costs, or shortages of key inputs can affect competitiveness and service levels. As global companies reassess their sourcing strategies in response to geopolitical uncertainty, India is increasingly being considered as an alternative manufacturing base. However, to fully capitalize on this opportunity, businesses must be able to demonstrate reliability, consistency, and transparency in their supply chains.
This is where traceability becomes
particularly relevant. For Indian companies, improving traceability can provide a clearer understanding of supplier dependencies, including exposure to regions affected by geopolitical instability. It enables organizations to identify potential risks earlier and evaluate alternative sourcing or logistics strategies before disruptions escalate. In sectors with complex, multi-tier supply chains, such visibility can make a meaningful difference in maintaining continuity and managing costs.
Traceability also supports more effective coordination across sourcing, production, and distribution activities. By linking information across these functions, companies can respond more quickly to changes in supply or demand conditions. For example, if a disruption affects a particular supplier or route, organizations with stronger traceability capabilities are better positioned to adjust production plans, reallocate inventory, or identify substitute inputs.
Beyond risk management, there is also a strategic dimension for India. As global supply chains evolve, there is increasing emphasis on building networks that are not only cost-effective but also resilient and transparent. Countries and companies that can offer greater visibility into their supply chains are likely to be viewed as more reliable partners. For India, this presents an opportunity to strengthen its position in global manufacturing and trade by aligning operational capabilities with emerging expectations around supply chain transparency and responsiveness.
In this context, traceability is not only a tool for managing disruption but also an enabler of competitiveness. It allows Indian businesses to operate with greater confidence in uncertain conditions while positioning themselves more effectively within a changing global supply chain landscape.
MOVING FROM EFFICIENCY TO RESILIENCE
For many years, supply chain strategies were primarily designed around efficiency. Organizations focused on optimizing costs, reducing inventory, and building lean, globally distributed networks that could take advantage of scale and
specialization. This approach delivered significant benefits, enabling companies to improve margins and expand their reach across markets. However, it also introduced dependencies that were not always fully understood or visible.
Recent geopolitical developments have highlighted the limitations of this model. Conflicts, trade disruptions, and instability in key regions have shown that supply chains optimized solely for efficiency can be vulnerable to external shocks. When disruptions occur, the lack of visibility into supplier networks, material flows, and logistics pathways makes it difficult to respond quickly and effectively.
As a result, there is a growing shift toward building more resilient supply chains. This does not imply abandoning efficiency, but rather balancing it with greater visibility, flexibility, and preparedness. Organizations are increasingly looking to understand where their critical dependencies lie, how disruptions may propagate through their networks, and what options are available to mitigate risk.
Traceability plays an important role in enabling this shift. By providing a clearer view of supply chain structures and flows, it allows companies to move from reactive responses to more informed and proactive decision-making. It supports better evaluation of sourcing strategies, improved coordination across functions, and more effective management of disruptions when they occur.
In an environment where geopolitical uncertainty is likely to remain a defining feature of global trade, the ability to understand and adapt supply chains becomes a key differentiator. Companies that invest in strengthening visibility and traceability capabilities are better positioned to navigate disruptions while maintaining service levels and operational stability.
Ultimately, the ‘War of Supply Chains’ is not about eliminating disruption, but about improving the ability to operate within it. As supply chains continue to evolve, traceability is becoming a foundational capability that supports resilience, informed decision-making, and long-term competitiveness.
DEMAND PLANNING AT THE EDGE: ORCHESTRATING PRECISION IN A VOLATILE WORLD
Stability has quietly exited the demand landscape—replaced by constant flux, fractured signals, and decisions that can no longer wait for certainty. In this environment, demand planning is being redefined in real time. No longer confined to forecasts and monthly cycles, it is emerging as a high-stakes, enterprise-wide discipline that directly shapes growth, agility, and competitive advantage. This cover story brings together incisive perspectives from industry leaders who are navigating this shift firsthand. Through their insights, we examine how organizations are rebuilding planning frameworks for continuous uncertainty, embedding AI-driven sensing to respond at speed, and breaking down structural silos that limit true Integrated Business Planning. The discussion extends to sustainability, ecosystem-driven visibility, and the expanding strategic role of planners. As the horizon stretches toward autonomous, intelligence-led supply chains, the question is no longer how to predict demand—but how to continuously align decisions with it.
FOR decades, demand planning rested on a comfortable assumption: the future could be predicted by extending the past. That assumption no longer holds. Volatility today is not an exception—it is the operating environment. Geopolitical shocks, fractured trade routes, shifting consumer behavior, and shrinking product lifecycles have turned demand into a moving target. Signals are faster, noisier, and often contradictory. In response, demand planning is being forced out of its traditional confines— evolving from a forecasting function into a decision engine that directly shapes growth, agility, and resilience. And yet, a paradox persists.
Even as organizations accelerate investments in advanced planning systems and AI, outcomes remain uneven. As highlighted by Boston Consulting Group, while a vast majority of companies have deployed digital planning tools, only a select few are translating these into measurable gains in accuracy, responsiveness, or service levels. Technology, it turns out, is not the constraint—execution is. The shift underway is more fundamental than a tech upgrade. It is a transition from technology-led planning to decision-led planning.
Leading organizations are no longer focused on refining forecasts alone. Instead, they are redesigning how decisions are made—how quickly signals are interpreted, how trade-offs are evaluated, and how cross-functional alignment is achieved in real time. Without this shift, even the most advanced algorithms risk reinforcing fragmented processes and legacy inefficiencies.
What differentiates leaders is their ability to embed planning into the core of enterprise decision-making. This means integrating data, processes, governance, and talent into a unified operating model—one that is responsive, collaborative, and anchored in clear decision rights. It also means redefining the role of the planner. As AI takes over routine forecasting, planners are stepping into more strategic roles—managing exceptions, orchestrating trade-offs, and aligning stakeholders across sales,
marketing, operations, and finance. The planner is no longer a forecaster, but a connector of insight and action.
The implication is unmistakable: demand planning is no longer a supply chain activity—it is an enterprise capability. As markets continue to shift, competitive advantage will not come from better predictions alone, but from better, faster decisions. The organizations that succeed will be those that build adaptive, decision-centric planning ecosystems— where technology amplifies human judgment, rather than attempting to replace it.
What does this transformation look like in practice? The following Q&A brings together perspectives from industry leaders, unpacking how demand planning is being rebuilt for a world defined by continuous uncertainty.
With demand volatility becoming a constant rather than an exception, how are organizations fundamentally redesigning their demand planning frameworks to operate in a state of continuous uncertainty?
Amrit Bajpai, Supply Chain Planning Leader – Global Supply Chain, Schneider Electric: Demand volatility is no longer an occasional disruption—it has become a defining characteristic of today’s operating environment. Structural shifts such as rapid electrification, expansion of digital infrastructure, evolving energy systems, and geopolitical realignments are reshaping demand patterns across industries. As a result, traditional planning approaches built around fixed forecasting cycles are increasingly inadequate. Organizations are therefore moving toward more adaptive and responsive planning frameworks. At Schneider Electric, demand planning increasingly integrates multiple signals—from market trends and customer demand patterns to supply constraints and supplier readiness—into a unified planning view. This allows the organization to evaluate demand and supply dynamics in parallel rather than sequentially.
Advanced digital planning platforms play an important role in enabling this shift. They allow teams to simulate multiple demand–supply scenarios and
assess the implications for production, inventory, and distribution decisions. The objective is not simply to improve forecast accuracy but to ensure that the supply chain can consistently deliver on customer commitments while maintaining resilience and efficient working capital management.
Rayapati Srinath Reddy, Head – Supply Chain Planning, The HEINEKEN Company: In the alcobev industry, demand has never been perfectly predictable — we deal with seasonality, regional festivities, changing consumer preferences, and regulatory shifts all at once. But what has changed fundamentally in the last few years is the frequency and overlap of disruptions. It is no longer about planning for one shock at a time; it is about building a planning muscle that expects turbulence as the baseline.
We have moved away from rigid annual demand plans anchored to a single consensus number. Instead, our frameworks now operate on rolling horizons with scenario-based overlays. At any point, we are working with a base plan, but alongside it, we carry two or three plausible scenarios — each one stress-tested against variables like raw material availability, excise policy changes, route-to-market disruptions, or sudden shifts in channel mix.
The real redesign is not just in tools or models — it is in mindset. Planning teams are being trained to think probabilistically rather than deterministically. We are embedding range-based forecasting and flexible response triggers into our processes so that when a deviation occurs, the organisation does not scramble — it pivots. The goal is not to predict the future perfectly but to be prepared for several versions of it.
Arpita Srivastava, Supply Chain Planning & Logistics Leader, Schreiber Foods: Such a relevant question… totally agree that demand volatility is not something that happens once a blue moon. We are observing same almost every day in different forms. And the way to manage same is that organizations are redesigning demand to be more dynamic, responsive and
Amrit Bajpai, Supply Chain Planning Leader – Global Supply
Chain, Schneider Electric
Demand planning today is undergoing a fundamental transformation. In an increasingly volatile and digitally connected global economy, the role of planning has moved well beyond traditional forecasting models. It is evolving into a strategic capability that connects market demand, supply chain execution, and customer value creation. At Schneider Electric, demand planning is viewed as a critical lever for delivering a reliable customer promise. The objective is not only to anticipate demand but to ensure that the entire supply chain—from sourcing and manufacturing to distribution and customer delivery—remains aligned and responsive. In this environment, supply chains must be resilient to disruptions, sustainable in their operations, and digitally connected to respond quickly to market signals.
iterative process rather than forecastdriven with once a month. The objective is to be able to sense and react to changing demand as quickly as possible rather than having a lower Mean Absolute Percentage Error (MAPE). Organizations are also strengthening cross-functional collaboration through Integrated Business Planning (IBP) where sales, marketing, supply chain, and finance review demand signals together and adjust plans quickly.
Advanced analytics and demand segmentation helps act in a way market demands. Instead of one model fits all, SKUs are classified based on demand patterns—stable, seasonal, or highly volatile—and planned differently.
For example, in an FMCG, demand for products can spike suddenly due to weather, promotions, or regional events. Instead of relying only on historical forecasts, organizations now combine real-time sales data, distributor inventory visibility, and weekly demand
reviews. When a sudden demand surge occurs at any node, the team can quickly reallocate production, rework on dispatch priorities, and redistribute inventory across distribution hubs.
Neha Sorathia, Sr. Principal Accenture – Strategy & Consulting, Accenture India: Volatility and disruption are now intrinsic to the planning landscape. Traditional planning models built around periodic forecasts and relatively stable demand patterns were designed for a very different operating context. Leading organizations are therefore redesigning demand planning around adaptability rather than point accuracy. This includes moving away from static monthly cycles toward more continuous sensing, frequent replanning, and faster decision loops. Scenario-based planning is becoming central to this shift. Instead of locking into one number, organizations are building dynamic frameworks that allow
them to sense changes early and respond in a structured manner. Ultimately, the future demand planning framework is less about predicting the demand perfectly and more about building systems that can respond intelligently when reality diverges from the plan.
Sanjay Desai, Independent Board Advisor / Mentor: Since the disruption caused by COVID, well-run organizations have realized that demand can no longer be expected to settle into predictable patterns. Instead, the priority today is agility — the ability to respond quickly to evolving customer needs. Rather than relying on a single monthly forecast, companies are increasingly updating demand plans more frequently, often continuously. Demand planning is also being repositioned as part of the broader commercial function, where the baseline forecast should originate. Organizations are therefore moving away from traditional weekly or monthly forecast cycles toward dynamic planning environments where forecasts are adjusted in real time, depending on the product category and market signals. The focus is gradually shifting from measuring periodic forecast accuracy to managing demand variability with agility and responsiveness.
How is demand planning evolving within your organization—from a forecasting function to a strategic capability that directly influences revenue growth, pricing agility, and market responsiveness?
Amrit Bajpai: Within Schneider Electric,
demand planning is closely integrated into our Integrated Business Planning (IBP) process. Demand signals feed directly into cross-functional discussions around capacity investments, supply prioritization, component allocation, and inventory positioning across global networks. This integration enables planning to influence key business decisions, supporting growth, faster responsiveness to market opportunities, and stronger service reliability for customers.
Rayapati Srinath Reddy: Historically, demand planning in alcobev sat squarely within the supply chain function — it was about generating a volume forecast, passing it downstream, and hoping production and logistics could deliver. That model is no longer sufficient, and frankly, it was never truly strategic. Within our organization, demand planning is now deeply connected to commercial strategy. When we launch a new variant, enter a new state, or adjust pricing in response to competitive action, the demand plan is not an afterthought — it is part of the decision-making table from the beginning. We actively model how promotional intensity, pricing elasticity, and distribution expansion will shape volume, and we use those insights to guide investment decisions.
This shift has turned demand planning into a revenue enabler. We can answer questions like: "If we increase our on-premise presence in a particular city by 20%, what should we expect in incremental volume — and do we have the supply architecture to support it?" That is a fundamentally different
conversation from "What did we sell last quarter, and what should we expect next quarter?" The planning function now influences where we grow, how aggressively we invest, and how quickly we respond to market signals — not just how much we produce.
Varun Kakkar, Senior General Manager – Supply Chain @ Birla Opus, Grasim Industries: Demand planning is no longer a back-end forecasting function—it has evolved into a strategic lever that directly influences business performance. Today, it plays a central role in CXO-level discussions, shaping decisions around revenue growth, pricing strategies, and market expansion. Within the organization, the demand planning function works closely with sales and marketing teams, contributing actively to market planning, seasonal strategies, and promotional effectiveness. This integration allows demand planners to not only anticipate demand but also actively shape it— through better alignment of supply with market opportunities, improved product mix decisions, and sharper pricing interventions. As a result, demand planning is increasingly being recognized as a value creator, driving both topline growth and margin enhancement while enabling faster and more informed responses to market dynamics.
Sanjay Desai: In many organizations, demand planning has moved out of the operational shadows and into the center of strategic business discussions. It is no longer just an input for supply planning; it now plays an active role in
shaping revenue targets, promotional strategies, and pricing decisions. Demand planners today provide critical insights into product mix dynamics, shifts in channel performance, and the impact of promotional peaks. These insights help leadership teams determine which customers, products, or markets should be prioritized — and where resources should be scaled back. As a result, the role has evolved significantly. What was once primarily about processing historical data has transformed into a capability that supports commercial decisionmaking through advanced analytics, scenario planning, and forward-looking market insights.
AI-driven demand sensing is rapidly advancing. How close are enterprises to building planning environments that can automatically detect shifts in demand patterns and recalibrate decisions in near real time?
Amrit Bajpai: Many organizations are already taking meaningful steps toward real-time demand sensing by leveraging advanced analytics and artificial intelligence. These technologies are capable of identifying demand anomalies, analysing order patterns, and generating predictive insights from large and diverse data sets. However, most companies still operate within a humansupported planning model. While digital tools provide powerful insights, planners continue to play a critical role in interpreting the broader business context. Factors such as macroeconomic shifts, large infrastructure project cycles, regional market dynamics, and customer
Varun Kakkar, Senior General Manager –Supply Chain @ Birla Opus, Grasim Industries
Demand volatility has fundamentally reshaped how organizations approach planning. Traditional linear forecasting models are being replaced by agile, adaptive frameworks that emphasize flexibility, scenario planning, and autonomous intelligence. Today’s demand planning systems are increasingly designed to sense, interpret, and respond to real-time signals, enabling organizations to stay ahead of rapid market fluctuations. A key shift has been the decoupling of front-end demand variability from back-end operational stability. The focus is now on building resilient, intelligence-led planning environments that can dynamically recalibrate without compromising efficiency or service levels.
With advanced tools, organizations are getting closer and closer to their customers/ point of sales with close to real-time visibility. AI tools can now combine signals from various sources like distributor sell-out data, retailer POS, weather, and social trends to detect demand shifts earlier than traditional monthly forecasts. For example, in FMCG, if heatwaves suddenly increase demand for cold drinks or ice cream, AI models can detect the spike within days and adjust replenishment. However, the real challenge isn’t prediction—it’s how quickly we act to those prediction / changes. Organizations are still building the capability to automatically translate insights into actions like demand reprioritization, production rescheduling or dynamic inventory allocation.
investment patterns often require human judgment to translate data signals into actionable decisions.
Over time, planning environments are expected to become increasingly closed-loop and digitally enabled. In such systems, technology will continuously sense changes in demand, assess supply constraints, and recommend adjustments across sourcing, production, and distribution networks. Human planners will increasingly focus on evaluating scenarios, managing exceptions, and collaborating across the ecosystem to ensure supply chains remain resilient and responsive.
Rayapati Srinath Reddy: The honest answer is that most organisations — including many in our industry — are still on a journey with AI-driven demand sensing. We are past the experimentation phase and into practical application, but we are not yet at the stage where decisions are being fully recalibrated autonomously in real time.
What we have been able to do meaningfully is layer machine learning models on top of our traditional statistical forecasts. These models ingest a wider set of signals — weather patterns, local event calendars, retail off-take trends, and even social media sentiment around certain categories — to sharpen shortterm forecasts. In alcobev, where a single festival weekend or a state-level policy change can move volumes dramatically, this kind of signal detection has proven genuinely valuable.
The gap, however, is in the "last mile" of decision-making. Sensing a demand shift is one thing; automatically triggering a supply response — adjusting
production schedules, redirecting inventory, modifying distribution plans — requires a level of system integration and organisational trust in algorithms that most companies are still building. I would say we are perhaps 60 to 70% of the way there on the sensing side, but only about 30 to 40% on the autonomous response side. The next few years will be about closing that gap — and it will require as much change management as it does technology investment.
Varun Kakkar: AI-driven demand sensing capabilities have made significant progress in recent years. Modern tools are now capable of identifying subtle shifts in sales momentum, detecting emerging patterns, and generating actionable insights with far greater speed and accuracy than traditional systems. These capabilities enable supply chain teams to respond more proactively—optimizing inventory, improving service levels, and reducing stock imbalances. However, while the technology has matured considerably in terms of sensing and analytics, fully autonomous decisionmaking is still evolving. At present, AI serves as a powerful decision-support engine rather than a complete decisionmaker. It augments human judgment, allowing planners to make faster, more informed decisions. Over time, as confidence in models and data integrity improves, we can expect a gradual transition toward more automated, selfcorrecting planning systems.
Neha Sorathia: AI models today can detect subtle shifts in demand patterns much earlier than traditional statistical methods, especially when
they incorporate diverse signals such as point-of-sale data, promotions, macroeconomic indicators, and digital behavior. That said, the limiting factor is rarely the model itself. The real challenge is organizational readiness to act on those insights. Many companies now have advanced sensing capabilities, particularly in more mature industries. Translating those signals into coordinated decisions across supply, manufacturing, and distribution still requires strong governance, clear decision thresholds, and tight integration across planning systems. Until those foundations are in place, fully autonomous adjustments remain aspirational rather than practical.
Sanjay Desai: Most organizations are currently somewhere in the middle of this journey. Modern planning systems can already analyse large datasets — including POS data, customer orders, and e-commerce signals — identifying patterns far more quickly than traditional approaches. Leading organizations with mature supply chain capabilities are increasingly integrating data science and advanced analytics into their planning processes to enhance efficiency and improve decision quality. In the near future, we may even see the traditional title of “demand planner” evolve into something closer to “demand data scientist.” Near realtime updates will become the norm once organizations establish clear governance rules that define when systems can act autonomously and when human intervention is required.
Despite widespread adoption of Integrated Business Planning
Rayapati Srinath Reddy, Head – Supply Chain Planning, The HEINEKEN Company
Beyond accuracy, demand planning can drive sustainability through smarter portfolio decisions. By understanding which pack sizes, formats, and channels drive the most efficient throughput with the least waste, we can shape the demand plan to favour sustainable outcomes without sacrificing commercial objectives. For example, promoting returnable glass bottles in certain on-premise channels or right-sizing production batches for slower-moving SKUs to avoid excess inventory. We are also beginning to factor carbon footprint into our distribution planning — optimising not just for cost and service level but for the environmental impact of moving goods across long distances.
(IBP), many organizations still struggle with fragmented decision-making. What leadership or structural shifts are required to make truly synchronized, enterprise-wide planning a reality?
Amrit Bajpai: Achieving synchronized planning requires more than the adoption of advanced technology platforms. While digital tools provide the necessary visibility and analytics, the real transformation lies in organizational alignment and decision ownership. One of the most important structural shifts is the move toward end-to-end accountability for planning decisions. Rather than operating in functional silos, demand, supply, procurement, and logistics teams must work within a unified framework where decisions are made with a holistic understanding of the entire value chain. This requires breaking down traditional barriers between commercial and supply chain teams.
Equally important is the alignment of performance metrics. When sales,
operations, and supply chain teams are evaluated against shared business outcomes—such as service levels, inventory health, and responsiveness to market demand—it naturally drives collaboration and better decisionmaking.
Governance also plays a critical role. Integrated Business Planning (IBP) processes provide the structured forum where cross-functional leaders can review demand signals, evaluate supply capabilities, and make coordinated decisions. When these structural elements come together—clear ownership, shared metrics, and disciplined governance— planning becomes deeply embedded in how the business operates rather than functioning as a standalone forecasting activity.
Rayapati Srinath Reddy: This is a question that resonates deeply because I have seen firsthand how IBP can look excellent on paper yet deliver underwhelming results in practice. The process exists, the meetings happen, the dashboards are built — but too often,
the decisions made in an IBP forum are revisited or overridden outside of it.
The root cause is rarely the process design. It is almost always a combination of leadership alignment and organisational incentives. When the sales team is measured purely on top-line volume and the supply chain team is measured on cost efficiency, you will inevitably get competing priorities showing up in the same planning room. No amount of process sophistication can fix misaligned incentives.
The structural shift required is threefold. First, IBP needs genuine executive sponsorship — not just attendance, but ownership. The seniormost commercial and operations leaders need to treat it as the primary decisionmaking forum, not a reporting ritual. Second, organisations need to align KPIs across functions so that trade-offs are made transparently and collectively. In alcobev, for instance, the decision to prioritise a high-margin SKU over a high-volume one has implications for production, warehousing, distribution, and sales — and everyone needs to own that trade-off together. Third, and perhaps most importantly, companies need to invest in planning talent that can speak the language of both finance and operations — people who can translate a demand signal into a P&L impact and communicate it across functions.
Varun Kakkar: Achieving the full potential of Integrated Business Planning (IBP) requires more than just process implementation—it demands a fundamental organizational shift. The most critical enabler is strong topdown alignment, with leadership actively championing IBP as a core business
discipline rather than a supply chain initiative. Equally important is the integration of finance into the planning process, ensuring that all decisions are aligned with financial goals and business priorities. Demand planning must act as the central anchor of the monthly planning cycle, connecting crossfunctional inputs from sales, marketing, operations, and finance into a unified plan. Organizations that successfully embed IBP into their operating rhythm benefit from greater transparency, faster decision-making, and improved alignment across functions. Ultimately, synchronized planning is as much about governance and culture as it is about tools and processes.
Arpita Srivastava: Well, one thing I always say is that any technology alone cannot fix fragmented decision-making; leadership alignment does. Many IBP implementation/processes fail because functions still optimize their own targets—sales pushes volume, supply focuses on efficiency, finance prioritizes cost; each working in their own silo. True synchronization requires shared KPIs and executive ownership of the end-toend plan. Leading organizations run a single monthly executive IBP meeting where commercial, supply chain, and finance align on one consensus plan. This alignment is critical when balancing production capacity between low margin but mandated products versus high-margin products. Structurally, organizations are moving toward crossfunctional planning teams and central demand control towers that integrate data, decisions, and accountability across the enterprise.
Neha Sorathia: Integrated Business Planning is fundamentally an organizational transformation. While platforms can connect data, they cannot automatically align incentives or resolve cross-functional trade-offs. Many IBP initiatives struggle because commercial, operational, and financial teams continue to operate with different metrics, assumptions, and success criteria. For IBP to truly deliver value, three structural shifts are essential :
First, leadership alignment around enterprise-level outcomes rather than functional optimization, supported by the right KPIs.
Second, planning forums must evolve from review meetings into decisionmaking forums where trade-offs are explicitly resolved.
Third, companies must establish a single version of the truth, ensuring that all functions operate from the same data, assumptions, and scenarios. When these elements come together, IBP becomes far more than a planning cadence; it becomes a mechanism for enterprise synchronization, enabling agility and organizational growth.
Sanjay Desai: It is useful to remember that Sales & Operations Planning (S&OP) emerged in the late 1980s, while Integrated Business Planning (IBP) began gaining traction in the early 2000s. Yet the fundamental challenge remains unchanged — clarity of decision ownership and the speed of execution. To make enterprise-wide planning truly
effective, leadership must strengthen accountability across functions and align teams around shared outcomes. The era of siloed functional KPIs is gradually giving way to unified organizational goals supported by coordinated execution across verticals.
Structurally, this also means simplifying governance. Instead of multiple fragmented meetings, organizations are moving toward integrated decision forums where finance, sales, supply chain, and product teams jointly review trade-offs and align on execution priorities. Without disciplined execution and clear decision rights, even the most sophisticated IBP frameworks risk becoming procedural rather than impactful.
As sustainability becomes a board-level priority, how can demand planning play a measurable role in reducing overproduction, excess inventory, and the overall environmental footprint of supply chains?
Amrit Bajpai: Demand planning has a significant but often underappreciated role in advancing sustainability across supply chains. Improved demand visibility allows organizations to operate with greater precision, reducing inefficiencies that often translate into both financial and environmental costs. When demand signals are clearer and more reliable, companies can minimize overproduction, avoid excess inventory accumulation, and reduce the likelihood of product obsolescence. This also reduces the need for emergency shipments and expedited logistics, which tend to carry a higher carbon footprint.
Neha Sorathia, Sr. Principal Accenture –Strategy & Consulting, Accenture India
Demand visibility today extends well beyond the enterprise boundary. Organizations are increasingly integrating sell-through data, distributor inventory levels, channel insights, and external market indicators into their planning architecture. This expanded data ecosystem enables planners to detect shifts in consumption earlier and respond more proactively. In more mature industries, this ecosystem-aware demand intelligence is increasingly being operationalized through real-time demand sensing, enabling faster, more informed decision-making across the value chain.
At Schneider Electric, sustainability is not treated as a separate agenda but is integrated into operational decisionmaking. Demand planning helps strike the right balance between customer service levels, operational efficiency, and environmental responsibility. By enabling more accurate production and distribution planning, it contributes to reducing waste, lowering logistics emissions, and supporting the company’s broader ambition of building sustainable and lower-carbon supply chains.
Rayapati Srinath Reddy: Sustainability in the alcobev industry is a particularly interesting challenge because we are dealing with agricultural raw materials, glass and packaging waste, waterintensive processes, and complex cold chain requirements. Demand planning sits at a critical intersection here because the most direct lever it controls is the accuracy of what we produce and where we place it. Every unit of overproduction carries an environmental cost — wasted raw materials, unnecessary energy consumption, excess packaging, and in some cases, product that reaches the end of its shelf life and must be written off. Improving forecast accuracy by even a few percentage points translates directly into reduced waste across the value chain. This is still early-stage, but the intent is clear: demand planning should be able to quantify the sustainability trade-off of every significant planning decision, and that visibility will only improve as we embed these metrics into our planning tools and governance processes.
Varun Kakkar: Demand planning is uniquely positioned to drive sustainability
outcomes, given its holistic view of business trends, future demand signals, and historical patterns. By improving forecast accuracy and aligning production more closely with actual demand, demand planners can significantly reduce overproduction, excess inventory, and associated waste. Beyond planning accuracy, demand insights can inform broader sustainability strategies— such as optimizing product portfolios, reducing slow-moving inventory, and supporting circular economy initiatives. At the same time, supply chain and manufacturing functions are advancing complementary efforts through green warehousing, sustainable logistics, ecofriendly packaging, and zero-discharge manufacturing facilities. Together, these initiatives position demand planning as a critical enabler of both operational efficiency and environmental responsibility, linking business performance with sustainability goals.
Arpita Srivastava: A very key impact demand planning brings from a sustainability point of view is by improved forecast accuracy and demand consolidation. Now suppose you need 100 units in a month; you can split it as 3-4 units every day to 100 units in one go. This split in line with market demand, production capacity and logistics planning can significantly impact waste we generate and energy we consume. No if this demand goes up or down, this will impact utilization of resources across the chain and hence increasing both losses and energy consumption. For short-shelf life products, overproduction directly leads to spoilage and disposal costs. By using better demand sensing
and shorter planning cycles, companies can reduce excess inventory and avoid unnecessary production runs. The sustainability impact is measurable: lower waste, reduced energy use in manufacturing, and fewer emergency shipments. Essentially, every percentage improvement in forecast accuracy translates into less waste and a smaller carbon footprint. Demand planning thus is significant as by improving forecast accuracy and aligning production to real consumption we optimize our environmental footprint.
Neha Sorathia: Many sustainability inefficiencies like excess production, obsolete inventory originates upstream in planning decisions. When demand signals are misinterpreted or planning cycles are slow to react, organizations end up producing more than the market ultimately absorbs. Improving demand planning therefore directly reduces waste, overproduction, and carbon-intensive logistics adjustments. Increasingly, organizations are embedding sustainability metrics into planning decisions, evaluating scenarios not only on cost and service but also on environmental impact. This is particularly relevant in industries like retail, where long planning lead times and high obsolescence amplify sustainability risks.
Sanjay Desai: Effective demand planning is one of the most practical ways to reduce waste across the supply chain. When demand signals become more accurate, organizations can significantly reduce emergency production runs, product expiries, and unnecessary transportation. In the future, demand
Sanjay Desai, Independent Board Advisor / Mentor
By 2030, demand planning will likely operate in a hybrid model where automation and human expertise work seamlessly together. Routine forecasting tasks will increasingly be handled by intelligent systems, allowing planners to focus on higher-value strategic activities. The role of the planner will evolve toward that of a data-driven strategist, combining analytical expertise with business acumen to design scenarios, anticipate risks, and guide strategic decisions. Planning will also become more tightly aligned with P&L accountability, with cross-functional teams proactively managing increasingly autonomous supply chains.
planners — or perhaps demand data scientists — will increasingly evaluate forecast performance not just against accuracy metrics, but also against waste reduction, markdowns, and avoidable CO₂ emissions. Over time, sustainability metrics will naturally converge with cost and efficiency metrics. When better planning simultaneously improves financial performance and reduces environmental impact, sustainability becomes embedded within everyday operational decisions and aligns seamlessly with broader ESG governance frameworks.
Demand visibility increasingly depends on ecosystem collaboration. How are you integrating distributors, channel partners, and downstream data streams into your planning architecture to create a more accurate and responsive demand picture?
Amrit Bajpai: The traditional model of demand planning—where forecasts were generated largely from internal sales data—is increasingly giving way to a more collaborative ecosystem approach. Today, a significant portion of demand intelligence originates outside the organization. Distributors, channel partners, system integrators, and suppliers all possess valuable signals about market activity. By integrating these inputs into planning systems, organizations can develop a far more accurate view of real demand patterns.
Digital collaboration platforms are playing an important role in enabling this integration. They provide visibility into channel inventory levels, project pipelines, order trends, and supplier commitments. This shared transparency allows companies to detect demand shifts earlier, anticipate potential supply constraints, and respond faster to market changes. As supply chains become more interconnected, the ability to incorporate ecosystem insights into planning processes will become a key differentiator for companies seeking to improve responsiveness and resilience.
Rayapati Srinath Reddy: In alcobev, the route to market is complex. We operate through a layered distribution
network that often includes state-level regulations, multiple tiers of distributors, and a mix of on-premise and off-premise channels. Getting a true picture of endconsumer demand from behind this distribution wall has always been one of our biggest planning challenges.
We have made meaningful progress by investing in distributor management systems that capture secondary and, in some markets, tertiary sales data in near real time. This has been a game-changer for understanding what is actually moving off shelves versus what is simply being pushed into the trade pipeline. When we combine this with point-of-sale data from modern trade partners and our own direct-to-consumer channels, we start to see a much richer demand picture.
The harder part is making this data actionable within the planning cycle. Raw data from distributors is often noisy, inconsistent, and arrives at different frequencies. We have invested in data harmonisation layers that clean and standardise these feeds before they enter our planning models. We are also working on collaborative forecasting pilots with key distribution partners — sharing our demand outlook with them and getting their on-the-ground intelligence in return. It is not yet a seamless twoway data flow, but the direction is clear. Ultimately, the organizations that win in demand visibility will be those that treat their ecosystem partners not as data sources to extract from but as planning collaborators to invest in. That requires technology, yes, but also trust, transparency, and shared incentives.
Varun Kakkar: Enhancing demand visibility requires a shift from enterprisecentric planning to ecosystem-driven intelligence. We are actively collaborating with distributors, dealers, contractors, and even end influencers such as painters to capture granular, real-time demand signals. Through the deployment of digital solutions such as track-andtrace systems, barcode scanning, and integrated data platforms, we are building a connected, data-driven planning ecosystem. These technologies enable seamless data flow across stakeholders, improving transparency and enabling more accurate demand forecasting. This integrated approach not only enhances
responsiveness but also elevates demand planning into a strategic capability— one that is deeply embedded across the value chain and capable of driving more informed, end-to-end decision-making.
Sanjay Desai: True demand visibility extends well beyond the factory walls and depends heavily on collaboration across the supply chain ecosystem. Many companies are now integrating data from distributors, retailers, and digital marketplaces to create a more comprehensive view of demand. This represents a shift from an “inside-out” approach to an “outside-in” perspective where market signals drive planning decisions. Achieving this requires both technological and cultural change. Technically, organizations need shared data platforms, standardized interfaces, and a unified data architecture. Culturally, success depends on trust, long-term partnerships, and clear data governance frameworks that ensure all stakeholders see tangible value in sharing information.
Over the next three to five years, which investments in technology, talent, and planning governance will most strongly determine whether organizations achieve intelligent, future-ready demand planning maturity?
Amrit Bajpai: The maturity of demand planning capabilities will largely depend on three foundational investments: technology, talent, and governance. Technology forms the backbone of modern planning systems. Advanced planning platforms, digital control towers, and analytics tools provide realtime visibility across the value chain and allow organizations to model complex scenarios. These technologies help planners move from reactive decisionmaking to proactive supply chain management. However, technology alone is not sufficient. Talent plays an equally critical role. The next generation of planners must combine strong supply chain knowledge with analytical capabilities and business decisionmaking skills. They must be able to interpret data, evaluate trade-offs, and collaborate effectively across multiple functions.
Finally, governance ensures that
The structural shift that will define this next frontier is the blurring of boundaries between planning, execution, and learning. Today, these are largely sequential — we plan, we execute, we review. In the future, they will be simultaneous. Execution data will feed back into planning models in real time, and the system will learn and adjust continuously. The organisations that get there first will not just be better at predicting demand — they will be better at shaping it.
these capabilities translate into effective action. Robust integrated planning processes—linking commercial, supply chain, procurement, and finance teams— create the discipline required to make aligned decisions. When these three elements work together, organizations can build planning capabilities that are both intelligent and adaptive.
Rayapati Srinath Reddy: If I had to prioritize the investments that will matter most over the next three to five years, I would group them into three buckets. On the technology front, the biggest unlock will come from investing in connected planning platforms that bring demand, supply, and financial planning into a single integrated environment. Most organisations, including ours, still operate with planning tools that are stitched together through spreadsheets and manual handoffs. Moving to a cloud-native planning ecosystem — with embedded analytics, scenario modelling, and real-time data ingestion — is foundational. Alongside this, selective investment in AI and machine learning for demand sensing and pattern recognition will continue to deliver incremental gains, particularly in shortterm forecasting accuracy.
On the talent side, the demand planner of the future looks very different from the demand planner of the past. We need people who are comfortable with data science concepts, who can interpret algorithmic outputs critically, and who can communicate planning insights in business terms. This means investing in
upskilling existing teams and recruiting from non-traditional talent pools — data analysts, business school graduates with a quantitative bent, and even people from outside the supply chain function who bring fresh perspectives.
On the governance front, the most underrated investment is in decision rights and planning discipline. Technology and talent mean little if the organisation does not have clear governance around how demand signals translate into supply decisions, who owns the trade-offs, and how performance is measured. Building a robust planning governance framework — with defined escalation paths, clear accountability, and regular performance reviews — is what separates companies that use planning tools from companies that are truly well-planned.
Varun Kakkar: Over the next five years, the organizations that succeed will be those that invest decisively across three critical pillars: technology, talent, and governance. On the technology front, there will be a strong shift toward AI-enabled, digital-first supply chains with end-to-end integration across planning and execution layers. However, technology alone is not sufficient— there is an equally pressing need to build talent that can effectively leverage these advanced tools. This includes upskilling existing teams and attracting new talent with expertise in analytics, AI, and data science. Finally, robust planning governance will be essential to ensure alignment, accountability, and consistency in decision-making.
Organizations that successfully integrate these elements will be better positioned to achieve true demand planning maturity and unlock sustained competitive advantage.
Arpita Srivastava: Major areas I see that investments can determine and/or influence any organizations’ existence and growth would be having right technologies at right place and right people to work on those technologies with structured frameworks driving dayto-day operations. On the technology side, companies are investing in AI-driven planning platforms and digital supply chain twins to simulate disruptions. But technology alone won’t bring the change. The biggest shift will be in talent. There will be need for stronger data literacy, the ability to interpret AI-driven insights, and the commercial acumen to challenge sales assumptions. Most of the roles in any organizations are evolving into a data-savvy business strategist who can interpret analytics and influence commercial decisions, someone who can grow at the pace of technology introduction. So this will call for extensive training programs and upgrading the talent pool that any organization has. And at times, job reallocation depending on current skillset will be required to manage. Governance is equally important—it will determine whether insights translate into action. Organizations will need faster IBP cycles, clear decision rights, and shared KPIs across sales, finance, and supply chain.
Neha Sorathia: Future demand planning maturity will depend on investments across three interconnected dimensions.
First, technology platforms that can integrate diverse signals, support scenario simulation, and generate explainable AI-driven insights.
Second, talent transformation, where demand planners evolve into analytical decision partners who combine business understanding with technology fluency.
Third, planning governance, ensuring that organizations can act on insights quickly through clear decision rights and disciplined escalation mechanisms.
Organizations that invest only in technology will see limited impact. The real advantage will come from total enterprise reinvention by aligning technology, talent, and operating model into a cohesive planning ecosystem.
Sanjay Desai: Three areas will clearly differentiate leaders from followers: technology, talent, and governance. Technology investments will increasingly focus on building strong data foundations and platforms capable of integrating
statistical models with machine learning capabilities. However, technology alone is not the solution. Talent will play an equally important role. Demand planners must develop stronger analytical capabilities and the confidence to challenge assumptions across sales, finance, and operations using datadriven insights and compelling business narratives. Finally, governance will be critical. Organizations need clearly defined decision-making frameworks, supported by senior leadership involvement when commercial priorities and system recommendations diverge.
Looking toward 2030 and beyond, how do you envision the demand planning function evolving in an era of increasingly autonomous, intelligenceled supply chains, and what structural shifts will define the next frontier of planning excellence?
Amrit Bajpai: By the end of this decade, demand planning is expected to evolve into a far more intelligencedriven capability powered by digital technologies. Advanced analytics, artificial intelligence, and machine learning will enable systems to detect shifts in demand patterns, assess
supply constraints, and recommend optimal responses across sourcing, production, and distribution networks. Rather than focusing primarily on generating forecasts, digital platforms will increasingly act as decision-support engines that continuously monitor the supply chain environment and propose actionable scenarios.
In this future model, the role of human planners will also evolve. Instead of spending large amounts of time on manual data consolidation and forecast adjustments, planners will focus on evaluating scenarios, collaborating with ecosystem partners, and managing strategic exceptions. Their role will be to apply judgment, contextual understanding, and cross-functional coordination to ensure that supply chains remain resilient and responsive. Ultimately, the evolution of demand planning will reflect a broader shift in supply chain management—from static forecasting processes to dynamic, sensing networks capable of responding to market changes in near real time.
Rayapati Srinath Reddy: By 2030, I believe the demand planning function will look fundamentally different from what most of us recognise today. The core shift will be from planning as a
periodic, human-driven process to planning as a continuous, intelligenceaugmented capability that operates in the background of every business decision. In practical terms, this means demand plans will not be "created" in the traditional sense — they will be continuously generated, updated, and refined by intelligent systems that monitor an ever-expanding set of signals. The planner's role will shift from building forecasts to governing and curating the intelligence that produces them, making judgment calls on exceptions, and ensuring that the algorithms are aligned with business strategy.
In the alcobev industry specifically, I expect we will see much tighter integration between demand planning and consumer insights. As direct-toconsumer channels grow and data from digital commerce, loyalty programmes, and social listening becomes richer, the demand plan will increasingly reflect not just "how much" but "who, where, when, and why." This level of granularity will enable hyper-local planning — tailoring assortments, pack sizes, and promotional strategies at a city or even neighbourhood level. The biggest leadership challenge in all of this will be letting go of the illusion of control. The best demand planning functions of 2030 will not be the ones that produce the most accurate singlepoint forecasts. They will be the ones that are most adaptive, most resilient, and most comfortable operating in a world where certainty is the exception, not the rule.
Varun Kakkar: By 2030, demand planning is expected to transition into a largely autonomous, intelligencedriven function. Advances in AI and data analytics will enable zero-touch planning for a significant portion of standard product categories, where systems can independently sense demand, generate forecasts, and trigger execution decisions. Beyond automation, the role of demand planning will expand significantly in strategic importance. It will evolve into a key driver of revenue and profit maximization, offering actionable insights not just within the organization but also to customers, dealers, and channel partners. By enabling better inventory
optimization, reducing dead stock, and improving sell-through rates, demand planning will become a critical enabler of value creation across the ecosystem. The next frontier of planning excellence will be defined by this shift—from reactive forecasting to proactive, intelligence-led orchestration of demand and growth.
Arpita Srivastava: With the pace of technological advancements and GPTs being available, by 2030, demand planning will look very different from the spreadsheet-heavy process many organizations still run today. Most of historical data analysis and forecast generation itself will become automated. Real time visibility across the chain will be enhanced. AI systems will continuously read signals like retailer POS data, distributor inventory, weather trends, and even social media cues to update demand in near real time. At the ground level, this means planners won’t spend most of their time debating numbers. Instead, they’ll focus on exceptions and decisions. Say, if sales of a drink suddenly spike in a region due to weather change, the system may automatically recommend increasing production, reallocating stock from nearby hubs, or shifting promotions to bridge supplydemand gap. The planner’s role will be to validate these decisions and align them with commercial priorities.
Structurally, companies will move toward centralized planning control towers that bring together demand, supply, and financial planning in one place. Functional boundaries might cease to exist. I also foresee major changes in the talent pool having more GenZ professionals taking up mid-to senior roles. This will for sure impact and challenge the current ways of working as they are naturally data-driven and comfortable working with AI tools. They will expect real-time dashboards, automated insights, and faster decision cycles rather than static monthly reports. This generational shift will accelerate the adoption of more agile, tech-enabled planning cultures.
Neha Sorathia: Routine forecasting tasks will become increasingly automated as AI systems continuously monitor signals and adjust baseline projections.
We will also see deeper convergence between demand, supply, and financial planning, supported by Agentic AI Agents that allow organizations to maximize for specific outcomes within a designed decision framework. In this future environment, the defining capability will not be the ability to predict demand perfectly. It will be the ability to sense change early, evaluate implications quickly, and orchestrate coordinated enterprise responses with agility. Demand planning will be one of the first functions to transition toward autonomous planning with humans in the lead for exception scenarios.
Sanjay Desai: By 2030, planning ecosystems will become more collaborative, integrating suppliers, distributors, and partners into shared decision frameworks. Ultimately, planning excellence will not be defined by the extent of automation alone, but by the clarity of human roles and an organization’s ability to sustain transformation through capability building and cultural change. Because, in the end, true transformation is not driven by technology — it is driven by people and the mindsets they bring to change.
Disclaimer: The views and opinions expressed in this article are solely experts’ own and do not represent the official policy, position, or views of their employers or any organization with which they are affiliated. The content is based on their independent analysis, experience and expertise.
WHEN GEOPOLITICS REWRITES TECH STRATEGY
As geopolitical tensions reshape global technology ecosystems, resilience is no longer a defensive layer but a strategic imperative. Enterprises must redesign their tech architecture for adaptability, visibility, and control in an increasingly fragmented world, highlights a McKinsey & Co. article.
FOR decades, enterprise technology was engineered for efficiency— centralized architectures, global vendor ecosystems, and seamless crossborder data flows designed to optimize cost and scale. That model is now under strain. Geopolitics, once a distant macro concern, has moved to the core of technology strategy. As highlighted in recent insights from McKinsey & Company, the convergence of geopolitical tensions, cyber threats, and supply chain fragmentation is fundamentally reshaping the technology landscape. What was once a back-office function is now a frontline determinant of business continuity. The shift is structural, not cyclical.
THE VISIBILITY GAP
Geopolitical dynamics—ranging from regulatory divergence to east–west decoupling—are exposing vulnerabilities embedded deep within technology ecosystems. Enterprises are discovering that their digital infrastructure is not just technical architecture; it is also a map of geopolitical exposure. A critical blind spot lies in visibility. Many organizations understand their direct vendors but lack insight into “nth-party” dependencies— extended supplier networks where risks often originate. In a fragmented world, a disruption in one geography can cascade across systems, talent, and operations, even when core infrastructure appears stable.
THE END OF CENTRALIZATION AS DEFAULT
This raises a fundamental question: can efficiency-led models survive in an age defined by volatility? The answer is increasingly no. The traditional logic of centralization is being challenged by a new imperative—resilience through flexibility. Governments are treating data and compute as strategic assets, imposing restrictions on cross-border flows. In response, organizations are
being forced to rethink how technology is structured, shifting toward modular, platform-based architectures that allow regional adaptability without systemic fragmentation. This is not about abandoning globalization, but about redesigning it.
Yet, resilience cannot be retrofitted in a crisis. Technology systems, by their very nature, are slow to reconfigure. Companies that wait for disruption before acting often find themselves constrained by legacy choices. McKinsey emphasizes the importance of proactive scenario planning—stress-testing systems against a range of geopolitical outcomes, including unlikely but highimpact events. The objective is not to predict every shock, but to reduce response time when disruption occurs.
FROM REACTION TO READINESS
This requires a shift in mindset—from reactive risk management to anticipatory design. Organizations must identify their most critical vulnerabilities, prioritize them, and build targeted safeguards rather than attempting to defend against every possible threat. Focus, not overengineering, becomes the differentiator.
LEADERSHIP AT THE NEW FAULT LINES
Equally significant is the transformation
of leadership roles. The CIO is no longer confined to optimizing systems; they are emerging as strategic advisors on geopolitical risk. Technology decisions— where data is stored, which vendors are engaged, how systems are architected— now carry direct geopolitical implications. As McKinsey notes, CIOs must translate global uncertainty into actionable technology strategies, engaging with the board and C-suite at the highest level.
OPTIONALITY AS ADVANTAGE
This elevation signals a broader shift: technology is no longer downstream of strategy—it is strategy. The implications extend beyond risk mitigation. Resilience, when embedded effectively, can become a source of competitive advantage. Companies that build adaptable systems, diversify dependencies, and maintain operational continuity in volatile environments will not only withstand shocks but also capture opportunities that disruption creates.
In this emerging order, resilience is not redundancy—it is optionality. The enterprises that succeed will not be those that attempt to predict the future with precision, but those that design systems capable of evolving with it. In a world where geopolitics is rewriting the rules of technology, resilience is no longer a defensive posture. It is the architecture of growth.
FROM BANKING DISCIPLINE TO SUPPLY CHAIN STRATEGY: REDEFINING LIQUIDITY IN MOTION
In an increasingly volatile business environment, Supply Chain Finance (SCF) is evolving from a transactional working capital tool into a strategic enabler of ecosystem resilience and growth. For Megha Kaushik, GM & Head – Supply Chain Finance, Patanjali Foods Ltd., this transformation is deeply shaped by her dual grounding in banking and corporate finance. Bringing together risk discipline, digital integration, and value chain alignment, she views liquidity not as a safeguard in isolation, but as a synchronizing force that connects suppliers, distributors, and internal operations. In this conversation, she explores how FMCG organizations in India are redesigning working capital strategies amid demand volatility and tighter credit conditions, the role of automation and AI in enabling predictive finance, and how structured SCF frameworks can extend stability deep into Tier 2 and Tier 3 networks—turning finance into a driver of sustainable, system-wide growth.
You began your career in banking and now lead Supply Chain Finance in a large FMCG organization. How has this dual perspective shaped your approach to liquidity management across the value chain?
Beginning my career in banking gave me a disciplined understanding of credit, risk evaluation, and capital allocation. I was trained to assess exposure conservatively and to view liquidity as a safeguard. Transitioning into the FMCG ecosystem expanded that lens. I began to see liquidity not merely as protection, but as a strategic enabler of growth across the value chain.
In banking, you evaluate risk from the outside. In corporate supply chain finance, you design structures from within. That shift fundamentally altered my approach. Liquidity management, in my view, is not about preserving cash in isolation — it is about ensuring
that suppliers, distributors, and internal operations remain financially synchronized.
A defining phase in this evolution was leading digitization efforts, including the implementation of a Treasury Management System. Integrating digital supplier onboarding, automated workflows, and structured financing mechanisms created transparency across stakeholders. It demonstrated how technology can reduce friction, accelerate decision-making, and optimize working capital without compromising governance.
This dual exposure has taught me that supply chains function as interconnected financial ecosystems. When visibility improves and funding mechanisms are thoughtfully designed, liquidity becomes stabilizing rather than reactive. My approach today is anchored in building resilient structures where capital flows
financial ecosystem. She is also known for strengthening supplier ecosystems and enabling inclusive financing across Tier 2 and Tier 3 networks.
Megha Kaushik is a seasoned supply chain finance leader with over 15 years of experience across banking and FMCG. She specializes in liquidity optimization, digital transformation, and risk management, and has led SAP-driven automation initiatives that improved cash flow visibility, reduced DSO, and built a scalable, data-driven, and value-generating
predictably, risk is anticipated early, and partnerships are strengthened through financial clarity.
In today’s environment, should Supply Chain Finance be viewed primarily as a working capital tool or as a strategic lever for ecosystem growth?
It would be reductive to see Supply Chain Finance as only one or the other. In practice, it evolves. At its foundation, Supply Chain Finance must begin as a working capital optimization mechanism. The immediate objective is to improve liquidity cycles, rationalize credit terms, and enhance cash flow predictability. Without this discipline, any broader ambition lacks structural stability.
However, once the fundamentals are in place — visibility, automation, credit governance, and stakeholder alignment — it naturally transitions into a strategic instrument. At that stage, it does more than release capital; it strengthens supplier relationships, enhances negotiating power, improves resilience during volatility, and enables scalable growth across the ecosystem.
Overemphasizing only the working capital angle can make it transactional. Treating it purely as a growth lever without financial rigor can introduce risk. The balance lies in sequencing — operational discipline first, strategic leverage next. When structured thoughtfully, Supply Chain Finance does not just optimize liquidity; it aligns incentives across the value chain and builds a more durable commercial ecosystem.
How are changing demand cycles and tighter credit conditions reshaping working capital strategies in FMCG? FMCG demand cycles today are less predictable than they were a decade ago.
Consumption patterns fluctuate faster, channel dynamics evolve rapidly, and credit conditions have become more disciplined. In such an environment, working capital strategy can no longer be static. What has changed significantly is the financial awareness across the value chain. Vendors, distributors, and retailers are far more informed about funding costs, credit structures, and opportunity trade-offs. Conversations are no longer limited to extending credit or negotiating payment terms; they now revolve around automation, cost of capital, structured financing models, and efficiency gains.
As credit tightens, the emphasis shifts from expansion to optimization. Companies are focusing on sharper receivables monitoring, calibrated inventory levels, and structured payables programs that balance liquidity with supplier stability. Automation plays a critical role here — eliminating manual interventions improves speed, transparency, and accuracy, which directly impacts working capital efficiency.
Tighter credit and volatile demand have forced FMCG organizations to move from reactive liquidity management to data-driven working capital design. The strategy today is less about stretching cycles and more about synchronizing them intelligently.
What are the foundational elements of a strong and scalable Supply Chain Finance framework?
A scalable Supply Chain Finance framework is built on three essentials: financial clarity, disciplined risk governance, and digital integration. Financial clarity ensures that the economics of the program — cost of capital, liquidity impact, and credit structure — are transparent and aligned
across stakeholders. Without this, adoption remains superficial.
Equally important is structured risk management. Exposure limits, credit evaluation, and continuous monitoring must be embedded from the outset; scalability without risk discipline can quickly become vulnerability.
Finally, data integration is nonnegotiable. A modern SCF framework must be automated, ERP-aligned, and supported by real-time visibility. Technology transforms it from a transactional tool into a resilient system. When these elements converge, Supply Chain Finance becomes not just efficient, but sustainable.
How do you design SCF programs that support suppliers and distributors without compromising on risk controls?
Designing an effective SCF program requires balancing commercial support with disciplined governance. The objective is not to dilute risk controls in the name of partnership, but to structure support intelligently.
My approach typically follows five principles.
1. First, technology integration. Programs must be embedded within core systems — ERP-linked workflows, automated approvals, and real-time exposure tracking reduce manual intervention and enhance control.
2. Second, data-led decision-making. Supplier segmentation, credit profiling, and transaction analytics allow differentiated structures rather than one-size-fits-all funding.
3. Third, structured risk architecture. Clear exposure limits, predefined eligibility criteria, and continuous monitoring ensure that liquidity
Supply Chain Finance is no longer confined to safeguarding cash—it is emerging as a strategic lever that synchronizes the entire value chain. When liquidity flows are structured with visibility and discipline, they align suppliers, distributors, and internal operations into a cohesive financial ecosystem. This shift transforms finance from a reactive function into a stabilizing force that enables growth, strengthens partnerships, and improves resilience in volatile market conditions.
SCF must begin with working capital discipline but cannot remain there. Once visibility, governance, and automation are established, it evolves into a powerful growth enabler. It enhances supplier confidence, strengthens negotiating power, and builds resilience across the network. The real impact lies in sequencing—operational rigor first, strategic leverage next—allowing organizations to unlock capital while maintaining financial stability and control.
support does not translate into unchecked credit risk.
4. Fourth, collaborative alignment. Suppliers and distributors should understand the commercial logic behind the program. When transparency exists, compliance improves naturally.
5. Finally, operational discipline. Defined processes, escalation mechanisms, and measurable KPIs ensure that the framework remains scalable and consistent. A well-designed SCF program does not choose between growth and control — it integrates both. The strength lies in creating liquidity pathways that are predictable, monitored, and strategically aligned with the broader business model.
What structural interventions are most effective in reducing Days Sales Outstanding (DSO) sustainably?
Reducing DSO sustainably requires structural alignment rather than shortterm collection drives. The focus must be on system design, not periodic pressure.
The first intervention is visibility. Real-time ERP integration and receivables dashboards allow early identification of aging patterns, credit utilization trends, and stress signals across channels. Without data transparency, DSO management becomes reactive.
Second is calibrated credit structuring. Credit limits, tenure, and incentives must be aligned with customer profiles and market realities. A uniform policy rarely works across diverse distributor networks.
Third, cross-functional coordination is critical. Finance cannot reduce DSO in isolation. Alignment with
sales, logistics, and production ensures that dispatch decisions, scheme structures, and payment terms are synchronized with liquidity objectives.
Fourth, automation strengthens discipline. System-driven reminders, automated reconciliations, and approval workflows reduce delays caused by manual intervention.
Finally, proactive risk assessment ensures sustainability. Monitoring exposure trends, segment-level stress, and market volatility allows early corrective action before receivables deteriorate.
Sustainable DSO reduction is not about tightening credit abruptly; it is about creating a controlled, transparent, and coordinated receivables ecosystem where incentives and accountability are clearly defined.
How can companies build earlywarning systems to prevent NPAs instead of reacting to them?
The objective of an early-warning system is not to predict the future with certainty — that is unrealistic. The objective is to detect stress patterns early enough to act before deterioration becomes irreversible.
An effective framework begins with behavioral analytics. Monitoring payment frequency, payment slippages, order volatility, credit utilization spikes, and sales velocity across geographies provides leading indicators of stress within dealer and distributor networks.
Second, data integration is essential. Early-warning signals must be embedded within ERP and credit monitoring systems rather than reviewed manually. Automated triggers based on predefined
thresholds ensure that exceptions are identified immediately.
Third, segmentation matters. Risk parameters should vary based on distributor scale, territory risk, and historical conduct. A uniform risk lens often fails to detect localized vulnerabilities.
Fourth, cross-functional escalation mechanisms must be clearly defined. Finance, sales, and regional teams should act collaboratively once stress indicators emerge. Early engagement with channel partners often prevents temporary liquidity constraints from becoming structural defaults.
Preventing NPAs is less about reacting to a missed payment and more about interpreting subtle financial behavior shifts over time. When systems are designed to capture these signals proactively, risk management becomes preventive rather than corrective.
You led the shift from manual payment systems to an SAPintegrated automated model. What were the biggest transformation challenges, and what changed structurally post-automation?
The most significant challenge in any automation initiative is not technological — it is behavioral. Transitioning from manual systems to an SAP-integrated framework required alignment across teams that were accustomed to legacy processes. Building confidence in the new system, addressing resistance to change, and pacing the transition carefully were critical to ensuring adoption.
Before implementation, we undertook a detailed business blueprint assessment, with particular focus on data hygiene and process mapping. Automation without clean data or clearly defined workflows only replicates inefficiencies at scale. Once the foundational architecture
was clarified, the transition became significantly more structured.
Post-automation, the changes were structural rather than incremental.
Receivables management became system-driven, with real-time tracking and automated reconciliation improving liquidity visibility and reducing DSO. A centralized funds transfer interface enhanced cash flow transparency, enabling more precise allocation and forecasting.
Operationally, automation reduced manual errors, shortened approval cycles, and improved processing efficiency — translating into measurable cost savings. Risk management also strengthened, with structured forex monitoring and hedging mechanisms embedded within the treasury framework to manage currency and interest rate exposure more proactively.
Perhaps most importantly, automation enabled the creation of a more integrated financial ecosystem — connecting banks, NBFCs, fintech partners, and internal stakeholders within a unified, transparent structure. The shift was not merely about digitizing payments; it was about redesigning the financial operating model to be scalable, resilient, and data-led.
How does ERP-linked automation strengthen negotiating power with suppliers and improve operational efficiency?
ERP-linked automation fundamentally shifts the quality of financial conversations with suppliers. When payment cycles, invoice validations, exposure limits, and cash forecasts are system-driven and transparent, discussions move from assumptions to data.
With real-time visibility into payables and liquidity positions, companies can design structured financing programs that offer suppliers predictability. Predictability reduces perceived risk. When suppliers are confident about payment timelines and funding access, negotiations evolve from defensive pricing to collaborative optimization. Automation also enhances internal efficiency. Approval hierarchies become streamlined, reconciliation cycles shorten, and manual intervention
reduces significantly. This improves turnaround time while minimizing operational errors and disputes. Importantly, ERP integration enables early identification of stress signals — delayed dispatches, credit utilization spikes, or payment irregularities. Addressing these proactively prevents friction from escalating into supply disruption.
In essence, automation strengthens negotiating power not through pressure, but through credibility. When financial processes are transparent, disciplined, and data-backed, supplier relationships become more stable — and operational efficiency improves as a natural outcome.
Many companies invest in dashboards but struggle to extract strategic insight. How can tools like TABLEAU move beyond reporting to enable predictive decision-making?
Dashboards fail when they remain descriptive. Data, in isolation, rarely drives decisions. It must be contextualized, interpreted, and linked to action.
Tools like Tableau become strategic only when they move beyond displaying historical metrics and begin surfacing patterns, correlations, and emerging risks. Visualization is not merely about charts; it is about structuring information in a way that reveals direction.
The real shift happens when organizations transition from reporting ‘What Happened’ to analyzing ‘Why It Happened’ and ultimately projecting ‘What Is Likely To Happen Next’. When dashboards integrate trend analysis, variance triggers, cohort comparisons, and scenario simulations, they begin to support predictive decision-making rather than passive monitoring.
Equally important is narrative discipline. Insights must be aligned with business priorities — liquidity sensitivity, credit stress signals, demand fluctuations, or margin compression. When data is translated into business implications, leadership can act with clarity rather than instinct alone.
In essence, business intelligence tools deliver value not by accumulating data, but by converting information into foresight. The objective is not better reporting — it is better anticipation.
What role do AI-driven analytics play in improving credit evaluation and cash flow forecasting?
AI-powered analytics enhance both the quality and responsiveness of credit assessment and cash flow forecasting. Their true advantage, however, lies less in automation and more in their ability to detect patterns that may not be immediately apparent through conventional analysis.
Traditional credit models are largely anchored in historical financial statements and fixed exposure thresholds. In contrast, AI facilitates dynamic risk profiling by evaluating behavioral indicators such as payment consistency, transaction cadence, sales momentum, regional dispersion, and seasonal variations. This enables organizations to identify early warning signals before they surface in standard financial reports, shifting the focus from retrospective evaluation to proactive risk sensing.
In the context of cash flow forecasting, AI introduces greater accuracy by synthesizing diverse inputs simultaneously — including receivable cycles, payable schedules, consumption trends, currency volatility, and credit utilization levels. Forecasting thus evolves from static, linear projections to continuously refined scenario modeling, allowing liquidity planning to remain agile amid changing conditions.
That said, AI should serve as an enabler rather than a substitute for financial prudence. Established governance mechanisms, exposure controls, and disciplined credit frameworks remain indispensable. The real strength of AI lies in reinforcing human judgment through enhanced insight and faster detection of deviations.
When seamlessly integrated with ERP and treasury platforms, AI-driven analytics reposition finance from reactive analysis to forward-looking stewardship. The benefit extends beyond improved projection accuracy; it strengthens confidence in strategic capital deployment and risk management decisions.
How can Supply Chain Finance support Tier 2 and Tier 3 channel partners who often operate with limited financial buffers?
Supply Chain Finance initially evolved around Tier 1 partners, where financial documentation and credit visibility were relatively strong. However, in many industries — particularly FMCG — the product ultimately reaches the consumer through Tier 2 and Tier 3 intermediaries. If liquidity constraints exist at these levels, the efficiency of the broader SCF framework is inevitably affected.
In recent years, NBFCs and fintech institutions have increasingly focused on these segments, recognizing both the opportunity and the structural necessity of extending credit deeper into the distribution chain. While challenges such as limited documentation, collateral constraints, credit divergence, and geographic dispersion remain, these risks can be managed through structured mechanisms.
One approach is anchor-led financing, where the credibility of the primary corporate strengthens the credit profile of downstream partners. In some cases, lenders adopt controlled disbursement and collection models — such as linking loan servicing directly to mapped collection accounts — to reduce diversion risk. Collateral-backed structures, transaction-based lending, and invoice-level funding models also help mitigate exposure.
The key lies in designing credit programs that combine risk safeguards with operational simplicity. When Tier 2 and Tier 3 partners are provided structured liquidity access, the entire supply chain becomes more stable, predictable, and resilient.
Extending financial support beyond Tier 1 is not merely an inclusion strategy; it is a structural reinforcement of the distribution ecosystem.
In your experience, can SCF evolve into a self-sustaining or revenuegenerating function? What mindset
shift is required at the leadership level?
Supply Chain Finance can absolutely evolve into a self-sustaining — and in many cases revenue-accretive — function, provided it is designed intentionally rather than treated as a peripheral support tool. In the early stages, SCF is often viewed purely as a working capital optimization mechanism. However, once a structured framework is implemented, organizations begin to recognize measurable value creation: reduced leakages, improved cash cycle efficiency, optimized credit utilization, and lower dependency on conventional borrowing lines.
When liquidity is unlocked systematically, surplus funds can be deployed more strategically within treasury operations, generating additional yield. Over time, the SCF vertical can operate with defined metrics, cost efficiencies, and even a distinct performance contribution that is visible at the management reporting level.
The required leadership shift is conceptual. SCF must be viewed not as a side instrument but as a financial architecture embedded within the supply chain. It requires ownership, dedicated governance, and performance tracking — much like any other strategic function. Once leadership recognizes that every structured intervention in payables, receivables, and channel financing translates into measurable financial impact, SCF transitions from a facilitative role to a value-creating engine. In mature organizations, it is no longer optional — it is foundational to sustainable supply chain performance.
How important is cross-functional alignment between finance, procurement, and sales in unlocking capital efficiency?
Procurement, production, logistics, and
sales form the operational backbone of the supply chain. Finance, however, is the liquidity engine that sustains it. Without alignment between these functions, capital efficiency remains theoretical. Working capital outcomes are rarely determined by finance alone. Credit terms are influenced by sales strategies, inventory levels by production planning, and payment cycles by procurement negotiations. If these decisions are made in silos, liquidity stress becomes inevitable.
Cross-functional alignment ensures that commercial ambition is matched with financial discipline. For example, extending credit to accelerate market penetration must be evaluated alongside cash flow implications. Similarly, procurement negotiations should balance cost savings with payment structures that preserve supplier stability.
From an SCF perspective, initiatives such as dynamic discounting, reverse factoring, ERP integration, and automated payment frameworks succeed only when all stakeholders operate within a synchronized structure. Shared KPIs, transparent data visibility, and collaborative planning are essential.
External partnerships with banks, NBFCs, and fintech institutions further strengthen this alignment by embedding structured funding solutions into the operational flow. Ultimately, capital efficiency is not unlocked by financial tools alone. It emerges when commercial strategy, operational execution, and liquidity management move in coordination rather than competition.
As supply chains become more digital and interconnected, what capabilities must the next-generation Supply Chain Finance leader build to stay ahead?
The next-generation Supply Chain Finance leader must operate at the
True supply chain resilience depends on liquidity reaching beyond Tier 1 partners. Structured SCF programs, supported by fintechs and NBFCs, are enabling credit access for Tier 2 and Tier 3 players through anchorled and transaction-based models. This not only reduces systemic risk but also strengthens the last-mile distribution network, making the entire ecosystem more stable, inclusive, and growth-ready.
Early-warning systems are redefining risk management by identifying stress before defaults occur. Monitoring behavioral indicators—such as payment patterns, credit utilization, and order volatility—allows organizations to act early. Integrated systems, automated triggers, and cross-functional coordination ensure that risks are addressed proactively. The focus shifts from reacting to missed payments to anticipating financial stress signals across the network.
intersection of finance, technology, and strategy. The role is no longer confined to optimizing payables or receivables — it demands architectural thinking.
First, technological fluency is nonnegotiable. Leaders must understand automation frameworks, ERP-integrated ecosystems, AI-enabled analytics, and evolving fintech models. It is not about coding expertise, but about knowing how digital tools can be embedded into financial workflows to create transparency, speed, and scalability.
Second, there must be a deep understanding of dynamic funding structures. Traditional instruments are giving way to hybrid and structured solutions — dynamic discounting, platform-based financing, embedded lending, and risk-participation models. A modern SCF leader must continuously evaluate and recalibrate these mechanisms to suit changing market
cycles.
Third, predictive analytical capability is critical. Data must move beyond reporting to foresight. The ability to interpret behavioral credit patterns, liquidity signals, and demand volatility through predictive models will differentiate proactive leaders from reactive ones.
Equally important is cross-functional orchestration. Siloed decision-making is incompatible with digital supply chains. Finance must collaborate seamlessly with procurement, sales, logistics, technology teams, and external financial partners to create synchronized capital strategies. Finally, risk intelligence must evolve. Volatility — whether geopolitical, currency-driven, or demand-led — requires structured mitigation tools, diversified funding channels, and disciplined governance frameworks.
The future SCF leader is not just
a finance manager, but a systems thinker — someone who blends data, discipline, collaboration, and innovation to build resilient and adaptive financial ecosystems.
Disclaimer: The views, opinions, and responses expressed in this document are personal and based on my individual professional experience. They do not represent or reflect the official position, policies, or views of my employer, its management, or any affiliated organizations. All information shared is intended solely for general discussion and knowledge-sharing purposes and does not disclose any confidential, proprietary, or sensitive business information. Any use, reproduction, or distribution of this material without the prior written consent of the author is not authorized, and the author assumes no liability for any outcomes, interpretations, or consequences arising from such unauthorized use.
Warehousing Reimagined: THE RISE OF INTELLIGENT LOGISTICS HUBS
Keeping pace with rapid technological change remains a persistent challenge for businesses worldwide. From automation to advanced digital systems, every innovation demands careful evaluation before organizations commit to large-scale investments. This dynamic is particularly evident in warehousing, where operators are under increasing pressure to enable faster fulfilment while maintaining efficiency, accuracy, and seamless logistics flows. As demand patterns grow more complex and delivery expectations tighten, businesses are rethinking warehouse design, technology adoption, and operational processes. Investments in automation, data visibility, and modern infrastructure are gradually transforming facilities into high-performance logistics hubs. This feature revisits industry perspectives that helped shape the conversation around next-generation warehousing—insights that remain highly relevant as the sector continues its shift toward smarter logistics ecosystems.
Gopala Krishna
Nirav Doshi
Rajan Ekambaram
Dr. Arunachalam R
Kapil Premchandani
Asim Behera
Kamal Kishore Kumawat
Nitin Joshi
Tannistha Ganguly
Deepak Jain
Godowns Fulfilment Engines to
As supply chains grow more dynamic and customer expectations intensify, warehouses are increasingly being repositioned as operational nerve centres rather than passive storage points.
Warehousing has undergone a significant transformation over the past decade. Once viewed primarily as storage facilities, warehouses today function as strategic enablers of supply chain performance. In an era defined by omnichannel commerce and rising consumer expectations, the warehouse has evolved into a critical node powering modern fulfilment ecosystems. The rapid growth of e-commerce and quick commerce has accelerated this shift. Delivery timelines that once stretched over several days have shrunk dramatically, forcing warehouses to manage thousands of SKUs while maintaining speed, accuracy, and operational efficiency. As a result, organizations are rethinking warehouse design, infrastructure, and
operational models to support faster and more responsive logistics networks.
According to Gopala Krishna, National Head – Supply Chain, Big Basket, excellence in warehousing— particularly in e-commerce and quick commerce—has evolved into a fundamental requirement. “Achieving excellence in warehousing operations, especially in e-commerce and quick commerce, is now a fundamental necessity. Over the past 12 years, I've witnessed this transformation firsthand at Big Basket… Technology has become the backbone of the entire warehousing process today. When we initially launched a warehouse… we were focusing on nextday and same-day deliveries. Over time, this shifted to two-hour deliveries…
Now, we’re offering deliveries in as little as 10 minutes, with around 500 stores in operation. Technology is playing a vital role across every aspect, from reducing labor intensity to optimizing space, managing inventory, and ensuring availability.”
This journey—from next-day fulfilment to hyperlocal ten-minute delivery windows—illustrates how warehouses have evolved from static storage facilities into dynamic fulfilment hubs capable of supporting real-time logistics operations. The pace of quick commerce has particularly redefined operational benchmarks.
“In the context of e-commerce, excellence might be defined as delivering within two or three days… But in the
case of quick commerce, where we aim for 10-minute deliveries, the definition shifts to one of speed and efficiency. In dark stores, you often have only a minute or two to pick, pack, and hand over the order to delivery. Sometimes you may even have less than a minute… Time is absolutely critical in ensuring excellence in this business,” explains Gopala Krishna.
Such operational realities have pushed companies to rethink warehouse networks and adopt decentralized fulfilment models that combine large distribution centres with smaller, strategically located dark stores. Beyond the quick commerce ecosystem, industry experts observe that warehouses have increasingly transitioned from cost centres into strategic enablers of business growth.
According to Rajan Ekambaram, Partner, Qwixpert Consulting, this shift is redefining how organizations measure warehouse performance. “In today’s dynamic supply chain landscape, warehousing and fulfilment centers have evolved from being cost centers to strategic enablers of customer satisfaction and business growth. OnTime In-Full (OTIF) is the single most critical metric… ensuring that the right product, in the right condition, reaches customers within the promised time frame. A prime example is Quick Commerce, where warehouses operate with near-instantaneous precision.
Orders are picked, packed, and handed over to delivery personnel in under two minutes, ensuring 10-minute deliveries with exceptional accuracy.”
This growing emphasis on customercentric metrics reflects how warehouse performance is increasingly linked to service reliability and delivery speed rather than purely operational cost. The changing nature of warehousing is also closely tied to the broader transformation of supply chains, particularly the shift from traditional B2B logistics models to B2C fulfilment frameworks.
Kapil Premchandani, FounderDirector, KD Supply Chain Solutions, notes that warehousing today must accommodate vastly different operational requirements across industries. “Warehousing has undergone significant evolution over time. Its primary function is to ensure that products ordered by customers are delivered accurately and on time… Each industry has its own unique set of needs. Over time, these processes have become more sophisticated, especially as our country transitions from traditional B2B (business-to-business) warehousing to B2C (business-to-consumer) models. As consumers, we care not just about how fast our orders arrive, but also about getting exactly what we ordered… To me, this is the essence of excellence— guaranteeing the timely delivery of the correct product to the right customer.”
The emergence of e-commerce
and omnichannel retail has therefore pushed warehouses to evolve into highly responsive fulfilment environments capable of handling both bulk distribution and individual order processing. This shift is also visible in how warehouse infrastructure has evolved across India.
According to Kamal Kishore Kumawat, Co-founder, Edgistify, the industry has moved steadily from traditional storage facilities toward more sophisticated fulfilment ecosystems. “Warehousing has undergone a significant transformation over the past decade, evolving from a mere cost centre to a key player in enhancing customer satisfaction. This shift has been characterized by the transition from traditional ‘Godown’ settings to state-of-the-art fulfilment centres and last-mile hubs or dark stores. A decade ago, delivery times ranged from 2 to 10 days, but today companies often promise delivery times as short as 10 minutes or even the same day.”
As delivery promises to shrink and customer expectations rise, the warehouse has become one of the most critical operational assets within the supply chain. What was once seen primarily as a storage facility has now evolved into a strategic engine powering speed, efficiency, and customer satisfaction across the modern logistics landscape.
The The Tech Engine Tech Engine Modern Warehousing Modern Warehousing behind behind
As warehouse operations grow more complex and fulfilment expectations accelerate, technology has become the backbone of modern warehousing. Digital systems are enabling warehouses to operate faster, smarter, and with far greater visibility across supply chains.
Today’s warehouses are no longer run through manual coordination or fragmented systems. Instead, they function within integrated digital ecosystems where warehouse
management systems (WMS), enterprise resource planning (ERP), automation tools, and analytics platforms work together to streamline operations. This digital infrastructure enables companies
to manage growing SKU volumes, coordinate high-frequency order processing, and maintain service levels in an increasingly demanding logistics environment.
According to Tannistha Ganguly, Global Head – Supply Chain WMS (IT), Kimberly-Clark, operational excellence in warehousing depends on combining strategic planning, efficient resource management, and effective use of technology. “I would say that a combination of Strategic Planning, Efficient Resource Management and Effective Utilization of Technology are key to achieving excellence in warehouse operations. While we are all aware of the growing role of technology in warehouse operations across India and other emerging markets, we also must realize that digital transformation in warehousing is not solely about large-scale overhauls; rather it involves optimization of space, processes, resources and ensures greater synchronization.”
Her perspective highlights a critical reality: warehouse modernization is not simply about deploying advanced technologies but about integrating them into operational workflows. In sectors such as FMCG, where supply chains handle extremely high product volumes, operational performance is often measured through tightly defined key performance indicators such as pick efficiency and On-Time In-Full (OTIF) delivery.
“For instance, in my current sector (FMCG), KPIs such as Pick Efficient and On-Time, In-Full (OTIF) Delivery are critical. If workforce turnover is recurring, then constant training of staff… adds to delays and cost overruns, as compared to implementing advanced routing
algorithms and layout optimization tools that ensure continuity and effective cost management. By leveraging intelligent systems, businesses can achieve greater accuracy in order fulfilment, reduce turnaround times, and improve overall warehouse productivity. Ultimately, the integration of technology in warehousing is no longer optional but a necessity for businesses seeking to maintain competitiveness and operational excellence.”
Technology adoption is also enabling organizations to operate warehouses in a far more responsive manner. Integrated ERP and WMS platforms provide granular operational visibility, allowing managers to monitor inventory, labour productivity, and order fulfilment in real time.
According to Rajan Ekambaram, building the right digital infrastructure is the starting point for improving warehouse efficiency. “For companies to improve warehouse efficiency, the right digital infrastructure is the foundation. This begins with implementing an ERP and a high-end WMS that not only manages daily operations but also captures granular, actionable data for continuous improvement. Once a strong digital footprint is in place, companies should focus on harnessing historical and real-time data to drive key operational decisions.”
These systems help optimize multiple aspects of warehouse operations— from intelligent product slotting and efficient picking routes to advanced
wave management and improved dock coordination. “Data-driven insights can significantly optimize warehouse operations in the areas of product slotting & picking strategies to reduce travel time and improve order fulfilment, wave management for efficient batch processing of orders, real-time visibility of order boards enabling dynamic dispatch and resource planning, cross-docking & dock management ensuring faster truck turnarounds, and resource allocation & workforce planning optimizing labour utilization based on Skill matrix.”
Beyond day-to-day operational improvements, digital systems are also shaping long-term warehouse strategies. Advanced analytics platforms allow organizations to redesign layouts, improve storage density, and identify areas where automation can deliver the greatest operational gains. However, experts caution that technology adoption must remain aligned with operational realities. While automation and digital platforms offer significant advantages, their success depends on how effectively they integrate with existing processes and workforce capabilities.
As warehouses continue evolving into intelligent logistics hubs, the integration of technology will remain central to this transformation—enabling organizations to build supply chains that are faster, more agile, and better equipped to handle the growing complexity of modern commerce.
Warehouses Think in Data that
As warehouse networks grow more complex and fulfilment timelines shrink, data is becoming the invisible engine driving operational efficiency.
Modern warehouse environments generate vast amounts of operational data—from inventory movements and picking speeds to dispatch timelines and workforce productivity. When leveraged effectively, this information can significantly improve how organizations plan, manage, and optimize their logistics operations. Yet many companies are still at an early stage in converting warehouse data into meaningful operational insights.
According to Tannistha Ganguly, the real opportunity lies in strengthening data awareness and developing structured data strategies rather than focusing solely on deploying new technologies. “In my opinion, companies in the Supply Chain sector should focus on Data Awareness and Data Strategy if they are to fully capitalize on available resources. Typically, warehouses are integrated
with ERPs… and large volumes of data flow between ERPs and Warehouse Management Systems (WMS). Most companies in our sector are still focussed on pure report generation and KPI tracking, instead of exploring insight generation from enterprise data. With AIbased technologies figuratively barging into every sector, the time is ripe for the Supply Chain players to define their enterprise Data Vision and formulate and implement Data Strategies.”
Her observation highlights a common challenge in many warehouse operations. While enterprise systems capture enormous volumes of operational information, organizations often remain focused on reporting rather than using data for predictive or strategic decisionmaking.
She recounts an internal assessment that revealed how deeper engagement
with data can uncover operational gaps. “For example, at my current organization… I found notable discrepancies. While one warehouse was manually processing data via MS XLS with a turnaround period of one week… the other claimed to have generated realtime insights via Power BI dashboards. A deep dive showed the data was 3–4 days old rather than real time… only four or five out of forty-four KPIs actually needed real-time reporting.”
In high-velocity fulfilment environments such as quick commerce, however, real-time data processing becomes essential. Warehouses must respond instantly to new orders, fluctuating demand patterns, and inventory changes.
According to Gopala Krishna, the success of quick commerce operations relies heavily on integrating automation
with real-time data systems. “In the fast-paced world of quick commerce (Q-Commerce), where every second counts, automation and data-driven insights are pivotal to success… With an operational goal of picking in two minutes, relying on manual methods such as paper tracking or manual data entry becomes impractical.”
Instead, automated systems transmit order information instantly to warehouse teams, allowing fulfilment processes to begin immediately and ensuring strict delivery timelines can be maintained. Data also plays a critical role in demand forecasting and inventory planning—two areas that directly influence warehouse efficiency.
“Demand forecasting… plays a critical role in managing stock levels, particularly during peak seasons like Diwali. Predicting customer demand accurately is essential… businesses must strike a fine balance between maintaining sufficient stock to meet demand and avoiding overstocking,” explains Gopala Krishna.
Real-time inventory visibility further strengthens warehouse responsiveness by enabling proactive stock replenishment. “If inventory is running low, the system can alert the team to replenish stock
before it becomes an issue. This type of proactive approach ensures that businesses can keep pace with customer expectations without facing supply shortages.”
Operational dashboards have also become powerful management tools in modern warehouses, allowing managers to monitor each stage of the fulfilment cycle—from picking and packing to dispatch—and identify bottlenecks quickly. Data analytics is also helping organizations tackle one of the biggest operational challenges facing modern warehouses: managing rapidly expanding SKU volumes within limited space. “Moreover, space management is a growing challenge as the number of SKUs continues to rise… warehouses might have handled 5,000–6,000 SKUs, but today that number can easily reach 10,000–15,000,” notes Gopala Krishna.
Advanced analytics helps address this challenge through intelligent slotting strategies that position fastmoving products closer to picking zones while optimizing storage density. For companies seeking to build more data-driven warehouse ecosystems, establishing a strong digital foundation remains critical.
According to Rajan Ekambaram,
this begins with implementing integrated enterprise systems capable of capturing actionable operational data. “For companies to improve warehouse efficiency, the right digital infrastructure is the foundation. This begins with implementing an ERP and a high-end WMS… Once a strong digital footprint is in place, companies should focus on harnessing historical and real-time data to drive key operational decisions.”
These insights allow organizations to optimize critical warehouse functions—from product slotting and wave planning to dock management and workforce allocation. “Data-driven insights can significantly optimize warehouse operations in the areas of product slotting & picking strategies… wave management… real-time visibility of order boards… cross-docking & dock management… and workforce planning.”
As warehouse systems become more digitally integrated, the challenge is shifting from collecting data to effectively interpreting and applying it— an evolution that also brings into focus another critical dimension of warehouse transformation: the human element behind these increasingly technologydriven operations.
The Human Side Smart Warehousing of
While technology is reshaping warehouses, the real challenge lies in managing people through this transformation. As operations become more digital, change management on the ground is proving just as critical as technology adoption.
Warehousing has long been labour-intensive, with deeply embedded processes. The shift to automation and digital systems therefore requires not just infrastructure upgrades, but also behavioural change—especially among frontline teams. Resistance to change remains a key barrier, often driven by uncertainty around roles and job security.
According to Gopala Krishna,
the challenge is less about technology and more about operational mindset. “Warehouse operations have significantly evolved… However, one of the biggest challenges in adopting practices like Industry 4.0… is not access to technology or finances, but resistance to change… particularly among the ground staff and operators.” He emphasizes that gradual implementation helps ease this
transition. “At Big Basket, successful transitions have been achieved through pilot-based implementation… introduced gradually.”
Training and data-backed outcomes further help build trust in new systems. “Training and upskilling have been key… data-backed decision-making has played a crucial role in showcasing tangible improvements…”
Tannistha Ganguly highlights the importance of empathy in managing workforce transitions. “Resistance to change… should not come as a surprise. The primary fear… would be loss of jobs… Empathy and transparency are key to building confidence…” She stresses structured change management. “Effective change management strategies… communication, feedback systems, training programs… and fair
compensation are keys to success.”
As warehouses adopt more advanced systems, workforce capability also becomes a constraint.
Rajan Ekambaram notes that skill gaps and ROI concerns often slow automation adoption. “Many businesses prefer manual operations due to high initial investment… and lack of skilled workforce…” He advocates a long-term value-driven approach. “Companies
Designing Speed and Scale for
must shift from cost-driven to valuedriven decision-making… Upskilling the workforce and phased implementation… can improve adoption.” Equally important is changing how warehouse roles are perceived.
Kapil Premchandani believes warehouses must be seen as essential infrastructure. “Let’s stop thinking of warehouses as just warehouses. They’re essential services, just like hospitals or fire brigades… most warehouses today are operating 24/7, 365 days a year…” This shift became especially visible during the pandemic, when uninterrupted warehouse operations were critical to supply continuity.
As warehouses evolve into high-performance, tech-enabled environments, success will depend on how effectively organizations align people with technology—building skills, trust, and a culture that embraces continuous change.
Warehouse design has become a strategic lever for performance, directly influencing efficiency, scalability, and long-term cost structures.
As fulfilment demands intensify and SKU complexity rises, warehouse design is no longer about space—it is about enabling seamless flow, speed, and adaptability. Decisions made at this stage determine how effectively a warehouse can support evolving supply chain requirements over time. According to Gopala Krishna, design must begin with process efficiency and operational flow.
“At the warehouse design stage, several key factors must be considered. Optimized process flow is essential for seamless FIFO and FEFO-based stocking… Automation integration
should be planned from the start, including RFID/barcode scanning, conveyor systems, and robotic picking, to avoid expensive retrofits.”
Embedding automation readiness at the design stage is increasingly critical. As warehouses integrate robotics and intelligent systems, retrofitting becomes both costly and disruptive. Designing with automation in mind ensures smoother transitions as operations scale.
Scalability is another key factor, particularly in dynamic fulfilment environments. “Scalability must also be accounted for… ensuring the infrastructure can handle seasonal SKU
pullbacks and format transitions… Additionally, compliance and safety measures should be embedded into the design,” adds Gopala Krishna.
Such foresight allows warehouses to adapt without requiring major structural changes. From a strategic standpoint, warehouse design must also align with long-term business growth. Rajan Ekambaram emphasizes the need for a forward-looking approach. “A datadriven, scientific approach to warehouse design is essential… companies must design warehouses with a three-tofive-year horizon, factoring in business growth projections…”
He further outlines the key pillars of effective warehouse design: “An optimal warehouse design integrates four critical elements: Infrastructure, Layout, Processes, and Systems & Technology… selecting the right storage systems… efficient flow paths… standardized workflows… and leveraging WMS, automation, and real-time data analytics.”
Aligning these elements ensures higher throughput, better space utilization, and improved operational stability. However, design decisions are often influenced by short-term cost considerations—sometimes at the expense of long-term efficiency. According to Kapil Premchandani, companies frequently underestimate the impact of infrastructure quality. “Often, companies focus too much on the rental price… but this narrow focus can lead to costly mistakes in the long run… elements like the approach and apron roads… are vital for maneuvering containers efficiently.”
Lower rental costs can often translate into operational inefficiencies. “Opting for a warehouse with a lower rental price… may seem like a good deal. However, this often results in
The
compromised efficiency… On the other hand, a Grade A warehouse… offers better infrastructure, leading to greater efficiency in the long run.”
Another often overlooked aspect is designing for sustainability readiness. “Another often overlooked consideration is the roof's collateral load capacity… Without sufficient load capacity… implementing solar solutions could become impractical,” notes Kapil Premchandani. As sustainability gains prominence, infrastructure must be future-ready to support renewable energy and efficiency initiatives.
10-Minute Delivery Effect
At a broader level, warehouse infrastructure itself is evolving— becoming more sophisticated and functionally closer to manufacturing facilities. “The industry… has undergone significant transformation… warehouses themselves have evolved into more sophisticated, manufacturing-like facilities,” he adds.
As supply chains become more complex and performance-driven, warehouse design is no longer a onetime decision—it is a long-term strategic investment that shapes efficiency, scalability, and competitiveness.
Quick commerce is compressing fulfilment timelines and reshaping warehouse networks around speed, proximity, and responsiveness.
The rise of quick commerce has fundamentally altered how warehouses are structured and operated. Traditional models built around bulk movement and predictable timelines are giving way to high-frequency, small-order fulfilment systems that operate within minutes. This shift is forcing companies to rethink network design, moving from centralized distribution to decentralized, hyperlocal fulfilment models.
Today’s warehouse networks
increasingly combine large distribution centres with micro-fulfilment hubs and dark stores located closer to consumption centres. This hybrid approach enables faster last-mile delivery while maintaining operational flexibility. As delivery expectations shrink—from days to hours and even minutes—warehouses must be designed not just for scale, but for speed and responsiveness.
According to Kamal Kishore Kumawat, the transformation has been
driven by the rapid rise of e-commerce and changing customer expectations. “Warehousing has undergone a significant transformation over the past decade… transitioning from traditional ‘Godown’ settings to state-of-the-art fulfilment centres and last-mile hubs or dark stores… A decade ago… delivery times ranged from 2 to 10 days, but today, companies often promise delivery times as short as 10 minutes or even the same day.”
This evolution reflects a broader shift toward customer-centric logistics, where fulfilment speed is a key differentiator. As a result, warehouse infrastructure is being redesigned to support hyperlocal operations and faster order processing cycles. The transformation is also closely linked to the expansion of digital commerce and improved infrastructure connectivity.
According to Nirav Doshi, MD, NIDO Group, multiple structural shifts have contributed to the evolution of warehousing in India. “Over the past decade, the warehousing landscape in India has undergone significant changes… transitioning from traditional godowns to large-scale multi-storey warehouses and logistics parks… fuelled by GST, rapid expansion of e-commerce, and improvements in transportation infrastructure.”
He further highlights how fulfilment complexity has increased. “With the rise of e-commerce and omnichannel retailing, warehouses are now dealing with more complex fulfilment needs… requiring greater flexibility in inventory management, order processing, and shipping methods.”
As order sizes shrink and channels multiply, warehouses must simultaneously support bulk distribution, individual e-commerce
orders, and hyperlocal deliveries. This has significantly expanded the operational scope of warehouses, making them more dynamic and multi-functional.
According to Dr. Arunachalam R, MD & CEO, IBOB – India sub-brand of SF International; Board of Director – SF Logistics Pvt Ltd., this shift has also redefined the role of logistics service providers. “The shifts in consumer behaviours… have driven significant change in warehousing operations… necessitating 3PL companies to offer value-added services beyond traditional storage and distribution… including inventory management, kitting and assembly, product customization, packaging design, reverse logistics and many more.”
At the same time, the operational intensity within warehouses has increased significantly. Higher order volumes, expanding SKU ranges, and rising service expectations require more advanced systems and tighter process integration.
“The widespread adoption of Advanced WMS, Automation and Robotics… and the rapid growth of e-commerce… have led warehouses to support omni-channel distribution models, where customers expect seamless shopping experiences across online and offline channels,” adds Dr. Arunachalam R.
A key outcome of this shift is the rise of hyperlocal logistics. Micro-fulfilment centres positioned within urban clusters are enabling faster delivery while reducing last-mile complexity. However, this decentralization also increases the need for real-time inventory visibility and coordinated decision-making across multiple nodes.
As warehouse networks become more fragmented yet interconnected, technology and data are playing a critical role in maintaining speed and accuracy. At the same time, improvements in infrastructure—such as logistics parks and multimodal connectivity—are supporting the development of more integrated and responsive supply chain ecosystems.
The 10-minute delivery promise is not just redefining customer expectations— it is fundamentally reshaping how warehouses are designed, located, and operated in the modern logistics landscape.
The
Automation Equation
Automation is becoming central to warehouse performance as operations scale in speed and complexity.
As fulfilment environments handle higher volumes, tighter timelines, and complex SKU mixes, automation is moving from isolated deployments to integrated ecosystems. Warehouses today are increasingly combining robotics, intelligent material handling, and advanced software to improve throughput, accuracy, and consistency.
According to Asim Behera, President, Daifuku Intralogistics India Pvt. Ltd., automation-led transformation is already reshaping warehousing. “Over the past decade, warehousing has undergone a profound transformation driven by… automation integration, data-driven decision making, the rapid expansion of e-commerce, sustainability initiatives, and workforce augmentation.”
Technologies such as ASRS, AGVs, AMRs, STVs, and advanced conveying systems are now core to modern warehouse operations. “These innovations optimize space utilization, streamline
material flow, and enhance operational efficiency, leading to faster throughput and improved order accuracy.” Software integration is equally critical. “Advanced Warehouse Management System (WMS) software orchestrates these automated processes, ensuring seamless integration and real-time optimization.”
Automation is also expanding beyond large warehouses into mid-sized and specialized facilities. According to Nirav Doshi, material handling technologies are enabling end-to-end efficiency. “Smart conveyors streamline material handling… automated putaway and picking solutions… robotic packing stations enable faster order processing and improve order accuracy.” Sorting systems further enhance throughput. “Sorter systems… significantly increase throughput and reduce the time required for order processing and shipment preparation.”
Automation is increasingly being complemented by AI, IoT, and data-driven
systems. According to Dr. Arunachalam R, these technologies are improving both efficiency and reliability. “Warehousing processes have seen a drastic revolution… automation, real-time collaboration, data analytics… robotic picking systems, AGVs, and conveyor systems.” They also enable predictive operations. “Smart warehousing systems improve safety, optimize space utilization… Predictive maintenance programs using IoT sensors… minimize downtime and reduce maintenance costs.” Automation is also enabling faster fulfilment models. “To address the challenge of delivering goods quickly… companies are exploring… micro-fulfilment centres located closer to urban areas.”
As automation matures, warehouses are steadily transitioning into intelligent, high-performance logistics environments capable of delivering speed, scale, and precision.
Sustainability Moves Inside the Warehouse
Sustainability is becoming a core pillar of warehouse strategy alongside efficiency and cost.
As warehouse networks expand, their environmental footprint—from energy consumption to packaging waste—is coming under scrutiny. Companies are increasingly embedding sustainability into infrastructure, operations, and transportation planning. According to Nitin Joshi, Head – Warehousing & Logistics, Fabindia Ltd., practical interventions can deliver measurable impact. “At Fabindia, sustainability lies at the heart of our brand ethos… we have implemented solar panels… to generate a part of the energy requirement. By consolidating shipments… we optimize transportation routes, reducing fuel consumption and emissions.” Operational practices are also evolving. “The majority of our local transportation is carried out using CNG vehicles… strategies such as right-sizing packaging… reuse of cartons… and utilizing totes…”
These measures improve both efficiency and environmental performance. “Additionally, we prioritize the use of biodegradable packaging materials… future initiatives will focus on expanding clean energy… and transitioning to electric vehicles (EVs).”
At an industry level, sustainability is increasingly influencing warehouse design and operations. According to Dr. Arunachalam R, green practices are gaining momentum. “The adoption of energy-efficient lighting, solar panels, and eco-friendly packaging materials… waste reduction and recycling… reduce the environmental impact.”
Technology is also enabling sustainability. As Asim Behera notes: “Environmental sustainability has emerged as a prominent consideration… companies are investing in eco-friendly practices such as energy-efficient
lighting, renewable energy sources, and waste reduction measures…” Automation and intelligent systems further support energy optimization and resource efficiency. As fulfilment intensity increases, sustainability is no longer optional—it is becoming integral to building resilient and future-ready warehouse networks.
The Rise of
Strategic Logistics Partners
3PLs are evolving from service providers to strategic enablers of supply chain agility
The decision between managing inhouse warehousing operations and outsourcing them to logistics partners often depends on balancing operational control with cost efficiency. For many organizations, outsourcing warehousing allows them to focus on core business functions while leveraging the operational expertise and infrastructure of specialized logistics providers.
According to Kapil Premchandani, outsourcing warehousing operations can offer significant flexibility, particularly when businesses face fluctuating demand patterns.
“The decision between managing your own supply chain or outsourcing it to third-party logistics providers is often a matter of balancing control and cost. Running your own warehouses offers complete control over processes, inventory, and operations, whereas outsourcing can help keep costs competitive while offering flexibility to respond to varying demand.”
Demand fluctuations, seasonal spikes, and regulatory complexities often make it difficult for companies to manage large warehousing networks independently. “The key advantage of
outsourcing lies in its flexibility. Demand patterns can be unpredictable, and there are regulatory complexities to consider. For example, operating large warehouses or regional distribution centers often requires multiple licenses—sometimes as many as eight or nine.”
For organizations launching new products or expanding into new markets, working with experienced logistics partners can simplify operational challenges. “When launching a new product, do you want to navigate the intricacies of obtaining these licenses, or would you prefer to work with experts who can handle this for you? Outsourcing allows you to focus on your core competencies while leaving regulatory challenges in the hands of professionals.”
Seasonal demand spikes represent another major reason companies rely on 3PL providers to scale warehouse capacity. “Consider an example from a company like Mondelez, which faces demand surges during peak seasons, such as Diwali. These spikes require additional warehousing capacity. Managing this internally can stretch resources, but a 3PL provider offers the necessary flexibility to
scale operations efficiently.” However, the effectiveness of outsourced warehousing ultimately depends on selecting the right logistics partner and establishing clear operational frameworks.
According to Nitin Joshi, organizations must treat 3PL providers as longterm strategic partners rather than transactional service vendors. “Looking at 3PLs as long-term strategic partners is essential. From my perspective, the process of selecting a 3PL should be rigorous. Many 3PLs specialize in specific industries, bringing with them extensive expertise.”
A structured evaluation process is therefore essential when choosing logistics partners. “It’s advisable to thoroughly check at least three to four references and visit operational sites in similar industries to assess their capabilities. When engaging with a 3PL, it’s crucial to provide them with sufficient data to accurately evaluate the nature of operations they will manage.”
Effective collaboration between companies and logistics providers also depends on clearly defined operational expectations and performance metrics. “Before implementation, we establish clear communication channels and define expectations through detailed discussions on Service Level Agreements (SLAs), Key Performance Indicators (KPIs), and operational requirements.”
As supply chains evolve, 3PL relationships are shifting toward deeper collaboration—moving from transactional outsourcing to integrated partnerships that enable long-term value creation.
The in
Next Chapter Warehousing
Future-ready warehouses will be defined by agility, intelligence, and integration.
According to Gopala Krishna, warehouse networks are already evolving toward more flexible and distributed fulfilment models designed to support faster deliveries and greater operational efficiency. “Looking ahead, warehousing is evolving toward a hybrid model, balancing dark store setups with large fulfillment centers to optimize efficiency. Predictive inventory management using AI will further reduce overstocking and unstacked inventory issues, while decentralized sourcing models are expected to minimize reliance on large distribution centers.”
The next phase of warehouse transformation will also be shaped by deeper integration between operational efficiency and emerging technologies. As Tannistha Ganguly observes, innovations such as smart operations and immersive technologies are likely to reshape how warehouses function.
“Interesting days ahead, I must admit. I see greater handshaking between efficiency and innovation and expect some ‘wild’ technological disruptions. Some of the trends that I keep my eye on are around Smart Operations (Inventory Tracking, Warehouse Automation), Wearables and Immersive Reality. Smart Wearables, like VR glasses and voice picking devices will play a major role in inventory management, dispatch and shipping, wastage and dumping reduction, cost-effective heavy equipment training (through simulation) among countless other applications.”
Infrastructure development and logistics connectivity will also play a critical role in shaping the future of warehousing. According to Rajan Ekambaram, improved infrastructure and increasing supply chain complexity are pushing warehouse operations toward higher levels of sophistication.
“Warehousing operations are
evolving rapidly, driven by improved road connectivity, increasing supply chain complexity, and rising business expectations. As a result, warehousing is becoming more sophisticated in infrastructure and technology adoption to enhance efficiency, scalability, and service excellence.” One of the most visible trends is the growing shift toward modern Grade-A warehouse infrastructure as well as the rapid expansion of hyperlocal fulfilment models.
“The rise of Grade-A warehouses— developed by real estate firms—is improving infrastructure standards, with a focus on better safety, sustainability, and operational efficiency,” he explains. At the same time, the quick commerce ecosystem is accelerating the development of smaller fulfilment nodes closer to urban consumption centres. “The quick commerce revolution is driving demand for micro-fulfillment centers and dark stores, strategically located in high-density urban areas. These enable hyperlocal deliveries, ensuring rapid order fulfillment within minutes.”
Looking ahead, the planning and design of warehouses themselves are also expected to become far more datadriven. Deepak Jain, Director, Argon & Co., believes that advanced analytics
and simulation tools will increasingly guide how warehouse infrastructure is designed and optimized.
“Technology advancements enable us to gather extensive warehouse data and leverage analytics for smarter layouts, optimized inventory, and process improvements. Using tools such as simulation software and digital modelling, companies can test warehouse configurations, equipment integration, and workflow efficiency before implementation.”
This ability to model warehouse environments virtually allows organizations to design facilities that are not only operationally efficient but also scalable for future demand. Taken together, these developments suggest that the warehouse of the future will be far more than a storage facility. It will function as a highly responsive logistics hub—powered by data, supported by automation, and integrated with transportation networks to enable faster and more resilient supply chains.
Editor’s PERSPECTIVE
Beyond the Archives: Mapping India’s Next Warehousing Hubs
India’s warehousing sector has undergone a remarkable transformation over the past decade. What was once perceived largely as a backend storage function has steadily evolved into a strategic pillar of modern supply chains—driven by the rise of e-commerce, the expansion of organized retail, and the increasing complexity of logistics networks.
As highlighted in this archival feature, the shift from traditional godowns to technology-enabled fulfilment hubs has fundamentally redefined warehouse operations. Automation, digital visibility, and data-driven decision-making are now central to ensuring speed, accuracy, and resilience across supply chains. At the same time, emerging fulfilment models such as quick commerce and omnichannel retail are pushing warehouse networks closer to consumption centres, further altering how logistics infrastructure is designed and deployed.
Yet the story does not end here. New insights from Colliers suggest that India’s warehousing landscape is entering another significant phase of expansion. The global real estate services firm recently identified 30 high-potential industrial and warehousing hotspots across the country, driven by improvements in logistics infrastructure,
manufacturing growth, and supportive policy initiatives.
These emerging hubs reflect the broader structural changes underway in India’s logistics ecosystem. Infrastructure programs such as the National Logistics Policy, Dedicated Freight Corridors, PM Gati Shakti, and Sagarmala are steadily improving connectivity between production centres, logistics parks, and major consumption markets. At the same time, the government’s push for domestic manufacturing under initiatives like Make in India and Production Linked Incentive (PLI) schemes is generating fresh demand for modern industrial and warehousing infrastructure.
The result is a gradual shift toward more distributed and integrated logistics networks. Tier-II and TierIII cities are increasingly emerging as important warehousing destinations, complementing established hubs around major metropolitan regions. These new locations offer strategic advantages such as lower land costs, improving infrastructure connectivity, and proximity to expanding consumption markets.
For businesses, this evolving landscape presents both opportunity and responsibility. As warehouses become central to supply chain competitiveness, companies must continue investing in smarter infrastructure, advanced technologies, and sustainable operational practices.
Looking ahead, the warehouse of the future will be defined not merely by size or storage capacity, but by its ability to function as a digitally enabled, agile, and environmentally responsible logistics node. The insights shared in this archival feature remain highly relevant today—but the next chapter of India’s warehousing journey promises to be even more dynamic, as the sector continues to expand, innovate, and redefine the backbone of the country’s supply chains.
