International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
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Heuristic Approach for Demand Forecasting under the Impact of Promotions 1Sahana.N, 2Dr.
N.V.R. Naidu
1Student,
MTech II Year, Department of IEM, Ramaiah Institute of Technology, Karnataka, India 2Principal, Ramaiah Institute of Technology, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract— A Balance between the demand and supply is one of the indicator of the position of your organization in the Market. Every organization tries to match their supply with the demand. However, balancing between the two is highly impossible because of the changing customer requirements. Retail stores whose customer are the end users is very dynamic system as it is prone to the fluctuations immediately. Hence balancing the supply and demand is very difficult. Forecasting the demand of products helps in making products available readily. If there are no changes in demand, forecasting the future demand would be very easy. However, in this uncertain environment this is a complicate work. Forecasting the demand of products when promotions are offered is a crucial step as maintaining high inventory would increase inventory cost and less inventory would lead to losing the customers. Time series method are the best techniques available for forecasting the demand when subjected to constraints. However, while considering a new variable, promotions these methods are not effective. Support Vector regression(SVR) is one such method that will produce the forecast the demand based on the different type of promotions planned by the stores
forecasts. In this paper Support Vector Regression(SVR) method is used to forecast demand when promotions are applied. The demand for the retail store under study was predicted using Simple moving average, weighted moving average and SVR. MAPE was used for comparing the results. Finally, an effective method was developed by coming the moving average and SVR to forecast demand of SKU’s at division level. 2. Literature Review Forecasting is a process of estimating a future event by casting forward the past data. The past data are systematically combined in a predetermined way to obtain the estimate of future. Prediction is the estimation of future event based on the various consideration other than just past data. Thus, forecasting is an estimate of future values of specified indicator relating to decisional/planning situation [6]. The number of variables/factors and degree of details required in the forecasting depends on the intend of use of the forecasted value. 2.1 Support Vector Regression
Key words— Support Vector regression, MAPE, Simple moving average, Weighted moving average, MAD, Retail stores, Demand forecast.
SVM and its regression version, Support vector regression (SVR) implicitly map instances to higher dimensional feature space using kernel function. SVR ideally seeks to identify a linear function in this space that is within epsilon distance to the mapped output points. One of the major shortcoming of the SVR methodology is its difficulty in giving explanation beyond prediction.
1. Introduction Demand planning can be said as an art of getting the right stock to the right store at the right time. The key challenge for any retail store is to minimize or eliminate shelf out of stock. Consumer behaviour has a significant value for retailers, more than ever. Today consumers are affected by various non-price factors such as quality, availability, store attribute, entertainment shopping and many such non-price attributes. To maintain this optimum inventory, the forecast of demand of the products needs to be made precisely. Promotions refers to raising awareness among the customers about the product in order to increase the sales of the product. Promotions also may refer to offers provided on the product which will attract the customers towards them and in turn increase the sale of the products.
The foundation for support vector machine was laid by Vapnik in 1995 and gaining popularity due to high empirical performance. The formulation uses Empirical risk minimization (ERM) principle, which has proven to be more superior than Structural risk minimization (SRM) principle that is used by conventional Neural networks [5]. Unlike the neural network where training is a search that may or may not yield the best model, the SVM is based on yields the best model for the inputs. Maximizing the margin of separation and minimizing the total empirical error in a balanced way, SVM have performed well where complex relationship between input attributes and output attribute exist [1].
Forecasting using time series methods provide an effective result. However, in the presence of constraints like promotions, these methods do not produce effective
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