Time Series Analysis For Blayer Pharmblayer Pharm Sells Two Types Of B Time Series Analysis for Blayer Pharm Blayer Pharm sells two types of blood pressure cuffs at more than 50 locations in the Midwest. The first style is a relatively expensive model, whereas the second is a standard, less expensive model. Although weekly demand for these two products is fairly stable from week to week, there is enough variation to concern management. There have been relatively unsophisticated attempts to forecast weekly demand but they haven't been very successful. Sometimes demand (and the corresponding sales) is lower than forecasts, so inventory costs are high. Other times, the forecasts are too low. When this happens, and on-hand inventory is not sufficient to meet customer demand, Blayer requires expedited shipments to keep customers happy—and this nearly wipes out Blayer’s profit margin on the expedited units. Profits would almost certainly increase if demand could be forecast more accurately. Data on weekly sales of both products appear in the file for this week. A time series chart of the two sales variables indicates what Blayer management expected—namely, there is no evidence of any upward or downward trends or of any seasonality. In fact, it might appear that each series is an unpredictable sequence of random ups and downs. For this Assignment, reflect on the scenario presented. Review the resources for this week and consider how you might apply time series analyses to address the case questions.
Paper For Above instruction In this analysis, we explore the application of different time series forecasting methods to predict weekly sales of two blood pressure cuff products sold by Blayer Pharm. The goal is to identify the most accurate forecasting approach, which can potentially reduce inventory costs and avoid costly expedited shipments. The data being analyzed are weekly sales figures for both product types, and initial observations suggest that these series display no clear trend or seasonal pattern, appearing more like random fluctuations. Two primary forecasting methods were applied to the dataset: (1) the Moving Average method and (2) the Exponential Smoothing method, specifically Holt-Winters exponential smoothing without seasonality, given the absence of seasonal patterns. These methods are selected due to their effectiveness for series with no trend or seasonality, and their simplicity makes them suitable for operational forecasting scenarios. Application of Moving Average Method The moving average method smooths out short-term fluctuations and highlights longer-term trends or