Sales Forecast Accuracy Analysis in Retailer Management

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Title: Sales Forecast Accuracy Analysis in Retailer Management
Author: Gao, Jing
Advisors: Robert Handfield, Committee Chair
Kevin McCormack, Committee Co-Chair
Henry Nuttle, Committee Member
Abstract: Sales Forecast Accuracy (SFA) is a critical issue in supply chain management. More and more people have noticed that poor estimates of future demand will lead to low revenue performance caused from high stock out or excess inventory. To achieve an SFA as high as possible, many new methods have been developed and a lot of effort and money has been committed by the retailers and manufactures. However, sometimes high SFA does not result in as high a profit as was expected. This paper analyses the importance of SFA for different items in retail management, and defines a new concept called 'key SFA' which is the SFA that has the right balance between cost and return for the situation. In this paper, a supply and demand chain simulation model, based on the actual GlaxoSmithKline (GSK) logistics system (SPIRiT), was developed. In this model, we generated a series of random variables as the real daily demand, which are evenly distributed in a fixed range. This range is determined by SFA. Four GSK products were selected as samples to run the simulation and their specific information was extracted from SPIRiT. We then developed assumptions about sales forecasts analysts' salary cost and effort to improve SFA and the qualification of expected earnings. Finally the key SFA of different items was determined based on the simulation results of annual profit, lost-demand cost and inventory cost. Our conclusion was in contrast to the general thinking of supply chain managers which is: the higher SFA, the better profit, no matter what level of accuracy or product. Instead, the conclusion from this research was that each item has its key SFA and a higher SFA will not bring an increase in profit worth pursuing. This diminishing returns analysis and the key SFA concept in this paper seems to hold true for most product segments. However, for product segments which have substantially different characteristics than our sample set, future research is required to validate this conclusion.
Date: 2006-05-23
Degree: MS
Discipline: Operations Research

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