Improving automotive battery sales forecast
Name
927169170-MIT.pdf
Description
Full printable version
Size
7.63 MB
Format
Adobe PDF
Checksum (MD5)
4b6903901a11c8a1ab694d30cef36367
Author(s)
Bulusu, Vinod
Kim, Haekyun
Advisor(s)
Roberto Perez-Franco.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
Improvement in sales forecasting allows firms not only to respond quickly to customers' needs but also to reduce inventory costs, ultimately increasing their profits. Sales forecasts have been studied extensively to improve their accuracy in many different fields. However, for automotive batteries, it is very difficult to develop a highly accurate forecast model because many variables need to be considered and their correlations are complex. Additionally, current sales forecasts are derived from historical data and thus do not include any other causal factor analysis. In this study we applied causal factor analysis to determine how the forecast accuracy could be improved. We focused on understanding the relationship between temperature and sales. Using regression modelling, we found that there is a quadratic relationship between temperature and battery sales. We validated the model by comparing the actual and predicted sales for various geographies and times. We concluded that the model is more robust for predicting sales across various times than through various geographies.
Description
Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 54-55).
Subjects
Engineering Systems Division.
MIT Department
Massachusetts Institute of Technology. Engineering Systems Division
Terms of Use
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