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Introduction and Risk Analysis in Supply Chains Read chapter Read first chapter

2023 | OriginalPaper | Chapter

2. Analytical Foundations: Predictive and Prescriptive Analytics

Author : Işık Biçer

Published in: Supply Chain Analytics

Publisher: Springer Nature Switzerland

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Abstract

In this chapter, we review important predictive and prescriptive models that can be applied to supply chain problems. We begin with linear models and outline basic assumptions of the ordinary least squares (OLS) approach. Then, we discuss how to extend the OLS method when some of the assumptions are violated. We focus on the generalized least squares (GLS), two-stage least squares (2SLS), generalized method of moments (GMM) and time series analysis as the methods of remediation of restrictive assumptions. We also review the machine learning regularization approaches and classification methods that are proven to be effective in dealing with high dimensionality and categorical variable issues. We later introduce some fundamental theories of predictive analytics that are used in the following chapters of this book.

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previous chapter Introduction and Risk Analysis in Supply Chains
next chapter Inventory Management Under Demand Uncertainty
Footnotes
1
We refer the reader to Shumway and Stoffer (2011, pp. 121–140), Chatfield (2004, Ch. 4) or Hamilton (1994, Ch. 5) for time series estimation methods.
 
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Metadata
Title
Analytical Foundations: Predictive and Prescriptive Analytics
Author
Işık Biçer
Copyright Year
2023
Book
Supply Chain Analytics

Print ISBN: 978-3-031-30346-3

Electronic ISBN: 978-3-031-30347-0

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-30347-0_2

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