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Analytical Foundations: Predictive and Prescriptive Analytics

In: Supply Chain Analytics

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  • Işık Biçer

    (York University)

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.

Suggested Citation

  • Işık Biçer, 2023. "Analytical Foundations: Predictive and Prescriptive Analytics," Springer Texts in Business and Economics, in: Supply Chain Analytics, chapter 0, pages 27-74, Springer.
    • Handle: RePEc:spr:sptchp:978-3-031-30347-0_2
    DOI: 10.1007/978-3-031-30347-0_2
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