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2023 | Book

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

Supply Chain Analytics

An Uncertainty Modeling Approach

Author: Işık Biçer

Publisher: Springer Nature Switzerland

Book Series : Springer Texts in Business and Economics

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This textbook offers a detailed account of analytical models used to solve complex supply chain problems. It introduces a unique risk analysis framework that helps the reader understand the sources of uncertainties and use appropriate models to improve decisions in supply chains. This framework illustrates the complete supply chain for a product and demonstrates the supply chain's exposure to demand, supply, inventory, and financial risks.

Step by step, this book provides a detailed examination of analytical methods that optimize operational decisions under different types of uncertainty. It discusses stochastic inventory models, introduces uncertainty modeling methods, and explains methods for managing uncertainty. To help readers deepen their understanding, it includes access to various supplementary material including an online interactive tool in Python.

This book is intended for undergraduate and graduate students of supply chain management with a focus on supply chain analytics. It also prepares practitioners to make better decisions in this field.

Table of Contents

Frontmatter
Chapter 1. Introduction and Risk Analysis in Supply Chains
Abstract
In this chapter, we introduce a unified supply chain framework based on the flows of products, information and capital. Using this framework, we identify five salient parameters that characterize supply chains and present some indicators of supply chain risks derived from the salient parameters. We later discuss the roles of demand management, supply chain integration and operational flexibility in managing uncertainties along supply chains. We emphasize the uncertainty modelling approach as an effective demand management tool and demonstrate how it helped a global manufacturer increase the bottom line substantially.
Işık Biçer
Chapter 2. Analytical Foundations: Predictive and Prescriptive Analytics
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.
Işık Biçer
Chapter 3. Inventory Management Under Demand Uncertainty
Abstract
Effective inventory management is crucial to long-term profitability of organizations. Companies that manage their inventories effectively can substantially improve their operating margins and gain a competitive edge in their industries. We elaborate on inventory management theories under demand uncertainty in this chapter. We first focus on inventory problems that have analytical solutions. One key message of this chapter is that decision-makers can find optimal or near-optimal solutions intuitively by applying marginal analysis without using exhaustive derivations. We also discuss how the assumptions on which analytical results are based are often violated in practice. We conclude the chapter with the Monte Carlo simulation, which has proven to be effective in solving complex supply chain problems when no analytical solution exists.
Işık Biçer
Chapter 4. Uncertainty Modelling
Abstract
Organizations attempt to create value for their customers while managing different uncertainties. Uncertainties in supply chains offer both opportunities and challenges to decision-makers. We start this chapter by reviewing three uncertainty types (i.e. truth, epistemological and ontological uncertainty) and discuss why the ontological uncertainty deserves special attention. We study the uncertainty modelling approach along two dimensions to address the challenges of managing the ontological uncertainty. First, we introduce the additive and multiplicative demand models to capture the evolutionary dynamics of uncertainty. Second, we focus on advanced methods, such as the fast Fourier transformation (FFT) and demand regularization, to model complex demand dynamics when demand is formed as combination of different uncertain elements.
Işık Biçer
Chapter 5. Supply Chain Responsiveness
Abstract
Supply chain responsiveness helps companies reduce the mismatches between supply and demand. When the demand uncertainty is high, companies with more responsive supply chains can better align their resources with fluctuating demand, thereby minimizing the mismatch risk. In this chapter, we present the methods used to enhance the responsiveness of supply chains, such as lead time reduction, multiple sourcing, and quantity flexibility. One challenge of establishing responsiveness is the difficulty of quantifying its value of organizations. To address this challenge, we propose analytical methods that help price the value of supply chain responsiveness depending on the evolutionary dynamics of demand uncertainty.
Işık Biçer
Chapter 6. Managing Product Variety
Abstract
Companies increase product variety to tailor their product offerings to unique tastes and needs of their customers. Although expanding product portfolio helps increase sales, it causes some operational challenges, potentially leading to higher operational costs. In this chapter, we focus on three aspects of managing product variety: (1) product selection, (2) resource allocation, and (3) establishing operational excellence. We first demonstrate that how the mean-variance analysis can be applied to the product selection problem. We later illustrate how the inventory management and supply chain responsiveness models discussed in previous chapters can be extended to manage product variety. We conclude this chapter by discussing how to combine the responsiveness and cost reduction strategies to establish operational excellence.
Işık Biçer
Chapter 7. Managing the Supply Risk
Abstract
In this chapter, we focus on supply risks such that the availability of products in the market can be disrupted due to some supply chain problems. Effective strategies to mitigate supply risks depend on the type of supply disruption and the ownership structure of inventory during the shipment. We consider risk mitigation inventory and reactive capacity strategies to reduce the negative impact of supply risks on firms’ profits. We then analyse the optimal risk mitigation strategies for three types of supply risks. We also link these risk types to the Incoterms of international trade to highlight how to insure inventory against supply risks depending on the Incoterm agreement between supply chain parties.
Işık Biçer
Chapter 8. Supply Chain Finance
Abstract
Supply chain activities directly influence the cash-flow performance of organizations. For example, reducing the production lead time helps companies not only improve supply chain responsiveness but also generate revenues in a short time period. Companies can also employ different instruments (i.e. early payment scheme, reverse factoring, letter of credit and dynamic discounting) to finance their operations. While an effective operational strategy would have positive implications for cash-flow management, it may fail to generate sustainable profits if it is not coupled with the right financial strategy. In this chapter, we discuss the characteristics of supply chain finance strategies. We also present analytical methods to quantify their values for companies.
Işık Biçer
Chapter 9. Future Trends: AI and Beyond
Abstract
In this chapter, we discuss the potential of artificial intelligence (AI) to transform supply chains digitally. We elaborate on some future trends of AI and how AI can be adapted to solve some important supply chain problems. We argue that the misspecification problems in artificial neural networks (ANNs) could be an obstacle hindering the adaption of AI in supply chain management. Another weakness of ANNs is their incapability of planning complex action sequences. Nevertheless, we are optimistic that once these issues are addressed by researchers, AI has the potential to revolutionize supply chains.
Işık Biçer
Backmatter
Metadata
Title
Supply Chain Analytics
Author
Işık Biçer
Copyright Year
2023
Electronic ISBN
978-3-031-30347-0
Print ISBN
978-3-031-30346-3
DOI
https://doi.org/10.1007/978-3-031-30347-0