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2023 | Book

Introduction Read chapter Read first chapter

Procurement Analytics

Data-Driven Decision-Making in Procurement and Supply Management

Author: Christian Mandl

Publisher: Springer Nature Switzerland

Book Series : Springer Series in Supply Chain Management

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

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About this book

This unique textbook explicitly addresses the intersection of advanced analytics and procurement. It is motivated by one core question: How can firms generate (economic) value from procurement data? It demonstrates that procurement is one of the major functions within a firm where data analytics, artificial intelligence, and operations research can successfully be leveraged to reduce cost and risk and to achieve resilience and sustainability goals.

The book provides a methods-based overview of data-driven optimization of purchasing decisions. Besides presenting key concepts and applications, it particularly focuses on implementation, so as to help (future) procurement managers and data scientists quickly evaluate the value generated by a given data-driven solution. What sets this textbook apart is its combination of rigorous, state-of-the-art methodologies from academic research and first-hand experience from various application-oriented consulting projects in a range of industries.

Though primarily intended for graduate students with a major in procurement and supply chain management, the book will also benefit purchasing managers with and without specific knowledge of advanced analytics techniques, and data scientists with and without specific experience in procurement.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
This chapter aims at pointing out the potential of data-driven procurement optimization and shows the current state of digitization in procurement and supply management. It gives a brief overview of latest technologies, innovations and trends in digital procurement along the source-to-pay (S2P) process such as artificial intelligence (AI), robotic process automation (RPA), chatbots, text mining or blockchain and highlights concrete procurement applications for advanced data analytics.
Christian Mandl
Chapter 2. Fundamentals of Data Analytics
Abstract
This chapter gives an introduction to the methodological foundations of data analytics that is required for an effective reading of the subsequent chapters of this textbook. The structure follows a widespread and accepted breakdown of analytics levels, i.e., descriptive analytics, predictive analytics and prescriptive analytics, presenting accuracy measures and methods for each field—always related to problem settings from the procurement function. The chapter covers areas such as data collection, data visualization, prediction and classification methods from machine learning, mathematical optimization, simulation and game theory. It builds the methodological basis for procurement analytics professionals that plan to develop or extend their own planning approaches. Readers who are familiar with the basics of data analytics such as correlation analysis, regression models, linear programming and classification models can easily skip this technical chapter.
Christian Mandl
Chapter 3. Data-Driven Spend Management
Abstract
This chapter targets the analysis of a company’s spend cube, which builds the data basis for data-driven optimization in procurement. We present standard and advanced spend classification methods from the field of machine learning, major key performance indicators for spend control from different dimensions (e.g., cost, time, risk and sustainability) as well as spend analytics and spend intelligence methods such as should-cost analysis or linear and nonlinear performance pricing for spend optimization through finding price inconsistencies and discrepancies in supplier data.
Christian Mandl
Chapter 4. Data-Driven Supplier Management
Abstract
This chapter presents analytical models and use cases for data-driven decision-making in buyer-supplier relationships. It covers standard sourcing strategies (e.g., make or buy), digital tender management, auction theory, bid analytics, game theory in supplier negotiations and multi-criteria supplier selection and evaluation techniques. It furthermore addresses analytical methods for supply network optimization under cost, sustainability, risk and resilience aspects and data-driven planning approaches for supply contracting and supply chain coordination.
Christian Mandl
Chapter 5. Data-Driven Inventory Management
Abstract
Over the last decades that were characterized by just-in-time supply chains, inventories were highly unpopular and avoided wherever possible in order to reduce capital lockup to a minimum. However, severe supply disruptions and inflation risk in the early 2020s showed that inventories can be of strategic importance in times of high volatility and economic uncertainty. This chapter focuses on single-item inventory optimization and addresses both deterministic and stochastic models as well as single-period and multi-period approaches to optimally control stock levels in a data-driven manner. The chapter introduces important inventory metrics for performance management, analytical safety stock planning models and latest developments in machine learning and deep learning for inventory management applications.
Christian Mandl
Chapter 6. Data-Driven Risk Management
Abstract
The world is increasingly becoming volatile and uncertain. In the procurement context, this gets obvious through severe supply disruptions for semiconductors and other basic purchase materials as well as through unforeseeable upward commodity price movements in the early 2020s at metal, energy and agricultural markets. In this chapter, we provide guidance on how to measure the degree of procurement risk that a company is facing and present analytical approaches to reduce risk. Doing so, we distinguish between three major exogenous risk factors that affect procurement performance: supply disruption risk, commodity price risk and exchange rate risk.
Christian Mandl
Chapter 7. Conclusion
Abstract
This short concluding chapter summarizes the content of the textbook and provides a brief outlook on potential developments in procurement analytics during the next decade based on advancements in, e.g., generative artificial intelligence, cognitive analytics, quantum computing, cloud computing and 3D printing.
Christian Mandl
Metadata
Title
Procurement Analytics
Author
Christian Mandl
Copyright Year
2023
Electronic ISBN
978-3-031-43281-1
Print ISBN
978-3-031-43280-4
DOI
https://doi.org/10.1007/978-3-031-43281-1