Creating Values with Operations and Analytics

This chapter introduces a framework that explores the wide range of models and methods that have been used to analyze supply chains. We discuss the origins and attributes of eight different approaches, ranging from science fiction to religion. In between we consider ideas and concepts from quantum physics, engineering, management science, economics, social science, and policy analysis. We note that there is a fundamental trade-off between model fidelity and tractability, which impacts the role of the different approaches in analyzing supply chains. The paper then discusses the implementation of model-based methods and draws insights based on an application case. The application case illustrates how new technology (big data, cloud computing, and machine learning) can be combined to develop better supply chain planning solutions. The paper concludes with observations suggested by the case and the examples explored in the paper, and notes that a key requirement is to achieve robustness in the application of supply chain models to real-world planning problems. This will ensure that the output and predicted results of a model are consistently accurate even as the input variables or assumptions change drastically due to unforeseen circumstances.

More and more firms use connected technologies to reshape fundamentally the way in which they interact with their customers. Rather than having few episodic interactions, companies are trying to create a continuous relationship with their customers that reduces friction and allows companies to anticipate the needs of their customers. In this chapter, we discuss new business models enabled by this development. We do so by reviewing some of the literature on after-sales services in general and some of Morris Cohen’s pioneering research in particular. We then extend this prior work by articulating a framework of “Connected Strategy” in the form of a taxonomy of four connected customer experiences. Finally, we apply our Connected Strategy framework to the domain of healthcare delivery.

This chapter reviews Morris Cohen’s scholarly contributions to new product development (NPD) and the other areas at the interface of marketing and production. Specifically, we examine how Morris and his co-authors’ pioneering work in NPD has generated follow-up work by a number of scholars, demonstrating the frequent tension between the marketing and production functions. The authors provide rigorous support for performance metrics used by practitioners in the NPD process. Their work on a data-driven decision support system shows the usefulness of their research to industry. In summary, this work on NPD reveals who Morris Cohen is as a scholar—someone with the rare ability to link rigorous research with practical implementation.

The triple bottom line, coined by the famed business writer John Elkington, consists of three elements—profit, people, and the planet. It maintains that companies should commit to focusing as much on environmental and social issues as they do on profits. This chapter reviews existing literature that focuses on the financial and environmental impacts of product design and presents recent work to motivate research needs on the social impact of product design. It also includes a general demand model that can serve as a starting point for future researchers to develop methodologies to help companies address product design issues with the triple bottom line.

The COVID-19 pandemic has not only caused unprecedented disruptions of global supply chains but also exposed unfair supply chain practices including unfair pricing, unfair trade, and unfair pay. Despite these unfair incidents, we observe that new industry initiatives have been developed to address various supply chain fairness issues. In this chapter, we discuss the notion of supply chain fairness as well as its strategic values and historical movements. We further outline the challenges of putting fairness to practice and various research opportunities in this area.

In commemorating Morris’s illustrious research career, in this chapter, we examine the genesis and legacy of the research on Performance-Based Contracting, the subject Morris focused on in the years since 2004. Based on the written notes and voice recordings from the early days of research, we provide first-hand accounts of how the research got its start, memorable anecdotes, and the process through which research was developed and conducted. We then discuss the impact of our early research effort by reviewing some of the relevant articles that have appeared in the operations management literature since our collaboration started. The chapter concludes with a discussion of the current state of performance-based contracting in practice, how it may evolve in the future, and what we learned in the process of developing this project.

Research on operations analytics has focused on the design and execution of processes and projects. For projects in particular, the emphasis is on factors such as plans, stakeholders, and uncertainty; and their effects on the outcomes of projects. Little attention is paid to a variable of considerable importance: ethical behaviour, particularly corruption in large projects. This matters because studies of corruption have shown that corruption inflates project budgets (by sometimes 30%) and thus imposes an unproductive tax. This chapter builds on an original dataset of 38 very large government projects in Nigeria to demonstrate that corruption not only inflates project budgets but also distorts decisions, rendering other project management success drivers less effective. The chapter demonstrates that corruption has negative interactions with the positive effect of project success drivers and illustrates on a detailed case example what these interactions look like in practice.

After sales service is a critical component of any firm’s customer acquisition and retention strategy, especially when the products are capital intensive, such as in aerospace and defense, heavy manufacturing, telecommunications, construction equipment, medical devices, process industry, consumer electronics, transportation, and semi-conductor equipment industries. These products are often deployed in mission critical environments, which makes ensuring uptime of these assets critical. Therefore, manufacturers of such products often sell service contracts designed to ensure consistent availability of these assets. Consequently, design and management of the infrastructure needed to deliver such services, also called as the service supply chain, is an extremely important topic for practitioners. Not surprisingly, a significant body of research has emerged in the operations management literature to address the underlying analytical challenges. Distinct attributes of the assets and parts needed to provide service, ways in which demand for service parts occurs and can be satisfied, definition of customer service, and the network of physical locations from where demand can be fulfilled make the underlying analytical problem particularly challenging, and, therefore, exciting for academic researchers. This chapter reviews the key foundational concepts related to the design and management of the inventory strategy needed to provide cost-effective service in spares parts management (SPM) systems. It also provides a brief description of the software solutions provided by some of the major software vendors in the SPM space. It concludes with a brief summary of some of the most exciting trends in this arena that could lead to opportunities for further research by academics and enhancements by industry solutions providers.

This chapter explores the potential of distributed ledger technology (DLT) in addressing supply chain shortages and competition for scarce resources. Specifically, we assess the effect of strategic information sharing on supply chain efficiency and the creation of virtual markets to improve supply chain performance. To facilitate this research, we designed a simulation platform called DISASTER (DLT In Sourcing And Strategic Trading Experimental Research), which hosts web-based, dynamic, and customizable supply chain simulations that leverage concepts of blockchain technology, and permit capturing of information regarding players’ ordering strategies and behavioral traits.

In this chapter, we describe the DISASTER platform and discuss two selected DISASTER simulations that probe supply chain retailers’ order behavior: the first investigates the role of information sharing among competing retailers; the second allows for the trading of tokens among competing retailers. In the first simulation, we find that decision makers act more strategically and closer to Nash equilibrium predictions as more information about historical orders of competitors is shared; however, the observed outcome is not invariably an improvement in efficiency as measured by profits across participants. In the second simulation, we observe that initial order quantities remain unchanged as compared to the baseline (non-trading) scenario, despite the possibility to trade on virtual markets; however, over time, more equitable distribution of inventory is achieved, and the supply chain efficiency as measured by profits increases.

Our findings highlight the value of empirical research and management games in shedding light on the role of decision makers’ behavioral characteristics and investigating real-life supply chain challenges and the potential of adopting blockchain-specific capabilities in that space.

The semiconductor industry has seen some of the most technological achievements in the last century with the most complex supply chains among all modern industries. To address the daunting challenges presented in this industry, operations management (OM) researchers have done numerous studies, which we group into three broad areas: (1) capacity expansion, allocation, and upgrading; (2) outsourcing, contracting, and procurement; (3) production, quality control, and maintenance. In this chapter, we highlight the development of the OM literature on the above three areas with applications to the semiconductor industry. In addition, the most recent information technology advancements, notably cloud computing and cloud-based artificial intelligence, not only have profound impacts on the existing semiconductor supply chains but also give rise to new industries. Hence, through our discussions of the existing studies and future research opportunities, we hope that this chapter will stimulate new research ideas that are either extensions to the previous studies or applications to new operational environments in the semiconductor and computing technology industries.

In 1999–2001, Morris Cohen led a research study of the US semiconductor equipment supply chain, of which the author was a student member. Through extensive field interviews and subsequent data collection, the team was able to empirically test the effectiveness of forecast sharing between a major US semiconductor manufacturer and its largest semiconductor equipment supplier using a novel “imputed cost” approach. This research eventually yielded two empirical papers published in Management Science (and a third one in Operations Research), which collectively elevated the standards of empirical research in Operations Management.

In this chapter, we pursue two main objectives, both related to health care operations. The first objective is to provide an overview of Morris Cohen’s main contributions to health care operations research and of the current research trends on the related topics: blood bank inventory management, patient in-hospital flows, and patient choice of hospitals. The second objective is to review recent developments on the adoption of telemedicine, a rapidly growing component of the health care delivery system, and the related research literature in the field of operations.

This chapter discusses risk management with a focus on the airline industry. The world has become acutely aware of major supply chain disruptions due to the COVID pandemic. Consumers, airline passengers, and companies are scrambling to understand and respond to these events. In that light, we begin the chapter with a brief overview of risk management, highlighting both common and catastrophic risks faced by companies and their supply chains. We then discuss approaches that companies employ to mitigate them. Our primary goal is to explore the risks that airlines face and the approaches they take to manage them, including fuel hedging, capacity management, and ticket pricing. Based on company interviews and our firsthand experience, we note that the airlines typically make these decisions in silos. Therefore, we introduce an analytical model that explicitly integrates them. We derive analytical results and propose directions for future research. We conclude with summary comments about managing risks once the world moves past COVID.

This chapter summarizes the last 8 years of collaborative research of a global group of scholars on supply chain management and especially on how companies are dealing with uncertainties and disruptions. Starting with analyzing the factors that drive changes in global supply chain designs, this chapter describes how companies are coping with new types of disruptions such as trade conflicts, natural disasters, and pandemics. Commonly suggested resilience strategies like reshoring or regionalization are de-mystified and discussed based on first-level insights from interviews and survey data. Moreover, we analyzed how companies have handled different types of disruption and the underlying efficiency-resilience trade-offs. The chapter then outlines the different types of complexity and obstacles to supply chain resilience that companies have to overcome based on their individual product characteristics, market environment, and supply chain setup. Finally, the need for measuring resilience is outlined and proposed resilience metrics are discussed.