Manufacturing and Service Operations Management

Telephone call centers are an integral part of many businesses, and their economic role is significant and growing. They are also fascinating sociotechnical systems in which the behavior of customers and employees is closely intertwined with physical performance measures. In these environments traditional operational models are of great value—and at the same time fundamentally limited—in their ability to characterize system performance.

We review the state of research on telephone call centers. We begin with a tutorial on how call centers function and proceed to survey academic research devoted to the management of their operations. We then outline important problems that have not been addressed and identify promising directions for future research.

We consider a simple supply-chain contract in which a manufacturer sells to a retailer facing a newsvendor problem and the lone contract parameter is a wholesale price. We develop a mild restriction satisfied by many common distributions that assures that the manufacturer's problem is readily amenable to analysis. The manufacturer's profit and sales quantity increase with market size, but the resulting wholesale price depends on how the market grows. For the cases we consider, we identify relative variability (i.e., the coefficient of variation) as key: As relative variability decreases, the retailer's price sensitivity decreases, the wholesale price increases, the decentralized system becomes more efficient (i.e., captures a greater share of potential profit), and the manufacturer's share of realized profit increases. Decreasing relative variability, however, may leave the retailer severely disadvantaged as the higher wholesale price reduces his profitability. We explore factors that may lead the manufacturer to set a wholesale price below that which would maximize her profit, concentrating on retailer participation in forecasting and retailer power. As these and other considerations can result in a wholesale price below what we initially suggest, our base model represents a worst-case analysis of supply-chain performance.

Bài báo này rà soát tài liệu về quản lý công suất chiến lược với nội dung liên quan đến việc xác định kích thước, loại và thời điểm của các khoản đầu tư và điều chỉnh công suất trong điều kiện không chắc chắn. Đặc biệt chú ý đến những phát triển gần đây trong việc tích hợp nhiều quyết định, nhiều loại công suất, phòng ngừa và sự không ưa rủi ro. Công suất là thước đo khả năng và giới hạn xử lý và được biểu diễn dưới dạng một vectơ các cổ phiếu của các nguồn lực xử lý khác nhau, trong khi đó, đầu tư là sự thay đổi công suất và bao gồm mở rộng và thu hẹp. Sau khi thảo luận các vấn đề chung trong vấn đề đầu tư công suất, bài báo này rà soát các mô hình đầu tư công suất dưới điều kiện không chắc chắn trong ba bối cảnh:

Bối cảnh đầu tiên rà soát đầu tư công suất tối ưu bởi các nhà quyết định không ưa rủi ro đơn lẻ và đa dạng trong một môi trường ổn định nơi công suất không thay đổi. Khi chấp nhận nhiều loại công suất, danh mục công suất tối ưu liên quan chỉ định số lượng và địa điểm của công suất an toàn trong một mạng lưới xử lý. Tính năng chủ yếu của nó là nó không cân bằng; nghĩa là, dù cho sự không chắc chắn được hiện thực hóa thế nào, người quản lý công suất thông thường sẽ không bao giờ tận dụng hết tất cả các công suất. Bối cảnh thứ hai rà soát việc điều chỉnh công suất theo thời gian và cấu trúc của động lực đầu tư tối ưu. Bài báo kết thúc bằng việc rà soát cách tích hợp sự không ưa rủi ro trong đầu tư công suất và đối chiếu các chiến lược phòng ngừa liên quan đến phương tiện tài chính so với phương tiện hoạt động.

The view that adopting an environmental perspective on operations can lead to improved operations is in itself not novel; phrases such as “lean is green” are increasingly commonplace. The implication is that any operational system that has minimized inefficiencies is also more environmentally sustainable. However, in this paper we argue that the underlying mechanism is one of extending the horizons of analysis and that this applies to both theory and practice of operations management. We illustrate this through two principal areas of lean operations, where we identify how successive extensions of the prevailing research horizon in each area have led to major advances in theory and practice. First, in quality management, the initial emphasis on statistical quality control of individual operations was extended through total quality management to include a broader process encompassing customer requirements and suppliers’ operations. More recently, the environmental perspective extended the definition of customers to stakeholders and defects to any form of waste. Second, in supply chain management, the horizon first expanded from the initial focus on optimizing inventory control with a single planner to including multiple organizations with conflicting objectives and private information. The environmental perspective draws attention to aspects such as reverse flows and end-of-life product disposal, again potentially improving the performance of the overall supply chain. In both cases, these developments were initially driven by practice, where many of the benefits of adopting an environmental perspective were unexpected. Given that these unexpected side benefits seem to recur so frequently, we refer to this phenomenon as the “law of the expected unexpected side benefits.” We conclude by extrapolating from the developmental paths of total quality management and supply chain management to speculate about the future of environmental research in operations management.

The terms pull and lean production have become cornerstones of modern manufacturing practice. However, although they are widely used, they are less widely understood. In this paper, we argue that while the academic literature has steadily revealed the richness of the pull/lean concepts, the practitioner literature has progressively simplified these terms to the point that serious misunderstandings now exist. In hopes of reducing confusion, we offer general, but precise definitions of pull and lean. Specifically, we argue that pull is essentially a mechanism for limiting WIP, and lean is fundamentally about minimizing the cost of buffering variability.

We consider a single-period inventory model in which a risk-averse retailer faces uncertain customer demand and makes a purchasing-order-quantity and a selling-price decision with the objective of maximizing expected utility. This problem is similar to the classic newsvendor problem, except: (a) the distribution of demand is a function of the selling price, which is determined by the retailer; and (b) the objective of the retailer is to maximize his/her expected utility. We consider two different ways in which price affects the distribution of demand. In the first model, we assume that a change in price affects the scale of the distribution. In the second model, a change in price only affects the location of the distribution. We present methodology by which this problem with two decision variables can be simplified by reducing it to a problem in a single variable. We show that in comparison to a risk-neutral retailer, a risk-averse retailer in the first model will charge a higher price and order less; where as, in the second model a risk-averse retailer will charge a lower price. The implications of these findings for supply-chain strategy and channel design are discussed. Our research provides a better understanding of retailers' pricing behavior that could lead to improved price contracts and channel-management policies.

Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which includes both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.

Two innovative financing schemes have emerged in recent years to enable suppliers to obtain financing for production. The first, purchase order financing (POF), allows financial institutions to offer loans to suppliers by considering the value of purchase orders issued by reputable buyers. Under the second, which we call buyer direct financing (BDF), manufacturers issue both sourcing contracts and loans directly to suppliers. Both schemes are closely related to the supplier’s performance risk (whether the supplier can deliver the order successfully), upon which the repayment of these loans hinges. To understand the relative efficiency of the two emerging schemes, we analyze a game-theoretical model that captures the interactions among three parties (a manufacturer, a financially constrained supplier who can exert unobservable effort to improve delivery reliability, and a bank). We find that, when the manufacturer and the bank have symmetric information, POF and BDF yield the same payoffs for all parties irrespective of the manufacturer’s control advantage under BDF. The manufacturer, however, has more flexibility under BDF in selecting contract terms. In addition, even when the manufacturer has superior information about the supplier’s operational capability, the manufacturer can efficiently signal her private information via the sourcing contract if the supplier’s asset level is not too low. As such, POF remains an attractive financing option. However, if the supplier is severely financially constrained, the manufacturer’s information advantage makes BDF the preferred financing scheme when contracting with an efficient supplier. In particular, the relative benefit of BDF (over POF) is more pronounced when the supply market contains a larger proportion of inefficient suppliers, when differences in efficiency between suppliers are greater, or when the manufacturer’s alternative sourcing option is more expensive.

The online appendix is available at https://doi.org/10.1287/msom.2017.0638 .

This paper has been accepted for the Manufacturing & Service Operations Management Special Issue on Interface of Finance, Operations, and Risk Management.

Problem description: We study the impact of credit ratings on operational and financial decisions of a supply chain with a supplier and a retailer interacting via an early payment discount contract. The retailer has a single opportunity to order a product from the supplier to satisfy future uncertain demand. Both the retailer and supplier are capital constrained, and the retailer can use both short-term bank loans and trade credits for his financing needs, while the supplier can use short-term bank loans and/or the retailers early payment. We analyze for all relevant operational decisions (wholesale price, trade credit rates, bank loans, and order quantity) for capital-constrained firms. Academic/practical relevance: We add a framework on who should finance inventories, and at what rates, in the presence of differential credit ratings of the supply chain parties. Methodology: Within a modified selling to the newsvendor Stackelberg game with the supplier as the leader, we derive the equilibrium trade credit rates, wholesale price, bank loans, and order quantity. Results: We show there exists a threshold such that if the supplier’s credit rating is above it, then the supplier offers trade credits with zero interest rate and the retailer uses trade credits only. Otherwise, the supplier sets a positive rate, which motivates the retailer to combine trade credits and bank loans. The supplier always benefits from working with good rating retailers. A retailer prefers to work with suppliers outside the supplier’s credit rating hole (a finite set of ratings) over suppliers with ratings within the range. Managerial implications: We provide insights on who should finance supply chain inventories and at what rates when there are differential credit rating between the supplier and retailer. We provide a plausible explanation for the practice of large and good credit rating retailers maintaining a small cash ratio and working with small suppliers in developing countries.

The online appendix is available at https://doi.org/10.1287/msom.2017.0669 .

This paper has been accepted for the Manufacturing & Service Operations Management Special Issue on Interface of Finance, Operations, and Risk Management.

We introduce a class of models, called newsvendor networks, that allow for multiple products and multiple processing and storage points and investigate how their single-period properties extend to dynamic settings. Such models provide a parsimonious framework to study various problems of stochastic capacity investment and inventory management, including assembly, commonality, distribution, flexibility, substitution and transshipment. Newsvendor networks are stochastic models with recourse that are characterized by linear revenue and cost structures and a linear input-output transformation. While capacity and inventory decisions are locked in before uncertainty is resolved, some managerial discretion remains via ex-post input-output activity decisions. Ex-post decisions involve both the choice of activities and their levels and can result in subtle benefits. This discretion in choice is captured through alternate or “nonbasic” activities that can redeploy inputs and resources to best respond to resolved uncertain events. Nonbasic activities are never used in a deterministic environment; their value stems from discretionary flexibility to meet stochastic demand deviations from the operating point.

The optimal capacity and inventory decisions balance overages with underages. Continuing the classic newsvendor analogy, the optimal balancing conditions can be interpreted as specifying multiple “critical fractiles” of the multivariate demand distribution; they also suggest appropriate measures for and trade-offs between product service levels. This paper shows that the properties of optimal newsvendor network solutions extend to a dynamic setting under plausible conditions. Indeed, we establish dynamic optimality of inventory and capacity policies for the lost sales case. Depending on the nonbasic activities, this also extends to the backordering case. Analytic- and simulation-based solution techniques and graphical interpretations are presented and illustrated by a comprehensive example that features discretionary input commonality and a flexible processing resource.