Knowledge that Transforms

The condition of the used products acquired by remanufacturing firms often varies widely. A firm can manage this variation by acquiring a quantity of used items that exceeds demand, enabling it to remanufacture a subset of the acquired items in the best condition. As more excess items are acquired, the firm can increase its selectivity and lower its remanufacturing costs. In this paper, we examine the tradeoff of acquisition and scrapping costs vs. remanufacturing costs when used product condition is widely varying and uncertain. We derive acquisition quantities that minimize total expected costs for several representations of condition variability and remanufacturing cost structures. We find that, when costs are linear, the optimal acquisition quantity has a closed form and increases with the square root of the degree of condition variability. Our models are based on experience with remanufacturers of cell phones and imaging supplies, and application of our results is illustrated using example data from industry.

We introduce a dynamic pricing model for a monopolistic company selling a perishable product to a finite population of strategic consumers (customers who are aware that pricing is dynamic and may time their purchases strategically). This problem is modeled as a stochastic dynamic game in which the company's objective is to maximize total expected revenues, and each customer maximizes the expected present value of utility. We prove the existence of a unique subgame‐perfect equilibrium pricing policy, provide equilibrium optimality conditions for both customer and seller, and prove monotonicity results for special cases. We demonstrate through numerical examples that a company that ignores strategic consumer behavior may receive much lower total revenues than one that uses the strategic equilibrium pricing policy. We also show that, when the initial capacity is a decision variable, it can be used together with the appropriate pricing policy to effectively reduce the impact of strategic consumer behavior. The proposed model is computationally tractable for problems of realistic size.

Extended Producer Responsibility (EPR) legislation focuses on the life‐cycle environmental performance of products and has significant implications for management theory and practice. In this paper, we examine the influence of EPR policy parameters on product design and coordination incentives in a durable product supply chain. We model a manufacturer supplying a remanufacturable product to a customer over multiple periods. The manufacturer invests in two design attributes of the product that impact costs incurred by the supply chain— performance, which affects the environmental impact of the product during use, and remanufacturability, which affects the environmental impact post‐use. Consistent with the goals of EPR policies, the manufacturer and the customer are required to share the environmental costs incurred over the product's life cycle. The customer has a continuing need for the services of the product and optimizes between the costs of product replacement and the costs incurred during use. We demonstrate how charges during use and post‐use can be used as levers to encourage environmentally favorable product design. We analyze the impact of supply chain coordination on design choices and profit and discuss contracts that can be used to achieve coordination, both under symmetric and asymmetric information about customer attributes.

Cooperative (co‐op) advertising is an important instrument for aligning manufacturer and retailer decisions in supply chains. In this, the manufacturer announces a co‐op advertising policy, i.e., a participation rate that specifies the percentage of the retailer's advertising expenditure that it will provide. In addition, it also announces the wholesale price. In response, the retailer chooses its optimal advertising and pricing policies. We model this supply chain problem as a stochastic Stackelberg differential game whose dynamics follows Sethi's stochastic sales‐advertising model. We obtain the condition when offering co‐op advertising is optimal for the manufacturer. We provide in feedback form the optimal advertising and pricing policies for the manufacturer and the retailer. We contrast the results with the advertising and price decisions of the vertically integrated channel, and suggest a method for coordinating the channel.

Managers are increasingly faced with pressure to think not just about profits, but also about their organization's environmental and social performance. This research provides a first examination of operational managers' experiences with and attitudes about employee well‐being and environmental issues, how these factors impact employee well‐being and environmental performance, and how the three performance measures interrelate. We use violations of Occupational Safety and Health Administration regulations and Toxic Release Inventory reports of emissions as proxies for employee well‐being and environmental performance. Our findings suggest that operational managers do not (yet) think in sustainability terms. However, employee well‐being and environmental performance do interact in a significant way with operational performance. Hence, operational managers would benefit from a more complete understanding of the relationships among the elements of the triple bottom line.

Whenever intrepid researchers venture into new terrain, they find that they require knowledge outside of their formal training. This paper reviews bodies of knowledge for operations management (OM) researchers interested in the new area of Behavioral Operations. We highlight theoretical constructs and empirical phenomena from cognitive psychology, social psychology, group dynamics, and system dynamics. We also provide a guide for where to go to learn more about each body of knowledge. Our overall goal is to lower the startup costs for new researchers in Behavioral Operations.

A key strategic issue in pre‐disaster planning for humanitarian logistics is the pre‐establishment of adequate capacity and resources that enable efficient relief operations. This paper develops a two‐stage stochastic optimization model to guide the allocation of budget to acquire and position relief assets, decisions that typically need to be made well in advance before a disaster strikes. The optimization focuses on minimizing the expected number of casualties, so our model includes first‐stage decisions to represent the expansion of resources such as warehouses, medical facilities with personnel, ramp spaces, and shelters. Second‐stage decisions concern the logistics of the problem, where allocated resources and contracted transportation assets are deployed to rescue critical population (in need of emergency evacuation), deliver required commodities to stay‐back population, and transport the transfer population displaced by the disaster. Because of the uncertainty of the event's location and severity, these and other parameters are represented as scenarios. Computational results on notional test cases provide guidance on budget allocation and prove the potential benefit of using stochastic optimization.

Product and waste take‐back is becoming more regulated by countries to protect the environment. Such regulation puts an economic burden on firms, while creating fairness concerns and potentially even missing its primary target: environmental benefits. This research discusses the economic and environmental impacts of extended producer responsibility type of legislation and identifies efficiency conditions. It is shown that the right policy would (i) make producers responsible for their own waste to avoid fairness concerns and (ii) favor eco‐design producers to create stronger environmental benefits. Furthermore, the efficiency of take‐back systems is also driven by environmental classification of products, industry structure, and end‐user willingness to participate in take‐back programs.

This paper examines supply chain design strategies for a specific type of perishable product—fresh produce—using melons and sweet corn as examples. Melons and other types of produce reach their peak value at the time of harvest; product value deteriorates exponentially post‐harvest until the product is cooled to dampen the deterioration. Using the product's marginal value of time (MVT), the rate at which the product loses value over time in the supply chain, we show that the appropriate model to minimize lost value in the supply chain is a hybrid of a responsive model from post‐harvest to cooling, followed by an efficient model in the remainder of the chain. We also show that these two segments of the supply chain are only loosely linked, implying that little coordination is required across the chain to achieve value maximization. The models we develop also provide insights into the use of a product's MVT to develop supply chain strategies for other perishable products.

We consider an integrated production–distribution scheduling model in a make‐to‐order supply chain consisting of one supplier and one customer. The supplier receives a set of orders from the customer at the beginning of a planning horizon. The supplier needs to process all the orders at a single production line, pack the completed orders to form delivery batches, and deliver the batches to the customer. Each order has a weight, and the total weight of the orders packed in a batch must not exceed the capacity of the delivery batch. Each delivery batch incurs a fixed distribution cost. The problem is to find jointly a schedule for order processing and a way of packing completed orders to form delivery batches such that the total distribution cost (or equivalently, the number of delivery batches) is minimized subject to the constraint that a given customer service level is guaranteed. We consider two customer service constraints—meeting the given deadlines of the orders; or requiring the average delivery lead time of the orders to be within a given threshold. Several problems of the model with each of those constraints are considered. We clarify the complexity of each problem and develop fast heuristics for the NP‐hard problems and analyze their worst‐case performance bounds. Our computational results indicate that all the heuristics are capable of generating near optimal solutions quickly for the respective problems.