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False quality claims have recently rocked the automobile industry. To promote customer demand while seemingly complying with regulations, manufacturers may cheat even at the risk of incurring sever...

Retailers are sources of environmental pollution, 80–90% of which is ultimately due to the processes that retailers set in motion by their orders for the products they carry and sell. The goal of t...

We consider a supply chain consisting of a supplier and a risk-averse retailer operating under endogenous demand in retail pricing. The demand potential is uncertain and is revealed at the beginning of the selling season when it is too late to order products. The product price, on the other hand, is not determined in advance and can be postponed until the demand is revealed. The goal is to study the effect of risk-aversion and postponed pricing on both the retailer’s decisions and the overall supply chain. We find that the risk-averse retailer does not necessarily order less than the risk-neutral one and may introduce a bias by choosing a specific demand distribution. We contrast two specific choices. One is symmetric (balanced) with respect to the mean demand potential. The other is skewed (pessimistic) with most observations expected below the mean demand potential. Our numerical results show that the binding downside risk constraint deteriorates the supply chain performance when the forecast is balanced and improves it when the forecast is pessimistic.

This paper provides a quantitative and comparative economic and risk approach to strategic quality control in a supply chain, consisting of one supplier and one producer, using a random payoff game. Such a game is first solved in a risk-neutral framework by assuming that both parties are competing with each other. We show in this case that there may be an interior solution to the inspection game. A similar analysis under a collaborative framework is shown to be trivial and not practical, with a solution to the inspection game being an ‘all or nothing’ solution to one or both the parties involved. For these reasons, the sampling random payoff game is transformed into a Neyman–Pearson risk constraints game, where the parties minimize the expected costs subject to a set of Neyman–Pearson risk (type I and type II) constraints. In this case, the number of potential equilibria can be large. A number of such solutions are developed and a practical (convex) approach is suggested by providing an interior (partial sampling) solution for the collaborative case. Numerical examples are developed to demonstrate the procedure used. Thus, unlike theoretical approaches to the solution of strategic quality control random payoff games, the approach we construct is both practical and consistent with the statistical risk Neyman–Pearson approach.

This paper addresses the pricing policy of durable value goods that do not depreciate over time. This implies that demands for this type of goods fluctuate with respect to their market price and social interactions between customers rather than with respect to the time elapsed since they have been produced or created. We suggest an analytical approach for optimally setting durable value product prices with respect to the interdependency between two customer groups characterized by asymmetric intergroup externalities. We demonstrate that cyclic pricing policies of harmonic form become optimal when the company is prepared to compromise its short-run net profit to ensure its lasting reputation. Furthermore, we show that the greater the difference between the product of the price and the externality effect of the two customer groups, the greater the frequency of the harmonic fluctuation.

The paper focuses on the control decisions in the area of multi-period, aggregate production planning. The plan consists of a number of sequential planning periods stretching out to a particular time horizon. Owing to machine downtime, quality, supply, and maintenance problems, true productivity differs from the expected. Therefore, at the end of each planning period, the planner must have immediate feedback about production outcome. The results are analysed for variations from the plan (shortfalls or surpluses) and any differences are used to modify subsequent plans. The goal is to minimize the expected total costs including productivity, overtime as well as over- and under-production costs. Although the presented solution is generally not of base-stock type, a correspondence between it and the solution of the classical newsboy problem is revealed. This paper was developed during a consultation assignment in the food industry where the planning results were tested, and the case is presented.

A short selling season and highly uncertain demands prior to the season characterize production and selling of fashion goods. Once the season starts and demands turn up with a peak interest in the beginning, monopoly becomes under tremendous pressure to produce the required amount so as not to disappoint its customers. It motivates the monopoly to prepare significant inventories by the opening day. Unfortunately, even the most advanced techniques for demand forecasting are likely to induce either an overestimate or underestimate of the initial inventories. Both affect the monopoly's profit. Overestimation results in surplus, which may never be sold, and excessive inventory holding costs. Underestimation implies sales as well as customer loyalty losses. Given inventory level at the beginning of the selling season, we derive policies of handling this inventory, production capacity and product prices in order to maximize the profit and thus diminish the effect of inherent inaccuracy of initial inventory estimation of fashion goods. A case of bookstore management illustrates the effectiveness of the suggested strategies.

This paper focuses on a dynamic, continuous-time control generalization of the unbounded knapsack problem. This generalization implies that putting items in a knapsack takes time and has a due date. Specifically, the problem is characterized by a limited production horizon and a number of item types. Given an unbounded number of copies of each type of item, the items can be put into a knapsack at a controllable production rate subject to the available capacity. The demand for items is not known until the end of the production horizon. The objective is to collect items of each type in order to minimize shortage and surplus costs with respect to the demand. We prove that this continuous-time problem can be reduced to a number of discrete-time problems. As a result, solvable cases are found and a polynomial-time algorithm is suggested to approximate the optimal solution with any desired precision.

A flexible manufacturing cell consisting of two machining centres, several automated storage/retrieval stations, and a mobile transporting robot is considered. The problem is to schedule jobs on machines so as to minimize the makespan, with the effects of transportation and set-ups to be taken into account. The problem is studied with the aid of a graph model, and an exact algorithm of cubic complexity is derived based on the Gilmore–Gomory algorithm for the travelling salesman problem.