Knowledge that Transforms

We consider scheduling issues at Beyçelik, a Turkish automotive stamping company that uses presses to give shape to metal sheets in order to produce auto parts. The problem concerns the minimization of the total completion time of job orders (i.e., makespan) during a planning horizon. This problem may be classified as a combined generalized flowshop and flexible flowshop problem with special characteristics. We show that the Stamping Scheduling Problem is NP‐Hard. We develop an integer programming‐based method to build realistic and usable schedules. Our results show that the proposed method is able to find higher quality schedules (i.e., shorter makespan values) than both the company's current process and a model from the literature. However, the proposed method has a relatively long run time, which is not practical for the company in situations when a (new) schedule is needed quickly (e.g., when there is a machine breakdown or a rush order). To improve the solution time, we develop a second method that is inspired by decomposition. We show that the second method provides higher‐quality solutions—and in most cases optimal solutions—in a shorter time. We compare the performance of all three methods with the company's schedules. The second method finds a solution in minutes compared to Beyçelik's current process, which takes 28 hours. Further, the makespan values of the second method are about 6.1% shorter than the company's schedules. We estimate that the company can save over €187,000 annually by using the second method. We believe that the models and methods developed in this study can be used in similar companies and industries.

We consider a dynamic pricing problem that involves selling a given inventory of a single product over a short, two‐period selling season. There is insufficient time to replenish inventory during this season, hence sales are made entirely from inventory. The demand for the product is a stochastic, nonincreasing function of price. We assume interval uncertainty for demand, that is, knowledge of upper and lower bounds but not a probability distribution, with no correlation between the two periods. We minimize the maximum total regret over the two periods that results from the pricing decisions. We consider a dynamic model where the decision maker chooses the price for each period contingent on the remaining inventory at the beginning of the period, and a static model where the decision maker chooses the prices for both periods at the beginning of the first period. Both models can be solved by a polynomial time algorithm that solves systems of linear inequalities. Our computational study demonstrates that the prices generated by both our models are insensitive to errors in estimating the demand intervals. Our dynamic model outperforms our static model and two classical approaches that do not use demand probability distributions, when evaluated by maximum regret, average relative regret, variability, and risk measures. Further, our dynamic model generates a total expected revenue which closely approximates that of a maximum expected revenue approach which requires demand probability distributions.

Managers at all stages of a supply chain are concerned about meeting profit targets. We study contract design for a buyer–supplier supply chain, with each party maximizing expected profit subject to a chance constraint on meeting his respective profit target. We derive the optimal contract form (across all contract types) with randomized and deterministic payments. The best contract has the property that, if the chance constraints are binding, at most one party fails to satisfy his profit target for any given demand realization. This implies that “least risk sharing,”that is, minimizing the probability of outcomes for which both parties fail to achieve their profit targets, is optimal, contrary to the usual expectations of “risk sharing.” We show that an optimal contract can possess only two of the following three properties simultaneously: (i) supply chain coordination, (ii) truth‐telling, and (iii) non‐randomized payments. We discuss methods to mitigate the consequent implementation challenges. We also derive the optimal contract form when chance constraints are incorporated into several simpler and easier‐to‐implement contracts. From a numerical study, we find that an incremental returns contract (in which the marginal rebate rate depends on the return quantity) performs quite well across a relatively broad range of conditions.

We consider the inventory management problem of a firm reacting to potential change points in demand, which we define as known epochs at which the demand distribution may (or may not) abruptly change. Motivating examples include global news events (e.g., the 9/11 terrorist attacks), local events (e.g., the opening of a nearby attraction), or internal events (e.g., a product redesign). In the periods following such a potential change point in demand, a manager is torn between using a possibly obsolete demand model estimated from a long data history and using a model estimated from a short, recent history. We formulate a Bayesian inventory problem just after a potential change point. We pursue heuristic policies coupled with cost lower bounds, including a new lower bounding approach to non‐perishable Bayesian inventory problems that relaxes the dependence between physical demand and demand signals and that can be applied for a broad set of belief and demand distributions. Our numerical studies reveal small gaps between the costs implied by our heuristic solutions and our lower bounds. We also provide analytical and numerical sensitivity results suggesting that a manager worried about downside profit risk should err on the side of underestimating demand at a potential change point.

We consider multitier push assembly systems with sequential supplier decisions and a wholesale price contract. We show that both an Original Equipment Manufacturer (OEM)–Contract Manufacturer (CM) assembly and a modular assembly with sequential supplier decisions are mathematically equivalent to the corresponding traditional assembly. We determine that, in most cases, the first mover supplier realizes a higher profit than the second mover supplier but we also identify the sufficient conditions for the reverse to occur. We provide conditions under which the order quantity, the second mover profit, total supplier profits, and the assembler profit are either higher or lower for a multitier system with sequential suppliers compared to simultaneous suppliers. We conclude that the first mover is always better off in a three‐tier sequential system while she can be either better off or worse off in a four‐tier sequential system compared to the corresponding simultaneous systems. We also analyze the impact of information asymmetry on the supplier and assembler profits in a three‐tier sequential system. Finally, we determine the profit threshold for an independent manufacturer in a three‐tier system to become a CM in a four‐tier system and vice versa.

Today, software supports many important tasks in a variety of industries. In the specialized nature of these environments, a common problem faced by software vendors is to correctly signal the true value of a software product to the end users. For example, telecommunications equipment manufacturers design complex software for important functions like provisioning new users in the network. These software products automate various functions that would otherwise need to be done manually. In order to enable potential customers—telecommunications providers—to evaluate and recognize the full value of the product, equipment vendors often provide a free, feature‐limited version of the product to the customer. As the specific features included in the feature‐limited version influence whether the full product is purchased or not, it is essential that the features included in the feature‐limited version be selected judiciously. While the importance of identifying the best set of features has been well recognized, there has been little research to date that systematically addresses this fundamental business decision. This study fills this gap in the literature by providing an objective approach to the design of demonstration software. We illustrate the benefits of our approach through a case study involving the design of a feature‐limited demo for a wireless telecommunications equipment manufacturer.

Brand commitment and the risk of product failure play an important role in the timing of upgrades for a durable product in the presence of stochastic technology advancements. Higher brand commitment makes a firm less vulnerable to sales erosion in an incumbent product due to a technology lag. However, the firm is more susceptible to profit loss from the risk of a failed product. We show that under these circumstances, a firm's optimal upgrade strategy is characterized by a threshold policy based on the level of pent‐up demand for their next generation product. Contrary to previous research, we find that a threshold policy based on technology may be suboptimal, since the risk of product failure is nonmonotonic in terms of the technology lag. We extend the model to determine whether a firm should offer a temporary price reduction and show that price promotions can be used to mitigate the risk of lost sales associated with a risky product upgrade. We perform numerical experiments to examine the impact of brand commitment under different market scenarios related to the stochastic dynamics of technology advancements. The implications of the findings are discussed in the context of the smartphone industry.

In Online Movie Rental Systems, customer desire to rent can often be observed before the actual consumption occurs. Desire represents uncensored (or true) demand information. Hence, the impact of inventory decisions (numbers of physical copies of different movies) can be accurately traced to the creation of desire (via Word‐of‐mouth), and then to rental. Word‐of‐Mouth (WOM) has been recognized as one of the most influential sources of information transmission, especially for experience goods. Poor inventory decisions may result in lost rentals in two ways: One is the loss of rentals because of low inventory (direct effect), and the other is the loss of the possible demand (rentals) that could have been created through WOM (indirect effect). We use data from an online DVD‐by‐mail firm to estimate the direct and indirect effects of inventory decisions, considering the circular relationship: Rental generates WOM, WOM creates Desire, and Desire turns into Rental. We find that the magnitude of indirect effects is significant, comparable to and sometimes even exceeding direct effects. The value of the empirical findings to facilitate better inventory allocation decisions is examined.

In this article, we study the control of stochastic make‐to‐stock manufacturing lines in the presence of electricity costs. Electricity costs are difficult to manage because unit costs increase with the total load, that is, the amount of electricity needed by the manufacturing line at a certain point in time. We demonstrate that standard methods for controlling manufacturing lines cannot be used and that standard analytic results for stochastic manufacturing lines do not hold in the presence of electricity costs. We develop a control policy that balances electricity costs with inventory holding and backorder costs. We derive closed‐form expressions and analytic properties of the expected total cost for manufacturing lines with two workstations and demonstrate the accuracy and robustness of the policy for manufacturing lines with more than two workstations. The results indicate that avoiding electricity peak loads requires additional investment in manufacturing capacity and higher inventory and backorder costs. Our approach also applies to companies which aim at reducing their carbon emissions in addition to their operating costs.