Knowledge that Transforms

This paper studies issues associated with designing process control systems when the testing equipment is subjected to random shifts. We consider a production process with two states: in control and out of control. The process may shift randomly to the out‐of‐control state over time. The process is monitored by periodically sampling finished items from the process. The equipment used to test sampled items also is assumed to have two states and may shift randomly during the testing process. We formulate a cost model for finding the optimal process control policy that minimizes the expected unit time cost. Numerical results show that shifts of the testing equipment may significantly affect the performance of a process control policy. We also studied the effects of the testing equipment's shifts on the selection of process control policies.

In this issue, Robert Hayes has written provocatively about the implications of the digital economy for Operations Management (om). Here I examine and illustrate a simple four‐stage framework for thinking about these implications: advances in digital technology (stage 1) lead to business developments (stage 2), which impact the views of OM‐relevant thought leaders (stage 3), which influence the conduct of OM activities (stage 4). This framework provides a perspective for viewing the evolution of OM and indicates how educators, researchers, and practitioners can steal the march on mainstream thinking.

Despite the enormous, enduring influence of Wick Skinner’s writing and ideas and the worldwide acclaim he has received during his lifetime, the memory of his disastrous first presentation at an academic seminar over 40 years ago still rankles in his mind. His critics on that cold December afternoon in 1960 did not argue much with his firsthand experience and observations; they simply disagreed with the conclusions he drew from them. Wick walked out of that experience bloodied but unbowed, and determined to see his views vindicated. He was not easily intimidated, even then. Although only in his third year of doctoral studies at the time, he was not your typical young faculty Lecturer. By then it already had been over 16 years since he had graduated from Yale with a Chemical Engineering degree (achieved in a war-accelerated 33 months). Immediately inducted into the Army, he soon was assigned to the Engineering Corps working on the Manhattan Project at Las Alamos. Most histories of that project focus on the theoretical breakthroughs that underpinned the development of the atomic bomb and neglect the fact that actually making the first bombs required a huge and complex engineering/manufacturing effort. Wick spent much of his time measuring material and manpower flows, and he was

Customer satisfaction can be achieved by providing rapid delivery of a wide variety of products. High levels of product variety require correspondingly high levels of inventory of each item to quickly respond to customer demand. Delayed product differentiation has been identified as a strategy to reduce final product inventories while providing the required customer service levels. However, it is done so at the cost of devoting large production capacities to the differentiation stage. We study the impact of this postponement capacity on the ability to achieve the benefits of delayed product differentiation. We examine a single‐period capacitated inventory model and consider a manufacturing system that produces a single item that is finished into multiple products. After assembly, some amount of the common generic item is completed as non‐postponed products, whereas some of the common item is kept as in‐process inventory, thereby postponing the commitment to a specific product. The non‐postponed finished‐goods inventory is used first to meet demand. Demand in excess of this inventory is met, if possible, through the completion of the common items. Our results indicate that a relatively small amount of postponement capacity is needed to achieve all of the benefits of completely delaying product differentiation for all customer demand. This important result will permit many firms to adopt this delaying strategy who previously thought it to be either technologically impossible or prohibitively expensive to do so.

We studied time‐based policies on pricing and leadtime for a build‐to‐order and direct sales manufacturer. It is assumed that the utility of the product varies among potential customers and decreases over time, and that a potential customer will place an order if his or her utility is higher than the manufacturer's posted price. Once an order is placed, it will be delivered to the customer after a length of time called “leadtime.” Because of the decrease in a customer's utility during leadtime, a customer will cancel the order if the utility falls below the ordering price before the order is received. The manufacturer may choose to offer discounted prices to customers who would otherwise cancel their orders. We discuss two price policies: common discounted price and customized discounted price. In the common discounted price policy, the manufacturer offers a single lower price to the customers; in the customized discounted price policy, the manufacturer offers the customers separately for individual new prices. Our analytical and numerical studies show that the discounted price policies results in higher revenue and that the customized discounted price policy significantly outperforms the common discounted price policy when product utility decreases rapidly. We also study two leadtime policies when production cost decreases over time. The first uses a fixed leadtime, and the second allows the leadtime to vary dynamically over time. We find that the dynamic leadtime policy significantly outperforms the fixed leadtime policy when the product cost decreases rapidly.

In just‐in‐time inventory management in any manufacturing setting, the general idea has been to release jobs as late as possible (to reduce inventory costs) while still having them arrive at bottleneck machines in time to maintain the desired throughput (by not starving a bottleneck machine). When a cyclic schedule is employed, the throughput is determined by a cyclic sequence of operations known as the cyclic critical path. These operations are not, in general, all performed on a single bottleneck machine. We present an algorithm for releasing jobs that treats this cyclic critical path as the bottleneck. Although this algorithm has the somewhat complex task of not delaying any of these operations on the cyclic critical path, it is greatly simplified by being able to take advantage of the fixed sequence of the cyclic schedule. The result is that the algorithm is relatively simple to implement. Although it uses a simulation‐based analysis, this analysis can all be done and the necessary results stored in advance of its use. We test the algorithm in a job shop environment with stochastic operation times. This algorithm is shown to be effective at reducing inventory while avoiding decreases in throughput.

This paper was motivated by the operational problems faced by Northco, a school uniform manufacturer in the Northeastern United States. Northco was facing high working capital costs while also incurring high stockout and markdown costs. This paper models the impact of inventory holding cost and reactive capacity on Northco's targeted understocking and overstocking cost and offers a solution methodology for such problems. We quantify the impact of varying inventory carrying costs (and hence, high working capital costs) on stockout costs and the value of additional capacity. Our results illustrate that apparel manufacturers with high working capital costs, and hence high inventory carrying costs, should target higher stockout costs and achieve lower capacity utilization. The results presented have application beyond Northco because high working capital cost is endemic to many supply chains.

Recent advances in predictive maintenance technologies have led many manufacturers to abandon traditional periodic maintenance policies and replace them with predictive maintenance policies. The models in this paper explicitly evaluate the decision to utilize both predictive and periodic maintenance when the objective is to minimize expected maintenance costs per unit time. Renewal theory is used to obtain optimal policies as unique solutions of integral equations that depend on the failure distribution and prediction capabilities. Based on this research, we recommend that practitioners do not abandon the traditional maintenance methods but follow our guidelines for utilizing periodic maintenance in conjunction with the new technologies.

Common components are used extensively for reasons including product postponement and expediting new product development. We consider a two‐stage assemble‐to‐order system with two products having uniformly distributed demand, one common component, and product‐specific components. We develop optimization models in which the cost‐minimizing inventory of the components must be determined and allocated to products in order to meet product‐specific service level constraints. We compare two different commonality models based on whether or not the products are prioritized. A distinctive feature of our study is the use of product‐specific service levels. We compare our results with models using aggregate service levels.

A growing number of sophisticated observers are coming to believe that the forces driving the so‐called New Economy are fundamentally reshaping world industry. Moreover, the combination of fast growth and the excitement associated with leading edge technologies has made New Economy companies magnets for management talent—and particularly for the ambitious young people who attend our management programs. Are we providing these potential managers with a good foundation for managing operations in such companies? Are the principles that we traditionally have taught in operations management (om) courses sufficiently robust that they can still be applied to New Economy operations? In this paper we argue that, although some of our familiar concepts and techniques continue to be applicable to information‐intensive operations, many are not. We sketch out a way to think conceptually about the important differences between the Old and the New Economies, and their implications for operations management teaching and research.