Knowledge that Transforms

Service failures do not need to result in permanent negative consequences as long as effective recovery activities are undertaken. Unfortunately, existing research has been limited in providing information to support prescriptive approaches for applying specific service recovery techniques. By using data from a large sample ( n = 861) of service failure incidents and employing the use of hierarchical and non‐hierarchical cluster analysis, this exploratory study creates and analyzes empirical types of service failures. The derived failure types, or common situations faced by service providers, focus on customer loyalty and the severity of the failure, and may be visualized in a two‐by‐two matrix. Regression analysis is then used to demonstrate how effective recovery strategies and supporting activities should vary, based on the location of the failure within the matrix. The approach and results offer important implications for strategy and service support activities as well as a foundation for systematizing service recovery efforts.

Low‐earth orbit satellite (LEO) systems continue to provide mobile communication services. The issue of cost containment in system maintenance is a critical factor for continued operation. Satellite finite life‐times follow a stochastic process, and since satellite replenishment cost is the most significant on‐going cost of operation, finding optimal launch policies is of paramount importance. This paper formulates the satellite launch problem as a Markovian decision model that can be solved using dynamic programming. The policy space of the system is enormous and traditional action space dominance rules do not apply. In order to solve the dynamic program for realistic problem sizes, a novel procedure for limiting the state space considered in the dynamic program is developed. The viability of the proposed solution procedure is demonstrated in example problems using realistic system data. The policies derived by the proposed solution procedure are superior to those currently considered by LEO system operators, and result in substantial annual cost savings.

Manufacturing firms would like to maximize customer satisfaction by providing them with what they need when they need it. This, however, would mean continual variations in production quantities, and component orders from suppliers. A flexible manufacturing system can help alleviate costs related to modification of production quantities. The capacity of such a system, however, has to be limited because of high investment cost. Further, unless there is a long‐term relationship, suppliers may levy a high surcharge for last minute changes in order size. We model a hybrid control policy comprising an advance (pre‐production) order size agreed upon with suppliers, and a provision for real‐time order revision at a given rate of surcharge. We show that a rank‐order of products can be used for real‐time revisions, and that a strong buyer‐supplier relationship that keeps these surcharges low can actually help increase profits for both parties. We study issues such as compatibility between JIT and flexibility, and the impact of market conditions on overall profitability.

In Corbett and Kirsch (2001), we used a simple regression in an exploratory investigation of drivers of global diffusion of ISO 14000 certification. We found that ISO 9000 certification levels, environmental treaties ratified, and exports as a proportion of GDP were the main significant variables, where the environmental measure may be moderated by GDP per capita. In his replication study, Vastag (2004, in this issue) analyzes the same data using more visual techniques, specifically regression trees, and finds support for the significance of ISO 9000 certification levels and environmental treaties ratified, but not for export‐propensity. Vastag raises a number of relevant methodological issues, to which we add some perspectives here.

Information technology has profoundly impacted the operations of firms in the service industry and service environments within manufacturing. Two models are introduced that establish a conceptual framework linking firm profit to attributes of the IT‐worker system. The framework considers the impact of IT capabilities (such as functionality and ease‐of‐use) and worker skill as drivers of output volume and quality. The framework contrasts attributes of the IT‐worker systems when services are mass‐produced (flow shop) versus customized (job shop). Mathematical models are introduced to formalize the conceptual framework. Numerical examples are presented that illustrate the types of insights that can be obtained from the models.

An electronic marketplace typically provides industrial suppliers an alternative option for selling their capacity in addition to the traditional open market. However, suppliers face different sets of costs and risks in open market and in electronic market. Consequently, suppliers participating in an electronic market are likely to offer their capacity at a different price compared with traditional open market. We analyze this problem and derive the price‐capacity function for the supplier. We also derive a basis for allocating buyer's requirements among multiple suppliers so as to minimize his cost. Our model shows that suppliers with large capacities would quote a lower price in the electronic market. It also predicts that the unit bid price increases with bid quantity in the electronic market. Based on the price‐capacity curve, we model a scenario where the buyer announces, a priori, the number of suppliers to be selected for award of a contract that will minimize its costs.

In this paper, the supplier of a key component to a global manufacturer offers a one‐time price discount; we study the firm's optimal response to the discount under two different strategies. In the first strategy, the firm does not pass along the discount to its customers (sales subsidiaries); the firm simply coordinates purchasing and production among the different factories to take advantage of this one‐time price discount. In the second strategy, the firm offers price discounts for its most profitable products in different sales subsidiaries to increase their demand. We carried out experiments for the two strategies based on a mathematical programming model, built around Toshiba's global notebook supply chain. Model constraints include, among others, material constraints, bill‐of‐materials, capacity and transportation constraints, minimum lot size constraints, and a constraint on minimum fill rate (service level constraint). Unlike most models of this type in the literature, which define variables in terms of single arc flows, we employ path variables, which allow for direct identification and manipulation of profitable and non‐profitable products.

This paper develops a distributed decision‐making framework for the players in a supply chain or a private e‐marketplace to collaboratively arrive at a global Pareto‐optimal solution. In this model, no player has complete knowledge about all the costs and constraints of the other players. The decision‐making framework employs an iterative procedure, based on the Integer L‐shaped method, in which a master problem is solved to propose global solutions, and each player uses his local problems to construct feasibility and optimality cuts on the master problem. The master problem is modeled as a mixed‐integer program, and the players' local problems are formulated as linear programs. Collaborative planning scenarios in private e‐marketplaces and in supply chains were formulated and solved for test data. The results show that this distributed model is able to achieve near‐optimal solutions considerably faster than the traditional centralized approach.

It is understood that quantity discounts provide a practical foundation for coordinating inventory decisions in supply chains. The primary objective of this research is to test, under a variety of environmental conditions, the effectiveness of quantity discounts as an inventory coordination mechanism between a buyer and a supplier. A comprehensive simulation experiment with anova has been designed to investigate the impacts of (1) choice of quantity discount‐based inventory coordination policies, (2) magnitude of demand variation, (3) buyer's and supplier's relative inventory cost structure, and (4) buyer's economic time‐between‐orders on the effectiveness of supply chain inventory coordination. The analytical results confirm that the quantity discount policies have managerial properties as a mediator for inventory coordination. The results also show that the performance of quantity discount‐based inventory coordination policies is influenced significantly by environmental factors, such as the magnitude of demand variation, the buyer's and the supplier's inventory cost structure, and the buyer's economic time‐between‐orders.

Service managers often find that available worker capacity does not match with actual demand during a given day. They then must attempt to modify the planned work schedule to improve service and increase profitability. This study, which defines such a setting as the real‐time work schedule adjustment decision, proposes mathematical formulations of the real‐time adjustment and develops efficient heuristic approaches for this decision. The study evaluates the relative effectiveness of these heuristics versus experienced service managers, investigates the effect of the degree of schedule adjustment on profitability, and assesses the effect of demand forecast update errors on the performance of the schedule adjustment efforts. First, the results indicate that the computer based heuristics achieve higher profit improvement than experienced managers. Second, there is a trade‐off between schedule stability and profitability so that more extensive schedule revisions (efficiency first heuristics) generally result in higher profitability. However, the incremental return on schedule changes is diminishing. Third, we find that active adjustments of work schedules are beneficial as long as the direction of demand change is accurately identified.