Interdisciplinary research in operations management

Interdisciplinary research in operations management

Int. J. Production Economics 147 (2014) 571–572 Contents lists available at ScienceDirect Int. J. Production Economics journal homepage: www.elsevie...

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Int. J. Production Economics 147 (2014) 571–572

Contents lists available at ScienceDirect

Int. J. Production Economics journal homepage:


Interdisciplinary research in operations management

Over the past 50 years, research in the operations management (OM) discipline has emerged from the shadow of operations research and industrial engineering and has become a major focus of business research, together with finance and marketing. It has gone through different stages of transformations and emphases on topics studied, such as MRP in the 1970s, JIT in the 1980s, and TQM in the late 1980s and early 1990s. Starting from the late 1990s, researchers recognized that operations is only the functional area and, to be successful, operations management researchers must interface with their peers in the research fields of marketing, finance, engineering, and other functional areas. Therefore, in addition to the knowledge of operations research tools, operations management researchers must also understand business strategy, marketing concepts, financial tools, and effectiveness of information technology (systems). Several recent research trends can be identified in the field of operations management. Among these are increasing numbers of studies that integrate operations management decision making with other area decisions such as the OM/marketing interface and the OM/finance interface. As the field of operations management has shifted its attention to interdisciplinary practices, the research scope for operations research and operations management has become richer and more vibrant than in the past. This special issue of the International Journal of Production Economics focuses on the interdisciplinary research of operations management with marketing, engineering, information technologies, and other disciplines. Papers collected include a variety of topics as follows:

 integration of pricing policies with product design, inventory  


control, and consumer returns, including the effect of dynamic pricing on various production/inventory systems; sustainability issues, including design for recycling and recoverable manufacturing systems; incorporation of information-technology decisions (e.g., RFID, provider selection/out-sourcing, e-commerce) with production planning, inventory accuracy, and the management of perishable inventories; impact of transportation on operations, including inventoryrouting problems and flexible delivery times of suppliers; and other issues such as supply-chain contracts, dynamic overbooking, etc.

They are summarized as follows. Ruiz-Benitez and Muriel (forthcoming) examine the effect of consumer returns on the wholesale price, order quantity, and

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coordination of a supply chain. The analysis includes both wholesale-price contracts and buy-back contracts between a manufacturer and a retailer facing stochastic demand. A number of conclusions based on extensive computational testing are provided, including the identification of situations in which the consideration of consumer returns is detrimental to the coordination of the decentralized supply chain. The multi-product, multi-period inventory routing problem in fuel delivery is considered by Vidović et al. (forthcoming). Mixed-integer programs as well as heuristic approaches are proposed for the solution of this problem. Computational results are presented illustrating the quality of the solution methodologies in a variety of situations. Herbon et al. (forthcoming) consider the management of perishable inventories utilizing RFID-supported time–temperature indicator-based automatic devices to monitor the quality of the product. Dynamic pricing is applied to encourage purchasing the items as they approach their expiry date. Mixed-integer nonlinear programs as well as a local search technique are presented. Extensive simulation experiments are conducted to evaluate the impact of the automatic devices on the profitability of the product. Tjader et al. (forthcoming) utilize an integrated model of the Analytic Network Process and the Balanced Scorecard methodologies to develop a firm level IT outsourcing strategy. A case company is used to study the feasibility of the model at firm level outsourcing decision making. The papers discusses the robustness of the proposed model and provides managerial insights through sensitivity analyses. Zhou et al. (forthcoming) examine the impact of alignment between information technology and supply chain practice on business performance. This paper investigates two supply chain practices (sourcing practice and delivery practice) and information quality. Scales measuring sourcing practice, delivery practice, and information quality were developed. Four strategic clusters of companies, using alternative supply chain strategies, are identified. This study concludes that firms need to align supply chain practice with the level of their information quality in order to achieve good overall business performance. The paper by Zhang et al. (forthcoming) focuses on the interface between manufacturing operations and cost management. They adopt a simulation-based approach for optimizing three operational variables (production speed, scrap rate and maintenance speed) in a stochastic parallel-machine production system to minimize the average cost per unit time. The ordinal optimization principle and the optimal computing budget allocation technique have been applied to reduce the computational burden in simulation. In addition, a particle swarm optimization algorithm is designed to search the continuous space of the operational variables. Numeric computations show that


Editorial / Int. J. Production Economics 147 (2014) 571–572

the proposed algorithm outperforms traditional simulation optimization methods. Based on the obtained results, sensitivity analysis is also conducted to study the relationship between the optimal operational variables and the production system parameters. A multi-objective production planning problem in the laborintensive manufacturing industry is investigated by Wong et al. (forthcoming). An intelligent and real time multi-objective decision-making model is developed to provide timely and effective solutions for this problem by integrating RFID technology with intelligent optimization techniques, in which RFID technology is used to collect real-time production data. An evolution strategy process with self-adaptive population size and recombination operation is proposed and is integrated with effective nondominated sorting and pruning techniques to generate Pareto optimal solutions for the real-world production. Fan et al. (forthcoming) consider the situation of a retailer subject to inventory inaccuracies stemming from the shrinkage problem. A newsvendor model is applied to analyze how to reduce inventory shrinkage problems by deploying RFID. The inventory shrinkage problems are studied by optimizing order quantities and expected profits in consideration with the effect of the available rate of ordering quantity, RFID read rate improvement, and the tag price, respectively. The results show whether the retailer deploys RFID depends on the relative value of the available rate of ordering quantity and RFID read rate improvement. Chen and Liu (forthcoming) study the pricing and design decisions concerning products made from virgin and recycled materials. The analysis is focused on a duopoly market under price leadership. They conclude that the arrangement where the non-green and green firms are the price leader and follower, respectively, leads to more environmentally friendly product design decisions. Financial incentives are also analyzed on how to promote sustainable product design. In the face of uncertainty in the quality, quantity, and timing of returned products, Xiong et al. (forthcoming) propose that dynamic pricing strategies could be adopted in the acquisition of used products to balance supply and demand over time. They model the dynamic pricing problem as a continuous-time Markov decision process designed to minimize different costs. Simple managerial guidelines that reflect the characteristics of an optimal pricing policy are also derived. In the selection of a data communication services provider, there are uncertainties and vagueness in the Quality of Service (QoS) levels. Pan et al. (forthcoming) construct a multi-objective fuzzy optimization model to handle the vagueness and ambiguity in the available information as well as the fuzziness in human judgment and preference. The objectives concern non-linear membership functions, multi-class services, price breaks, different QoS levels, and penalty definitions. A numerical example is presented to illustrate the effectiveness of the proposed model. Qian (forthcoming) models product or service demand as a linear function of price, guaranteed delivery time, service level, or quality of a product or service. A customer-oriented approach is proposed regarding how to select the optimal supplier from alternative suppliers with variations in cost, delivery time, service level, or quality. For better profitability, the author argues a firm's operation characteristics—concerning performance in cost, delivery time, service level, or quality—must match the market characteristics, such as customer sensitivity to the price, guaranteed lead time, service level, etc. This special issue bridges the gap between operations management and other disciplines. We hope that this special issue serves the

purpose of providing new directions for operations management researchers. Acknowledgment The guest editors would like to thank all the authors for their contributions to this special issue and the reviewers for their dedicated time and insight. We also would like to thank Professor T.C. Edwin Cheng, Asian-Pacific Editor, and Professor Robert W. Grubbström, Editor-in-Chief of the International Journal of Production Economics, for agreeing to publish this special issue and for their patience during the review process. We are also indebted to Thiyagarajan Boopathy, Journal Manager of Elsevier, for tremendous editorial support. References Chen, C., Liu, L.Q., 2013. Pricing and quality decisions and financial incentives for sustainable product design with recycled material content under price leadership. Int. J. Prod. Econ.. (forthcoming). Fan, T., Chang, X.-Y., Gu, C.-H., Yi, J.-J., Deng, S., 2013. Benefits of RFID technology for reducing inventory shrinkage. Int. J. Prod. Econ.. (forthcoming). Herbon, A., Levner, E., Cheng, T.C.E., 2013. Perishable inventory management with dynamic pricing using time–temperature indicators linked to automatic detecting devices. Int. J. Prod. Econ.. (forthcoming). Pan, W., Yu, L., Wang, S., Wang, X., 2013. A fuzzy multi-objective model for provider selection in data communication services with different QoS levels. Int. J. Prod. Econ.. (forthcoming). Qian, L., 2013. Market-based supplier selection with price, delivery time, and service level dependent demand. Int. J. Prod. Econ.. (forthcoming). Ruiz-Benitez, R., Muriel, A., 2013. Consumer returns in a decentralized supply chain. Int. J. Prod. Econ.. (forthcoming). Tjader, Y., May, J., Shang, J., Vargas, L., Gao, N., 2013. Firm-level outsourcing decision making: a balanced scorecard-based analytic network process model. Int. J. Prod. Econ.. (forthcoming). Vidović, M., Popović, D., Ratković, B., 2013. Mixed integer and heuristics model for the inventory routing problem in fuel delivery. Int. J. Prod. Econ.. (forthcoming). Wong, W.K., Guo, Z.X., Leung, S.Y.S., 2013. Intelligent multi-objective decisionmaking model with RFID technology for production planning. Int. J. Prod. Econ.. (forthcoming). Xiong, Y., Li, G., Zhou, Y., Fernandes, K., Harrison, R., 2013. Optimal dynamic pricing for used products in remanufacturing. Int. J. Prod. Econ.. (forthcoming). Zhang, R., Chiang, W.-C., Wu, C., 2013. Investigating the impact of operational variables on manufacturing cost by simulation optimization. Int. J. Prod. Econ. (forthcoming). Zhou, H., Shou, Y., Zhai, X., Li, L., Wood, C., Wu, X., 2013. Supply chain practice and information quality: a supply chain strategy study. Int. J. Prod. Econ.. (forthcoming).

Wen-Chyuan Chiang n, Timothy L. Urban The University of Tulsa, Tulsa, OK 74104 USA E-mail addresses: [email protected] ([email protected] (T.L. Urban)

JianQiang Hu, Hongyu Li, Yifan Xu Fudan University, Shanghai 200433, China E-mail addresses: [email protected] (J. Hu), [email protected] (H. Li), [email protected] (Y. Xu)

Shanling Li McGill University, Montreal, Quebec, H3A 1G5 Canada E-mail address: [email protected]


Corresponding author.