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Three Essays on Performance Evaluation in Operations and Supply Chain Management

Liang, Hongyan
http://rave.ohiolink.edu/etdc/view?acc_num=kent1504827189112207

Abstract Details

2017, PHD, Kent State University, College of Business and Entrepreneurship, Ambassador Crawford / Department of Management and Information Systems.
In today’s globally competitive marketplace, organizations are challenged to increase their levels of customer service while under pressure to simultaneously reduce operating costs and the time to market of products and services. In meeting these challenges, organizations have adopted performance measurement systems to gauge current performance and to set benchmarks for improving future performance. As discussed in Neely et al. (1995), managerial success in improving performance is perquisite on having a formal performance measurement system that provides management with meaningful short term (day-to-day) as well as long term performance goals. Within the operations and supply chain management literatures the importance of integrating performance measurement systems into decision making has been addressed by many researchers (see for example, Ramaa et al., 2013; Martin & Patterson, 2009; Shepherd & Gunter, 2006; Gunasekaran et al. 2004). For effective performance measurement, formal quantitative models for performance measurement are needed (Suwignjo et al., 2000; Bititci et al., 2001). In this dissertation, we examine three different classes of performance evaluation models, which are used by decision makers in operations and supply chain management. The general forms of these three classes of models are: i) learning-based models for continuous improvement, ii) stochastic inventory models for shortages, and iii) cost-volume profit models for decision analysis. Despite a vast supporting literature for each class of model, there are adaptations of these models that can lead to further contributions that will be of interest to both the academic and practitioner communities. In the research of improving supply chain delivery performance, Guiffrida & Nagi (2006) developed a cost-based delivery performance model whereby improvement in delivery performance is achieved by reducing the variance of the delivery time distribution using a learning-based function. A limitation of Guiffrida & Nagi (2006) is the failure to include forgetting into the learning process. Given the discrete nature of the delivery process, learning can be lost during the time periods that accrue between deliveries. The first essay extends the research of Guiffrida & Nagi (2006) to include forgetting into the learning based approached for improving supply chain delivery performance. The literature on green and sustainable inventory management is quite limited and has mainly focused on the carbon footprint resulting with inventory management decisions (Bouchery et al. 2012). An examination of review papers on sustainable inventory models exposes the lack of integration of green and sustainable measures into stochastic inventory models. Of particular interest are stochastic inventory models with stockouts which examine the tradeoffs between backorders and lost sales. A limitation of these models is the failure to address environmental concerns in the stochastic inventory models with stockout decisions. The second essay integrates green and sustainable measures into stochastic inventory models which examine tradeoffs between backorders and lost sales. A review of stochastic cost-volume-profit (CVP) analysis models indicates that the inputs of the models as well as the resulting profit function are modeling using either the normal or the lognormal distribution. Using normal and lognormal distributions in stochastic CVP modeling represents a limitation to the general applicability of the models. In the third essay, we employ Mellin Transforms to expand and generalize the stochastic CVP model.
Alfred Guiffrida (Committee Co-Chair)
Butje Eddy Patuwo (Committee Co-Chair)
Michael Y. Hu (Committee Member)
77 p.
Business Administration; Operations Research
Supply chain delivery performance; learning forgetting curve model; stochastic inventory model; Cost-volume-profit analysis

Recommended Citations

Citations

  • Liang, H. (2017). Three Essays on Performance Evaluation in Operations and Supply Chain Management [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1504827189112207

    APA Style (7th edition)

  • Liang, Hongyan. Three Essays on Performance Evaluation in Operations and Supply Chain Management. 2017. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1504827189112207.

    MLA Style (8th edition)

  • Liang, Hongyan. "Three Essays on Performance Evaluation in Operations and Supply Chain Management." Doctoral dissertation, Kent State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1504827189112207

    Chicago Manual of Style (17th edition)

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This open access ETD is published by Kent State University and OhioLINK.