Abstract
Companies recognize the central role performance measurement plays in their success and are therefore becoming increasingly enthusiastic about their performance measurement efforts. Performance measurement indicators not only support the daily operation of the organization, but they are valuable in formulating any emerging problems as well. Logistics encompasses a complex set of activities that require an equally complex collection of metrics to adequately measure performance. However, many performance measurement systems have neither kept up with the changing role and scope of logistics nor have they been systematically examined or evaluated. Performance measurement systems should be evaluated at both the individual metric and system-wide levels in order to maintain relevance and effectiveness. This study aims to present the empirical findings and lessons learned from a field research on the development of the performance measurement system (PMS) for the logistics department of an Oil & Gas company. The implementation focuses on the procedure rather than on the structure of PMS offering a conceptual procedural framework with information and insights on how to design, implement, use, and assess a PMS, addressing an important gap identified in the literature. This research focuses on the evaluation of staff of the supply chain. Specifically, we propose a set of recommended criteria that can be applied to evaluate staff of the relevant department and demonstrate the use of these criteria through the evaluation of drivers’ performance. The primary motivation for evaluating performance at that level is that measurement systems guide management decisions. A well-crafted system of metrics will lead toward better decision-making by managers. The entire research effort lasted almost 6 months, involving multiple interactions between a researcher from academia and a practitioner from the company. Ultimately, the main findings are as follows: (1) how we can evaluate a drive; (2) the indicators and their parameters that contribute in revealing our assumptions about “good” and “bad” driver; (3) developing a prototype PMS; and (4) the use and review of the new PMS has led to improvements in people’s behavior, development of organizational capabilities, and improved performance results.
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Akhtar, M., & Mittal, R. K. (2015). Implementation issues and their impact on strategic performance management system effectiveness – an empirical study of Indian oil industry. Measuring Business Excellence, 19(2), 71–82.
Bichou, K., & Gray, R. (2004). A logistics and supply chain management approach to port performance measurement. Maritime Policy & Management, 31(1), 47–67.
Bititci, U., Garengo, P., Dörfler, V., & Nudurupati, S. (2012). Performance measurement: Challenges for tomorrow. International Journal Manager Review, 14(3), 305–327.
Burns, N., & Grove, S. (2005). The practice of nursing research: Conduct, critique, and utilization (5th ed.). Philadelphia, PA: WB Saunders.
Caplice, C., & Sheffi, Y. (1995). A review and evaluation of logistics performance measurement systems. The International Journal of Logistics Management, 6(2), 61–74.
Christodoulakis, N. (2015). Analytical methods and multicriteria decision support systems under uncertainty: The Talos system. Piraeus: University of Piraeus (UNIPI).
Corbin, J., & Strauss, A. (2008). Basics of qualitative research: Techniques and procedures for developing grounded theory (3rd ed.). Thousand Oaks, CA: Sage.
Creswell, J. (2007). Qualitative inquiry and research design: Choosing among five approaches: International student edition. Thousand Oaks, CA: Sage.
Delias, P., & Matsatsinis, N. (2009). A genetic approach for strategic resource allocation planning. Computational Management Science, 6(3), 269–280.
Dyer, J. (2005). MAUT—multiattribute utility theory. In Multiple criteria decision analysis: State of the art surveys (pp. 265–292). New York: Springer.
Eccles, R. (1991, January–February). The performance measurement manifesto. Harvard Business, 131–137, 69.
Elg, M., & Kollberg, B. (2012). Conditions for reporting performance measurement. Journal of Total Quality Management & Business Excellence, 23(1), 63–77.
Folan, P., & Browne, J. (2005). A review of performance measurement: Towards performance management. Computers & Industrial Engineering, 56(7), 663–680.
Gunasekaran, A., Patel, C., & Gaughey, R. E. M. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333–347.
Hoek, R. I. v. (2001). The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain. International Journal of Operations & Production Management, 21(1/2), 15–29.
Jacquet-Lagrèze, E., & Siskos, Y. (2001). Preference disaggregation: 20 years of MCDA experience. European Journal of Operational Research, 130(2), 233–245.
Johnson, R. (1997). Examining the validity structure of qualitative research. Education, 118(2), 282–292.
Matsatsinis, N., & Delias, P. (2003). AgentAllocator: An agent-based multi-criteria decision support system for task allocation. In Holonic and multi-agent systems for manufacturing (pp. 1082–1083). Berlin: Springer.
Neely, A. (1999). The performance measurement revolution: Why now and what next? International Journal of Operations & Production Management, 19(2), 205–228.
Neely, A., & Al Najjar, M. (2006). Management learning not management control: The true role of performance measurement. California Management Review, 48(3), 101–114.
Platts, K. W. (1994). Characteristics of methodologies for manufacturing strategy formulation. Computer Integrated Manufacturing Systems, 7(2), 93–99.
Platts, K. W., & Gregory, M. J. (1990). Manufacturing audit in the process of strategy formulation. International Journal of Production and Operations Management, 10(9), 5–26.
PSAC. (2017). Industry overview. Calgary: Petroleum Service Association of Canada.
Punch, K. (1998). Introduction to social research. London: Sage.
Siskos, Y., & Yannacopoulos, D. (1985). UTASTAR: An ordinal regression method for building additive value functions. Investigação Operacional, 5(1), 39–53.
Siskos, J., Spyridakos, A., & Yannacopoulos, D. (1993). MINORA: A multicriteria decision aiding system for discrete alternatives. Journal of Information Science and Technology, 2(2), 136–149.
Siskos, Y., Grigoroudis, E., & Matsatsinis, N. F. (2005). UTA methods. In Multiple criteria decision analysis: State of the art surveys (pp. 297–334). New York: Springer.
Taylor, A., & Taylor, M. (2013). Antecedents of effective performance measurement system implementation: An empirical study of UK manufacturing firms. International Journal of Production Research, 51(18), 5485–5498.
Thurmond, V. (2001). The point of triangulation. Journal of Nursing Scholarship, 33(3), 254–256.
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Gegitsidis, F., Delias, P. (2019). A Performance Measurement System for Staff of the Logistics Section: A Case Study for an Oil & Gas Company. In: Sykianakis, N., Polychronidou, P., Karasavvoglou, A. (eds) Economic and Financial Challenges for Eastern Europe. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-12169-3_3
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