Performance Measurement System for Automotive Spare Parts Supply Chain: A Categorization Approach

Yıl 2019, Cilt: 4 Sayı: 1, 31 - 50, 30.04.2019

Zineb Achetoui , Charif Mabrouki Ahmed Mousrij

Öz

This article provides a categorization approach that encompasses the required categories and sub-categories for the performance measurement of automotive spare parts supply chain with a focus on independent distributor belonging to the independent channel. In fact, the special characteristics of spare parts have led to the emergence of many scientific contributions related to inventory management and demand forecasting methods. However, little research has focused on the measurement of spare parts supply chain performance despite its big importance. In this paper, we attempt to fill this gap in the literature, in particular for the automotive aftermarket, by proposing a framework that will lead to the measurement of the overall automotive spare parts supply chain performance.

Anahtar Kelimeler

Performance, Supply chain, Spare parts, Automotive, Categorization

Kaynakça

• Aronis, K. -P., Magou, L., Dekker, R. and Tagaras, G. (2004) ‘Inventory control of spare parts using a Bayesian approach: A case study’, European Journal of Operational Research, Vol. 154, No. 3, pp. 730-739. Bacchetti, A. and Saccani, N. (2012) ‘Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice’, Omega, Vol. 40, No. 6, pp.722-737.
• Barkawi and Partners GmbH (2002) ‘Global Study on Spare Parts Logistics’, Munich.
• Barnabè, F. (2011) ‘A “system dynamics-based Balanced Scorecard” to support strategic decision making’, International Journal of Productivity and Performance Management, Vol. 60, No. 5, pp. 446-473.
• Berrah, L. (2013) ‘La quantification de la performance dans les entreprises manufacturières : de la déclaration des objectifs à la définition des systèmes d'indicateurs’, Informatique [cs], Université de Savoie (Doctoral dissertation).
• Bourguignon, A. (1995) ‘Peut-on définir la performance ?’, Revue française de comptabilité, Vol. 269, pp. 61-66.
• Boylan, J. E., Syntetos, A. A. and Karakostas, G. C. (2008) ‘Classification for forecasting and stock control: a case study’, Journal of the Operational Research Society, Vol. 59, No. 4, pp. 473-481.
• Boylan, J. E. and Syntetos, A. A. (2008) ‘Forecasting for inventory management of service parts’, in Complex System Maintenance Handbook, Springer Series in Reliability Engineering, Springer, London, pp. 479-506.
• Braglia, M., Grassi, A. and Montanari, R. (2004) ‘Multi-attribute classification method for spare parts inventory management’, Journal of Quality in Maintenance Engineering, Vol. 10, No. 1, pp. 55-65.
• Caglar, D., Li, C. -L. and Simchi-Levi, D. (2004) ‘Two-echelon spare parts inventory system subject to a service constraint’, IIE Transactions, Vol. 36, No. 7, pp. 655-666.
• Chan, F. T. S., Chan, H. K. and Qi, H. J. (2006) ‘A review of performance measurement systems for supply chain management’, International Journal of Business Performance Management, Vol. 8, Nos. 2-3, pp.110-131.
• Chang, P. -L., Chou, Y. -C. and Huang, M. -G. (2005) ‘A (r, r, Q) inventory model for spare parts involving equipment criticality’, International Journal of Production Economics, Vol. 97, No. 1, pp. 66-74.
• Cobbaert, K. and Van Oudheusden, D. (1996) ‘Inventory models for fast moving spare parts subject to ‘‘sudden death’’ obsolescence’, International Journal of Production Economics, Vol. 44, No. 3, pp. 239-248.
• De Leeuw, S. and Beekman, L. (2008) ‘Supply chain-oriented performance measurement for automotive spare parts’, International Journal of Automotive Technology and Management, Vol. 8, No. 1, pp. 56-70.
• Dekker, R., Kleijn, M. J. and De Rooij, P. J. (1998) ‘A spare parts stocking policy based on equipment criticality’, International Journal of Production Economics, Vol. 56-57, pp. 69-77.
• Demeestère, R., Lorino, P. and Mottis, N. (1997) ‘Contrôle de gestion et pilotage’, Nathan, Paris.
• Do Rego, J. R. and De Mesquita, M. A. (2015) ‘Demand forecasting and inventory control: A simulation study on automotive spare parts’, International Journal of Production Economics, Vol. 161, pp. 1-16.
• Eaves, A. H. C. and Kingsman, B. G. (2004) ‘Forecasting for the ordering and stock-holding of spare parts’, Journal of the Operational Research Society, Vol. 55, No. 4, pp. 431-437.
• EFQM (1991), The Business Excellence Model, EFQM Publication, Brussels.
• Figge, F., Hahn, T., Schaltegger, S. and Wagner, M. (2002) ‘The Sustainability Balanced Scorecard – linking sustainability management to business strategy’, Business Strategy and the Environment, Vol. 11, No. 5, pp. 269-284.
• Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R. and Voss, C. (1991) ‘Performance Measurement in Service Businesses’, The Chartered Institute of Management Accountants.
• Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D. and Neely, A. (2007) ‘Towards a definition of a business performance measurement system’, International Journal of Operations & Production Management, Vol. 27, No. 8, pp. 784-801.
• Gaiardelli, P., Saccani, N. and Songini, L. (2007) ‘Performance measurement systems in after-sales service: an integrated framework’, International Journal of Business Performance Management, Vol. 9, No. 2, pp. 145-171.
• Gajpal, P. P., Ganesh, L. S. and Rajendran, C. (1994) ‘Criticality analysis of spare parts using the analytic hierarchy process’, International Journal of Production Economics, Vol. 35, Nos. 1-3, pp. 293-297.
• Ghalayini, A. M., Noble, J. S. and Crowe, T. J. (1997) ‘An integrated dynamic performance measurement system for improving manufacturing competitiveness’, International Journal of Production Economics, Vol. 48, No. 3, pp. 207-225.
• Grimaldi, S. and Rafele, C. (2007) ‘Current applications of a reference framework for the supply chain performance measurement’, International Journal of Business Performance Management, Vol. 9, No. 2, pp. 206-225.
• Gunasekaran, A., Patel, C. and Tirtiroglu, E. (2001) ‘Performance measures and metrics in a supply chain environment’, International Journal of Operations & Production Management, Vol. 21, Nos. 1-2, pp.71-87.
• Hemeimat, R., Al-Qatawneh, L., Arafeh, M. and Masoud, S. (2016) ‘Forecasting Spare Parts Demand Using Statistical Analysis’, American Journal of Operations Research, Vol. 6, No. 2, pp. 113-120.
• Hua, Z. S., Zhang, B., Yang, J. and Tan, D. S. (2007) ‘A new approach of forecasting intermittent demand for spare parts inventories in the process industries’, Journal of the Operational Research Society, Vol. 58, No. 1, pp. 52-61.
• Ittner, C. D. and Larcker, D. F. (1998) ‘Are Nonfinancial Measures Leading Indicators of Financial Performance? An Analysis of Customer Satisfaction’, Journal of Accounting Research, Vol. 36, pp. 1-35.
• Ittner, C. D., Larcker, D. F. and Randall, T. (2003) ‘Performance implications of strategic performance measurement in financial services firms’, Accounting, Organizations and Society, Vol. 28, Nos. 7-8, pp. 715-741.
• Johnson, H. T. and Kaplan, R. S. (1987) ‘Relevance Lost: The rise and fall of management accounting’, Harvard Business School Press, Boston, MA.
• Kalchschmidt, M., Zotteri, G. and Verganti, R. (2003) ‘Inventory management in a multi-echelon spare parts supply chain’, International Journal of Production Economics, Vol. 81-82, pp. 397-413.
• Kanji, G. K. and e Sá, P. M. (2002) ‘Kanji’s Business Scorecard’, Total Quality Management, Vol. 13, No. 1, pp. 13-27.
• Kaplan, R. S. and Norton, D. P. (1992) ‘The balanced scorecard: Measures that drive performance’, Harvard Business Review, Vol. 70, No. 1, pp. 71-79.
• Kennedy, W. J., Patterson, J. W. and Fredendall, L. D. (2002) ‘An overview of recent literature on spare parts inventories’, International Journal of Production Economics, Vol. 76, No. 2, pp. 201–215.
• Laitinen, E. K. (2002) ‘A dynamic performance measurement system: evidence from small Finnish technology companies’, Scandinavian Journal of Management, Vol. 18, No. 1, pp. 65-99.
• Lambert, D. M. and Pohlen, T. L. (2001) ‘Supply Chain Metrics’, International Journal of Logistics Management, Vol. 12, No. 1, pp. 1-19.
• Länsiluoto, A. and Järvenpää, M. (2008) ‘Environmental and performance management forces: Integrating “greenness” into balanced scorecard’, Qualitative Research in Accounting and Management, Vol. 5, No. 3, pp. 184-206.
• Lynch, R. L. and Cross, K. F. (1991) Measure Up – The Essential Guide to Measuring Business Performance, Mandarin, London.
• Maltz, A. C., Shenhar, A. J. and Reilly, R. R. (2003) ‘Beyond the Balanced Scorecard: Refining the Search for Organizational Success Measures’, Long Range Planning, Vol. 36, No. 2, pp. 187-204.
• Medori, D. and Steeple, D. (2000) ‘A framework for auditing and enhancing performance measurement systems’, International Journal of Operations & Production Management, Vol. 20, No. 5, pp. 520-533.
• Neely, A., Gregory, M. and Platts, K. (1995) ‘Performance measurement system design: A literature review and research agenda’, International Journal of Operations & Production Management, Vol. 15, No. 4, pp. 80-116.
• Neely, A., Adams, C. and Crowe, P. (2001) ‘The performance prism in practice’, Measuring Business Excellence, Vol. 5, No. 2, pp. 6-13.
• Ng, W. L. (2007) ‘A simple classifier for multiple criteria ABC analysis’, European Journal of Operational Research, Vol. 177, No. 1, pp. 344-353.
• Olugu, E. U., Wong, K. Y. and Shaharoun, A. M. (2011) ‘Development of key performance measures for the automobile green supply chain’, Resources, Conservation and Recycling, Vol. 55, No. 6, pp. 567-579.
• Otley, D. (1999) ‘Performance management: a framework for management control systems research’, Management Accounting Research, Vol. 10, No. 4, pp. 363-382.
• Parasuraman, A., Zeithaml, V. A. and Berry, L. L. (1985) ‘A conceptual model of service quality and its implications for future research’, Journal of Marketing, Vol. 49, No. 4, pp. 41-50.
• Partovi, F. Y. and Anandarajan, M. (2002) ‘Classifying inventory using an artificial neural network approach’, Computers & Industrial Engineering, Vol. 41, No. 4, pp. 389-404.
• Porras, E. and Dekker, R. (2008) ‘An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods’, European Journal of Operational Research, Vol. 184, No. 1, pp. 101-132.
• Ramanathan, R. (2006) ‘ABC inventory classification with multiple-criteria using weighted linear optimization’, Computers & Operations Research, Vol. 33, No. 3, pp. 695-700.
• Rampersad, H. K. (2005) ‘Total performance scorecard: the way to personal integrity and organizational effectiveness’, Measuring Business Excellence, Vol. 9, No. 3, pp. 21-35.
• Romeijnders, W., Teunter, R. and van Jaarsveld, W. (2012) ‘A two-step method for forecasting spare parts demand using information on component repairs’, European Journal of Operational Research, Vol. 220, No. 2, pp. 386-393.
• Scudder, G. D. (1984) ‘Priority Scheduling and Spares Stocking Policies for a Repair Shop: The Multiple Failure Case’, Management Science, Vol. 30, No. 6, pp. 739-749.
• Stewart, G. B. (1991) ‘The Quest for Value: A Guide for Senior Managers’, Harper Business, New York, NY.
• Supply Chain Council (2012). ‘Supply chain operations reference model’, Overview of SCOR version 11.0.
• Sureshchandar, G. S. and Leisten, R. (2005) ‘Holistic scorecard: strategic performance measurement and management in the software industry’, Measuring Business Excellence, Vol. 9, No. 2, pp. 12-29.
• Teunter, R. H. and Klein Haneveld, W. K. (2002) ‘Inventory control of service parts in the final phase’, European Journal of Operational Research, Vol. 137, No. 3, pp. 497-511.
• Willemain, T. R., Smart, C. N. and Schwarz, H. F. (2004) ‘A new approach to forecasting intermittent demand for service parts inventories’, International Journal of Forecasting, Vol. 20, No. 3, pp. 375-387.
• Wong, H., Cattrysse, D. and Van Oudheusden, D. (2005) ‘Stocking decisions for repairable spare parts pooling in a multi-hub system’, International Journal of Production Economics, Vol. 93-94, pp. 309-317.
• Yang, Q. and Chen, Y. (2012) ‘Auto parts demand forecasting based on nonnegative variable weight combination model in auto aftermarket’, International Conference on Management Science and Engineering 19th Annual Conference Proceedings, pp. 817-822.
• Zhou, P. and Fan, L. (2007) ‘A note on multi-criteria ABC inventory classification using weighted linear optimization’, European Journal of Operational Research, Vol. 182, No. 3, pp. 1488-1491.
• Zhu, S., Dekker, R., van Jaarsveld, W., Renjie, R. W. and Koning, A. J. (2017) ‘An improved method for forecasting spare parts demand using extreme value theory’, European Journal of Operational Research, Vol. 261, No. 1, pp. 169-181.