A demand classification scheme for spare part inventory model subject to stochastic demand and lead time

References

• Bacchetti, A., and N. Saccani. 2012. “Spare Parts Classification and Demand Forecasting for Stock Control: Investigating the Gap between Research and Practice.” Omega 40: 722–737.10.1016/j.omega.2011.06.008
   Web of Science ®Google Scholar
• Botter, R., and L. Fortuin. 2000. “Stocking Strategy for Service Parts: A Case Study.” International Journal of Operations & Production Management 20 (6): 656–674.
   Web of Science ®Google Scholar
• Burgin, T. A. 1975. “The Gamma Distribution and Inventory Control.” Journal of the Operational Research Society 26: 507–525.10.1057/jors.1975.110
   Web of Science ®Google Scholar
• Cavalieri, S., M. Garetti, M. Macchi, and R. Pinto. 2008. “A Decision-making Framework for Managing Maintenance Spare Parts.” Production Planning & Control 19 (4): 379–396.
   Web of Science ®Google Scholar
• Childe, S. J. 2011. “Case Studies in Operations Management.” Production Planning & Control 22 (2): 107.
   Web of Science ®Google Scholar
• Croston, J. D. 1972. “Forecasting and Stock Control for Intermittent Demands.” Journal of the Operational Research Society 23: 289–303.10.1057/jors.1972.50
   Web of Science ®Google Scholar
• Das, C. 1976. “Approximate Solution to the (Q, r) Inventory Model for Gamma Lead Time Demand.” Management Science 22 (9): 1043–1047.10.1287/mnsc.22.9.1043
   Web of Science ®Google Scholar
• Downing, M., M. Chipulub, U. Ojiakoc, and D. Kaparisd. 2014. “Advanced Inventory Planning and Forecasting Solutions: A Case Study of the UKTLCS Chinook Maintenance Programme.” Production Planning & Control 25 (1): 73–90.
   Web of Science ®Google Scholar
• Driessen, M., J. Arts, G. J. V. Houtum, J. W. Rustenburg, and B. Huisman. 2014. “Maintenance Spare Parts Planning and Control: A Framework for Control and Agenda for Future Research.” Production Planning & Control 1–20. doi:10.1080/09537287.2014.907586.
   Web of Science ®Google Scholar
• Dunsmuir, W. T. M., and R. D. Snyder. 1989. “Control of Inventories with Intermittent Demand.” European Journal of Operational Research 40: 16–21.10.1016/0377-2217(89)90267-1
   Web of Science ®Google Scholar
• Eaves, A. 2002. “Forecasting for the Ordering and Stock-holding of Consumable Spare Parts.” PhD thesis, Department of Management Science, Lancaster University, Lancaster.
   Google Scholar
• Eaves, A., and B. Kingsman. 2004. “Forecasting for the Ordering and Stock-holding of Spare Parts.” Journal of the Operational Research Society 55 (4): 431–437.10.1057/palgrave.jors.2601697
   Web of Science ®Google Scholar
• Eppen, G., and R. Martin. 1998. “Determining Safety Stock in the Presence of Stochastic Lead Time and Demand.” Management Science 34 (11): 1380–1390.
   Web of Science ®Google Scholar
• Feeney, G., and C. Sherbrooke. 1966. “The (S − 1, S) Inventory Policy Under Compound Poisson Demand.” Management Science 12: 391–411.10.1287/mnsc.12.5.391
   Google Scholar
• Ghobbar, A. A., and C. H. Friend. 2003. “Evaluation of Forecasting Methods for Intermittent Parts Demand in the Field of Aviation: A Predictive Model.” Computers & Operations Research 30: 2097–2114.
   Web of Science ®Google Scholar
• Hax, A., and D. Candea. 1984. Production and Inventory Management. New Jersey, NJ: Prentice-Hall.
   Google Scholar
• Hill, R., M. Omar, and D. Smith. 1999. “Stock Replenishment Policies for a Stochastic Exponentially-declining Demand Process.” European Journal of Operational Research 116: 374–388.10.1016/S0377-2217(98)00223-9
   Web of Science ®Google Scholar
• Hua, Z. S., B. Zhang, J. Yang, and D. S. Tan. 2007. “A New Approach of Forecasting Intermittent Demand for Spare Parts Inventories in the Process Industries.” Journal of the Operational Research Society 58: 52–61.10.1057/palgrave.jors.2602119
   Web of Science ®Google Scholar
• Huiskonen, J. 2001. “Maintenance Spare Parts Logistics: Special Characteristics and Strategic Choices.” International Journal of Production Economics 71: 125–133.10.1016/S0925-5273(00)00112-2
   Web of Science ®Google Scholar
• Janssen, F., R. Heuts, and T. de Kok. 1998. “On the (R, s, Q) Inventory Model When Demand is Modelled as a Compound Bernoulli Process.” European Journal of Operational Research 104: 423–436.10.1016/S0377-2217(97)00009-X
   Web of Science ®Google Scholar
• Johnston, F., and J. Boylan. 1996. “Forecasting Intermittent Demand: A Comparative Evaluation of Croston’s Method.” International Journal of Forecasting 12: 297–298.10.1016/0169-2070(95)00642-7
   Web of Science ®Google Scholar
• Kourentzes, N. 2013. “Intermittent Demand Forecasts with Neural Networks.” International Journal of Production Economics 143: 198–206.10.1016/j.ijpe.2013.01.009
   Web of Science ®Google Scholar
• Krever, M., S. Wunderink, R. Dekker, and B. Schorr. 2005. “Inventory Control Based on Advanced Probability Theory, an Application.” European Journal of Operational Research 162: 342–358.10.1016/j.ejor.2003.06.040
   Web of Science ®Google Scholar
• Krupp, J. 1997. “Safety Stock Management.” Production and Inventory Management Journal 38 (3): 11–18.
   Google Scholar
• Liu, P., S. H. Huang, A. Mokasdar, H. Zhou, and L. Hou. 2013. “The Impact of Additive Manufacturing in the Aircraft Spare Parts Supply Chain: Supply Chain Operation Reference (Scor) Model Based Analysis.” Production Planning & Control 25 (13–14): 1169–1181.
   Web of Science ®Google Scholar
• Nahmias, S. 2004. Production and Operations Analysis. 5th ed. New York: McGraw-Hill College.
   Google Scholar
• Namit, K., and J. Chen. 1999. “Solutions to the <Q, r> Inventory Model for Gamma Lead-time Demand.” International Journal of Physical Distribution & Logistics Management 29 (2): 138–151.
   Google Scholar
• Nenes, G., S. Panagiotidou, and G. Tagaras. 2010. “Inventory Management of Multiple Items with Irregular Demand: A Case Study.” European Journal of Operational Research 205 (2): 313–324.10.1016/j.ejor.2009.12.022
   Web of Science ®Google Scholar
• Porras, E., and R. Dekker. 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 184: 101–132.10.1016/j.ejor.2006.11.008
   Web of Science ®Google Scholar
• Presutti, V., and R. Trepp. 1970. “More Ado about Economic Order Quantities (EOQ).” Naval Research Logistics Quarterly 17: 243–251.10.1002/(ISSN)1931-9193
   Google Scholar
• Regattieri, A., M. Gamberi, R. Gamberini, and R. Manzini. 2005. “Managing Lumpy Demand for Aircraft Spare Parts.” Journal of Air Transport Management 11 (6): 426–431.10.1016/j.jairtraman.2005.06.003
   Web of Science ®Google Scholar
• Romeijnders, W., R. Teunter, and W. van Jaarsveld. 2012. “A Two-step Method for Forecasting Spare Parts Demand using Information on Component Repairs.” European Journal of Operational Research 220: 386–393.10.1016/j.ejor.2012.01.019
   Web of Science ®Google Scholar
• Silver, E. 1970. “Some Ideas Related to the Inventory Control of Items Having Erratic Demand Patterns.” Canadian Operational Research Journal 8: 87–100.
   Google Scholar
• Silver, E., C.-M. Ho, and R. Deemer. 1971. “Cost Minimizing Inventory Control of Items Having a Special Type of Erratic Demand Pattern.” INFOR 9: 198–219.
   Google Scholar
• Silver, E., D. Pyke, and R. Peterson. 1998. Inventory Management and Production Planning and Scheduling. 3rd ed. New York: Wiley.
   Google Scholar
• Strijbosch, L., R. Heuts, and E. Schoot. 2000. “A Combined Forecast-inventory Control Procedure for Spare Parts.” Journal of the Operational Research Society 51 (10): 1184–1192.
   Web of Science ®Google Scholar
• Syntetos, A. A., and J. E. Boylan. 2005. “The Accuracy of Intermittent Demand Estimates.” International Journal of Forecasting 21: 303–314.10.1016/j.ijforecast.2004.10.001
   Web of Science ®Google Scholar
• Syntetos, A. A., and J. E. Boylan. 2006a. “Comments on the Attribution of an Intermittent Demand Estimator.” International Journal of Forecasting 22: 201–201.10.1016/j.ijforecast.2005.09.001
   Web of Science ®Google Scholar
• Syntetos, A. A., and J. E. Boylan. 2006b. “On the Stock-control Performance of Intermittent Demand Estimators.” International Journal of Production Economics 103: 36–47.10.1016/j.ijpe.2005.04.004
   Web of Science ®Google Scholar
• Tijms, H. 1994. Stochastic Models, An Algorithmic Approach. Chichester: Wiley.
   Google Scholar
• Tyworth, J., and R. Ganeshan. 2000. “A Note on Solutions to the Q_r Inventory Model for Gamma Lead Time Demand.” International Journal of Physical Distribution & Logistics Management 30 (6): 534–539.
   Google Scholar
• Voss, C., N. Tsikriktis, and M. Frohlich. 2002. “Case Research in Operations Management.” International Journal of Operations & Production Management 22 (2): 195–219.
   Web of Science ®Google Scholar
• Wagner, S. M., and E. A. Lindemann. 2008. “Case Study-based Analysis of Spare Parts Management in the Engineering Industry.” Production Planning & Control 19 (4): 397–407.
   Web of Science ®Google Scholar
• Willemain, T., C. Smart, and H. Schwarz. 2004. “A New Approach to Forecasting Intermittent Demand for Service Parts Inventories.” International Journal of Forecasting 20: 375–387.10.1016/S0169-2070(03)00013-X
   Web of Science ®Google Scholar
• Willemain, T., C. Smart, J. Shockor, and P. DeSautels. 1994. “Forecasting Intermittent Demand in Manufacturing: A Comparative Evaluation of Croston’s Method.” International Journal of Forecasting 10: 529–538.10.1016/0169-2070(94)90021-3
   Web of Science ®Google Scholar
• Williams, T. 1984. “Stock Control with Sporadic and Slow-moving Demand.” Journal of the Operational Research Society 35 (10): 939–948.10.1057/jors.1984.185
   Web of Science ®Google Scholar
• Yeh, Q. 1997. “A Practical Implementation of Gamma Distribution to the Reordering Decision of an Inventory Control Problem.” Production and Inventory Management Journal 38 (1): 51–57.
   Google Scholar
• Yin, R. K. 2003. Case Study Research: Design and Methods. 3rd ed. Thousand Oaks, CA: Sage.
   Google Scholar