Warehouse manpower planning strategies in times of financial crisis: evidence from logistics service providers and retailers in the Netherlands

The economic crisis that is experienced in Europe has a large influence on transport and logistics companies. Since turnover typically drops strongly during a crisis, companies try to reduce costs in order to survive. The study reported in this paper has investigated how manpower planning in warehouses has been used to counter effects of the crisis and what the results are of the measures taken. A survey was carried out among warehouses run by retailers and logistics service providers. The results of the survey show that there is a significant relation between a decrease in turnover and the four investigated manpower planning strategies. Furthermore, the study shows that the most effective manpower planning strategies are flexible planning of employees and balancing the workload. Hence, the study concludes that in particular better operational planning is a key strategy to counter the effects of the financial crisis, which is an important insight for the management of warehouses

Guidelines for the deployment and implementation of manufacturing scheduling systems

It has frequently been stated that there exists a gap between production scheduling theory and practice. In order to put theoretical findings into practice, advances in scheduling models and solution procedures should be embedded into a piece of software - a scheduling system - in companies. This results in a process that entails (1) determining its functional features, and (2) adopting a successful strategy for its development and deployment. In this paper we address the latter question and review the related literature in order to identify descriptions and recommendations of the main aspects to be taken into account when developing such systems. These issues are then discussed and classified, resulting in a set of guidelines that can help practitioners during the process of developing and deploying a scheduling system. In addition, identification of these issues can provide some insights to drive theoretical scheduling research towards those topics more in demand by practitioners, and thus help to close the aforementioned gap.Framiñan Torres, JM.; Ruiz García, R. (2012). Guidelines for the deployment and implementation of manufacturing scheduling systems. International Journal of Production Research. 50(7):1799-1812. doi:10.1080/00207543.2011.564670S17991812507Baek, D. H. (1999). A visualized human-computer interactive approach to job shop scheduling. International Journal of Computer Integrated Manufacturing, 12(1), 75-83. doi:10.1080/095119299130489Comesaña Benavides, J. A., & Carlos Prado, J. (2002). Creating an expert system for detailed scheduling. International Journal of Operations & Production Management, 22(7), 806-819. doi:10.1108/01443570210433562Bensana, E. 1986. An expert-system approach to industrial job-shop scheduling. In: Proceedings of the 1986 IEEE international conference on robotics and automation. 1986. Vol. 3, pp.1645–1650.Berglund, M., & Karltun, J. (2007). Human, technological and organizational aspects influencing the production scheduling process. International Journal of Production Economics, 110(1-2), 160-174. doi:10.1016/j.ijpe.2007.02.024Besbes, W., Teghem, J., & Loukil, T. (2010). Scheduling hybrid flow shop problem with non-fixed availability constraints. European J. of Industrial Engineering, 4(4), 413. doi:10.1504/ejie.2010.035652Bhattacharyya, S., & Koehler, G. J. (1998). Learning by Objectives for Adaptive Shop-Floor Scheduling. Decision Sciences, 29(2), 347-375. doi:10.1111/j.1540-5915.1998.tb01580.xBitran, G. R., & Tirupati, D. (1988). OR Practice—Development and Implementation of a Scheduling System for a Wafer Fabrication Facility. Operations Research, 36(3), 377-395. doi:10.1287/opre.36.3.377Buxey, G. (1989). Production scheduling: Practice and theory. European Journal of Operational Research, 39(1), 17-31. doi:10.1016/0377-2217(89)90349-4Chen, J.-F. (2004). Unrelated parallel machine scheduling with secondary resource constraints. The International Journal of Advanced Manufacturing Technology, 26(3), 285-292. doi:10.1007/s00170-003-1622-1Collinot, A., Le Pape, C., & Pinoteau, G. (1988). SONIA: A knowledge-based scheduling system. Artificial Intelligence in Engineering, 3(2), 86-94. doi:10.1016/0954-1810(88)90024-6Cowling, P. (2003). A flexible decision support system for steel hot rolling mill scheduling. Computers & Industrial Engineering, 45(2), 307-321. doi:10.1016/s0360-8352(03)00038-xDudek, R. A., Panwalkar, S. S., & Smith, M. L. (1992). The Lessons of Flowshop Scheduling Research. Operations Research, 40(1), 7-13. doi:10.1287/opre.40.1.7Dumond, E. J. (2005). Understanding and using the capabilities of finite scheduling. Industrial Management & Data Systems, 105(4), 506-526. doi:10.1108/02635570510592398Fox, M. S., & Smith, S. F. (1984). ISIS?a knowledge-based system for factory scheduling. Expert Systems, 1(1), 25-49. doi:10.1111/j.1468-0394.1984.tb00424.xFraminan, J. M., & Ruiz, R. (2010). Architecture of manufacturing scheduling systems: Literature review and an integrated proposal. European Journal of Operational Research, 205(2), 237-246. doi:10.1016/j.ejor.2009.09.026Freed, T., Doerr, K. H., & Chang, T. (2007). In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations. International Journal of Production Research, 45(21), 5075-5093. doi:10.1080/00207540600818351Gao, C and Tang, L. 2008. A decision support system for color-coating line in steel industry. In: Proceedings of the IEEE international conference on automation and logistics, ICAL 2008. 2008. pp.1463–1468.Grant, T. J. (1986). Lessons for O.R. from A.I.: A Scheduling Case Study. Journal of the Operational Research Society, 37(1), 41-57. doi:10.1057/jors.1986.7Graves, S. C. (1981). A Review of Production Scheduling. Operations Research, 29(4), 646-675. doi:10.1287/opre.29.4.646HALSALL, D. N., MUHLEMANN, A. P., & PRICE, D. H. R. (1994). A review of production planning and scheduling in smaller manufacturing companies in the UK. Production Planning & Control, 5(5), 485-493. doi:10.1080/09537289408919520Higgins, P. G. (1996). Interaction in hybrid intelligent scheduling. International Journal of Human Factors in Manufacturing, 6(3), 185-203. doi:10.1002/(sici)1522-7111(199622)6:33.0.co;2-6Kanet, J. J., & Adelsberger, H. H. (1987). Expert systems in production scheduling. European Journal of Operational Research, 29(1), 51-59. doi:10.1016/0377-2217(87)90192-5Kathawala, Y., & Allen, W. R. (1993). Expert Systems and Job Shop Scheduling. International Journal of Operations & Production Management, 13(2), 23-35. doi:10.1108/01443579310025286Kerr, R. M. (1992). Expert systems in production scheduling: Lessons from a failed implementation. Journal of Systems and Software, 19(2), 123-130. doi:10.1016/0164-1212(92)90063-pKnolmayer, G., Mertens, P., & Zeier, A. (2002). Supply Chain Management Based on SAP Systems. doi:10.1007/978-3-540-24816-3Leachman, R. C., Benson, R. F., Liu, C., & Raar, D. J. (1996). IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation—Semiconductor Sector. Interfaces, 26(1), 6-37. doi:10.1287/inte.26.1.6MACCARTHY, B. L., & LIU, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59-79. doi:10.1080/00207549308956713McKay, K. N., & Black, G. W. (2007). The evolution of a production planning system: A 10-year case study. Computers in Industry, 58(8-9), 756-771. doi:10.1016/j.compind.2007.02.002McKay, K. N., Safayeni, F. R., & Buzacott, J. A. (1988). Job-Shop Scheduling Theory: What Is Relevant? Interfaces, 18(4), 84-90. doi:10.1287/inte.18.4.84McKay, K. N., Morton, T. E., Ramnath, P., & Wang, J. (2000). ?Aversion dynamics? scheduling when the system changes. Journal of Scheduling, 3(2), 71-88. doi:10.1002/(sici)1099-1425(200003/04)3:23.0.co;2-0MCKAY, K., PINEDO, M., & WEBSTER, S. (2009). PRACTICE-FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*. Production and Operations Management, 11(2), 249-258. doi:10.1111/j.1937-5956.2002.tb00494.xMissbauer, H., Hauber, W., & Stadler, W. (2009). A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry. International Journal of Production Research, 47(15), 4147-4172. doi:10.1080/00207540801950136Numao, M and Morishita, S. 1989. A scheduling environment for steel-making processes. In: Proceedings of the 5th conference on artificial intelligence applications. 1989. pp.279–286.Olhager, J., & Rapp, B. (1995). Operations Research Techniques in Manufacturing Planning and Control Systems. International Transactions in Operational Research, 2(1), 29-43. doi:10.1111/j.1475-3995.1995.tb00003.xPerez-Gonzalez, P., & Framinan, J. M. (2009). Scheduling permutation flowshops with initial availability constraint: Analysis of solutions and constructive heuristics. Computers & Operations Research, 36(10), 2866-2876. doi:10.1016/j.cor.2008.12.018Pinedo, M., & Yen, B. P.-C. (1997). Annals of Operations Research, 70, 359-378. doi:10.1023/a:1018986524234Portougal, V., & Robb, D. J. (2000). Production Scheduling Theory: Just Where Is It Applicable? Interfaces, 30(6), 64-76. doi:10.1287/inte.30.6.64.11623Reisman, A., Kumar, A., & Motwani, J. (1997). Flowshop scheduling/sequencing research: a statistical review of the literature, 1952-1994. IEEE Transactions on Engineering Management, 44(3), 316-329. doi:10.1109/17.618173Steffen, MS. 1986. A survey of artificial intelligence-based scheduling systems. In: Proceedings of the fall industrial engineering conference. 1986.Storer, R. H., Wu, S. D., & Vaccari, R. (1992). New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling. Management Science, 38(10), 1495-1509. doi:10.1287/mnsc.38.10.1495Tang, L., & Wang, G. (2008). Decision support system for the batching problems of steelmaking and continuous-casting production. Omega, 36(6), 976-991. doi:10.1016/j.omega.2007.11.002T’kindt, V., Billaut, J.-C., Bouquard, J.-L., Lenté, C., Martineau, P., Néron, E., … Tacquard, C. (2005). The e-OCEA project: towards an Internet decision system for scheduling problems. Decision Support Systems, 40(2), 329-337. doi:10.1016/j.dss.2004.04.001Wiers, VCS. 1997. Human–computer interaction in production scheduling: Analysis and design of decision support systems for production scheduling tasks. Ph.D. Thesis, Technische Universiteit Eindhoven, NetherlandsWiers, V. C. S. (2002). A case study on the integration of APS and ERP in a steel processing plant. Production Planning & Control, 13(6), 552-560. doi:10.1080/09537280210160321Wiers, V. C. S., & Van Der Schaaf, T. W. (1997). A framework for decision support in production scheduling tasks. Production Planning & Control, 8(6), 533-544. doi:10.1080/095372897234876Zhang, L., Krishnamurthy, A., Malmborg, C. J., & Heragu, S. S. (2009). Variance-based approximations of transaction waiting times in autonomous vehicle storage and retrieval systems. European J. of Industrial Engineering, 3(2), 146. doi:10.1504/ejie.2009.02360

A review of the applicability of OR and AI scheduling techniques in practice

This paper presents a review of the applicability of scheduling techniques in practice. Both the operations research and the artificial intelligence research communities have produced a number of reports on the applicability of techniques, often in isolation from each other, although the problems encountered seem largely similar in each case. Moreover, studies on the role of humans in production scheduling are reviewed. Papers that discuss the use of techniques by humans are also discussed. The paper suggests ways in which the applicability of scheduling techniques might be improved. © 199

A review of the applicability of OR and AI scheduling techniques in practice

This paper presents a review of the applicability of scheduling techniques in practice. Both the operations research and the artificial intelligence research communities have produced a number of reports on the applicability of techniques, often in isolation from each other, although the problems encountered seem largely similar in each case. Moreover, studies on the role of humans in production scheduling are reviewed. Papers that discuss the use of techniques by humans are also discussed. The paper suggests ways in which the applicability of scheduling techniques might be improved. © 199

A quantitative field study of the decision behaviour of four shop floor schedulers

In this article the decision behaviour of four production schedulers in a truck manufacturing company is investigated by means of a quantitative model. The model consists of there parts: performance variables, action variables and disturbance variables. The outcomes show that there is a large difference between schedulers that apparently have the same type of decision problem. Another interesting finding is that some scheduling actions work positively in the short term, but negatively over a longer term. Other results, along with methodological issues of quantitative research, are discussed

From anecdotes to theory: a review of existing knowledge on human factors of planning and scheduling

no abstrac

A review of capicity planning techniques within standard software packages

This paper gives a review of capacity planning techniques from which today's standard software packages for production control make their choice. The following techniques are discussed in the paper: four variants of the rough cut capacity check, capacity requirements planning with infinite and finite loading, input/output planning without and with individual work orders, and a number of sequencing techniques. An important issue throughout the paper is the concept of robustness and nervousness of planning techniques, Aspects of interaction between techniques and human planners arc given. The human planner is still an important factor in capacity planning