Citation
Hilletofth, P., Hilmola, O.-P. and Wang, Y. (2016), "Simulation based decision support systems in the supply chain context", Industrial Management & Data Systems, Vol. 116 No. 2. https://doi.org/10.1108/IMDS-11-2015-0477
Publisher
:Emerald Group Publishing Limited
Simulation based decision support systems in the supply chain context
Article Type: Guest editorial From: Industrial Management & Data Systems, Volume 116, Issue 2.
The supply chain and its management, is of highest significance in most industries. At an overall level, it concerns collection and analysis of data to provide a better basis for logistics decision-making (Lummus and Vokurka, 1999). This is a complex task since supply chains span within and across many firms on different levels (Gimenez and Ventura, 2005). The intensified globalization has also increased the complexity since firms and their facilities now tend to be geographically separated with many barriers between them (Meixell and Gargeya, 2005). Accordingly, there is a huge demand for sophisticated decision support in the supply chain context. Decision support systems can be created in several ways (e.g. Power, 2002). To make them efficient and effective in the supply chain context they should provide decision-makers with appropriate and accurate information and be able to predict the outcome of decisions and how these may affect the entire supply chain (Hilletofth et al., 2010b).
One type of decision support systems, which currently is receiving a lot of attention in the literature, is simulation-based decision support systems (e.g. Petering, 2011; Acar et al., 2010; Fröhling et al., 2010; Lättilä et al., 2010). This decision support system means that the real system of interest is modelled and implemented in simulation software. The simulation model is then used to support the decision-making of the real system through repeated simulations (Hilletofth and Lättilä, 2012). Different modelling and simulation approaches may be used.
The research area of simulation-based decision support has grown substantially in recent years. Several types of systems have been presented and analyzed in the literature, primarily from a theoretical and conceptual standpoint. However, some have had simulation applied and/or field experiments conducted. The maturity level of the approaches presented in the literature is low (e.g. Hilletofth et al., 2010a). Hence, there is a lack of research concerning industrial case studies in general and real-life implementations and applications (deployed systems) in particular.
This special issue aims to contribute to the understanding of simulation-based decision support systems in the supply chain context by illustrating its connection and value in real-life. Our main interest is in practical implementations and applications (deployed systems), even if theoretical and conceptual research works in the area are also considered. The research works in this special issue cover such issues as:
simulation of make-to-order supply systems concerning flexibility, price and cost (Kuo et al., 2016);
optimization of different aspects of containerized supply chains (Salam and Khan, 2016);
utilization of agent-based simulation and geographic information system data to understand and improve local public healthcare services (Rouzafzoon and Helo, 2016);
construction of system dynamics simulation model for entire supply chain to reveal what kind of collaboration and flexibility is needed from different parties in varying situations (Zhu et al., 2016);
identification of Pareto optimal solutions for supply chain simulations (Aslam and Ng, 2016); and
simulation of additive manufacturing process within make to order setting (Chiu and Lin, 2016).
Industrial cases are often the base of the research works in this special issue. For example, these cover Asian fishing net manufacturer and its suppliers, Thai motor cycle parts supply chain, Finnish healthcare service in particular sub-region and Taiwanese lamp manufacturer with additive manufacturing investments being considered. In general, based on the included research works, we may conclude that companies need to use more simulation-based decision support systems within complex- and order-driven production systems. Research in such environments are more interested in lead time performance and customer satisfaction rather than from old plain cost world.
Associate Professor Per Hilletofth - Department of Industrial Engineering and Management, School of Engineering, Jönköping University, Jönköping, Sweden
Professor Olli-Pekka Hilmola - Department of Industrial Management, Lappeenranta University of Technology, Kouvola, Finland
Professor Yacan Wang - Institute of Value Added Logistics, Beijing Jiaotong University, Beijing, China
References
Acar, Y., Kadipasaoglu, S. and Schipperijn, P. (2010), “A decision support framework for global supply chain modelling: an assessment of the impact of demand, supply and lead-time uncertainties on performance”, International Journal of Production Research, Vol. 48 No. 11, pp. 3245-3268
Aslam, T. and Ng, A. (2016), “Combining system dynamics and multi objective optimization with design space reduction”, Industrial Management and Data Systems, Vol. 116 No. 2, pp. 291-321
Chiu, M.-C. and Lin, Y.-H. (2016), “Simulation based method considering design for additive manufacturing and supply chain: an empirical study of lamp industry”, Industrial Management and Data Systems, Vol. 116 No. 2, pp. 322-348
Fröhling, M., Schwaderer, F., Bartusch, H. and Rentz, O. (2010), “Integrated planning of transportation and recycling for multiple plants based on process simulation”, European Journal of Operational Research, Vol. 207 No. 2, pp. 958-970
Gimenez, C. and Ventura, E. (2005), “Logistics-production, logistics-marketing and external integration: their impact on performance”, International Journal of Operations and Production Management, Vol. 25 No. 1, pp. 20-38
Hilletofth, P. and Lättilä, L. (2012), “Agent based decision support in the supply chain context”, Industrial Management and Data Systems, Vol. 112 No. 8, pp. 1217-1235
Hilletofth, P., Aslam, T. and Hilmola, O.-P. (2010a), “Multi-agent based supply chain management: case study of requisites”, International Journal of Networking and Virtual Organisations, Vol. 7 Nos 2/3, pp. 184-206
Hilletofth, P., Lättilä, L., Ujvari, S. and Hilmola, O.-P. (2010b), “Agent-based decision support for maintenance service provider”, International Journal of Services Sciences, Vol. 3 Nos 2/3, pp. 194-215
Kuo, Y., Yang, T., Parker, D. and Sung, C.-H. (2016), “Integration customer and supplier flexibility in a make-to-order industry”, Industrial Management and Data Systems, Vol. 116 No. 2, pp. 213-235
Lummus, R. and Vokurka, R. (1999), “Defining supply chain management: a historical perspective and practical guidelines”, Industrial Management and Data Systems, Vol. 99 No. 1, pp. 11-17
Lättilä, L., Hilletofth, P. and Lin, B. (2010), “Hybrid simulation models: when, why, how?”, Expert Systems with Applications, Vol. 37 No. 12, pp. 7419-8914
Meixell, M. and Gargeya, V. (2005), “Global supply chain design: a literature review and critique”, Transportation Research Part E, Vol. 41 No. 6, pp. 531-550
Petering, M.E.H. (2011), “Decision support for yard capacity, fleet composition, truck substitutability, and scalability issues at seaport container terminals”, Transportation Research Part E, Vol. 47 No. 1, pp. 85-103
Power, D.J. (2002), Decision Support Systems: Concepts and Resources for Managers, Quorum Books, Westport, CT
Rouzafzoon, J. and Helo, P. (2016), “Developing service supply chains by using agent based simulation”, Industrial Management and Data Systems, Vol. 116 No. 2, pp. 255-270
Salam, M. and Khan, S.A. (2016), “Simulation based decision support system for optimization: a case if Thai logistics service provider”, Industrial Management and Data Systems, Vol. 116 No. 2, pp. 236-254
Zhu, Q., Krikke, H. and Caniëls, M. (2016), “Collaborate or not? A system dynamics study on disruption recovery”, Industrial Management and Data Systems, Vol. 116 No. 2, pp. 271-290
About the Guest Editors
Per Hilletofth (PhD) is an Associate Professor of Logistics at the Jönköping University in Sweden. He holds a PhD in Technology Management and Economics (with specialization in Logistics and Transportation Management) from Chalmers University of Technology (Sweden). His research focuses on business logistics and supply chain management with an emphasis on strategy, demand and supply planning, interfaces, information systems and sustainability. He has published articles in various international journals including Industrial Management and Data Systems, Expert Systems with Applications, International Journal of Shipping and Transport Management, and European Business Review. He is currently in the Editorial Board for Industrial Management and Data Systems, World Review of Intermodal Transportation Research, International Journal of Logistics Economics and Globalization, and International Journal of Management in Education. Associate Professor Per Hilletofth is the corresponding author and can be contacted at: mailto:per.hilletofth@ju.se
Olli-Pekka Hilmola (PhD) is working as a Professor at the Lappeenranta University of Technology (LUT), in research unit located in the city of Kouvola, Finland. He is affiliated with numerous int. journals through editorial boards, including Expert Systems with Applications, Industrial Management and Data Systems, as well as Decision Support Systems. He holds 140 journal publications and has been involved in numerous int. research projects from logistics.
Yacan Wang (PhD) is currently a Professor of closed-loop supply chain at the University of Beijing Jiaotong, PRC. Her primary research stream focuses on closed-loop supply chain, reverse logistics, green supply chain and marketing of green products. This research stream encompasses both the supply-side and demand-side aspects of remanufacturing operations, including network design, inventory control, recovery option decision and also promotion of remanufactured products. She is currently the Vice Director of value-added logistics institution, Beijing Jiaotong University. She has published in international journals such as International Journal of Production Economics, International Journal of Physical Distribution and Logistics Management, Resources, Conservation and Recycling, International Journal of Advanced Manufacturing Technology, etc.