Foreword
It is our pleasure to introduce this special issue of Decision Support Systems on ‘Rapid Modeling for Sustainability’. It brings together core papers on Rapid Modeling, focusing on the rising dimensions of sustainability, which is raised on top of technical and financial modeling. Rapid Modeling (ref) refers to the basic models behind the structured process of decision making to manage and improve time based performance. As its was originally applied for manufacturing systems and supply chains, it has spread through decision making areas like private and public services, office operations and new product development. All these areas cope with a substantial effort of short and/or on-time delivery. Rapid refers to strong calculation possibilities so that vvarious what-if scenario’s can be run in a short period of time. In this way Rapid Modeling is very well suited to support both individual as group decision making processes. Therefore, we are honored to edit this special issue for Decision Support Systems.
The conference ‘Rapid Modeling for Sustainability’ held in Leuven in 2011, was the third in a row, following the successful1st Rapid Modeling Conference in 2009 held in Neuchatel that focused on ‘Rapid Modeling for Increasing Competitiveness’. The main focus of the 2nd conference in 2010 also held in Neuchatel, ‘Rapid Modeling - Quick Response: Intersection of Theory and Practice‘ was about the transfer of knowledge from theory to practice, providing the theoretical foundations for successful performance improvement.
Rapid Modeling serves the purpose of understanding the complexity of flow systems, id est, where demand meets capacity in a dynamic and stochastic environment. This allows us to optimize towards operational, value and financial objectives. These insights have been the underpinning for the analysis of manufacturing systems, service systems and supply chains. Material and service flows have been studied in relation to the resources they consume. This leads to the study of utilization, the key driver of operational performance in stochastic environment. On top of the utilization, many amplifying and levering factors are studied within Rapid Modeling: the impact of stochastic behavior, the influence of variability elements (like breakdowns, setups, scrap, etc.), the consequences of heterogeneity (for instance mass customization), the modeling of complexity (in terms of technology, globalization, outsourcing, supply chain coordination, etc.) and last but not least, managerial decision making as it is present under the form of the numerous decision variables (e.g. lot sizing, supplier selection, scheduling, and planning). Rapid Modeling links all these elements together in a stochastic setting, wherein the various buffers are obtained to reach the desired operational performance: safety stock, safety capacity and safety time. In this way, Rapid Modeling makes it possible to study contemporary issues like Lean, Continuous Improvement, Six Sigma and various Planning aspects (e.g. Sales & Operations Planning, Inventory Management, and Scheduling).
Obviously, the deployment of these buffers has clear financial consequences. Rapid Modeling techniques increasingly more help to change operational decisions by considering such issues as cash flow, investments, costs, economic value added, customer equity and the like. The optimal operational decisions will definitely not coincide with the optimal mid and long term financial decisions.
Nowadays, and even more in the future, another relevant dimension by which the systems can be evaluated, is increasingly studied.[h1]Rapid Modeling needs to include performance measures related to what is generally summarized under the umbrella of ‘sustainable values’ in the broad sense. Examples are ample these days and include issues of a sustainable environment and ecology, reverse flows and remanufacturing, humanitarian and ethical concerns as well as human centered and societal aspects. All of these are upper layer performance measures which are more and more integrated into the mainstream Rapid Modeling approaches.
We can look back on a nice set of contributions to this special issue. The interest, both from the conference contributions as well as others, was substantial. We like to thanks all the authors for this effort. From this base,nine papers finally made it through the blind review process. It was nice to see that not only classical themes were tackled but also new applications, alternative methodologies and novel areas were investigated, including many illustrations from real life settings in various countries.
We were very happy to receive the manuscript ‘A Review of Modeling Approaches for Sustainable Supply Chain Management’ from Stefan Seuring. He concludes with an excellent review of the quantitative models used to study sustainability in f forward supply chains. With the social side of sustainability is hardly present in the literature, there is inclusion of the environmental part through the modeling of life cycle assessment and impact criteria issues. He concludes with the review of three dominant approaches: equilibrium models, multi-criteria decision making and analytical hierarchy process. He stresses the need to further develop these issues within Rapid Modeling.
This brings us directly to the mind expanding paper of Nico Vandaele and Catherine Decouttere ‘Sustainable R&D Portfolio Assessment’, where linear programming is used to evaluate a companies’ R&D portfolio. Herein the triple bottom line where financial, operational and ecological/human factors interfere is modeled as a full-fledged multi-dimensional assessment. The Rapid Modeling aspect relates to the rapid recalculation when new or upgraded pieces of information become available and to support group decision making. Nicely, this novel approach is illustrated with two industrial cases: one case from consumables and one case from materials manufacturing (polymers).
The third contribution from the hand of Krisztina Demeter ‘Time-Based Competition – the Aspect of Partner Proximity’, relates time-based competition to the aspect of partner proximity. Relating to the previous paper, product design modularity turns out to be moderating the partnering decision. The same holds for the ordering policies and the origin of the parent company. This paper underscores the fact that the efforts of Rapid Modeling for decision support are to be placed in a macro-economic context.
In a the fourth paper BoualemRabta, Johannes Fichtinger, Gerald Reiner and Reinhold Schodl go deep into the methodological aspects of Rapid Modeling in ‘A Hybrid Analysis Method for Multi-Class Queuing Networks with Multi-Server Nodes’. In this paper the combination of simulation and analytic decomposition is investigated to further improve Rapid Modeling objective: a quick calculation of performance without sacrificing accuracy too much. The latter is the key of the value of Rapid Modeling. The approach is illustrated to support decision making in the area of alternative system designs, which also relates back on the second paper of this special issue.
Subsequentially, some new grounds of application are explored. In the their contribution ‘Optimization of a Stochastic Remanufacturing Network with an Exchange Option’, Pieter Colen, Marc Lambrecht and Kris Lieckens dive into the area of remanufacturing as a way to preserve sustainability. Rapid Modeling turns out to be extremely valuable to model the construction of a network of remanufacturing facilities combined with the appropriate inventories and capacity levels to preserve a pre-specified service level. For evaluating two options, replacing immediately or remanufacturing, the authors rely on a non-linear model with non-linear constraints solved with differential evolutions techniques.
The sixth contribution explores the application of Rapid Modeling for the analysis of staffing problems. Mieke Defraeye and Inneke Van Nieuwenhuyse explore the possibilities of Rapid modeling for service systems in ‘Controlling Excessive Waiting Times in Small Service Systems with Time-Varying Demand: an Extension of the ISA Algorithm’. Rapid Modeling in systems with non-constant arrival patterns is of great interest to service industries. They are able to control the probability of experiencing excessive waiting times especially prevalent in small service systems like for instance emergency units.
Sustainability gets an extremely human touch when dealing with the contribution of Jeroen Belien, Jan Colpaert, Liesje De Boeck, Stijn Devesse and Filip Van den Bossche entitled ‘Optimizing the Facility Location Design of Organ Transport’. It is also related to the paper of remanufacturing networks as it deals with the location of organ transplant centers. In organ transplant, timely transporting is the key and Rapid Modeling is a way to model the criticality of the process after the organ’s removal. The model is applied to the Belgian organ transplant path.
The final two contributions deal with the area of transportation. In the paper ‘Analyzing the Impact of Disruptions in Intermodal Transport Networks: a Micro Simulation-Based Model’ Gerhard Bauer, Wolfgang Burgholzer, Werner Jammernegg and Martin Posset show the use of simulation for Rapid Modeling purposes. Identifying areas where alternative paths can be extremely useful in case of network disruptions is key and turns out to be very challenging in the case of intermodal transportation networks. In this paper, Rapid Modeling is realized with the aid of traffic micro simulation. The approach is applied to the Austrian intermodal transport network.
The last and closing contribution entitled ‘Sustainable Revenue Management: an Agent-Based Modeling Approach for the public Transportation Industry’ is from the hand of Milan Lovric, Ting Li and Peter Vervest. Public transportation operators operate in a complex societal context. In this respect they behave in a multi-objective sustainability framework which relates this the paper on R&D portfolio assessment. An agent-based modeling and simulation approach underscores the Rapid Modeling concept. Here also, the model is illustrated with smart card transaction data from a major Dutch public transport authority.
Being happy to introduce you to this special issue, allow me to acknowledge all the authors, the referees and the participants of the RMC11 meeting for making this special issue to a landmark in its field.
The special volume editors,
Olli-Pekka Hilmola*, Gerald Reiner, and Nico Vandaele
Leuven, May 2012
Biographical Details of Guest Editors
Lappeenranta University of Technology, Kouvola Research Unit, * corresponding author
Prikaatintie 9, FIN-45100 Kouvola, Finland
Fax: +358 5 344 4009
E-mail:
PhD Olli-Pekka Hilmola is working as a Professor in Lappeenranta University of Technology (LUT), in research branch unit located in Kouvola, Finland. He is affiliated with numerous international. journals through editorial boards, including Baltic Journal of Management, Industrial Management and Data Systems, Decision Support Systems, and International Journal of Shipping and Transport Logistics. Dr. Hilmola has published more than 100 refereed journal manuscripts.
University of Neuchâtel, Faculty of Economics and Business
Rue A.-L. Breguet 1, 2000 Neuchâtel, Switzerland
E-mail:
Gerald Reiner holds a doctorate degree and habilitation in Business Administration from the Vienna University of Economics and Business (Austria). He worked as an assistant professor at the Vienna University of Economics and Business and is currently full professor in Production and Logistics Management at the University of Neuchatel (Switzerland). His research interests include Production Management, Supply Chain Management, and Service Supply Chain Management. He published in international journals like, e.g., International Journal of Production Economics, International Journal of Production Research, International Journal of Operations and Production Management, Operations Management Research, OR Spectrum.,
Katholieke Universiteit Leuven, Faculty of Business and Economics
Research Center for Operations Management
Naamsestraat 69, 3000 Leuven, Belgium
E-mail:
Nico Vandaele holds a degree Commercial Engineering (1990) and obtained a PhD. in Applied Economics, Operations Research and Operations Management from the K.U. Leuven in 1996. He is currently Full Professor Operations Management at the Katholieke Universiteit Leuven, Faculty of Business and Economics. He is a research member of the Research Center of Operations Management. He is also a visiting researcher at CORE and IAG (Université Catholique de Louvain). Nico Vandaele teaches courses in statistics, operations research and operations management. His research interests are situated in modeling of manufacturing and service systems, performance measurement, the design of planning systems, factory physics, health care management and traffic modeling. Recently new research has been set up in the area of decision support for product design and development. He published in leading journals like IIE Transactions, Decision Support Systems, Managements Science, Transportation Research, European Journal of Operational Research, Interfaces, MSOM journal, Robotics and Intelligent Systems, International Journal of Production Economics, Computers and Operations Research, among others. He is active in several executive training programs, both national and international, and has served as consultant/advisor for major global companies, like Ab!nbev, Atlas Copco, IBM, Baxter, Johnson & Johnson, Continental, Glaxo-Smith Kline, Monsanto, Bekaert, Procter & Gamble, as well as small and medium sized companies. Since 2007 he is executive director of the Innovation and Incubation Center at Kortrijk, and Nyo Alatus, a KULeuven spin-off company.
[h1]I do not understand the meaning of this sentense.