This research focused on mid-term planning representing the tactical level of supply chain management among manufacturers. Mid-term planning is based on established frame contracts between suppliers and customers. Demand forecasts and early customer orders (from retailers or consumers) are the starting point for performing planning runs involving logistical functions such as procurement, production, storage, and distribution. The aim is to draw up consistent plans among supply chain partners

Beyer, Ingo

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Communications of the IIMAVolume 6 | Issue 3 Article 132006Information Technology-Based Logistics Planning:Approaches To Developing A CoordinationMechanism For Decentralized PlanningIngo BeyerBerlin University of TechnologyFollow this and additional works at: http://scholarworks.lib.csusb.edu/ciimaPart of the Management Information Systems CommonsThis Article is brought to you for free and open access by CSUSB ScholarWorks. It has been accepted for inclusion in Communications of the IIMA byan authorized administrator of CSUSB ScholarWorks. For more information, please contact scholarworks@csusb.edu.Recommended CitationBeyer, Ingo (2006) "Information Technology-Based Logistics Planning: Approaches To Developing A Coordination Mechanism ForDecentralized Planning," Communications of the IIMA: Vol. 6: Iss. 3, Article 13.Available at: http://scholarworks.lib.csusb.edu/ciima/vol6/iss3/13Information Technology-Based Logistics Planning Beyer Information Technology-Based Logistics Planning: Approaches To Developing A Coordination Mechanism For Decentralized Planning Ingo Beyer Department of Logistics and SCM, Institute of Technology and Management, Berlin University of Technology, Strasse des 17. Juni 135,10623 Berlin, Germany, phone +49 30 314 26749, fax +49 30 314 25992, bever@,logistik.tu-berlin.de ABSTRACT This research focused on mid-term planning representing the tactical level of supply chain management among manufacturers. Mid-term planning is based on established frame contracts between suppliers and customers. Demand forecasts and early customer orders (from retailers or consumers) are the starting point for performing planning runs involving logistical functions such as procurement, production, storage, and distribution. The aim is to draw up consistent plans among supply chain partners. INTRODUCTION Motivation for our Research Question The planniag concepts that are being discussed in practice and science often face implementation obstacles. In the past, concepts focusing on centralized planning prevailed, as they were imderpinned by reasoning theories, available software solutions, and consulting driven. The most prominent is the concept of the so called fourth-party logistics provider acting as a central supply chain manager of the entire network. However, this concept has never been realized among autonomous companies on the tactical planning level. A pilot project in the automotive industry (vi^hich was cancelled) had aimed at setting up centralized tactical plaiming comprising a three-tier network. Available software solutions do not take into account feedback mechanisms and operate under the assumption of isolated supply chains. Each OEM, for instance, maintains a separate system for supplier capacity checking. This leads to the so called multi-monitor syndrome at the supplier site. These solutions aim at providing OEMs with isolated optimization in their supply chain. Complex value networks, however, consist of many overlapping supply chains. The centralized approach is unlikely to be implemented, due to barriers such as computational complexity, resistance to disclose confidential information and to receive instructions from centralized decision making. There might be obstacles to successful business usage even in intra-enterprise plaiming via production plants utilizing advanced planning and scheduling systems. Nevertheless, there are some new initiatives in industry to optimize both intra- and inter-enterprise planning. They project for instance to establish multi-tier collaboration (supply chain monitoring). However, even logistics managers themselves are critical of the advantages that might be expected. Companies are facing increased business pressure from intensifying competition and dynamic demand in the markets. This leads to growing expectations for solution capabilities of concepts like supply chain management and collaboration. Fortunately, there might be new or unexploited potentials. Advanced planning and scheduling systems offer functionality for collaborative planning which has rarely been used in practice. The software agent oriented technologj' is regarded to provide a revolutionary potential for solutions in the area of decentralized planning and control. Various research theories could help to take up challenges in logistics and supply chain management, e.g. theory of complexity, and theory of real options. There is a controversy going on between centralized control oriented researchers (e.g. operations research and logistics management institutes arguing with theory of constraints, hierarchical planning, or new roles of logistics service providers) and decentralized control oriented researchers (e.g. information technology, sociological, and natural science institutes making a case for agent-oriented technologies, theory of complexity, adaptive systems, or ubiquitous computing). 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This is the precondition for the next (strategic) level of the model. It raises the question which degree of decentralization is desirable. This question should be answered by the interviewees on the basis of the coordination mechanisms which they consider to be desirable. Therefore, further dimensions of the coordination mechanisms are the planning policy and automation. This policy deals with the trade-off between having the system under control versus utilizing dynamic solution generations. The relevant categories are mathematical optimization, rule-based heuristics, or dynamic negotiations with transfer prices. The automation of the solution process distinguishes between manual maintenance and communication, information technology supported processes, and entirely automated planning and control by multi-agent software systems. The resulting degree of decentralization with its respective dimensions provides a solution to identifying the appropriate degree of decentralization on the tactical level of the model. Appropriate coordination mechanisms are designed on the basis of literature reviews, expert interviews, and own solution development. Carrying out Experiments to Evaluate the Decentralized Planning Model Selected coordination mechanisms are integrated into a software tool for evaluation purposes. This ranges from scheduling lines without confirmation, over iterative exchanges of scheduling lines, transfer payments on bottlenecks, to negotiations and trading of real options. Various scenarios are available based on simulated volatile demand from retailers, the availability of raw materials and production capacities, and the different supply network configurations with altemative cost structures. When we organize plaiming games in computer-based classes, we arrange competitions for the students. In addition, we perform simulation runs in order to evaluate the findings. In both cases comprehensive sets of key performance indicators such as profit margins, costs, inventory levels, and multiple s<;rvice levels are recorded and evaluated. The software component Supply Network Planning of the solution mySAP SCM® (formerly Advanced Plarmer and Optimizer*) is utilized to engineer a centralized supply network model. Simulation runs of this central supply chain manager serve as a benchmark for decentralized planning. RESULTS The research results provide appropriate coordination mechanisms necessary for autonomous supply chain partners to make decisions in tactical planning and to draw up consistent plans. We want to highlight some aspects as follows: bilateral, iterative negotiation mechanism is based on software agent technology using prices on bottleneck capacities which figure high on the list of priorities during the allocation process. This approach is extended by the concept of real options. These capacity options are subject to trade if they are highly recommended by partners or obsolete. This makes all participants of the network highly flexible in the way they respond to the dynamics of ultimate customer demands and to network capacity constraints. This holistic approach combines local hierarchical optimization of intemal supply chains with collaborative planning within the network. REFERENCES Arunachalam, R., Sadeh, N.M., Eriksson, J., Finne, N., Janson, S. (2004). The Supply Chain Management Game for the Trading Agent Competition. Technical report. Pittsbtngh: Camegie Mellon University. Dudek, G. (2004). Collaborative Plarming in Supply Chains: A Negotiation-Based Approach. Berlin et al.: Springer. Kelly, K., Allison, M. (1999). The Complexity Advantage: How the Science of Complexity Can Help Your Business Achieve Peak Performance. New York. Spinier, S., Huchzermeier, A., Kleindorfer, P.R. (2002). An Options Approach to Enhance Economic Efficiency in a Dyadic Supply Chain. Cost Management in Supply Chains, Physica, 349-360. Straube, F. and Beyer, I. (2005). Permanent Planning Readiness in Supply Networks. Industrial Management 21(5), 37-40. Communications of the IIMA 119 2006 Volume 6 Issue 3 Information Technology-Based Logistics Planning Beyer Communications of the HMA 120 2006 Volume 6 Issue 3 

Information Technology-Based Logistics Planning: Approaches To Developing A Coordination Mechanism For Decentralized Planning

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