Application of Dynamic Analysis in a Centralised Supply Chain

Application of Dynamic Analysis in a Centralised Supply Chain

Mu Niu, Petia Sice, Ian French, Erik Mosekilde
DOI: 10.4018/978-1-60960-135-5.ch003
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This chapter presents dynamic analysis of a model of a centralised supply chain. The research was conducted within the manufacturing sector and involved the breathing equipment manufacturer Draeger Safety, UK A simplified model of the Draeger Safety, UK centralised supply chain is developed and validated. Simulation and analysis are performed using System Dynamics, control theory, nonlinear dynamic and chaos theory. The findings suggest that destructive oscillations of inventory could be generated by internal decision making practices. Bifurcation diagram is plotted to indicate the bifurcation status of the model with different internal decision policies. A management microworlds is developed for managers to experiment with different decision scenarios and learn how the supply chain performs.
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The contemporary business environment is characterised by an acceleration of the process of change due in part to improved communication and analytical capabilities brought about by modern Information and Communication Technologies (Sterman, 2000; Terzi and Cavalieri, 2004; Ortega and Lin, 2004). This poses an ever increasing demand for periodical review of strategies and organisational processes, and for faster and more effective learning to deal with problem situations more quickly. Furthermore, companies increasingly need to react to customer requirements (Hong-Minh, Disney and Naim, 2000). Naylor et al (1999) suggested the principles of agile manufacturing, i.e. responding to customer requirements with very short lead-times can be applied to supply-chains. Global companies are realising that the efficiency of their businesses is dependent on the collaboration with their suppliers and customers (van der Zee, 2005; Yusuf, Gunasekaran, 2004; Adeleye and Sivayoganatahn, 2004; Cox et al, 2004). The concept of supply chain management (SCM) is concerned with the strategic approach of integrating producers, suppliers and customers in a collaborative network with the objective of improving the overall responsiveness of the network, but at the same time preserving the organisational autonomy of each unit (Terzi and Cavalieri, 2004). More accurate data and better information available to all nodes of the supply chain are considered crucial in improving its performance. Thus, companies invest heavily in intra-company information and communication platforms and E-collaboration tools, such as data warehouses or Enterprise Resource Planning (ERP) and Manufacturing Resource Planning systems (Lyneis, 2005; Li & Li, 2005; Marquez, Bianchi and Gupta, 2004).

Contemporary Manufacturing Resource Planning (MRP) is a structured approach to manufacturing management in which a suite of integrated computer software covering the main operational business functions is used to assist the planning of materials, capacity and cash flow, in accordance with company policy, to meet customer delivery requirements (Ackermans & Dellaert, 2005). It is a total approach to managing a business (Li & Li, 2005). It includes inventory control data, purchasing and accounts payable, suppliers’ data, master schedules, production schedules, resource capacities, manufacturing orders, and so on. MRP systems are developed under the assumption that more data and better information lead to better decisions. The quality of this data is believed to be accurate or at least good enough to be reliable (Ackermans and Dellaert, 2005). However, even with the introduction of resource planning systems, the performance of the supply chain often remains problematic (Lyneis, 2005; Fowler, 1999). The weakness of such systems is that when used on their own they fail to address the complexity of management situations for two reasons: firstly, they do not take into account the inherent ‘messiness’ of situations that contain human decision making within the process that is considered problematic (Maturana & Varela, 1987; Beer, 1979, 1994); secondly, such tools do not promote learning (Senge, 1984, 1996; Venix, 1994; Morecroft, 2007) or effective decision support as they do not include simulation facilities to allow what-if analysis on alternative strategies (Terzi and Cavalieri, 2004).

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