Supply Chain Management Based on Modeling & Simulation: State of the Art and Application Examples in Inventory and Warehouse Management Francesco Longo
Modeling & Simulation Center – Laboratory of Enterprise Solutions (MSC-LES) Mechanical Department, University of Calabria Via P. Bucci, Cubo 44C, third floor, 87036 Rende (CS) Italy 1. Introduction The business globalization has transformed the modern companies from independent entities to extended enterprises that strongly cooperate with all supply chain actors. Nowadays supply chains involve multiple actors, multiple flows of items, information and finances. Each supply chain node has its own customers, suppliers and inventory management strategies, demand arrival process and demand forecast methods, items mixture and dedicated internal resources. In this context, each supply chain manager aims to reach the key objective of an efficient supply chain: ‘the right quantity at the right time and in the right place’. To this end, each supply chain node (suppliers, manufacturers, distribution centers, warehouses, stores, etc.) carries out various processes and activities for guarantying goods and services to final customers. The competitiveness of each supply chain actor depends by its capability to activate and manage change processes, in correspondence of optimistic and pessimistic scenarios, to quickly capitalize the chances given by market. Such capability is a critical issue for improving the performance of the ‘extended enterprise’ and it must take into account the complex interactions among the various supply chain nodes. The evaluation of correct trades-offs between conflicting factors, such as inventory reduction and fill rates, customers’ satisfaction and transportation cost, sales loss and inventory costs, resources management and internal costs, are (among others) the most important tasks of a competent supply chain manager. Therefore, supply chains have to be regarded as complex systems; a wide range of factors usually affects the behaviour of complex systems. The ways in which such factors interact and the stochastic nature of their evolution over the time increase the complexity of many real-world supply chains up to critical levels, where the use of ad-hoc methodologies, techniques, applications and tools is the only way to tackle problems and succeed in identifying proper and optimal solutions (Castilla and Longo, 2010).
Supply Chain Management
To this end, Modelling & Simulation (M&S) has been widely recognised as the best and most suitable methodology for investigation and problem-solving in real-world complex systems in order to choose correctly, understand why, explore possibilities, diagnose problems, find optimal solutions, train personnel and managers, and transfer R&D results to real systems (Banks, 1998). In addition, M&S, regardless of the application domain, usually provides innovative solutions and new user-friendly tools, with special attention to integration into business processes and management. The identification of proper and optimal solutions in complex real-world systems often requires the solution of multi-objective problems involving multiple stochastic variables. As stated in Chen (2003), real world optimisation problems involve contrasting and competing objectives and require the definition of multiple performance measures. In such a context, where the whole is greater than the sum of parts, successful approaches require something more than simple mathematical or stochastic models. M&S capabilities to recreate (with high level of accuracy) the intrinsic complexity of real-world systems allows to find out and test alternative solutions under multiple constraints and to monitor, at the same time, multiple performance measures. In this chapter the use of M&S as enabling technology is investigated, highlighting the contribution of this approach in supply chain management (with a specific focus on supply...
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