Towards a model for managing uncertainty in logistics operations – A simulation modeling perspective
Abstract: Uncertainty rules supply chains. Unexpected changes constantly occur on all levels; strategically through globalization, introduction of novel technology, mergers and acquisitions, volatile markets, and on an operational level through demand fluctuations, and events such as late arrival of in-bound material, machine equipment breakdown, and quality problems. The problem with uncertainty is increasing as the focus on cost reductions and efficiency in the industry tends to stretch supply chains to become longer and leaner, thus making them more vulnerable to disturbances. The aim of this thesis is to explore strategies for evaluating and managing uncertainties in a logistics context with the objectives; “to propose a method for modeling and analyzing the dynamics of logistics systems with an emphasize on risk management aspects”, and “to explore the impact of dynamic planning and execution in a logistics system”. Three main strategies for handling uncertainties are being discussed; robustness, reliability, and resilience. All three strategies carry an additional cost that must be weighed against the cost and risk of logistical disruptions. As an aid in making this trade-off, a hybrid simulation approach, based on discrete-event simulation and Monte Carlo simulation, is proposed. A combined analytical, and simulation approach is further used to explore the impact of dynamic planning and execution in a solid waste management case. Finally, a draft framework for how uncertainty can be managed in a logistics context is presented along with the key reasons why the proposed simulation approach has proven itself useful in the context of logistics systems.
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