Discrete-Event Simulation, Operations Analysis, and Manufacturing System Development: Towards Structure and Integration
Abstract: After more than four decades of presence in the manufacturing industry, discrete event simulation has established itself as a powerful technology for a wide range of strategic, tactical, and operational applications in production, logistics, and supply chain management. Discrete event simulation software packages - although not entirely adequate - are relatively capable, and methodologies for performing simulation projects are reasonably well-developed and documented. As evidence of what Discrete-Event Simulation (DES) technology can do, successful cases from various industrial sectors abound, showing that DES use can have significant positive impacts on the quality, cost, and time aspects of Manufacturing System Development (MSD) and the Product Realization Process (PRP). Despite a seemingly rosy picture, however, this thesis argues that several problems associated with the adoption and use of DES still exist in industry. First, a majority of companies do not use simulation at all, and many of these do not have enough belief in what simulation can do for their organizations to even consider using it in the future. Second, companies that use simulation do not seem to have realized the full potential of this technology. In the terminology of this thesis, they have not fully integrated simulation into their MSD process. Often, simulation is used on a one-shot basis only, troubleshooting specific problems such as bottlenecks, usually in late stages of the manufacturing system life-cycle, or as a stand-alone tool, both of which reflects a low level of simulation integration, a concept introduced by this author. Despite that reasons for this modest and non-integrated use of simulation in the manufacturing industry have been less than satisfactorily explored (empirical studies in particular are scarce), some conclusions can be drawn as to the nature of these reasons. In brief, these have been found to be attributed to reductionist views on and unstructured approaches to DES integration. At the same time, it seems that academia is not fully addressing the issues needed to overcome this situation. Other research has reported on the barriers and enablers affecting the adoption, diffusion, and integration of DES, but it has done so to a very limited extent, and neither placed these factors in a conceptual or explanatory context, nor provided adequate models or methodologies to guide the process of diffusion and integration. Instead, simulation research on integration aspects often deals with specific cases of system and application integration, or what can be referred to as functional issues, such as integrating and connecting simulation to other systems and tools, rather than structural, hierarchical, and procedural integration aspects as part of a methodological approach. Finally, and perhaps most importantly, simulation use and adoption often lacks strategic focus. However, it is just as important to extend the scientific analysis to new domains, or in other words: learn from others. In this context, it is argued that we need to look beyond the narrow scope of simulation techniques, methodologies, and software, to a more holistic perspective where DES is seen as an innovation that is adopted and diffused through an organization through a social process, and as subject to competition with other methods, models, tools, ideas, and resources. From a systems perspective, and based on industrial experience and case studies this thesis looks at the activities and knowledge needed to integrate DES into the MSD process, and outlines a framework for a structured approach to integration. This framework rests on three pillars - (i) a holistic view on simulation integration, (ii knowledge fromother disciplines, and (iii) an integration methodology - and it extends over four simulation integration domains; here defined as strategy, operations, data, and enablers (DOSE). It is concluded that both simulation users and non-users could benefit from incorporating such a framework into their simulation integration efforts, but also that several research challenges remain, including further development of the methodology and the models, if the approach is to gain industrywide acceptance.
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