Model for handling uncertainty management in production with elements of engineering change management.
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This thesis examines combined fields of Engineering Change Management in production, Uncertainty concepts and aspects of decision making. The main objective of this research is to identify uncertainty in production phase, to find a method to analyse how they affect elements associated and in turn the overall project values which may lead to negative effects on project success. Engineering change management is a vital part of every industry with their competency in production and require flexibility in their process; they are usually constructed in the form of specific guidelines or frameworks on how to work towards minimising their impacts (high cost and multiple delays). The first goal is understanding key concepts that make the spectrum of this combined field of uncertainty and engineering change, their commonality and differentiating aspects are contemplated. This in turn helps formulate an approach to ascertain the uncertainty involved in production phase and how best to map/analyse it. For any given production scenario, it is agreed that poorly managed ECs could result in complexities leading to higher costs, decreased performance and increased lead times. Thus, the need for managing ECs should be of priority and it s inevitable that every company eventually will decide to form their own approach in handling them. In cases where new product development is considered, the number of uncertainties associated with them is numerous resulting in high uncertainty, increased lead time in design phase and a questionable profitability throughout its life-cycle. A well planned project goes into production phase with the confidence of a good plan to execute and an emphasis on control to avoid unpredictable outcomes. But, nothing is certain when factors including missed details, human aspect and changing stakeholder interests are considered. Hence, more than half the amount of total ECs account to production phase. To predict this major uncertain issue and dissolve it before it causes a failure, lies in providing an uncertainty analysis method instead of an impact minimisation approach or framework on dealing with effects of EC implementation. Important Contributions and findings include formulating ECM process as bulk constituent of uncertainty present in production, an influence diagram is depicted to represent total system with its EC factors as variables and production conditions as its limitations and a new Bayesian model for quantifying the engineering change management process which takes into account the elements of EC in production phase. As a result an uncertainty analysis model (Bayesian Network) tailored for the execution phase of a project and it could also work as a failure model in cases is achieved.