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Title: A strategic decision making model on global capacity management for the manufacturing industry under market uncertainty
Author: Sabet, E.
Awarding Body: Nottingham Trent University
Current Institution: Nottingham Trent University
Date of Award: 2012
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Multi-national, large-scale and complex manufacturing systems, such as those for automotive manufacturers, often require a significant investment in production capacity, as well as great management efforts in strategic planning. Capacity-related investment decisions are often irreversible or prohibitively expensive and time-consuming to change once they are in place. Furthermore, such companies operate in uncertain business environments, which can significantly influence the optimal decisions and the systems’ performance. Therefore, a strategic question is how to globally and interactively set production resources for such systems so their optimal performance can be achieved under business uncertainty. Conventional optimisation models in this field often suffer from one or more drawbacks, such as deterministic styles, non-inclusive and non-comprehensive decision terms, non-integrated frameworks, non-empirical approaches, small size practices, local/non-global approaches or difficult-to-use methods/presentations. This research develops a new scenario-based multi-stage stochastic optimisation model, which is capable of designing and planning the production capacity for a multi-national complex manufacturing system over a long-term horizon, under demand and sales price uncertainty. Unlike many other stochastic models, this model can simultaneously optimise many strategic capacity-related decisions in an integrated framework, which helps to avoid sub-optimality. These decisions comprise capacity volume, location, relocation, merge, decomposition, product management, product-to-market decisions, product-to-plant planning, flexibility choices, etc. Furthermore, an enumerated scenario approach, which rightly fits real strategic decision making practices, has been employed in the model development. This model is also empirically designed for non-OR specialist users (managers), exploiting a programming technique and a more user-friendly input & output interface, which potentially makes the model more practical in real-scaled industrial applications. The model’s ability and its contribution to practice in real systems are demonstrated in two case studies from the automotive reference system, after a set of validations and verifications with fourteen hypothetical cases. Finally, in a systematic analysis the models’ features and abilities are compared with other newly developed analytical models and state-of-the-art researches in this field and the contribution to knowledge of this research is established.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available