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Title: Paints supply chain optimisation
Author: See-Toh, Yoong Chiang
ISNI:       0000 0004 2675 269X
Awarding Body: Imperial College London (University of London)
Current Institution: Imperial College London
Date of Award: 2008
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In production planning for strong seasonal demand products, it is uneconomical to configure the supply chain for throughputs equivalent to the demand peaks. Instead, a holistic approach to supply chain optimisation is adopted where forward demand forecasts drive the production planning process. In this thesis, the medium-term supply chain planning components of forecasting, production planning and evaluation are addressed through studies on a paints production facility. With a large number of specialised products, family-level forecasting is adopted for its simplicity and practicality in applying forecast techniques, coupled with its benefits on the inception of new products into markets. A time-series component was incorporated into traditional clustering techniques for segmenting products into families. The dominant cluster profiles identified are attributed as the seasonal component for the subsequent generation of demand profiles. In multi-purpose batch plants, production planning involves the twin decisions of batch sizing and lot sizing, often performed in series. This campaign is optimised through augmenting the batch sizing operation within a lot-sizing model. In the Mixed Integer Linear Programming model developed here, the degrees of freedom are the monthly batch sizes of each product, integer number of batches of each product produced each month, amount of monthly overtime working and outsourcing required as well as the time-varying inventory positions across the chain. Values for these are selected to balance the trade-offs in batch costs and inventory costs as well as the overtime and outsourcing costs. The final section sees the development of stochastic, dynamic supply chain models to predict the effect of different inventory policies, taking into account forecast accuracy, as derived from clustering. Using Monte Carlo based simulations, the various supply and production decisions are assessed against process manufacturing performance indicators. These planning components are then reconfigured to derive an optimal paints supply chain.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available