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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/6600

Title: Optimal production planning and scheduling, case study: Guinness Ghana Brewery Limited, Kumasi
Authors: Derkye, Adom
Issue Date: 16-Oct-2014
Abstract: Effective production planning and scheduling is paramount to the success of every manufacturing company. Regardless of your manufacturing industry, finding the best way to purchase, allocate and utilize your production resources to efficiently satisfy your customers while minimizing cost is a constant challenge. But without the right production planning and scheduling solution, it is near impossible. With the aid of computer software, a new approach makes it easier to bring out the basic principles involve and lead to a simple solution with respect to the manual iterations. This project seeks to avoid common problems such as high inventory levels, poor customer delivery times, low yield, high scrap and inefficient usage of capacity and production capabilities. With the use of TORA computer software, the company can use a tactical production planning to manage real-time interaction between sales, planning and production and maintain low inventory while promising realistic delivery dates to keep their customers satisfied to save money and resources. The production was modelled as a balanced transportation problem and solved using computer software TORA to obtain the optimal production plan. The results from the analysis showed that overtime production is not necessarily throughout the year but should apply effective planning and scheduling to their production activities to ensure optimum output. The main aim is to maximize profit and minimize cost in a more convenient approach than the tedious calculations of the three main methods, Northwest Corner Rule, Least Cost and the Vogel approximation method (VAM) involved in transportation.
Description: A thesis submitted to the Institute of Distance Learning, Department of Mathematics in partial fulfilment of the requirement for the award of the Master of Science degree in Industrial Mathematics, 2014
URI: http://hdl.handle.net/123456789/6600
Appears in Collections:Distance Learning

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