Implementation of fuzzy methods to evaluate manufacturing performances as a basis for a high profit manufacturing resource plan in the fast moving consumer goods industry

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NO FULL TEXT AVAILABLE. This thesis contains 3rd party copyright material. ----- This thesis describes a new manufacturing performance evaluation and production planning method called Fuzzy-MRP-II (F-MRP-II) model, which deals with uncertain information in the MRP-II system and then shows the consequences of uncertain situations to decision-makers. This model enables decision-makers to prioritize customers' orders, evaluate the best suppliers and the most suitable production lines. The output of this model is a production plan and strategies which result in the best production benefits and reduce risks. This model is based on fuzzy set theory, fuzzy evaluation method, analytical hierarchy process (AHP) method and fuzzy logic techniques to improve the traditional MRP-II system so that it can deal with decisionmaking on the basis of uncertain information. This F-MRP-II model considers and covers all of the five essential manufacturing elements: manpower, equipment, raw material, production techniques and environment. The model is divided into three parts. These are: first, customers' uncertain ordering behaviour including market forecasting, secondly imprecise and flexible production processes and environments and thirdly the best selection of suppliers. The result of the model's first step shows the best selection for each factor under different conditions and the correct evaluations, such as the suppliers' performance evaluation and the performance of production lines. Then based on these results and the company' s correct production and sales' strategies, F-MRP-II helps decision-makers make correct production plans and decisions. The following is a brief abstract for each chapter in this thesis to provide a quick overview. Chapter 1 "Introduction and Background". This chapter describes traditional MRP/MRP-II systems as applied in industries to manage the supply chain. From this author's experiences and from discussions with colleagues, we have found that, especially in the Fast Moving Consumer Goods (FMCGs) industry, there is much uncertain information used in decision-making. It is well recognized within the FMCGs industry that the problem of uncertain information is greater than in most other industries. Unfortunately, the MRP systems cannot deal with the problems and the decision-makers cannot see the uncertain situation clearly. This makes it difficult to make appropriate selections of suppliers and production plans. Chapter 2 "Purpose of study and contributions". This chapter describes the author's motivation for the main task of the thesis as well as the contributions of the project. It then summarizes this study's contributions to knowledge and the model's potential in industry. Chapter 3 "Literature Review and discussions". This chapter discusses first, the different methods and models in the literature relating to the manufacturing production planning area. It then discusses different decision-making methods when dealing with uncertain and imprecise information such as the fuzzy logic method, which is widely used in the automatic control area. This. chapter also provides an analysis of the disadvantages of traditional MRP/MRP-II systems, and the safety stock inventory methods for dealing with uncertain customer requirements. One method is the just-intime method. However, it is risky when there is a high level of uncertainty or imprecision. Chapter 4 "Methodology". This chapter contains the research methodology used in this project as a general self-lead research project. Then it describes the author's F-MRP-II model and its application. Chapters 5 and 6, deal with F-MRP-II modeling and the results and discussion from a case study. Chapter 5, "Evaluation and Selection Models of Manufacturing Elements Based on AHP Method" presents the details of the AHP modeling steps and calculation process. Chapter 6, "Using a Fuzzy Logic Method to Simulate High Profit Production Plan" presents the fuzzy logic modeling process. Based on a case study analyzing the results of the model it shows how the model can benefit a company. All the data for the case study were collected from a Global FMCGs food company. Based on a Business Confidential Agreement, all data and information are treated in confidence. Details of the data and calculation process are shown in Appendix 1. Chapter 8, "Conclusions". This chapter deals with the relevance, validity and further research and development potential derived from the methodology. Key words: Manufacturing Resource Planning (MRP-II), Fast Moving Consumer Goods (FMCGs), Analytical Hierarchy Process (AHP), Fuzzy logic
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