Proposição de um modelo de apoio à tomada de decisão do mix de produção usando programação linear

• Autor(a) principal: Sippel, Faber Taciano
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UCS
• Texto Completo: https://repositorio.ucs.br/11338/4681
• topic: Administração da produção; Processo decisório; Programação linear; Production management; Decision making; Linear programming

The production mix can influence a company's profitability in two ways: rationalizing the use of resources and indicating what is the best possible combination to maximize a company's net income. Within this scope, this dissertation proposes a mathematical model to determine the optimum production mix allowing for different strategies evaluation, intended to support decision-making when planning production. In the literature review, discussed concepts are related to the decision process model, the production strategy and planning system, and the production mix itself. The research method has a quantitative approach, and development of the method's routines follows nine sequential steps in a manufacturing company located in Serra Gaúcha, southern Brazil. The proposed mathematical model was developed using linear programming. Its objective function aims to maximize the net profit of a company, considering the following parameters: selling price of the product, cost of production over regular and overtime hours, inventory keeping, setup cost, and the cost of starting a new work shift. This objective function is subject to production capacity restrictions on regular and overtime hours, capacity balancing, minimum and maximum demand and inventory capacity. Results obtained through scenario generation for six different production strategies quantitatively showed the effect strategy choice can have on the optimal production mix result, reaching an 18.09% difference between the analyzed strategies. Research included many memorable, positive aspects such as the ease of use of the model and parameter adjustments for scenario generation, the volume of relevant data obtained as a solution to the problem and the possibility of using the tool to optimize future investment intelligence and help better understand the production system. The documented results shew the potential of the model's implementation to improve decision processes in production planning, adding significant competitive advantage to the company. Recommendations for future work are better demand forecasting methods, sensitivity analysis to assess the possible effects on the objective function's result when changing parameters or altering restrictions, and finally, applying a multicriteria decision-making method, such as the AHP (Analytic Hierarchy Process).