Análise e proposta de melhoria do processo de previsão de demanda em uma pequena empresa do setor de cosméticos

Abstract

Nowadays, even small-sized enterprises are investing in integrated management systems, called ERP (Enterprise Resource Planning), as a way to solve planning problems and to better control their cash flow. However, these enterprises end up neglecting one of the main inputs to improve the PPC (Production Planning and Control): the demand forecast. Thus, planning and cash flow problems persist, and no matter how flexible the enterprise is, it cannot follow the demand due to limited resources. Another factor is related to the responsibility of elaborating the sales forecast, which a single area or department is in charge of and reflects all the development of the enterprise, in case values are not reliable. This work fits context, once its main goal is to present a process which combines various methods or forecasting techniques, integrating areas or departments as a whole, and sharing the responsibilities to obtain a consensual preview accepted by all. This process aims to align the revenue goals defined by the board and make possible actions with advance, in case it is seen that the objectives cannot be achieved in revenue terms, promoting coordinated actions between the commercial and productive areas, searching for a common outcome in financial terms. The proposed method also aims to improve the way of communication among areas, integrating the information and trying to suggest an evaluation and analysis process of the performed forecasts, apart from proposing an incentive to the commercial area to search for accuracy in the forecasts. The final outcome of demand forecast process will be a master production scheduling. A theoretical-empirical analysis is presented, and the research, based on literature, identifies, from the production system evolution, the production planning and control the demand forecast process, its forecasting techniques and the evaluation of errors. Next, demand forecasting processes is presented, as a way to improve the demand forecast identified in the case study and demonstrate the expected outcomes after the implementation of this demand forecast model.