Abstract
The given article aims to evaluate different quantitative demand forecast methods through a case study on a glass tempering company. The analysis were held based on historical data series, which allowed the use of a part of this data for method application and another part for comparison and validation of the model`s results. The methods were compared based on obtaining the mean absolute error. In the studied company, the raw material request for the suppliers was made when new orders are ordered (pulled production). This method results in longer responsiveness, mainly due to the waiting time of raw material arrival. The application of those different demand forecasting models were analysed over three types of products on the tempered glass category, which represents a total volume of 65% of the company's costs. As a result, two methods were better adapted to the real data, providing absolute errors between 0.25 and 0.29. This given work showed that the application of the demand forecasting methods would reduce orders delivery time, what could lead to real gains to the analyzed company.
Highlights
Management decisions affect competitiveness, growth, achievement of strategic objectives and the economic outcomes of organizations
In case of stock scarcity of the necessary raw material, the suppliers are contacted for procurement
The usual raw material delivery lead time is three to five days, but there may be delays, especially in the periods when the suppliersfurnaces are under maintenance
Summary
Management decisions affect competitiveness, growth, achievement of strategic objectives and the economic outcomes of organizations. The orientation of short, medium and long-term planning variables can aid managers to achieve better results in key organizational issues such as: improve logistics efficiency, faster decision-making, greater robustness over adversities and costs reduction. When a problem or need is addressed beforehand, companies can develop better plans to solve them, what makes demand-forecast methods a tool of great relevance for increasing their competiveness (LEMOS, 2006). According to Lemos (2006), forecasting methods can be applied to several areas, such as "finance and accounting, engineering and research, production, distribution and logistics, human resources, marketing and sales.". The present work is based on the sales data of products on a small glass tempering company, located in Campinas-SP. The given company does not use any forecast method to plan its future demand and manage its inventories levels. In order to verify a method that suits the demands of this type of company, five methods of forecasting demands were analysed and compared: last period method, global simple average method, simple average method per period, moving average method and least squares method
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