Customer Demand Forecast Using Time Series Approach

Abstract

Nowadays we are living in an uncertain time (pandemics, wars, inflation, shortages) and for companies, predicting the future of the business becomes more and more difficult. From this point of view, we can resort to statistical forecasting methods that, based on on past data and current external trends, can predict figures for the short and long term planning. The motive, the scope, behind this study is to develop a forecasting model using time series that can help the organisations and the managers in taking better decisions, on time, for the future of the company. The data that were used for this paper are focusing on forecasting the demands for the sales budgets only, starting with the historical data from a company in the glass mould manufacturing area. If we examine the current literature, we can find that there a few studies, with real business data regarding budgeting, therefore this study is relevant and important for the researchers and practitioners. This paper aims to illustrate how we can use the time series statistical method and the linear equation regression that can help the organisations to forecast and plan the business. The main findings after developing the model are that it allows smoothing the fluctuations of the series over time (especially if we find outliers) and eliminating the influence of seasonality, in order to obain in the end a good accuracy of the predictions. In order to measure the quality of the forecasting we can use many indicators, such as Mean Absolute Percentage Error.