Abstract
The functioning of production enterprises is based on satisfying the needs of customers through the timely manufacture of products in accordance with the demand existing on the market. The availability of the offered range of products is guaranteed by a correct preparation of forecasts of potential orders. This article presents a multiple-regression-method-based tool supporting the planning of production volumes in an enterprise depending on the calendar month. Reliability analysis of the developed model through the analysis of residuals and their autocorrelations and partial autocorrelations is also presented.
 JEL classification: C2, C22.
Highlights
Knowledge of the level of demand for products is extremely important in the functioning of production enterprises
One of its essential features should be its readiness to carry out the tasks assigned to it
The article proposes a method of estimation of production volume using a multiple regression method, which will be determined on the basis of calendar variables, i.e. subsequent months of the year
Summary
Knowledge of the level of demand for products is extremely important in the functioning of production enterprises. The term ‘readiness’ has its source in the theory of exploitation of technical objects (Żurek et al, 2017; Borucka, 2018) and means the ability to remain in a state allowing to perform the required functions and tasks at a given moment or interval of time and under accepted conditions. It is often used both in relation to machine and equipment elements (Świderski et al, 2019), and in relation to vehicles as overall reliability structures (Borucka, 2018). The aim of this article is to present a multiple regression method as a classic cause and effect model used to forecast the production volume in the plastics industry, as well as analyse the developed model based on the verification of the normality of residuals distribution and the function of their autocorrelation
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