INTRODUCTION
Forecasting is a process of vital part of business organization as it provides the basis for planning and decision making (Jacobs, Chase and lummus, 2021). It is simply a statement about the future. It is clear that we must distinguish between forecast per se and good forecasts. Good forecast can be quite valuable and would be worth a great deal. Long-run planning decisions require consideration of many factors: general economic conditions, industry trends, probable competitor’s actions, overall political climate, and so on.
For prediction, good subjective estimates can be based on the manager’s skill experience, and judgment. One has to remember that a forecasting technique requires statistical and management science techniques.
In general, when business people speak of forecasts, they usually mean some combination of both forecasting and prediction (Daim and Hermandez, 2020). Forecasts are often classified according to time period and use. In general, short-term (up to one year) forecasts guide current operations, Medium-term (one to three years) and long-term (over five years) forecasts support strategic and competitive decisions (Pan. D., 2015).
According to Thomopoulos, (2021), “The desire to forecast rises from the need of predicting future economic conditions and the wish to eliminate uncertainties and risk”. The forecasting discipline is defined as “a planning tool that helps management in its attempts to cope with the uncertainties of the future, relying mainly on data from the past and present and analysis of trends.”
However, the complex nature of the world economy can make it difficult to predict future values for various variable, in spite of sophisticated mathematical models. A great deal of visibility, information sharing and communication between different divisions of a company is thereby a necessity (Granger and Persaran, 2020).
1.1 Background of the Study
Forecasting is a means of predicting what is going to happen in the future, next month, year, decade etc. Accurate forecasting requires high quality data, application of the application of the appropriate forecasting technique and knowledge interpretation. The accuracy of such forecasts depends in large measure on the degree to which the past is a good guide to the future (Synder R., 2017).
Prior to building a forecast model, the first step is to clearly understand the problem in order to establish the forecast range and objective (Granger and Persaran, 2020). Therefore, the four steps of forecasting are:-
The research work on sales forecasting system is a sales system using linear regression model to forecasting sales of products in an organization.
Many companies find it so difficult to identify products that will be on demand in the future and this has destabilized their relationship with their customers. In a country where lots of businesses thrive, i am motivated to carry out this study on sale forecasting because It is essential and highly beneficial for companies to develop a forecast of the future values of some important metrics such as demand for its product or variables that some important metrics such as demand for its product or variables that describes the economic climate.
The current system in use by many companies is Casual method. Casual forecasting is a strategy that involves the attempt to predict or forecast future events in the marketplace, based on the range of variables that are likely to influence the future movement within that market. The limitation of the casual model is that the forecast is, in turn, dependent on indicators that must themselves be forecast. Also isolating the influence of each factor on demand is a complex statistical process involving expert intervention and use of computers.
This study centers on the use of regression model for forecasting. Linear regression is time series methods that use basic statistics to project future values for a target variable. All organizations, big or small have at their disposal men, machines and materials, the supply of which may be limited. If the supply of these resources were unlimited, the need for managerial tool like linear programming would not rise at all. Supply of sources being limited, the management must find the best ablation of its resources in order to maximum the profit or minimize the loss or utilize the production capacity to the maximum. However, this involves a number of problems which can be overcome by quantitative methods, particularly the linear programming in different financial institutions. By understanding and implementing a well functioning forecasting process companies can increase their forecast accuracy, thus reduce their stock outs and increase their customer satisfaction (Jacobs, Chase and Lummus, 2021).
A good forecasting process is central for daily operational management and vital for every significant management decision, as it eases business planning and makes it more efficient (Diaz, Talley and Tulpule, 2017). The objective is to provide a continuous flow of information, hence enabling organization to cope with the ever-changing shift in demand and supply, increase operational efficiency and manage mitigate risk within a market (Vlahogianni, Golias and Karlaftis, 2016). The aim of a forecasting process is to provide its executives and management with a proper tool to improve their performance and competitive positive while adjusting to rapid changes in the economy (Pal Singh Toor and Dhir, 2021). By designing a forecasting process that aligns with strategic goals, a company can use the forecasting process as a mean of sustaining competitive advantages.
By embedding the forecasting process in the organizational decision making process, a clearer picture of the forecasting contribution to organizational effectiveness will emerge. The construction of a process that reflect a realistic assessment of current business environment can help companies prepare and respond to dynamic market situation, thus increase effectiveness and competitive advantage.
Conducting a forecasting process that is based on the most important key performance indicators for effective performance of a company will support improvement business process (Janes and Faganel, 2013). Hence, forecasting processes should align with the strategic goals of an organization. Companies that don’t align their forecasting process with strategic planning of their operations can experience lack of clarity regarding the structure and responsibility, and miss opportunities to reallocate resources and take advantage of market opportunities.
According to Diaz, Talley and Tulpule 2021, improved forecasting processes will have a direct and positive impact on several aspects enhance coordination in establishing plans consistent with corporate across an organization will enhance coordination in establishing plans consistent with corporate strategy, hence improve organization alignment and financial performance. A good forecasting process can fail through poor integration. A well-integrated forecasting process is therefore a necessity in order to enhance the results of the process.
1.2 Statement of the Problem
There are many problems concerned with sales forecasting such as:
1.3 Aims and Objectives
The aim of this research is the design and implementation of a sales forecasting system using linear regression model and with an added point of sale system.
The specific objectives are:
1.4 Significance of the Study
This research work will provide a more benefits to users such as:
1.5 Scope of the Study
This research work is to develop a system capable of managing sales on a daily basis using a “Point of Sale” (POS) method and also being able to forecast sale based on the sale history using linear regression model.
This system will not incorporate in its development all the models use for sales forecasting but will focus only on the forecasting of products with historical report of sales.
1.6. Limitation of the Study
Some problems cropping up in the course of carrying out this research are;
1.7 Definition of Terms