The quantity of a product or service in the market is determined by a set of factors, the main one being the price. Selling of goods and services to customers requires a price factor that can greatly influences good returns. The process needs to be keenly put into consideration by the individuals doing the selling. Demand often implies that it is more than a need or want to customers. It is more of willingness and desires the customers have to buy a commodity. This desire and willingness to purchase a product must be accompanied by the purchasing power that is the capacity to pay for the commodity. In this case, demand is the quantity of the commodities the consumers desire to have and are ready to pay for it. However, an increase in the price of a product tends to reduce the number of consumers purchasing a given product and vice versa (Yunishafira 2018). The inverse relationship between price and quantity of goods is known as the law of demand while its algebraic equation is referred to as demand function (Kilimci et al., 2019). Therefore demand equation is a list of prices of commodities with their corresponding quantity that individuals are willing to buy at a given period.
Demand forecasting is the estimation and prediction made on future consumption of products and services. Demand forecasting is a critical process in organizations as it determines their survival in the market (Yunishafira 2018). Mostly, a set of variables are applied to forecast future demand of the commodities effectively. Such variables include the competitor’s pricing strategy, price changes, and inflation of consumer’s income. The method used to forecast commodity demand includes survey method, market study method, and regression data analysis. Historical data or time series forecasting is mostly used by forecasters in economics to predict the past performance of a business to know the trend of the future (Kilimci et al., 2019). It is used in conjunction with statistical regression analysis, which is used to find the relationship between the independent and dependent variables. Regression data analysis technique uses historical data to predict the future demand of a commodity or service.
Delegate your assignment to our experts and they will do the rest.
Historical data may be recovered from the client’s invoices, the financial statement of the company and the macro-economy data such as the consumer confidence index and interest rates. However, using historical data can be very difficult and tricky more so when regression analysis is not being performed. This explains why historical data and regression are used hand in hand to predict the future of commodity demand and pricing. Regression data analysis and historical data have their limitations in business forecasting too. When there are competitive products which follow each other’s price change, very little information will be obtained from regression analysis, making it difficult to predict the future. Historical data analysis relies entirely on the assumptions that the past cycle will be repeated in the future, and the market variables will remain constant. In this case, some predictions fail and negatively affect the business because the future trends of commodity pricing may change due to various factors.
Client suitability is another challenge faced when using regression analysis since not all clients are suitable. For instance, real estate developers and investments holdings companies do not have a high volume of transactions, which is one factor that is determined to produce historical data. Identifying forecasters that lead to all-encompassing model is a critical challenge in performing regression analysis. Manufacturers and trading firms with a small number of suppliers of raw materials and goods produce strong models. For instance, cocoa butter delivery data supplied by the leading cacao butter supplier to forecast income from sale of chocolate provide much reliable analysis. Most auditors are insufficiently acquainted with statistics to produce financial statements audit which becomes difficult to use regression analysis and historical data.
References
Kilimci Z.H, Akyuz A.O., Uysal M., Akyokus S., U ysal M.O., Bulbul B.A. and Ekmis M.A. (2019). An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain. Hindawi Journal, 2019, 1-15.
Yunishafira A. (2018). Determining the appropriate demand forecasting using time series method: Study case at garment industry in Indonesia . International Conference on Economics, Business and Economic Education , 553-564.
