Economic, Technological and Operations Management Demand Forecast
Forecasting is very important in management. It is majorly concerned with the use of past and present trends in the market to determine what may happen in the future (Clements & Hendry, 1998). It helps firms, organizations and countries prepare in advance on how to deal with future uncertainties. Despite having many similarities, economic forecasting, technological forecasting and operations management demand forecasting also have significant differences. The main difference between the three types of forecasting is the element that is foreseen (Clements & Hendry, 1998). Precisely, technological forecasting deals with the anticipation of possible changes in technology and its effects on other operations (Henry, 1991).
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Therefore, it solely deals with technological inventions and their impacts (Henry, 1991). On the other hand, economic forecasting deals with future economic possibilities (Marquez, 2002). This is usually done by countries, individual firms and other organizations.
Unlike technological and economic forecasting, operations management demand forecast deals with the resources that organizations may need for efficient production in the future and possible changes in consumer behaviour (Kumar & Suresh, 2009). It is mainly concerned with the type and amounts of good and services their customers may demand in the future (Kumar & Suresh, 2009).
Strategic Importance of Forecasting to Human Resources, Production Capacity and Supply Chain Management
Forecasting helps ensure that the process of production is efficient. Managers who carry out analyses of market trends always ensure that the use of resources in the production process is controlled. The raw materials are used according to the number of products likely to be demanded. In the long-run, there are very minimal wastages.
Forecasting also ensures that the supply chain does not break at some point due to sudden changes (Molnar, A2010). A firm that carries out its forecasting process properly always prepares in advance for changes even before they occur. As a result, the production and supply processes are continuous since they are not affected by the changes. Such readiness also ensures that the production capacity of the firm is not affected negatively. In fact, good predictions always help firms maintain or increase their production capacities depending on the projected demand.
Qualitative and Quantitative Methods of Forecasting
The Delphi Method involves interrogating a panel of experts about their opinions on probable events (Naik, 2004). However, the interrogation is not done in a boardroom. Instead, each of the experts is questioned independently. The questioning is then followed by a compilation of all their responses by an outsider, who then brings them more questions. This process repeats itself until a common stand is reached. Its main purpose is to prevent the experts from arriving at a consensus prior to the interview.
This method completely eliminates the possibility of mob psychology and external pressure as the experts do not meet.
- The level of reliability is usually very low
- The experts rarely reach a consensus.
The Simple Moving Method involves the analysis of trends in an adjustable period (Nikolopoulos, 2010). The length of the period does not change. Its main purpose is to quantify the performance of the business in given periods in order to use them in predicting future trends. This method is commonly used by mobile phone manufacturers in determining the number of devices people buy each year.
It is more reliable since it compares trends over a uniform period. Its predictions are usually very accurate.
It is not good at predicting sudden changes.
Clements, M., & Hendry, D. (1998). Forecasting economic time series. Cambridge: Cambridge University Press. Web.
Henry, B. (1991). Forecasting technological innovation. Dordrecht: Kluwer Academic. Web.
Kumar, S., & Suresh, N. (2009). Operations management. New Delhi: New Age International. Web.
Marquez, J. (2002). Understanding Economic Forecasts. International Journal of Forecasting, 18(3), 464-466. Web.
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Molnar, A. (2010). Economic forecasting. New York: Nova Science Publishers. Web.
Naik, G. (2004). The structural qualitative method: a promising forecasting tool for developing country markets. International Journal of Forecasting, 20(3), 475- 485. Web.
Nikolopoulos, K. (2010). Forecasting with quantitative methods: the impact of special events in time series. Applied Economics, 42(8), 947-955. Web.