The Vitality of Forecasting in Operations Management

TheVitality of Forecasting in Operations Management

TheVitality of Forecasting in Operations Management

Today’sbusinesses should constantly use strategies that improve theirperformance both in present and future terms. Now that the future isunpredictable, forecasting techniques offer management with thepossibilities of the future. The market is very dynamic, andbusinesses should use forecasting to make postulations about thepossible changes that may affect the business positively ornegatively. The aim of business forecasts is to combine statisticalanalyses and domain knowledge to develop acceptable forecasts thatwill determine planning activities by management. A good businessforecast focuses on the defining measures that constitute keybusiness indicators that affect sales and revenue and provides themanagement with business decisions that are informed by the rightstatistical analysis. Contemporary business organizations areoperating in the information age and are likely to benefit from thegrowing need for the application of conceptual age features in themarket place.

Forecastingis a tenet of the conceptual because the available information thatguides statistical analysis determines the innovations the businessinitiates to create growth and value on investments. Businesses alsoneed to take advantage of the technological improvements that havemade it easier to process and prepare business forecasts. Theadvances have been a response to increased complexity andcompetitiveness of the global business environment (Fildes,Nikolopoulos, Crone, &amp Syntetos, 2008). Complexity increases therisk of associated with business decisions due to the huge amount ofdata and information base. Management of different businessorganizations are aware of the need for forecasts, but they are notaware of the techniques applicable in the modern businessenvironment. Traditionally, regression analyses have been the mainforecasting techniques, but techniques such as the Box-Jenkins andneural networks have significantly expanded the field of forecasting.


Coca-ColaInventory Forecast

CocaCola decision to use the conceptual framework model as theforecasting tool could make it meet its competitive objectives. Forsoft drink producer such as Coca-Cola the main determinants ofoptimal demand are the timing and quantity of the product availableon the market. The introduction of Minute-maid called for extra waysof understanding the market so that the strategies to mitigate tocustomer demand uncertainties. Moreover, Coca-cola could be a softdrinks giant, but all companies that have such a huge market presencecan only manage customer demand to a certain level. Hence, theconceptual framework provides the tools through which Coca cola’smanagement can streamline the supply chain to conform to the rightamount of Minute-maid in the market, certainty in the workconditions, adjusting the cost structure to fit the forecasted demandwithin stipulated timeframes.

Wal-martsForecasts of future decline

Indeed,Wal-Mart is among the world business giants that have effectivelytaken advantage of forecasting to make value-based business decisionson the retail market. Wal-Mart’s use of accounting and MIS topredict its market position in future regarding sales plausiblyprovides opportunities for strategic decision making rather thanmerely a moment of panic. Perhaps, Wal-Mart needs to employcollaborative forecasting as well. As a retail giant, Wal-Mart tradeswith other business partners in different product segments of thecommodity market. Now that the forecasts indicate a bleak regardingprofits (12% decrease) and only shares (10% drop), collaborativeforecasting with other business partners provides an opportunity forWal-Mart to co-ordinate the forecasting process (Holimchayachotikul &ampPhanruangrong, 2010). The result of a collaborative forecastingstrategy is accuracy in forecast figures in the retail supply chain.


Fildes,R., Nikolopoulos, K., Crone, S. F., &amp Syntetos, A. A. (2008).Forecasting and operational research: a review. Journal of theOperational Research Society, 59(9), 1150-1172.

Holimchayachotikul,P., &amp Phanruangrong, N. (2010, January). A Framework for ModelingEfficient Demand Forecasting Using Data Mining in Supply Chain ofFood Products Export Industry. In Proceedings of the 6thCIRP-Sponsored International Conference on Digital EnterpriseTechnology (pp. 1387-1397). Springer Berlin Heidelberg.