In the last class we studied the concept of demand forecasting and its importance in the business environment, especially in the 21st century. This topic was particularly interesting to me since I have recently experienced problems related to inaccurate forecasting at work.
I work for a supply and distribution company that deals with the fast moving consumer goods (FMCG) industry. While previously we had only undertaken the distribution of home and personal care products, recently our company decided to add several food product brands to our portfolio. This was a strategic move since we are experienced in dealing with various Middle Eastern retail establishments. However, the mistake our team made was to purchase the product inventory from manufacturing companies without accurately forecasting the demand for those products.
The result? We ended up with far more inventory than we could sell. Food products are perishable; their expiration deadlines are much shorter than for other consumer goods. As those expiry dates approached, a considerable percentage of the inventory we had bought was wasted in our own warehouse. Needless to say, the company suffered some heavy losses.
Few forecasts are absolutely accurate since the future is inherently uncertain. To add fat to the fire, many companies still use informal forecasting methods such as educated guesses by top management and intuition or “gut feeling”. Some use quantitative methods such as historical sales trends and adjust it according to the forecasting officer’s own personal experience or opinion. When forecasting methods are based on such subjectivity, how accurate can they really be?
Concerned about the accuracy of my company’s forecasting methods, I researched various forms of quantitative and qualitative forecasting. I came across an article by Kesten Green and Scott Armstrong of Wharton. The article proposed that only structured forecasting methods should be used and more qualitative techniques such as focus groups, unstructured meetings and intuition should be avoided. Even when some judgment must be used (possibly due to the lack of sufficient data), Armstrong and Green (2005) recommended that forecasts should be based on more structured procedures such as the Delphi method or structured analogies. The structured analogies method involves using results from similar situations from the past to predict the outcome of the current situation using a structured, formal process.
Even when accurate forecasts are made, it is not always easy to implement them in business decisions. When research results reveal figures contradictory to what the top management expected, they may even be ignored. To increase the acceptance of forecasts, decision-making managers can be asked to agree on what methods should be used before any forecast results are presented. The scenario approach can also be used; decision-makers can be asked what steps they will take in different possible future situations, before revealing forecast results. For instance, the managers at my company could have been asked what they plan to do if forecasts reveal that demand is considerably lower than our inventory of food products, and what if demand exceeds supply. This way, they are more likely to act on the results of the forecast.
Whatever the method used, companies should focus on maximizing the accuracy of their forecasts. In today’s fast-paced world where competition is ready to grab your market share at the slightest miscalculation, I feel forecasting is critical to business success.