Supply Chain Forecasting Digest: Volume 2 Issue 2

The environment has of late become much more challenging for forecasters and planners in most industry settings. As the effects of volatile commodity prices, rising energy costs, and ever rising costs of oil and gasoline ripple through the world's economies, the demand patterns for products and services of all types are being affected – both positively and negatively.

Much of the operational forecasting and planning of companies is based upon the use of time series models, which are heavily reliant upon the consistency and replication of data patterns through time. That is, they assume a stable demand pattern for the company's products and services through time.

So, the risks of greater forecast inaccuracy, higher inventory, and reduced customer service levels are looming for all of us in this shifting environment if care is not exercised in developing forecasts under conditions of higher uncertainty.. Of course, the challenge is to get the right products and services to the right place for customers and consumers at the right time.

So, one implication is that forecasters and planners will have to rely more heavily on gathering information and assumptions that may not be reflected in the historical information that they are using for their time series forecast models. Making inferences from information that may not directly relate to prior cyclical phenomenon for the business will also be necessary. This requires an effective and thorough forecasting and planning process as well as knowledgeable and well trained forecasters and planners within the company. This requires more than technical skills or the expert intelligence of the forecasting and planning software that is available (and important to supporting forecasting efforts).

Given this changing business environment, it will be more important than ever that forecasts reflect the market intelligence and the perspectives of key functions across the organization in order to "get a handle" on the drivers changing demand patterns. This means that the assumptions surrounding the forecasts and plans need careful consideration and scrubbing in an effort to maintain and improve accuracy. Some cause and effect modeling may be appropriate to determine which drivers are the most important and the relative magnitude of their effects on demand.

This also means that carefully and thoughtfully developing the estimates of the effects of the assumptions as well as the program effects built into the plans will be more essential than ever for achieving the desired level of accuracy for the company. The expected error and range of error would most likely expand in the kind of environment in which businesses find themselves.

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