In the modern supply chain, forecasting is necessary for companies that manufacture their own inventory — especially for items that are not made to order. Manufacturers will use material forecasting to ensure they produce enough stock to satisfy their customers without resulting in an overstock.
But at the same time, the forecast must not fall short so the manufacturer finds itself without enough inventory to fulfill its orders. The cost of failing to maintain an accurate forecast can be financially catastrophic.
How Forecasts are Developed
Forecasts are developed for a company’s finished goods, components and service parts. The forecast is used by the production team to develop purchase order triggers, quantities and safety stock levels.
The forecast is not static and should be reviewed by management on a regular basis. This is to ensure information on future trends, the internal or external environment is incorporated into the forecast to give a more accurate calculation.
Forecasts can be either statistical or non-statistical.
The forecast is a calculation that is fed data from real-time transactions and is based on a set of variables configured for a number of statistical forecast situations.
Planning professionals are required to use software to provide the best forecast situation possible. This is often left unchecked without any review for long periods. To best use the forecasting techniques in the supply chain software, planners should review decisions related to the internal and external environment. They should adjust the calculation to provide a more accurate forecast based on the current information they have.
Statistical forecasts are best estimates of what will occur in the future based on the demand in the past. Historical demand data can be used to produce a forecast using simple linear regression. This gives equal weighting to the demand of the historical periods and projects the demand into the future.
But forecasts today give greater emphasis on the more recent demand data than older data. This is called smoothing and is produced by giving more weight to the recent data. Exponential smoothing refers to ever-greater weighting given to the more recent historical periods. Therefore, a period two months ago has a greater weighting than a period six months ago.
Weighting is called the Alpha factor. The higher the weighting — or Alpha factor — the fewer historical periods are used to create the forecast.
For example, a high Alpha factor gives high weighting to recent periods. On the other hand, demand from one or two years ago are weighted so lightly, they have no bearing on the overall forecast. A low Alpha factor means historical data is more relevant to the forecast.
Historical periods generally contain demand data from a fixed month, say June or July. But there can be room for error using this method, as some months have more days than others, while others have holidays. This can create a variation in the number of workdays.
Some companies use daily demand to alleviate this error. But if the forecaster understands the error, monthly historical periods can be used along with a tracking indicator to identify when the forecast deviates from the actual demand. The level at which the tracking signal flags the deviation is determined by the forecaster or software and vary between industries, companies and products.
A small deviation may require intervention when the product being forecasted is high-value, whereas a low-value item may not require the forecast be scrutinized to such a high level.
Non-statistical forecasting is found in supply chain management software where demand is forecasted based on quantities determined by the production planners.
This occurs when the planner enters in a subjective quantity he believes the demand will be without any reference to historical demand. The other non-statistical forecasting occurs when demand for an item is based on the results of materials requirements planning (MRP) runs.
This takes the demand for the finished good and explodes the bill of materials so a demand is calculated for the component parts. The component demand can then be amended by the planner based on their assessment and knowledge of the current environment.
The resulting forecast is based on current demand and will not incorporate any demand from previous periods. Many companies will use a combination of non-statistical and statistical forecasting across their product line.
Why is Strategic Forecasting Important?
There are several factors why a manufacturer may want to adopt forecasting as part of its strategy. Here are a couple of them:
- Retaining customer satisfaction. Forecasting will help predict supply so it will keep production on time. This, in turn, will keep customers happy because there is no delay in delivering and fulfilling orders.
- Keeping costs down. Since companies can forecast how much of a product they'll need, they can cut down on production and storage over storage costs, especially for items that are not made to order. By knowing just how much to make, it reduces use of facilities, labor and warehousing. This, in turn, can help keep pricing competitive, which, in turn, also leads to return customers.
The Bottom Line
The forecast gives the planner a guide to future demand, but no forecast is totally accurate. The planners' experience and knowledge of the current and future environment is important in determining the future demand for a company’s products.