With the variety of execution techniques in practice today, such as Justin-Time and demand flow, why do we need to forecast at all? The answer is simply that there is a need to deliver products to the customer in less than the total aggregate time required to produce or procure that product.
In a manufacturing environment, a company may be able to make the product required immediately when the order is received, but with what? Components, of course! But where did they come from? A pull signal to a supplier right across the street in the purest JIT implementations, but those situations are not common. More likely, the components are there as needed because purchasing ordered a lot size in a period prior to the expected need, or to bring stock back up to a predefined buffer or reorder point quantity. Perhaps the vendor was supplied a schedule of requirements from the end item manufacturer. Or if the end item manufacturer doesn't use these techniques or MRP, perhaps it was due to a manual reorder point.
But buffers or reorder points aren't forecasts. Or are they? Even a simple reorder point based on past usage or demand or sales history is, in effect, a forecast. You are forecasting that demand over lead time will remain exactly the same as past history. Whether the inventory is finished goods in a shipping warehouse, buffer stocks on the production line, or components in a storeroom, it is there in anticipation of customer demand that will need to be filled in less than your company's total lead time to provide the product. Whenever total lead time, either internal or external to your company, exceeds delivery promise time, a forecast of some type, be it a formal MRP output or schedule given to a supplier, a traditional statistical forecast, or a reorder point, exists at some point in the supply chain.
Let's face it, most of us do not work for missile or airplane manufacturers that receive an order, then start to buy the components required to make it, so we must forecast. Since we must, there is a need to do it as accurately as possible to minimize waste and missed customer deliveries, and also to eliminate wasted time and effort for those who forecast. This is the purpose of this presentation. I will share thought-provoking information, time-saving ideas, tips on how to make the best calls with dubious information, and insights that I have learned in the trenches over 20 years that can help you get better results.
MAKING OR BREAKING YOUR FORECAST
Aggregates are, simply put, the sum of the forecasts or demands for all the items in a group. Many folks put great stock in aggregate forecasts and their measures, and they do have their place. They can be used effectively to increase or decrease production or purchasing efforts if a product line forecast is "not performing." Forecast nonperformance is a term frequently used to describe a situation where demands are far below or above the forecast. I ask you to start thinking of this as DEMAND non-performance. For if the aggregate (or item, for that matter) forecast was made in either a formal or informal sales/marketing/operations plan with decisions about forecast volumes and timing made with true consideration of the factors that influence demand, the question truly becomes why
the demand was different than expected. And that question is much more critical to a company than the variance itself.
If the group forecast was developed after careful analysis of the demand drivers, the forecast or demand error is a symptom of the real problem, and the symptoms are just as ugly as sneezing and a fever when you have the flu. Back orders, expediting, frustration, or overstocks, layoffs, and even more frustration are the order of the day. But the real problem is that there was a lack of understanding (or in some cases, an act of God) of the assumptions made and how they would effect the future demand.
History is only a starting point, whether you use a statistical forecasting package or do it manually. No computer knows why your past demand was the way it was, or whether that will continue as it was or change. It is critical that you and any participants in making the forecast truly understand the relevant issues to avoid variances.
To Be Continued
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