Aggregate Forecasts or Detail Forecasts Some Considerations

Transcription

1 Aggregate Forecasts or Detail Forecasts Some Considerations Ultimately, in any business forecasting application, the goal is to develop a forecast of some value (quantity ordered, sales dollars, raw material requirements, commodity price, etc.) whose behavior over time is meaningful to the operation of the business. Since the most common applications of forecasting are related to manufacturing, distribution/wholesale and retail requirements planning, we will keep this discussion focused on demand forecasts of end item products for customers. Keep in mind, though, that the same reasoning can be applied to many other forecasting environments. What is the best strategy for developing a "system" forecast (the term we will use for a forecast that is generated based on a statistical methodology)? Should we create a "topdown" or "bottom-up" forecast, and what are the differences between the two? Top-Down We use the term Aggregate Forecast to denote a forecast that is created in a top-down method. In this approach, we generate a forecast based on the combined, period by period history of a group of like items. The definition of "like items" can be based on any commonality in one or more of any of the fields associated with the underlying data records (product class, product line, channel of distribution, and so on). This approach has the advantage of working with larger numbers in any given period and of dampening out the impact of random "noise." Noise is present in all demand streams, but is potentially more significant at lower levels of detail where the overall demand values (the "signal") are smaller. The practical implications are: An historical timeseries that is made up of predominantly low-integer values or is highly intermittent may restrict the system's choice of forecasting algorithms, limiting it to methods that will produce a "flat" forecast, absent of any seasonality or trend. The higher the signal/noise ratio in a timeseries, the less error we can expect in a forecast of that time series. The term Detail Forecast is straightforward. Each "fact" or demand data point has a "lowest level" - the level at which the demand transaction is stored in the supply chain solution database. Typically, that level will be Item Number by Customer Sold-To or Item Number by Customer Ship-To. In some cases it may be even lower - it can be as detailed 2016 Stellium Inc., Lighthouse and SCM Solutions, LLC, , john.barry@stelliuminc.com, Page 1 of 7

2 as Item by Division by Plant by Warehouse by Ship-to by Day! A very low level of detail may be very important in terms of the flexibility provided for analysis and reporting, but it may also be too granular to support reliable forecasts at that same level. Bottom-Up What would be the benefit of a bottom-up system forecast? The first is obvious: companies don't manufacture and sell "categories" or "classes," they manufacture some variety of individual and distinguishable products (end-items) and sell them to customers. Sooner or later, there needs to be a forecast for the salable item in order to support appropriate planning of the manufacturing, assembly or sourcing of that salable product. Sooner or later, you have to get to the detail...at least the product detail. Secondly, just because the low-level detail can be low volume or intermittent doesn't imply that it must be. If the detail level volume is robust, forecasts at the detail level can reveal trends that could be covered up in aggregate forecasts - up trends in one model at the expense of another, growth in one customer that might have marketing or margin implications, shifts in geographical demand that might impact distribution capacity planning, and so on. Lastly, as the management of the supply chain extends further toward the end consumer on one end, and toward the raw material/component vendor on the other, the opportunity to more efficiently (and profitably) manage inventory requires more visibility to detail on the demand side. Collaborative forecasting processes that focus on customers demand the ability to tune forecasts by customer. Vendor-managed or comanaged replenishment initiatives demand the ability to tune forecasts at even lower levels (product by customer location). It is reasonable to use both aggregate and detail forecasts in our forecasting process. The challenge, then, is to determine when it is appropriate to use one approach over the other. It is also important to understand that, regardless of the "level" of the source data and the system forecast produced, supply chain solutions require that a forecast exist at the detail level within the database. Different methods can be used to produce this required detail forecast, and these methods ensure that the sum of the detail forecasts will equal the aggregate forecast. Stable Demand Consider the products manufactured and marketed by a fictitious demonstration company, BEI. One division of BEI makes and sells greeting cards. Some types of greeting 2016 Stellium Inc., Lighthouse and SCM Solutions, LLC, , john.barry@stelliuminc.com, Page 2 of 7

3 cards are more or less a constant-demand commodity. Sympathy cards for a death in the family of a friend are a good, if rather unwelcome, example. Congratulations cards welcoming a new baby are a similar, not to mention happier, example. The demand for each of these types of cards is unlikely to display any distinct seasonality because the circumstances driving the demand for the product are not related to the particular time of the year; there are no overwhelming differences in the correlation in demand between any two periods. When we look at the total demand for each of these types of cards it is relatively stable over time, exhibiting gradual uptrends or downtrends that are reflective of general changes in the birth rate or mortality rate at large. Why would we prefer to develop demand forecasts for either of these groups at an aggregate level (say "all sympathy" cards or "all new baby" cards) instead of a detail level (by end item number - different for each inscription/cover image combination)? Actually, in this example, it is quite possible that there is not much (or any) difference in the validity of the forecasts regardless of whether they were generated at the detail level or any higher, aggregated level. After all, the difference in product-to-product demand within these categories across time is likely to be random - a function of the taste of the individual consumer purchasing the card and their perception of the needs and tastes of the recipient. Some consumers will want hearts & flowers, some comedy, some spiritual messages. If you are collecting demand history across a large enough group of customers (i.e., stores), you will observe the impact of all different varieties of consumer tastes and requirements in any given time period. There is no particular reason to prefer an aggregate forecast Stellium Inc., Lighthouse and SCM Solutions, LLC, , john.barry@stelliuminc.com, Page 3 of 7

4 Intermittent Demand Let's suppose, though, that the underlying detail is at the End Item by Customer Ship-To level. The demand history for any given item at this level is likely to be low volume, intermittent or both. The demand history and system forecast generated at this level of detail would look something like this: If all of the detail level demand streams and forecasts generated at that level are of this form, the resulting summation to the end item level (the level of manufacturing concern), would take a form similar to this: 2016 Stellium Inc., Lighthouse and SCM Solutions, LLC, , john.barry@stelliuminc.com, Page 4 of 7

5 The magnitude of the end item forecast that is the result of the sum of the detail forecasts generated at the end item/ship-to level could be quite different than the magnitude you would expect from the total history. In fact, it could fall anywhere in a range from obviously too high to obviously too low, depending on the variation in each contributing detail element s history. Even though neither the history nor the forecast hints at any trend or seasonality, the magnitude of the forecast is potentially concerning. We can easily demonstrate another real scenario where the low level detail history is low volume and seemingly sporadic, but when viewed in total, the summed history exhibits strong trend, seasonality or both. The sum of the detail level forecasts in this situation could seem quite at odds with the summed demand: 2016 Stellium Inc., Lighthouse and SCM Solutions, LLC, , john.barry@stelliuminc.com, Page 5 of 7

6 The potential forecast generated from the aggregate level, however, has a higher likelihood of capturing the trend and seasonality: Conclusion 2016 Stellium Inc., Lighthouse and SCM Solutions, LLC, , john.barry@stelliuminc.com, Page 6 of 7

7 A good approach is to take some time at the outset to consider your demand environment and the objectives your demand management process hopes to achieve: Explore the demand data before you start generating forecasts. Become familiar with where it is strong and weak. Identify the forecast level or levels that are critical to the business. What uses will be made of the forecasts. Experiment with different aggregate forecasts for different sets of data, and determine which yield the most sensible results. Keep in mind that you can change your approach as the situation requires. The supply chain solution should be flexible and will accommodate your changing needs and constraints. A central feature of a solution is its support of collaborative forecasting. No statistical forecast will ever reflect the total reality found in a dynamic marketplace. Good forecasts require knowledgeable input on external conditions driving demand - that is, good market intelligence. The statistical system forecast is a very important starting point, but it is not the final word Stellium Inc., Lighthouse and SCM Solutions, LLC, , john.barry@stelliuminc.com, Page 7 of 7