Multiple Demand Streams are No Problem for IBP
Accommodating the Complicated Myriad of Planning Exceptions
By Ryan Rickard, Director SCMO2
Remember having long, drawn out discussions trying to decide which (single) date and order element should feed into the actuals/historical data stream when forecasting on a legacy platform? Thankfully those painful discussions are no longer necessary with SAP IBP. Even in cases where multiple demand streams are input, analyzed and processed, IBP can handle it.
So what do actuals mean in your planning tool and where does the actuals data come from?
- Are actuals the quantities the customer ordered?
- Are they the quantities that were shipped to the customer?
- Are they the quantities that were actually delivered?
Today’s complicated supply chain systems create myriad exceptional situations. Does your company accept backorders, filling an order partially and shipping the balance when available? If not, what data is used for actuals when customers later place a short order to make up for the balance? Are multiple shipments recorded in different periods, as they are shipped? Which date is attached to that order—the original date that the customer wanted the product, or the date it was actually shipped? Or the date it was delivered?
IBP Demand Sensing Functionality
These scenarios can be nearly impossible to accommodate on a regular basis in other platforms, but IBP provides straightforward, standard processes to bring in orders, shipments, deliveries and even point-of-sale data with ease. Taken further, IBP’s demand sensing functionality will actually analyze these trend exceptions and automatically react to recent market activities, suggesting short-term forecast adjustments for your consideration.
Users can then select which demand stream to apply in creating the desired statistical forecast. Most best-in-class organizations use orders as their input to statistical forecasting. Basically the data is driven by ‘how much’ and ‘on what date’ did the customer originally want fulfillment. To best predict what future needs may be at the same time next month, or next year, then using order information is the right tactic. But if an incomplete order was shipped two weeks late, then using shipment information could be valuable to trigger a future production and supply action as necessary.
Each of these two scenarios can be accounted for in different ways in our forecast. The benefit of IBP easily allows the planner to decide which demand stream should be used as input in statistical forecasting going forward—orders, shipments or deliveries. If useful, a planner could actually create multiple statistical forecasts using all these scenarios and measure the accuracy of each to determine how to proceed.
Point-of-Sale Data Forecasting
If you also get point-of-sale (POS) data from some of your customers, that too would be valuable to analyze relative to your forecast. The customer’s POS data may help forecast what future purchases could be expected and planned for. In IBP, POS data can also be easily imported and stored as a separate key figure (data row).
A statistical forecast using the historical POS data can then be created to project what that customer may order downstream. Isn’t that what all good retailers do anyway? Knowing what inventory is on hand, and forecasting the anticipated market demands, drives their purchase plan and optimized the use of cash. That POS forecast can then be compared to a demand plan to determined the most accurate course of action.
An example of an IBP view which shows actuals (orders), POS, and statistical forecast using orders as the input, and the POS statistical forecast.
Any demand planning platform can handle the rote, routine scenarios. But let’s all admit that success comes from effectively handling the exceptions. By employing a forecasting system that easily accommodates these exceptions, and allows the planner to make a more educated decision, organizations can more efficiently and effectively allocate resources to fulfill their demand.