Using Forecasting Software to Drive Specific Business Improvements – Example One

I am going to veer off from best practices for just a blog or two – we were recently going through a couple of project review sessions with clients and it struck me that there were some good examples of how to specifically apply improvements with forecasting software.  So thought I would share them; and for a special treat, I happened to have been running through the trails by my home and for some reason the Beverly Hillbillies sprang into my mind so the opening paragraph – please read it to the opening stanza of the BH theme song:

Want to tell you a little story about some companies, looking to establish best practices in S&OP.

Then one day, as they were boarding on despair, they discovered the miracle of forecasting software.

Okay – that was lame but it made me giggle, and I have always enjoyed the incredible gift of cracking myself up.

The point here is that the specific improvement example is based on using the more accurate forecast from the forecasting software to drive performance improvements in both inventory and customer service.

Specifically, this example happened with two different companies in different types of business: one in paper and one in lawn care.  So this type of work should have broad application.

Upon applying the best practice of measurement (please see earlier blog on S&OP software investment), these companies were able to identify opportunities with their top customers.  One – they had indeed improved their forecasting in excess of 25%.  In addition, they were able to show that the forecast supplied by their customers had error in excess of 50% (don’t think I need to bring up the dart board analogy here).  Please remember that we are talking about forecast error measured at the execution level, which in one case is by SKU by customer and in the other is SKU by ship to location.  Lastly, they were able to show what the implications were in terms of product inventory that was held throughout the value chain – specifically customer stores and distribution centers as well as our clients’ warehouses.

Incidentally, I should also point out that the companies were now running their computer forecasting based on the combined input of historical internal information (shipment, inventory, etc.) along with Point of Sale data from their customers (both sales at stores and inventory at stores and DCs).

Armed with this revealing information, these companies approached their customers.  Based on the data and information that was provided (of course having gone through a verification process), the partners in the value chain were able to take specific action.

With regards to the paper company, they were able to work with their client to streamline the new product introduction process.  Since they can sense the sales activity throughout the value chain, they can more specifically match the timing of introducing the new product upgrade to when the old product will sell out in the normal course of business.  For the retail customer, they were able to cut out almost $2 million in working capital carrying unneeded inventory at their DCs and stores.  Our client was able to save $1 million plus per quarter in promotion incentives used to clear out old product in anticipation of the new product intro.

Focusing on lawn care, our client’s retail partner was ordering far too much product in anticipation of the spring season, which took up space in DCs, required extra space in the stores, and far exceeded actual demand.  While I do not have the exact figures, we do have verification that this unnecessarily consumed working capital and sub-optimized the performance of many of their stores as that retail space could be used for other products with equal demand.  Our client in turn was able to avoid the inevitable return process and accompanying issue of product credits.  In addition, they were able to utilize constrained capacity for a broader array of products, some with higher profit margin.  They are estimating improved profitability of approximately 6% and counting.  The counting part stems from the application of the new practices to their remaining base of retail partners.

The most accurate forecast possible derived from your forecasting software is worthless unless you apply it to specific components of your value chain and back it up through measurement-in other words, discover your “bubbling crude” and “Texas Tea”. Hopefully these two examples will provide the basis for some creative thinking within your value chain, and some measurable benefits to your bottom line.


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