In the 1990’s when I was with Baxter Healthcare, we implemented a statistical forecasting solution for our European affiliates. In going through the user training I was intrigued by the functionality around aggregate level forecasting and the improved accuracy achieved.

The example they used was a company manufacturing bicycles. Rather than forecast the demand for different colors of a particular model the company would aggregate historical demand at the model level and then run their statistical modelling.

At this aggregate level the forecast accuracy was much better and downstream painting could be driven by Make to Order with much shorter lead times, a reorder point (ROP), or through a disaggregation technique for the higher level forecast.

At the time I was managing the European distribution of sterile surgical gloves and was excited about trying this approach. We had two SKU’s for each of the eight sizes for the five different types of surgical gloves, each with ten languages. We sourced these 80 SKU’s from our Malaysian manufacturing site into our European distribution center in Belgium and then shipped weekly to our twenty affiliates based on their actual inventories, forecast, and safety stock target.

I started by building a pyramid structure in our forecasting tool that allowed me to aggregate historical demand for these 80 SKU’s. I then began forecasting at this level each month and compared to the sum of the affiliate forecasts.

The results were astounding and I was able to demonstrate a greater than 20 point improvement in forecast accuracy using this method. I easily convinced my boss that we should use these forecasts for our manufacturing site in Malaysia.

I then calculated a ROP for each affiliate for each SKU based on historical demand variability and lead times and developed a dBase application to calculate weekly replenishment quantities based on actual inventory and the ROP. Getting commercial buy-in for this approach took more time but we did get agreement.

I also met monthly with our European product manager to ensure that any market intelligence was captured on top of the statistical model. This process worked so well that we were able to tell our affiliates that they no longer needed to spend time forecasting these products. We also well over achieved on our inventory targets.

A few years later I moved to our biotech division. I remember when my boss needed to provide a projection of QIV European sales for a blockbuster hemophilia product and asked me how much I thought we would sell.

I aggregated historical demand at the three dosage form levels, 250 AU (activity unit), 500 AU and 1000 AU lyophilized product in vials. I ran the statistical models and told him 90 million AU’s. Actual QIV sales came in close to 100 million IU’s and my forecast was much better than what he had received from Finance in the affiliates.

Since those days I have been with three different biopharmaceutical companies and have built a large network across the industry. It amazes me that not once have I seen this technique applied to improve forecast accuracy.

For many products the bulk unpackaged tablet, capsule, vial, syringe is the same across many markets and even globally. By aggregating demand at this level and then generating a forecast biopharmaceutical companies would be running their most constrained and expensive manufacturing operations with a much more accurate demand signal.

It goes without saying that this approach would have a profound impact on inventory levels. I am not suggesting that it be applied carte blanche but it should be strongly considered for any product from 3 – 5 years after launch through to late stage lifecycle.

With this approach one could use one of the strategies I mentioned above for downstream packaging and distribution. Make to Order would not work in this industry but reorder point is an option or using a technique to disaggregate the tablet/capsule/vial/syringe forecast down to the country level.