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.

Question

Dear Dr. Jain,

I work for a large multinational FMCG – it is considered a group norm to use a high level, statistically modeled forecasting tool for our demand forecasting. However, I believe the tool we have for this is old fashioned (as is the company’s thinking).

Within the section of the business I work in, we use a more granular forecast process (surrounding big retailers, customer implants, dedicated business planners etc.) but we are not happy with the overall result (either at customer level or top line). Let’s say in this model we have 8 customers at 80% of our total volume.

The top down decision is to implement the high-level forecasting tool and work on an aggregated forecast for all of these customers combined.

I am struggling to see that this is the correct and modern-thinking approach. Everything I have learned says that going to customer detail (when you have said detail) is the correct approach when dealing with sophisticated customers with their own promotional plans etc. But I am struggling to convince my peers.

What do you think on this point? Is there any modern best practice I can refer to?

Thanks,

Chris,
Large UK based FMCG company

Answer

Dear Chris,

I agree with you that we should forecast by customer, particularly where a large percentage of sales come from just a few customers. Forecasts are likely to improve if we incorporate their plans, especially their market plans, into the forecasting process. I am not sure whether your company has tried enough. Maybe you would like to see whether or not customer-based forecasts are better. If customer-based forecasting does not give the accuracy you want, then we have no choice other than to forecast at an aggregate level, which your company is currently doing. Forecasts tend to improve when forecasted at an aggregate level.

Better forecasting tools always help. But if you are thinking about forecasting tools only in terms of sophistication of forecasting models, then I am not sure. Forecasting is simply matching the data pattern with the pattern that model captures. With the right marriage between the two, we can have the best forecasts. It is not unusual for a data pattern to match with the pattern that a simple model captures. Among three different types of forecasting models (Time Series, Cause and Effect and Judgement), Time Series models are the easiest and most often used. Based on the recent IBF survey, 62% of the companies use Time Series models. Within Time Series models, 56% of them use the simplest models, which are, Averages and Trend. The best approach, therefore, should be to start with simple models. If they don’t give the accuracy you need, then move to more sophisticated ones. 

I hope this helps.

Dr. Chaman Jain

St. John’s University