My career in forecasting has been challenging, at times frustrating, and above all immensely rewarding. This is my story of how I learnt to predict consumer trends, and revolutionize the fortunes of the companies I have worked for.[bar group=”content”]

It all started at AIWA Consumer Electronics in the early 1990s. Here I was, a fresh MBA in my first Associate Product Manager role for personal audio products. Every year, new versions would replace the prior year’s products. End of year was filled with clearance specials of products just sitting in our warehouse that had failed to sell. The more that accumulated in warehouses, the further away we were from our profit targets.

One of my product marketing responsibilities was the demand plan. The inherited budget and forecast was prior year sales + 10% expected growth rate on all designs. My counterparts covering other categories used the same approach, and worked at selling the current inventory. It was a basic approach, to put it kindly. This is a story of how that paradigm changed completely.

Overall forecast error at the time was around 20% for the 5-month order-to-warehouse cycle. Not bad by marketing team reasoning but nowhere near good enough by management’s standards, or ours come to think of it. Collaboration meetings would open with our distribution manager, Joe Sparta, saying “You guys are selling plenty of what we don’t have and not enough of we do have”.

The lack of insight into what our customers wanted and how to service that need was painfully obvious. The inefficiency in supply chain was shocking.

In graduate school, I had the pleasure of taking Chaman Jain’s graduate forecasting class where I learned the power of measuring MAPE. I applied this method to my new role and running the numbers by SKU at AIWA, we had 105% MAPE. No wonder we needed clearance specials, and Joe was having a difficult time with customer service!

Something needed changing, and fast.

Luckily, Chaman Jain was open to helping out an old student. He has an immense wealth of knowledge and experience, and I was fortunate enough to be taken under his wing. Chaman Jain founded IBF, is the author of the Fundamentals of Demand Planning book and is Editor of the IBF’s Journal of Business Forecasting. I couldn’t have hoped for a better mentor.

Chaman had been where I was at that time, and understood the challenges I faced. What’s more, he knew intimately all the mistakes we were making. What I was trying to do was desperately make sense of vast amounts of data that seemed more like a quagmire than a clear insight into customer demand.

We discussed the challenges of relating older products to the newer versions. Considering the seasonality of my category (Christmas, Dads & Grads bumps), he recommended starting with multiplicative decomposition. I took shipment seasonality of the category rather than prior products because of the changes in features at price point. The next step was treating distribution penetration as a trend level. The distribution multiplier became the retailer share of consumer sales published by the trade magazine TWICE (This Week in Consumer Electronics). This dropped my MAPE to the sixties. We weren’t where we wanted to be, but changes had been implemented and definite progress was made. Joe was happier, and we had put the company on track to greater profitability.

The next step to improve accuracy was to use NPD INTELECT POS data. Clark Johnson, the sales VP who provided our data, provided in-depth guidance on data limitations. Using consumer purchases, I fine-tuned these simple models as the data became available (there was a 2-month delay).

My first step in changing category seasonality from shipments to consumption dramatically improved the forecast accuracy after initial pipe fills. This meant while we still had a good call for the highest seasonal period of Christmas, we had a weaker call for Dads and Grads when POG (Plan-O-Grams) were being reset. Overall, our MAPE for the 5 month order period improved to under 40%. Joe was getting happier.

Sales and retailers were not as happy, however. The more efficient our supply chain, the fewer opportunities there are for discount clearances. My manager and VP were very happy that profits were up for my category. This resulted in a promotion to Product Manager of Personal Stereo, as well as assuming additional responsibilities forecasting the other 4 categories.

My move to a forecasting career was solidified by one product. Our team designed an innovative new product – the first 100 watt, simple to operate, shelf top stereo system. Before this, all US shelf top stereos were between 10 and 30 watts of power. The product and sales teams believed that focusing on lower watt stereos were holding back our US market share and that the new stereo would be a game changer.

Launching the new product was quite a gamble to say the least.

The initial forecast was to move AIWA from a 12% share to a 18% share of the shelf top category. The first hint of success was 100% distribution with all major retailers placing the product in their POGs (Plan-O-Grams). We sold out of the initial container loads as soon as they arrived and increased our forecasts.

The gamble had paid off big time.

Management’s key question was how big could this get? We did not know if retailers were loading inventory because of expected shortages (common at that time – see MAPE comments above). But when the consumption data came in, we knew we had a hot seller. Using two months of fully distributed consumption sales divided by the category seasonality of those two months, we had an estimate of between a 55 and 65% share of our projected category sales (compared to our budgeted goal of 18%). This was a huge success for the company.

We only doubled our forecast because management did not completely buy into the seasonality based forecast. When the third month of consumption confirmed the same 55 to 65% share, our senior management team had the Malaysia factory to go to triple shifts. We manufactured for the conservative 55% share number and ended up selling out of almost all the containers coming into the US, exceeding the quantity. AIWA grew the category and become the dominant category leader.

Forecasting’s role in driving the company from $100 million to $300 million sales changed my career. To say it was an exciting time is an understatement.

I saw the power of forecasting and I was hooked.

Soon after, Roger Brown recruited me to Duracell. He exposed me to the consumption paradigm with rich data sources to drive the forecast. As part of Roger’s team, we regularly attended IBF forecasting conferences. I now had the opportunity to work with, and learn from, some of the best in our profession.

It all started with a conversation with Chaman Jain and joining IBF. Forecasting through demand planning and applying predictive analytics is a vocation that still inspires, challenges and motivates me today. And, why wouldn’t it? When done properly, demand planning sales to retailers through understanding the consumer allows you to predict the future. It enables even established companies to reach unimaginable levels of profitability.