FilterX, with sales over $100 million, used the SaleForekast™ methodology to complement its bottom-up forecast. This tempered the forecast for the Company’s Board and enabled a smoother exit for the private equity firm that owned FilterX.
FilterX sells highly engineered materials into a range of end-markets including Industrial, Consumer, and Medical & Biotechnology. The materials are designed for precise filtration, diffusion, and separation of liquids. FilterX focused on higher margin, higher growth products and end-marketing. At the time, more than 60% of FilterX’s sales were in the US, though the company had a global presence. FilterX was owned by a leading private equity firm with the goal of value creation.
FilterX was interested in understanding which end-markets were driving sales as well as providing external validation for its bottom-up sales forecasts. At the same time, the company was investing in both Research & Development for new products and in expanding geographically. Thus, it would expect to exceed the forecasts based on historical trends and estimates of the sales resulting from new investments.
SalesForekast™ provided FilterX with a capability that helped validate internal sales forecasts and budgets as well as provide greater credibility to its business model. The company used this process nearly every quarter.
Business Case SUmmary
This case study presents forecasts on the quarterly sales for 2009 to 2013. These forecasts are then compared with the actual sales for 2014 and 2015. Note: At the time, the computations were more manual as the SalesForekast software had not yet been fully developed, but the analysis is identical to the software available today.
FIrst, the software determined which macroeconomic indicators most closely correlate with FilterX’s sales history, providing valuable insight into what drives FilterX sales.
FilterX correlated significantly, above .94, with a number of economic indicators:
- Industrial Production - key end market for the company
- Motor Vehicle Sales - good measure of consumer spending
- Real GDP
- Healthcare Spending - key sector for FilterX and a high-growth area
- Personal Consumption, Food Services - good measure of consumer spending
The other correlations were not as significant, though the model uses the top eight correlations to build the best-fitting regression equation. Note: At times, indicators, such as Total Business Inventories, probably have less of a direct impact on the company. This happens due to possible mathematical correlations and can usually be ignored.
The regression equation that resulted included only three of these variables, with industrial production having the highest impact (See Below: industrial production coefficient of 1.2 vs. the others at below 0.1).
The model then took the forecasts of the variables in the resulting regression equation and used these to forecast sales for the following three years. In addition, SalesForekast™ looked at the sales trends themselves to find the best fit. For the case study, the model used actuals for 2014 and 2015 since these are now available.
The actual projections are indicated below:
The model forecasted sales of $125MM for each of 2014 and 2015 (at constant currency, to take out the effect of currency variations over this period).
The actual sales were:
- 2014: $124.8MM
- 2015: $126.5MM.
Each of these results were within 1% of the SalesForekast™ projected number in 2013.
FilterX also used the methodology to develop forecasts for its key markets: industrial, consumer and healthcare (but those data are not being disclosed publicly).