Over the years, NFPA has brought a variety of leading indicators for the fluid power industry to our members’ attention through our market data and reports.
Several leading indicators include:
- Total U.S. Industrial Production;
- Purchasing Managers Index (PMI);
- Durable Goods New Orders;
- Retail Sales Excluding Autos;
- and NFPA’s State of the Fluid Power Industry Survey (SOFP).
Why use leading indicators? Leading indicators can be key pieces of information to assist you in your company’s strategic planning process. A reliable leading indicator allows your business to anticipate changes in the industry before they actually occur, giving you the competitive advantage.
What is NFPA’s SOFP? This monthly opinion-based survey is conducted within our manufacturer and distributor membership and includes seven multiple choice questions concerning the current and future state of the industry that are compared to previous benchmarks. Results are shown in both a percentage and indexed format, much like the Purchasing Managers Index (PMI).
Why is SOFP a leading indicator? Analysis performed on the SOFP data revealed that there is a high correlation between SOFP data and NFPA’s fluid power industry data available in NFPA’s Confidential Shipment Statistics Report (CSS). Both data sources follow a similar pattern, with the SOFP data leading the CSS data by three to five months depending on which combinations of data you use. Such a high correlation means that the SOFP data is a very reliable leading indicator for fluid power industry data in CSS.
In figure 1, we see three trend graphs, SOFP Current 12/12 Ratio Data in red and CSS Shipments 12/12 Ratio Data in black. Both data series move in a very similar pattern, but the SOFP data seems to react before the CSS data does. This is the first sign that the SOFP data may be a leading indicator. To verify this, in figure 2, we use NFPA’s Stats Toolkit to run a correlation analysis on all three data series comparisons to identify just how strong the relationship is. We quickly discover all three have a very strong relationship when we apply a monthly lag to the SOFP data (i.e. move the SOFP data series forward month-by-month and analyze the correlation between the two data series each month) and single out the highest correlation percentage. Further trend analysis in figure 3 shows that when the highest correlation percentage lag is applied to the SOFP data series and is re-graphed, both data series appear to have an even stronger relationship and move together in a very similar pattern. This visually verifies that the SOFP data series is a reliable leading indicator for the CSS data series.
How can you receive access to data and analysis from SOFP, CSS, and the Stats Toolkit? By participating in the SOFP and CSS programs. For more information on how to participate in these valuable member benefits, please visit http://nfpahub.com/stats/ or contact Eric Armstrong at email@example.com or (414) 778-3372.