I recently wrote a piece called “The Reset Economy” where I argued that for various macroeconomic reasons, demand may not bounce all the way back after the recession ends.  Consequently, many manufacturers may end up with more capacity than they need.  I believe this is the biggest supply chain problem many companies will confront over the next few years.  

If that is the case, then companies need to conduct some strategic planning and scenario analyses -e.g., if demand does not fully bounce back, which factories should I shut?  I have been asking myself another question lately:  How much advance warning can companies get about demand patterns once the economy rebounds? 

I came across an article by the Infosys supply chain consulting team where they made the following statement:  “Economic data such as wage inflation and price inflation should be leveraged in econometric forecast models particularly for CPG, Hi-Tech and Automobile sectors.”  

This motivated me to go out and look for firms that can help companies incorporate econometric models into industry, or company specific, demand plans.  It turns out that there are consulting firms made up of economists that do this kind of work.  One of the biggest is IHS Global Insight, and they published an article called “Selecting a Marketing Mix Strategy: Why the Economy Matters,” where the authors (Antonia Prlic and Hemant Sangwan) make the following points:

• Consumer goods manufacturers and retailers regularly make critical decisions about pricing, product, distribution, and promotion (the “marketing mix”).  During a recession, consumers become more cost conscious, buying lower priced store brands, for example.  If a manufacturer’s product is priced lower that competing products, they should highlight this point in their promotions.  They should also promote large product packages, which have lower unit prices, more extensively than smaller packaged items. 
• Manufacturers and retailers should keep track of key economic indicators (e.g., consumer confidence, fuel prices, disposable income, etc.) and take into account overall macroeconomic conditions when developing their marketing mix strategy.
• Tracking some of these macroeconomic indicators can provide advance warnings into a category’s sales! 

For example, the authors provide a chart in the article that shows the historical change in gross domestic product, along with their forecast for coming quarters, and a second chart that shows the percentage change in consumer spending on furniture and food categories based on GDP.  The correlation between the two charts is impressive.

Econometric models can be far more complex, taking into account things like discretionary versus nondiscretionary spending, consumer spending, household income, tax rates, employment rates, and other variables.  Most companies would probably need help developing the initial models, but afterwards  they could track the statistical correlation to the various variables themselves, as well as make the appropriate changes to their demand models. 

From the perspective of longer term demand-supply balancing, these models are only helpful if the longer term forecast of the underlying macroeconomic  variables, like GDP, are reasonably accurate.  We certainly know that economists have not been very good at forecasting recessions.  Still, I’m intrigued by this.  I’d love to hear from you.  Are any of you using econometric forecasts effectively at your companies?