Good forecasts are critical to planning, and it surprises me how many are simple time series regression models. Time is a dimension, but it’s not a driver – sales don’t just happen because the sun comes up! I also see a lot of forecasts based on environmental variables alone, like industry or commodity forecasts. Correlations between industry indicators and enterprise level value chains are usually weak, and so the forecasts are also weak as a basis for major capital or operating investment.
I tell my students that forecasting is the process of projecting transactions into another timeframe (one of our class exercises is building forecasts based on transactional sales scoring). The most insightful and resilient models are often an ensemble of industry and transactional models. Backlog and pipeline are excellent sources of transactional data (those would fall in the RD1 – revenue driver group 1 above). But that’s not enough, and you can usually pursue transactional data with predictive quality further up the chain. RD2 describes the next stage e.g., your customer’s orders and specific demand drivers in your market. RD3 would be your customer’s customers orders, and drivers in that market. The same approach works on the CD (cost driver) side.
The most obvious benefit to this approach is higher quality forecasts. But there are other benefits: the exercise of meeting with customers, and customer’s customers, builds relationships, understanding, and shared purpose. We all have a tendency to be insular. But the answers may not be in the boardroom and spreadsheets – sometimes, we have to get outside and seek the drivers.