We're live blogging Commodity EDGE today and tomorrow. If you can't join us at the event, join us virtually on Spend Matters and MetalMiner!
Omer Abdullah, Co-Founder and Managing Director of The Smart Cube, led off the final session of the afternoon focused on forecasting, statistical modeling and building internal competencies for tackling commodity management and risk (I can't live blog the panel that I moderated on advanced sourcing and commodity technologies which was sandwiched in between Omer's presentation and the first, but I will share my notes on it tomorrow). He began by suggesting that forecasting is being taken more seriously inside advanced procurement organizations today (and others are following suit).
But forecasting is not just about creating models with hard and fast numerical inputs (e.g., GDP growth, unemployment, new housing starts, PPI, PMI) -- Omar suggests, in fact, "forecasting must incorporate both quantitative and qualitative in its pursuit of accuracy" and must also encompass a worldview.
In Spend Matters' experience, when companies are looking to develop a perspective on where a category or commodity market might be headed in the future, the organization will invariably say it wants to pursue a specific commodity price forecast strategy. Some even express an interest in running statistical regressions to build out predictive modeling and forecasting tools on their own.
This is a big change in the procurement sector from decades past. Indeed, commodity market volatility has transformed sourcing and supply chain organizations. Leaders have moved up a curve from simple purchasing and requisition while paying minimal attention to price trends to intelligence and analytics to better understand cost factors impacting commodity prices, seasonality trends and market dips. Today these select leaders have moved to outright forecasting to better time purchases and deploy strategic sourcing strategies.
For leaders, it's important to remember that the ultimate goal is not simply to develop a forecast and then map it to a broader hedging and commodity risk mitigation program. Rather, top performing organizations are as interested in exploring and getting into the forecasting sausage-making, attempting to understand when correlations (e.g., GDP, new housing starts, unemployment, aggregate credit ratings, BLS data) work and break down.
As one company told Spend Matters, "we want to get better at explaining the unexplainable." These organizations are most prepared for cooking in a kitchen with a lot of variables. Metaphorically speaking, as anyone who cooks Mexican food knows, the spiciness of a particular type of pepper can vary tremendously and ruin a dish if you're not careful.
To understand how hot that pepper is, Omar recommend taking a five-step approach to forecasting, beginning with an emphasis on taking a deep dive into commodity intelligence. Understanding the primary feedstock and the manufacturing process, secondary derivatives, commodities and related by-products are key elements of this. But in addition, organizations must consider the end-user sector for commodity demand, usage and trends as well as seasonal factors and more general price trends. These are just a start, depending on industry and specific categories. Other areas include steps in the production process, energy requirements, key inputs to the process and production yields.
The second step revolves around "rigorous and analytical" interrogation of data and requirements. Third, organizations must begin to evaluate alternative scenarios and determine probabilities as well as risk mitigation strategies. Fourth, it's critical to continually refresh this information and fifth, companies must be "action oriented." We'll explore steps two through five in more detail in a forthcoming post as well as use cases from Omar's presentation for steps involved in building commodity intelligence. Stay tuned as our coverage from Commodity EDGE continues!