Solving the dilemma of forecasting between building and buying
John Frechette is an economist and founder of Sourced Economics, an Arlington, Virginia-based research firm that provides consulting and analytical services to corporate strategy, finance, and supply chain departments. The views are those of the author.
Today’s volatile economic conditions increase the costs and level of effort required of CFO finance teams to prepare forward-looking financial plans, and generally make the task of forecasting extremely difficult.
Business forecasts that previously assumed stable conditions in, for example, commodity markets are now faced with significant input cost inflation and large unpredictable market shocks, underscoring the need for more
specialization in forecasting.
This leaves finance teams struggling to master the complex web of spreadsheets and software models that together produce supply and demand forecasts, affecting every aspect of their business.
But companies can weather the storm by focusing on “build versus buy” decisions in these forecasting processes. Too often it seems like these teams go into construction for various reasons and end up being too vertically integrated in their forecasting operations.
Let’s take a step back. Build or buy decisions are analyzed daily in the context of manufacturing and development operations. However, just like the transformation of raw materials into products, forecasts undergo a similar transformation. National production forecast information is obtained, implicitly or explicitly, and internal knowledge is used to transform this information into business unit forecasts. Over-reliance or under-reliance on external “vendors” can then have serious consequences for the accuracy of a finance team’s forecasts and, by extension, their planning and budgeting decisions.
Suffice it to say, especially in times of economic volatility and uncertainty, the forecasting process deserves more
Warning. Believe it or not, it is common for finance teams to maintain their own forecasts based on proprietary, albeit combined, methods of obtaining global commodity prices.
Understanding where market forecasting, using readily available external projections, should stop, and where business forecasting, using proprietary internal knowledge, should begin, can drive significant improvements.
This build-or-buy decision is then essential to optimize the forecasting process and to establish operational resilience in the face of uncertain economic conditions.
According to transaction cost theory, firms “build” where internal transaction costs are low relative to the cost of contracts and trade with suppliers, and vice versa to “buy”. In other words, markets can be expensive and in some processes internal governance is more economical. When forecasts do not require corporate knowledge, they must be sourced.
The objective of the financial teams, regarding this “purchase” decision, is to source increasingly enlightened forecasts, for increasingly relevant markets. Rather than general energy price forecasts, reliable sources forecasting light sweet crude oil prices, if any, are preferred. The source of a forecast can be as simple as a website
or a government publication, among many other possibilities.
When forecasts require specialized knowledge of the company’s operations, customers or products, they should be developed in-house. Here, the role of finance teams vis-à-vis this “build” decision is to continue to discover ways to combine external forecasts with internal proprietary information, to produce increasingly precise forecasts adapted to the situation. ‘company.
A clear example is demand planning, where technology systems may only use internal historical records to make forecasts. Another example is cost forecasting where CFOs can maintain forecasts for raw materials with large external markets, without reference to external forecasts.
Speed and frequency
While in practice it is difficult to predict prices and quantities, especially in some markets, finance managers can prepare even better for the future by analyzing best and worst case scenarios. These can
be performed to estimate the impacts of these scenarios on the overall supply and demand of a company’s portfolio, products, supply categories and even specific geographies.
Finally, there is the importance of speed and frequency. While one source of competitive advantage is having the most informed forecasts, another is having the most consistent updates.
Quickly determining the implications of supply or demand shocks will become a source of arbitrage. For example, by adjusting inventory management forecasts faster than competitors in response to an event, companies can capture additional market share or, alternatively, reduce the opportunity costs of otherwise inactive inventory.
In short, accurate and profitable forecasts do not come from a single methodology or a single data set. Instead, it is an ongoing process of bringing together vast amounts of disparate knowledge. By understanding the build versus buy decisions involved in making these forecasts, CFOs and their businesses can better
plan and prepare for future uncertainties.