Editor's Note:John Frechette is an economist and founder of Sourced Economics, a research firm based in Arlington, Virginia, that provides consulting and analytical services to corporate strategy, finance, and supply chain departments. The views expressed in this article are solely those of the author.

The current turbulent economic environment is continuously raising the costs and workload for CFO finance teams in preparing forward-looking financial plans, making forecasting tasks exceptionally difficult. Corporate forecasts that previously assumed stable conditions in markets such as commodities now face significant input cost inflation and large unpredictable market shocks, highlighting the need for greater specialization in the forecasting function.

Finance teams are consequently struggling to manage a web of interconnected complex spreadsheets and software models, which together generate supply and demand forecasts and influence every aspect of business operations.

However, companies that focus on the "make-or-buy" decision in their forecasting processes can hope to weather the storm. In reality, these teams often choose to build in-house for various reasons, ultimately leading to over-vertical integration of forecasting operations.

Let's step back. The make-or-buy decision is analyzed daily in manufacturing and development operations. Yet, forecasting undergoes a process similar to converting raw materials into products. Information about national output forecasts is procured explicitly or implicitly, while internal knowledge is used to transform this information into business unit forecasts. Over-reliance or under-reliance on external "suppliers" can severely impact the accuracy of finance teams' forecasts, which in turn affects planning and budgeting decisions.

Undoubtedly, especially during periods of volatility and economic uncertainty, the forecasting process deserves more attention. Believe it or not, it is not uncommon for finance teams to maintain forecasts of global commodity prices on their own, based on proprietary but patchwork methods.

Understanding where market forecasts (leveraging available external forecasts) should end and where corporate forecasts (leveraging internal proprietary knowledge) should begin can lead to significant improvements.

Transaction Cost Theory

This make-or-buy decision is key to optimizing the forecasting process and building operational resilience in the face of uncertain economic conditions.

According to transaction cost theory, when internal transaction costs are lower relative to the costs of contracting and exchanging with suppliers, companies choose to "make"; otherwise, they choose to "buy." In other words, markets can be costly, and in certain processes, internal governance is more economical. When forecasting does not involve proprietary corporate knowledge, external sourcing should be sought.

The goal of finance teams in "buy" decisions is to obtain more informed forecasts for increasingly relevant markets. Rather than relying on generic forecasts of energy prices, forecasts of light sweet crude oil prices (where applicable) from reliable sources should be preferred. Forecast sources can be as simple as a website or government publication, among many other possibilities.

When forecasting requires proprietary knowledge of business operations, customers, or products, it should be developed internally. Here, the role of finance teams in "make" decisions is to continuously explore ways to combine external forecasts with internal proprietary information to generate increasingly accurate and company-specific forecasts.

A clear example is demand planning, where technical systems may forecast using only internal historical records. Another example is cost forecasting, where financial managers may maintain forecasts of raw materials (with large external markets) based solely on internal data, without referencing any external forecasts.

Speed and Frequency

Although in practice forecasting prices and quantities (especially in specific markets) is quite difficult, financial managers can better prepare for the future by analyzing best-case and worst-case scenarios. These analyses can be used to estimate the impact of these scenarios on the company's overall portfolio supply and demand, products, procurement categories, and even specific regions.

Finally, the importance of speed and frequency cannot be overlooked. One source of competitive advantage is having the most informed forecast, and another is having the most frequently updated forecast.

Quickly determining the impact of supply and demand shocks will become an arbitrage opportunity. For example, if a company can adjust its inventory management forecasts based on events faster than competitors, it can capture more market share or reduce the opportunity cost of idle inventory.

In short, accurate and cost-effective forecasting does not come from a single methodology or dataset, but rather is a process of continuously integrating vast amounts of dispersed knowledge. By understanding the make-or-buy decisions in forecast development, CFOs and their companies can better plan and prepare for future uncertainties.