A colleague recently shared this LinkedIn article by Jack and Suzy Welch with me. I receive many article recommendations daily, but this one included Jack Welch’s name and the catchy title “This is the Most Mind-Numbing Ritual in Business (But Doesn't Have to Be)” convinced me to take the time to click on it. The article argues that companies should ground their annual budgeting exercise in reality and link it to actual operational what-if planning that can energize corporate and field teams behind common goals. This is not new territory for those who have read Jack: Straight from the Gut. Jack provides a lot of detail on how he conducted GE’s annual budgeting exercise in the book. On that note, it’s an excellent read for anyone wanting to transform their budgeting process.
However, the article set me thinking about why anyone would restrict this grounded-in-reality, market-based approach to an annual exercise. With the rate of change most businesses deal with today, it is undoubtedly time well spent to have this same discussion. Can we at least agree to omit sections of the budgeting process when there are no significant changes in the business environment?
The key to conducting operational planning more frequently is adopting processes that allow stakeholders to express their viewpoints on important business metrics with accompanying analysis, assumptions, and plans. The teams then review these inputs to arrive at a consensus plan.
On the demand side, key inputs I proactively engage with my clients on include:
- Statistical Forecasts – Leverages one or more statistical models validated upfront, typically managed by Supply Chain teams.
- Customer Forecasts – Gathered from a Collaborative Planning, Forecasting, and Replenishment (CPFR) process, typically managed by Supply Chain teams.
- Account Management Forecasts – Gathered in meeting with Key Account teams (typically managed by Sales Operations teams.
- Market-based Forecasts – Generated using syndicated data that represents the overall market size, typically managed by Category teams.
Given each of these is done at different levels (e.g., Market-based inputs at Subcategory by Month level, whereas an account team provided their forecast at Key Account by Month x Key SKUs level), there needs to be a standard way to compare and contrast these forecasts at a uniform level of granularity to then drive a consensus. Teams accustomed to looking at absolute numbers need to get comfortable thinking about ranges. For example, if each of these forecasts is in a range of +/- 5%, you’ve got a winner; choose a number and discuss assumptions, upside scenarios, and execution plans.
Suppose the ranges are further apart, as they are apt to be given a wide range of inputs, a robust discussion of assumptions, and Forecast Value Add (read: “Who recently did the best job of forecasting?”) becomes critical. Based on these inputs, a consensus number can be reached with an accompanying storyline on assumptions that need to pan out to make that outcome possible. However, remember that the consensus number is typically at an aggregate level and that operationalizing the forecast for execution purposes will require it to be disaggregated down to the lower levels (e.g., Category->SKU, Month->Week, Account->Store).
If this sounds like a monumental challenge—and I have had clients tell me it does—that might explain why Jack recommends an annual instead of a monthly consensus process. But shouldn’t we be able to leverage modern technology to meet this challenge? And if so, what would be the core set of capabilities it must fulfill? I have posited an initial below for your consideration:
- Allow each department to create a forecast based on their worldview. Moreover, restrict sharing of that information until the appropriate time.
- Collaborate with internal and external partners
- Serve as a repository for syndicated Market Data and leverage that to develop forecasting algorithms
- Aggregation/Disaggregation
- Contextual assumption creation and sharing
- Versioning forecasts
- Accuracy reporting such as Forecast Accuracy, Forecast Value Add
- What-if planning – What’s the sensitivity of the forecast to a change in a particular variable?
- Scenario planning – How will changes in business drivers impact input variables, affecting the output forecast?
If I missed something, please let me know in the comments.
At o9, we used these requirements to architect our decision management platform, mPower. The result is that large-scale enterprises looking to adopt a more rapid budgeting process can now do so at a frequency aligned with this world's pace, all without increasing the burden on the workforce.
Lastly, for you finance folks, if "mark to market" is your rallying cry, we hear you. Come with us, and we’ll take you on a journey that moves your company toward an ideal, reality-based budget.
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