Rising inflation. Market uncertainty. Are we approaching an economic AI bubble? A market downturn like the ecommerce bubble in 2000 where it took supply chain organizations over six months to align to a new market reality? I think so.

Reflection

This week, economist Mohamed El-Erian warned that the while the underlying system of the evolving economy is intact, that investors should brace for significant individual losses within the Artificial Intelligence (AI) sector. As more and more economist warnings flood my inbox, each day, I feel that a market downturn is more likely.

Investment in Generative AI infrastructure in North America in the last two years exceeded $500B. Much like the investment in fibre optic telecoms infrastructure in the late 90’s and early 2000’s, value will be derived, but not during the hype cycle.

Although a market crash would surprise almost nobody, few have thought about its consequences. The root of the vulnerability for supply chains is the impact on the American consumer. This paragraph from The Economist resonnated with me:

“Stocks account for 21% of the country’s household wealth—about a quarter as much again as at the height of the dotcom boom. Assets related to AI are responsible for nearly half the increase in Americans’ wealth over the past year. As households have become wealthier, they have grown comfortable saving less than they did before the covid-19 pandemic (albeit not as little as during the subprime boom). The Economist projects that a fall in stocks comparable to the dotcom bust would reduce American households’ net worth by 8% resulting in a reduction in consumer spending. By one rule of thumb, the pullback would amount to 1.6% of GDP—enough to push America, where the labour market is already suffering, into a recession. “

Source Alvaro Bernis, The Economist, November 2025

We are in the middle of the AI hype cycle. AI is everywhere, but nowhere in supply chain planning. Many pilots, lots of confusion, and hype.

It has been estimated that this investment has added 4% to US GDP in the last months, and without that investment, growth would be flat. If the stock market bubble bursts expect tech stocks to crumble with impact on crypto.

What Does This Mean for Planners?

When markets plunge, growth slows, demand latency and volatility increase, bullwhip grows, and supply becomes more eratic. Internal teams become more political and less collaborative.

The reality is that supply chain teams pedal better uphill than downhill. Internal pressures are intense. The average team takes six months to align and focus on a market reality.

To prepare, ensure that you have a clear planning system of record. For example, SAP IBP has many limitations as a modeling technology, but performs well as a system of record. (A system of record synchronizes and enables the visualization of the output of multiple planning technologies for business leaders that are not planners to access.)

Prepare to run “what-if analysis” continuously using some version of a digital twin. The digital twin should run in parallel to your normal batch planning jobs, and enables ad hoc what-if analysis. Don’t limit your planning to scheduled batch jobs. Instead, turn to solutions like Gains, Hack and Craft (H&C), Lyric, Optilogic, or Riverlogic to help you build digital twin modeling.

Brace For An Increase in Politics

While finance will attempt to elongate payables and squeeze price, sales teams will ask for more investment in promotions/price incentives. Debates will be heated. Sidestep irrational politics, and focus on tracking and improving core planning metrics.

Reduce Waste and Improve Value

Communicate progress on planning metrics regularly. Get good at marketing results. Translate the improvement to currency and measure/market operating margin improvements through better planning. Here is where I would start:

• Forecast Value Added (FVA). Shift from a focus on forecast error to measure Forecast Value Added (FVA) by demand stream. Recognize that demand has many streams. (A recent video by o9 gives a good explanation.) Define and measure each stream based on forecastability patterns. Strive to reduce obsolescence. Measure the impact of improvements in the demand stream to waste reduction in currency (dollars and euros saved) and margin impact.
• Inventory Health. Translate FVA insights into inventory holding strategies. With market turbulence, lead times will change in unpredictable ways. If intransit inventory slows, account for the impact of growing in-transit inventories on cash-to-cash measurements. Use lead time actuals in the calculation of safety stock. As FVA analysis matures, identify the impact of FVA improvements on inventory waste of old and new processes. Focus on improving inventory health.
• Order Fulfillment. Shorts and Backlog. Returns. Focus on root cause analysis to maximize order reliability to commit dates.
• Leadtime. Collect data on inbound shipments (planned versus actual arrival), purchase order variance (planned versus actual) and manufacturing lead time (planned versus actual). Use machine learning to update leadtimes based on market conditions. Publish and track actual leadtimes to synchronize the decision making for procurement, logistics, and outsourced manufacturing. Push your findings into your planning batch logic.
• Bullwhip. Measure and track the bullwhip at each node. Continously model to analyze how to reduce the bullwhip. Measure progress and communicate the results to the organization. Where possible quantify the impact of the bullwhip on margin and customer service.
• Measure and Analyze Complexity. Complexity is like cholesterol. There is good and bad complexity. Good complexity drives customer and brand loyalty while bad complexity drags down margin. Use the what-if analysis in your digital twin to analyze the impact of potential product portfolio impacts on margin and cash-to-cash.

Summary

An economic downturn often results in irrational behavior. As fools rush in, dazzle them with progress being made in supply chain planning.

Update on Outside-in Planning

This week, I wrap up my fourth year of teaching outside-in planning. The number of class participants now tops 350. I think that this may be my last class. I will be placing the models in AskLora in January. Over the next couple of months, I will be working with developers to use the combination of Agentics and Generative AI to build interactive maturity models using my research. (Stay tuned.)

The good news is advancements in semantic reconciliation now enables market-to-market bi-directional orchestration without a company having to turn to custom models. The bad news is that traditional planning technologists cling to traditional planning definitions like Linus clasps his blanket. When students join the class, there are many misconceptions:

• Outside-in Planning Redefines Demand. Contrary to popular belief, outside-in planning redefines both demand and supply processes using market data. It is just as important for the redefinition of supply as demand. The goal is self-service insights at the speed of business for business leaders. The redefinition of work reduces data latency and improves the visualization/portability of insights across supply chain roles.
• Outside-in Initiatives Hinge on Change Management. We quickly debunk this myth early in the class. Building outside-in processes and improving the usability of market data requires unlearning. Teams must first unlearn the current planning paradigm to adapt to new opportunities. The good news is the rapid improvements in technology make this easier than it was four years ago when I started teaching the classes. The bad news is that aggregate supply chain acumen is much lower than today than a decade ago. Academic programs in business schools are not equal to the challenge on online programs like those pushed recently from CSCMP on LinkedIn learning do not close the gap.
• Not a Big Change. The outside-in process changes discussed redefine supply chain planning to improve value. As a result, it is not an evolution. Instead, it is a step change requiring the learning of a new vocabulary.

So, how do we evolve to outside-in processes while redefining our relationship with data, code, and models? The answer is evolving, but it starts with a clear vision of what is possible. To do this, we need to step away from the current definitions of work and redefine organizational workflows holistically. Role redefinition –a demand planner, a supply planner, procurement buyer, manufacturing planner, S&OP planning–should not be redefined in isolation. Instead, it requires the redefinition of work.

If you are reading this research, I would love your help to participate in research on the redefinition of supply chain platforms. My goal is to give to you through my writings when you give to me by filling out research studies. My business model is base on I give to you and you give to me. I will share the results of the survey openly here on this blog and through future reports/training. Click the link to share your insights.

The survey is open to all–business users, technologists, and consultants. All respondent data will be shared confidentially without attribution. If you can complete the survey, a big thanks!