The cultural roots of forecasting failures

Editor’s note: This article first appeared on the University of Tennessee, Knoxville’s Global Supply Chain Institute’s blog. It is being reprinted with permission. You can read the original post here.

Author’s note: The forthcoming white paper, “Love to Hate the Forecast: Segmenting Planning Demand Triggers to Drive Total Value,” co-authored by Haslam College of Business faculty members Mike Burnette and Lance Saunders, and edited by Ted Stank and Dan Pellathy, marks the 40th white paper released by the UT Global Supply Chain Institute. The paper was released during the Spring Supply Chain Forum and is available for digital download.

I have been actively involved in supply chain work since 1980, across Procter & Gamble, consulting, lecturing, and conducting applied research at the University of Tennessee. During these decades, I have interfaced with hundreds of supply chain organizations. In the last 20 years, virtually all the company leaders I’ve interviewed have included forecasting issues as a top-five improvement opportunity. Despite continuous investment in systems and analytics, forecasting remains a widely recognized barrier to achieving business goals and a significant source of organizational frustration.

Why is forecasting such a persistent obstacle?

The Global Supply Chain Institute white paper, “Love to Hate the Forecast: Segmenting Planning Demand Triggers to Drive Total Value,” argues that the answer is not simply mathematical—it is cultural.

A professional, robust corporate volume forecasting process has been in place in leading supply chains since the 1960s. In the 1980s, organizations began formally documenting forecasting systems and measuring results. Statistical regression methods became commonplace tools for improving accuracy. Over five decades, SKU-family level MAPE—Mean Absolute Percentage Error, a common forecasting metric—has improved incrementally. Yet forecast bias has not improved at the same rate.

This reality forces us to examine two foundational questions: Are forecasts negatively impacted by organizational and functional cultural norms? And are statistical forecast models appropriate for all demand patterns?

Cultural influencers

Historically, supply chains have created at least two—and sometimes as many as four or five—aggregated forecasts covering a 1-to-12-month planning horizon. One forecast (often conservative) is shared externally with enterprise owners or shareholders. A second forecast—typically higher—is deployed internally as the business commitment. In some organizations, additional forecasts exist to support functional reward systems.

Through monthly forecast deployment, the supply chain is tasked with creating a supply plan that delivers the aggregate commitment while meeting customer service, inventory, and cost objectives. Historically, the internal business commitment is “bias high”—in other words, consistently exceeding actual demand—more than 90% of the time.

In response, many supply chains create their own SKU-level forecast through a process known as Left-to-Right (LtR) planning. LtR planning incorporates supplier capability, operational efficiencies, cost targets, and safety stock into the demand signal. Inventory buffers are built to protect against variation in demand, cycle time, lead times, and operational reliability.

While rational, LtR planning shifts the system’s focus from delivering actual consumer demand to managing internal efficiency and protection. Over time, this dynamic generates waste.

Normalized dysfunction

Unproductive behaviors often become normalized. Inventory is added to SKUs experiencing service issues. Inventory is reduced across the board during cash crises. Sales rewards are tied to beating the forecast. General manager compensation may be linked to forecast comparisons. When service or inventory targets are missed, forecast accuracy becomes the convenient explanation.

When forecasting is not working, several warning signs typically appear:

• Monthly forecast results are either bias low or high more than 60% of months
• Customer service results lag competition
• Inventory days on hand lag benchmarks
• High level of demand variation is driven internally

In these environments, the forecast is not the root problem but a symptom of deeper organizational defects.

The role of demand-supply integration (DSI)

True demand-supply integration is focused on creating a single-number business plan that every function executes. Demand plans must be based on unconstrained consumer demand; supply plans must reflect demonstrated capacity. Leadership—across demand, supply, finance, and general management—must own the process. DSI must be a disciplined decision-making drumbeat, not a reporting ritual.

Changing the culture is the first step toward eliminating waste from forecasting systems. Without eliminating internal variation and aligning incentives, new tools will simply reinforce old dysfunction.

Written by UT professors in collaboration with GSCI partners, white papers translate rigorous research into practical insights for business leaders. The institute’s applied research has been featured in Forbes, Harvard Business Review, Supply Chain Management Review, and The Wall Street Journal. To learn more about how your company can partner to explore advanced supply chain management concepts, visit ASCC.

About the author

Michael H. Burnette is a Global Supply Chain Institute fellow at the University of Tennessee, Knoxville. Burnette came to UT after a 33-year career as a supply chain executive at Procter and Gamble. Most recently, he was the P&G Global Supply Chain leader for Skin Care ($2 billion+ Olay brand) and P&G Global Supply Chain Leader for Hair Care ($4 billion Pantene and Herbal Essence brands). His supply chain leadership and expertise include supply strategy/design, manufacturing, logistics, innovation, PLCM, acquisitions and human resources.

Burnette teaches supply chain courses and manages multiple GSCI projects, including coordinating and publishing white papers based on research conducted between UT faculty and industry leaders. He is a consultant, speaker and co-author of the book Supply Chain Game Changers.

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