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 will be released during the Spring Supply Chain Forum and be 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 forthcoming 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.
In Part 2, we examine how leading-edge companies are moving beyond forecasting as the default demand trigger.
Written by UT professors in collaboration with GSCI partners, our 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 with us to explore advanced supply chain management concepts, visit ASCC.
Contact
Related News
Beyond the Forecast: Rethinking Demand-Driven Planning
Benchmark supply chains are shifting from internally driven planning to a Right-to-Left model synchronized with actual consumption. The result: lower...
Read ArticleTariffs, Protectionism, and Uncertainty: How US Economic Policy Affects Global Supply Chains
Economics professor Georg Schaur joined Tom Goldsby on stage at the Fall Supply Chain Forum for a discussion on the...
Read ArticleTranslating Military Leadership to Today’s Supply Chain Challenges
At the Fall Supply Chain Forum, retired Rear Admiral Kevin Sweeney discussed lessons from his 32-year Navy career and how...
Read ArticleBeyond the Headache: Smarter Returns Management with the 5Ps
The latest GSCI white paper shows that leaders can reduce costs and improve customer loyalty by managing returns holistically using...
Read Article