AI in the Chain

Introduction

For decades, forecast accuracy has been the gold standard of supply chain performance management. Teams are measured, rewarded, and often judged based on how closely their forecasts match actual demand. Significant effort is invested in improving statistical models, cleansing data, and refining planning processes to reduce forecast error.

Yet many organizations with excellent forecast accuracy still struggle with excess inventory, service failures, missed revenue opportunities, and slow responses to disruption. Paradoxically, better forecasts do not always translate into better outcomes.

The issue is not forecasting capability. It is the assumption that accurate predictions automatically lead to effective decisions.

Expert Context: Why KPIs Are Evolving

Research and practitioner insights increasingly challenge forecast-centric performance management. ASCM and MIT research emphasize outcome-based measurement rather than input accuracy. Gartner predicts a shift toward decision-centric KPIs as organizations adopt decision intelligence platforms. Lora Cecere consistently argues that forecast accuracy without execution discipline creates a false sense of control.

Across these perspectives, a shared conclusion emerges: supply chains should be measured by the quality of decisions they make, not just the quality of their predictions.

The Limits of Forecast Accuracy

Forecast accuracy is an important input, but it is an incomplete performance indicator.

Forecasts Do Not Capture Trade-Offs

Forecast accuracy measures how well demand was predicted, not how well trade-offs were managed. Decisions often involve balancing service, cost, risk, and sustainability — none of which are captured by forecast error metrics.

Forecasts Are Evaluated After the Fact

Accuracy is calculated once actuals are known. It does not reflect whether decisions made at the time were reasonable given available information.

Forecasts Do Not Measure Action

A perfect forecast creates no value if it does not lead to timely and effective action.

Practical Example:
A product forecast is highly accurate, but inventory is positioned incorrectly due to capacity constraints and delayed decisions. Despite strong forecast KPIs, service levels decline.

Practitioner Context: Nicolas Vandeput on Forecasting and Decisions

Nicolas Vandeput consistently emphasizes that forecasting should never be evaluated in isolation from decisions. In his practitioner-focused work, he highlights that forecast error metrics are diagnostic tools — not objectives. According to this perspective, organizations fail when they optimize forecast accuracy without asking whether forecasts actually improve decision-making.

Vandeput also stresses the importance of evaluating forecasts based on decision usefulness: whether a forecast helps reduce error where it matters most, removes bias that drives wrong actions, or improves downstream decisions such as inventory positioning or capacity planning.

This aligns directly with the shift toward decision accuracy. The question is not whether a forecast was statistically optimal, but whether it supported a better decision under uncertainty.

What Is Decision Accuracy?

Decision accuracy measures whether the right decision was taken, given the information available at the time, constraints, objectives, and risks.

Unlike forecast accuracy, decision accuracy focuses on actions and outcomes rather than predictions.

Key questions decision accuracy answers include:

• Was inventory positioned correctly?
• Was capacity adjusted early enough?
• Was risk mitigated proactively?
• Were trade-offs aligned with business priorities?

Where Forecast Value Added Fits

Forecast Value Added (FVA) is one of the most practical bridges between classic forecasting metrics and decision-centric performance.

At a simple level, FVA asks: did a forecasting step add value compared to a benchmark? The benchmark is often a naive model (e.g., seasonal naive, moving average, or last-year same-week) or the output of an automated statistical forecast.

Why this matters: many organizations improve forecast accuracy “on paper” through manual overrides or extra modeling complexity, but those changes may not improve — and can even harm — downstream decisions.

FVA in plain language

• If a planner override improves forecast quality vs the benchmark, FVA is positive
• If it makes the forecast worse (or adds bias), FVA is negative
• If it changes nothing, FVA is zero

Practical Example: The ‘Helpful Override’ Myth

A team manually increases a forecast ahead of a perceived promotion.

• The promotion doesn’t materialize.
• The override increases inventory and drives obsolescence.
• The baseline statistical forecast would have stayed closer to actuals.

In FVA terms, the manual step is negative. In decision terms, it produced a worse outcome even if the intent was correct.

How FVA Connects to Decision Accuracy

FVA can be extended beyond forecast error into decision outcomes:

• Did the change improve service level where it matters most?
• Did it reduce costly expedites or stockouts?
• Did it avoid excess inventory or waste?

This is the decision-centric evolution of FVA: not just “was the number better?” but “was the decision better?”

FVA and Decision Outcomes: Practical Comparison

The table illustrates a critical insight: a step can look beneficial from a forecasting perspective while still destroying value downstream. FVA highlights this early, before poor decisions compound.

Leadership Prompts

• What is our benchmark forecast for each product family (and do we trust it)?
• Which planning steps consistently show negative FVA?
• Where do overrides improve forecast error but worsen inventory or service outcomes?
• Are we measuring FVA against the KPI that matters (revenue, service, working capital), not just accuracy?

Decision accuracy measures whether the right decision was taken, given the information available at the time, constraints, objectives, and risks.

Unlike forecast accuracy, decision accuracy focuses on actions and outcomes rather than predictions.

Key questions decision accuracy answers include:

• Was inventory positioned correctly?
• Was capacity adjusted early enough?
• Was risk mitigated proactively?
• Were trade-offs aligned with business priorities?

Practical Examples of Decision Accuracy

Example 1: Inventory Positioning

An AI system recommends reallocating inventory to protect high-margin customers during a demand surge. The decision slightly increases forecast error but preserves revenue and service.

Example 2: Capacity Adjustment

Demand forecasts are uncertain, but AI recommends securing flexible capacity options early. The decision reduces upside risk even though actual demand turns out lower.

Example 3: Risk Mitigation

A supplier risk signal triggers early dual sourcing. Forecasts remain unchanged, but supply continuity is protected.

Key Insight

Perfect forecasts do not create value — correct decisions do.

How AI Enables Decision Accuracy Measurement

AI makes decision accuracy measurable by capturing context, alternatives, and outcomes.

Capturing Decision Context

AI records what information was available, what constraints existed, and which objectives were prioritized.

Evaluating Alternatives

AI simulations allow organizations to compare actual decisions with plausible alternatives.

Learning from Outcomes

AI links decisions to outcomes, enabling continuous refinement of decision policies.

New KPI Categories for 2025 and Beyond

Decision Latency

How long it takes to move from signal detection to action.

Decision Outcome Variance

The gap between expected and realized outcomes.

Value at Risk Avoided

The financial impact of proactive decisions.

Override Frequency

How often AI recommendations are overridden and why.

Practitioner Insights: Making the KPI Shift Work

Organizations that successfully shift KPIs share several practices:

1. They keep forecast accuracy as a diagnostic metric, not a performance target
2. They introduce decision KPIs alongside traditional metrics
3. They review decisions, not just numbers, in performance forums
4. They reward outcome-aligned behavior, even when forecasts are imperfect

Leadership Prompts

• Which KPIs drive behavior in our organization today?
• Where do we reward accurate predictions instead of effective decisions?
• How often do we review decision outcomes rather than forecast errors?
• Which decisions create the greatest financial leverage?

Implementing Decision Accuracy in Practice

To implement decision accuracy KPIs, leaders should:

• Identify high-impact decisions across the supply chain
• Define desired outcomes and acceptable trade-offs
• Use AI to simulate alternatives and capture decision context
• Redesign performance reviews around decision effectiveness

Final Thought

The future of supply chain performance management is not about predicting the future with greater precision. It is about making better decisions in the face of uncertainty — and measuring success accordingly.