Your on-time-in-full score sits at a respectable 94%. Leadership celebrates the improvement. Customer complaints, meanwhile, keep climbing. Something doesn't add up.

The disconnect isn't a mystery—it's a measurement problem. Standard OTIF calculations aggregate performance in ways that obscure rather than illuminate. They tell you what your average delivery looks like, but average deliveries don't exist. Real customers experience specific failures with specific consequences, and those details vanish into the aggregate.

The metrics aren't lying, exactly. They're answering questions you didn't mean to ask. When you calculate a single OTIF number across thousands of orders, dozens of customers, and multiple product categories, you create a statistical phantom that represents no one's actual experience. Breaking free from this measurement trap requires understanding where aggregation distorts reality—and building diagnostic metrics that reveal where your supply chain actually needs attention.

Consider a company with two customer segments. Strategic accounts receive 99% OTIF performance because planners prioritize their orders. Smaller accounts experience 85% performance because they absorb the variability. The blended metric shows 92%—a number that describes neither group accurately.

This distortion compounds across dimensions. Product categories with stable demand inflate the average, masking chronic problems with promotional items. Strong performance during normal periods obscures terrible results during peak seasons. The aggregate improves even as specific failure modes persist uncorrected.

The statistical crime here isn't averaging itself—it's losing the distribution. A 94% OTIF could mean consistent 94% performance everywhere, or it could mean half your customers get perfect service while the other half suffers. The mean tells you nothing about the spread, and the spread is where improvement opportunities hide.

Segment-level reporting reveals these hidden patterns. Break OTIF by customer tier, product family, geographic region, and time period. Look for variance as much as level. A segment showing 90% OTIF with low variance might need less attention than one showing 95% with high variance—the inconsistency signals an unstable process that could deteriorate without warning.

Takeaway

Never report a single OTIF number without its segments. The aggregate tells you how to feel; the breakdown tells you where to act.

A late delivery is a symptom, not a diagnosis. The shipment might have left the warehouse late due to inventory shortages. The warehouse might have had stock, but picking delays pushed the shipment past the carrier cutoff. The carrier might have picked up on time but encountered transit delays. Each failure type requires different interventions.

Effective root cause attribution requires instrumenting the order lifecycle. Track promise-to-plan (did planning create a feasible schedule?), plan-to-execution (did operations execute the plan?), and execution-to-delivery (did external factors hold?). This decomposition transforms OTIF from a lagging indicator into a diagnostic tool.

The framework extends to in-full failures as well. Distinguish between no-stock situations (demand exceeded available inventory), allocation conflicts (inventory existed but was committed elsewhere), and quality holds (inventory existed but couldn't be released). Each category points to different functional owners and different corrective actions.

Building this attribution capability requires process discipline as much as technology. Someone must record why each failure occurred, not just that it occurred. Exception codes need standardization and training. The investment pays off through targeted improvement—instead of general pressure to 'improve OTIF,' teams receive specific feedback on the failure modes they control.

Takeaway

Treat every delivery failure as data, not just a problem. Systematic root cause coding converts complaints into a prioritized improvement roadmap.

Not all delivery failures carry equal consequences. Missing a routine replenishment to a well-stocked distributor differs fundamentally from missing a critical component for an automotive assembly line. Standard OTIF treats these as equivalent—both count as one failure. Business impact weighting corrects this distortion.

Start by classifying delivery criticality. Some orders support customer safety stock and tolerate delays. Others feed just-in-time operations where late delivery triggers production shutdowns. Emergency orders carry implicit urgency regardless of customer size. This classification can come from customer contracts, product characteristics, or explicit criticality flags on orders.

Layer in customer value dimensions. Revenue contribution matters, but so does strategic importance and growth potential. A delivery failure to a key development account might damage more future value than a failure to a larger but stable relationship. Construct weighting factors that reflect these priorities and apply them to calculate an impact-weighted OTIF.

The resulting metric aligns supply chain performance with business outcomes. Teams focus improvement efforts where failures hurt most. Resources flow toward protecting critical deliveries rather than chasing aggregate numbers. When impact-weighted OTIF diverges from standard OTIF, you've found exactly the gap between operational activity and strategic value creation.

Takeaway

Weight your metrics by what failures actually cost. An unweighted OTIF optimizes for counting orders; a weighted OTIF optimizes for protecting customer relationships.

The problem with OTIF isn't the metric—it's how we've chosen to calculate it. Aggregation hides variation, lack of root cause data prevents targeted action, and equal weighting misaligns effort with impact.

Fixing this requires more than dashboard redesign. It demands a philosophical shift from performance reporting to performance diagnosis. Good metrics should make you uncomfortable precisely where improvement is needed, not comfortable everywhere through the magic of averaging.

Build segmented views that expose variation. Instrument your order lifecycle to capture why failures happen, not just that they happen. Weight outcomes by business impact. The result won't be a prettier number—it will be a more useful one.