A plant-wide OEE number can look perfectly healthy while individual products underneath it are quietly losing money on every batch. Averaging performance across every SKU hides exactly the detail a plant manager needs most, because a fast-running commodity item can mask a complex, low-margin product that struggles every time it comes up on the schedule. Breaking OEE down by product, recipe, and batch exposes that hidden loss directly, and you can book a demo to see your own product mix analyzed this way.
Plant-Wide OEE Hides the Products Actually Losing You Money
iFactory breaks OEE down by product, SKU, recipe, and batch so hidden losses from changeovers, speed variation, and quality issues are visible at the level where they actually happen.
Why a Healthy Plant-Wide Number Can Still Be Misleading
Plant-level OEE is a useful headline metric, but it is calculated as a blend across every product a line runs, which means a strong-performing high-volume SKU can offset a weak-performing specialty product without anyone noticing the imbalance. Over time, that hidden product mixes into pricing decisions, capacity planning, and profitability reports that all quietly assume every SKU behaves the same way on the line.
The Losses a Plant-Wide Number Cannot Show You
Products that require frequent recipe or tooling changes accumulate changeover losses that a blended average spreads thin and hides.
Some SKUs only run well below rated speed, and that gap disappears when averaged against faster-running products on the same line.
Certain formulations or batch sizes generate disproportionate scrap, a pattern only visible once quality is tied back to the specific SKU.
Infrequent SKUs are often the least understood, since there is rarely enough isolated data to know how they truly perform.
Find Out Which Products Are Actually Costing You OEE
iFactory breaks your blended plant average down to the SKU level so hidden losses become visible and fixable.
An Example of What SKU-Level OEE Analysis Reveals
| Product Category | Typical Loss Driver | Recommended Focus |
|---|---|---|
| High-volume staple | Minor speed loss during long runs | Micro-stop reduction |
| Frequent changeover SKU | Setup and changeover time | Changeover standardization |
| Complex formulation | Batch-to-batch quality variation | Process parameter control |
| Low-volume specialty | Limited operator familiarity | Documented run procedures |
Turning Batch Data Into Product-Level Insight
Every production run is automatically tagged to its SKU, recipe, and batch so OEE data never has to be manually sorted afterward.
Time lost to changeovers is attributed to the specific product transition responsible, not lumped into general downtime.
First-pass yield and scrap rates are tied directly to product and batch identifiers for accurate profitability analysis.
OEE data can be combined with cost data to show which products are truly profitable once real production losses are counted.
Moving From Plant Averages to Product-Level Visibility
Map the Product Catalog
Every SKU, recipe, and batch type running through the plant is catalogued and linked to production data sources.
Tag Historical Runs
Past production data is tagged retroactively where possible to establish an initial product-level performance baseline.
Track Ongoing Production
New runs are tagged automatically going forward, building a continuously growing product-level performance record.
Act on Product Insights
Underperforming SKUs are flagged for process review, pricing reconsideration, or scheduling changes based on real data.
Outcomes Reported After Product-Level OEE Analysis
Questions Teams Ask About SKU-Level OEE Analysis
Stop Letting Averages Hide Your Worst-Performing Products
iFactory shows you exactly which SKUs are costing OEE and profit, and why, down to the batch.
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