AI for Engineering Productivity
Most engineering dashboards track activity, not delivery risk. Here are the KPIs backed by real benchmark data, and the vanity metrics to drop.
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9 min read
Dr. Maor Farid
Maor Farid is the Co-Founder and CEO of Leo AI, the first AI platform purpose-built for mechanical engineers. He holds a PhD in Mechanical Engineering and completed postdoctoral research at MIT as a Fulbright fellow. A Forbes 30 Under 30 honoree and former AI researcher and Mechanical Engineer in an elite military intelligence, Maor leads Leo AI's mission to transform how engineering teams design better products faster.
BOTTOM LINE
Most engineering dashboards are full of activity metrics: hours logged, ECOs closed, drawings released. They feel like progress because they move every week, but none of them predict delivery. The metrics that do, ECO cycle time, design review turnaround, ECO cost as a share of NPD cost, first pass yield, and time-to-find-an-answer, share one property: they measure waiting, rework, or friction rather than effort. A small set of four to six metrics, mixing leading indicators with one trusted outcome measure, reviewed on a fixed cadence, catches a slipping program weeks before a status report would.
Most engineering leaders have a dashboard. Fewer have one that tells them, three weeks before a milestone slips, that it is about to slip. The gap between those two things is not a tooling problem. It is a metric-selection problem. Teams default to the numbers that are easiest to pull from a PLM or ticketing system, then treat a healthy-looking dashboard as evidence the program is on track, right up until it is not.
This piece separates the engineering KPIs that correlate with delivery from the ones that mostly measure how busy a team looks, using published benchmark data where it exists rather than intuition.
The Metrics That Feel Like Progress But Do Not Predict Delivery
Hours logged is the most common offender. It rewards time spent, not problems closed, and a team can log a full week against a task that has not moved because the actual blocker (a missing test fixture, an unanswered question to a supplier, a part that will not mate) never showed up in the timesheet. Two teams can log identical hours in a sprint and be in completely different positions on the same milestone.
Count of ECOs closed has the same flaw from a different angle. It looks like throughput, but a team under schedule pressure can hit a closure target by batching trivial changes and deferring the difficult ones, which inflates the number while leaving the riskiest work exactly where it was. The same is true of drawings released per week: it counts output, not whether that output survives the next design review without rework.
Meeting count and status-report cadence measure communication overhead, not progress, and a program with more meetings is not more on track than one with fewer. None of these numbers are useless on their own. The problem is treating them as leading indicators of delivery when they are, at best, activity logs. A team can max out every one of them and still miss the milestone, because none of them measure whether the underlying design work is converging toward a state that can actually ship.
IN PRACTICE
We're less dependent on outsourced engineers. We do it all in-house. We get answers in a few minutes instead of a few days.
- Harel Oberman, CEO, Oberman Industrial Designs
Cycle Time Metrics That Actually Move: ECO Cycle Time and Design Review Turnaround
Engineering change order cycle time, the days from a change request landing to that change being implemented in production, is one of the few metrics with real published benchmark data behind it. APQC's cross-industry benchmarking puts the median ECO cycle time at 7.0 days across a sample of more than 4,000 companies. That number is less useful as an absolute target than as a baseline: what matters for prediction is whether a specific program's ECO cycle time is trending up or down relative to its own history as it approaches a milestone. A program where ECO cycle time is climbing week over week is accumulating a backlog of unresolved changes, and that backlog is a far better early warning than a flat "on track" status update.
Design review turnaround, the time between a design being submitted for review and a review verdict coming back, behaves the same way and is easier for most teams to instrument since it usually lives inside the same PLM workflow that already timestamps review requests and approvals. A lengthening review queue almost always shows up weeks before the schedule slip it causes becomes visible in a Gantt chart, because the queue is the leading edge of the same bottleneck.
Both metrics share a property that makes them more trustworthy than activity counts: they measure the time a piece of work sits waiting on a decision, not the effort put into producing that piece of work. Waiting time is what turns into schedule risk, and it is measurable from timestamps that most PDM and PLM systems already capture without any new instrumentation.
The Cost Metric Almost No One Puts on a Dashboard
ECO cost as a share of total new product development cost rarely appears on an engineering dashboard, even though it is one of the more direct measures of how much rework a program is absorbing. APQC's benchmark puts the median at 10.0 percent of total NPD cost across a sample of 325 companies, meaning a meaningful share of program budgets go to changing decisions that were already made rather than to making new ones. That number does not distinguish between a change driven by a supplier substitution and a change driven by a design error caught late, but a rising trend in either case means cost that was not planned for is being absorbed somewhere in the schedule.
The reason this metric matters for prediction specifically, not just for cost control, is the well documented cost-of-change curve: a change caught in a design review is inexpensive, the same change caught after tooling is cut is not, and the same change caught after a customer ship is the most expensive version of all. A dashboard that tracks only how many changes happened, without tracking how late in the process they happened, cannot distinguish a healthy program that catches its own mistakes early from a program quietly pushing risk downstream into a more expensive phase.
First pass yield, the percentage of units or assemblies that pass inspection or test without rework, is the manufacturing-side complement to ECO cost. A falling first pass yield on a part that has been in production for months is often the first visible sign that an upstream engineering decision, not a shop floor problem, is the actual cause, and it tends to surface before the ECO that eventually corrects it does.
Search Time and Tribal Knowledge: The Leading Indicator Hiding in Plain Sight
None of the metrics above capture a cause that shows up earlier than any of them: how long it takes an engineer to find out whether a problem has already been solved. Engineers report spending roughly 35 percent of their time redesigning parts that already exist somewhere in the organization (engineering part reuse), simply because the part could not be found in time. That is not a search-box inconvenience. It is time that does not show up as "blocked" in any tracking system, because the engineer is not idle, they are working, just on a problem that a faster search would have made unnecessary. A PDM system with weak search (PDM search that cannot find parts) turns every design decision into a small research project, and the cumulative effect of thousands of those small delays is exactly the kind of schedule slip that an hours-logged metric will never catch, because the hours were logged.
This is the specific gap Leo closes as an AI intelligence layer on top of an organization's existing PDM, PLM, and technical documentation, rather than a replacement for any of them. When an engineer can ask a direct question against the organization's own drawings, standards, and prior design decisions and get a cited answer in minutes, the search-and-rediscovery tax that normally hides inside "engineering hours" starts showing up as fewer redundant designs and fewer late-stage surprises traced back to a decision someone else had already made. A KPI dashboard that never measures time-to-find-an-answer is missing one of the earliest signals available for whether a program is about to slow down.
Building a Small KPI Set Engineering Leaders Will Actually Use
A dashboard with twenty metrics gets ignored, and a dashboard with one metric gets gamed. Somewhere between four and six is the range where a KPI set still gets checked every week and still resists being optimized in a way that hides the real problem. A workable set mixes both directions on purpose:
One waiting-time metric, such as ECO cycle time or design review turnaround, to catch bottlenecks before they show up in a status report.
One rework metric, such as ECO cost as a share of NPD cost or first pass yield, to catch decisions that are being remade rather than made.
One knowledge-friction metric, such as time-to-find-an-answer or the share of designs that duplicate an existing part (part standardization), to catch the delays that never register as "blocked."
One outcome metric that everyone already trusts, such as schedule adherence against the committed milestone, so the leading indicators above have something concrete to be checked against.
The set should get reviewed on a fixed cadence, not whenever a program looks like it might be in trouble, because that is precisely when a team is most tempted to reach for the metric that looks best rather than the one that is most honest. It is also worth revisiting the set itself every few quarters: a metric that was a genuine leading indicator on one program's structure, say a hardware-heavy build with a long supplier chain, can become close to meaningless on a software-heavy one with a completely different change-management flow (the engineering to manufacturing handoff). And on programs with deep, nested assemblies, cost rollups (multi-level BOM management) deserve their own line rather than being folded into a single generic cost metric. The goal is not a permanent dashboard. It is a small, current one that a team actually looks at before the milestone, not after it.
FAQ
Engineering change order cycle time and cost benchmarks: APQC, Engineering Change Order (ECO) Cycle Time in Days and APQC, Engineering Change Order (ECO) Costs as a Percentage of Total New Product Development Cost.
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