{"id":4575,"date":"2023-08-14T10:14:52","date_gmt":"2023-08-14T08:14:52","guid":{"rendered":"https:\/\/merca.presspulp.com\/21-indicateurs-de-performance-industrielle\/"},"modified":"2026-09-16T16:29:06","modified_gmt":"2026-09-16T14:29:06","slug":"21-industrial-performance-indicators","status":"publish","type":"post","link":"https:\/\/merca.team\/en\/21-indicateurs-de-performance-industrielle\/","title":{"rendered":"The 21 industrial performance indicators"},"content":{"rendered":"

An industrial performance indicator measures the gap between what a facility was supposed to produce and what it actually produced, in terms of volume, quality, lead time, or cost. The 21 indicators selected here are divided into seven categories, ranging from equipment to team skills.<\/p>\n\n\n\n

Each one is described along with its formula, a numerical reference, and its main pitfall when interpreting the data. The references come from public sources (AFNOR, Assurance Maladie, Ipsos bva) and a study conducted using data from the Mercateam platform.<\/p>\n\n\n\n

This study covers 210 industrial sites monitored from August 2025 to August 2026. It quantifies a rarely measured aspect of production dashboards: the ability of teams to perform their duties according to established procedures.<\/p>\n\n\n\n

210<\/span>industrial sites monitored over a 12-month period<\/span><\/div>
59 298<\/span>employees whose skills have been assessed<\/span><\/div>
3.65 M<\/span>Scheduled assignments analyzed<\/span><\/div>
5<\/span>comparative sectors, from aerospace to cosmetics<\/span><\/div><\/div>\n\n\n\n
\n

How do you interpret the study's metrics?<\/p>\n

Each rate is calculated on a site-by-site basis and then summarized by sector. The median corresponds to the middle site, with half of the sites performing better. The first quartile (P25) and the third quartile (P75) enclose the middle half of the sites.<\/p>\n<\/div>\n\n\n\n\n\n\n\n

What is an industrial performance indicator?<\/h2>\n\n\n\n

An industrial performance indicator, or KPI (key performance indicator), is a numerical metric calculated at regular intervals that compares a production outcome to a target. It involves a fixed formula, a target, and a person responsible for it.<\/p>\n\n\n\n

There are two types of indicators. Outcome indicators reflect a fact that has already occurred, such as the weekly OEE or the monthly scrap rate. Capacity indicators measure the factors that influence these outcomes, thereby providing time to take action before a deviation occurs.<\/p>\n\n\n\n

A balanced scorecard combines both. The 21 selected indicators start with equipment and work their way up to the teams, and five additional, more financial indicators round out the list.<\/p>\n\n\n\n

\n

The Seven Categories of Industrial Performance Indicators<\/p>\n

\n\n\n
KPI<\/th>Family<\/th>Indicators<\/th>Nature<\/th>Question asked<\/th><\/tr><\/thead>\n
1 to 4<\/td>Facilities<\/td>TRS, TRG, MTBF, MTTR<\/td>Result<\/td>Do the machines produce when they're supposed to?<\/td><\/tr>\n
5 and 6<\/td>Productivity and Throughput<\/td>Labor Productivity, Cycle Time<\/td>Result<\/td>Is production keeping pace with demand?<\/td><\/tr>\n
7 to 9<\/td>Quality<\/td>Scrap Rate, RPP, Cost of Non-Quality<\/td>Result<\/td>Can you get it right the first time, and at what cost?<\/td><\/tr>\n
10 to 12<\/td>Costs and Timelines<\/td>Unit cost, lead time, OTD<\/td>Result<\/td>Are deliveries made on time and at a reasonable price?<\/td><\/tr>\n
13 to 15<\/td>Teams and Schedule<\/td>Absenteeism, employee turnover, schedule stability<\/td>Capacity<\/td>Are the projected headcounts in place?<\/td><\/tr>\n
16 to 18<\/td>Skills<\/td>Versatility, Job Coverage, Monopolies<\/td>Capacity<\/td>Are the people here capable of handling these positions?<\/td><\/tr>\n
19 to 21<\/td>Security and Compliance<\/td>Frequency rates, compliant assignments, up-to-date matrix<\/td>Earnings and Capacity<\/td>Are we following the rules and working with reliable data?<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n\n\n\n

What metrics should be tracked for equipment?<\/h2>\n\n\n\n

Four indicators describe the production equipment. OEE and TRG measure its efficiency, MTBF measures its reliability, and MTTR measures the time to resume operation after a failure.<\/p>\n\n\n\n

1. Total Return (TRS)<\/h3>\n\n\n\n
\n
What it measures<\/div>
The efficiency of a piece of equipment over the time it was supposed to be in production. Known as OEE in English, it is standardized in France by NF E60-182 (AFNOR, 2002).<\/div><\/div>\n
Formula<\/div>
TRS = availability rate \u00d7 performance rate \u00d7 quality rate<\/div><\/div>\n
Landmark<\/div>
85 % corresponds to the \"world-class\" standard set by Seiichi Nakajima, the founder of TPM. Most discrete manufacturing sites operate closer to 60 % (OEE.com<\/a>).<\/div><\/div>\n<\/div>\n\n\n\n

Equipment with a capacity of 90 %, operating at 85 %, and producing 95 % of good parts has an OEE of 72.7 %. The aggregate figure does not indicate where time is lost; it is its three components that pinpoint this.<\/p>\n\n\n\n

Performance depends directly on the operator: settings, micro-stops, and maintaining a steady pace. If performance drops only during certain shifts, find out who was working those shifts before opening a machine trace.<\/p>\n\n\n\n

\n
\n
\n <\/span>\n Quality & TRS<\/span>\n <\/div>\n

Boost your TRS with qualified operators<\/span> in the right place<\/h2>\n

The majority of quality and output discrepancies are due to poorly staffed positions. Visualize the real skills of each operator and assign the right people to the right jobs.<\/p>\n \n Book a demo\n <\/line><\/polyline><\/svg>\n <\/a>\n

\n \n <\/polyline><\/svg>\n Skills matrix\n <\/span>\n \n <\/polyline><\/svg>\n Up-to-date authorizations\n <\/span>\n \n <\/polyline><\/svg>\n Quick to set up\n <\/span>\n <\/div>\n <\/section>\n<\/div>\n\n\n\n

2. Internal Rate of Return (IRR)<\/h3>\n\n\n\n
\n
What it measures<\/div>
Productivity relative to total operating time, including scheduled downtime: breaks, scheduled maintenance, and periods with no orders.<\/div><\/div>\n
Formula<\/div>
TRG = effective time \/ opening time<\/div><\/div>\n
Landmark<\/div>
It is always less than or equal to the TRS, since its denominator is broader.<\/div><\/div>\n<\/div>\n\n\n\n

The gap between TRS and TRG highlights work stoppages initiated by management. If this gap widens without a reduction in workload, the site is leaving capacity unused\u2014for example, due to a lack of qualified operators to start an additional shift.<\/p>\n\n\n\n

3. MTBF (Mean Time Between Failures)<\/h3>\n\n\n\n
\n
What it measures<\/div>
The average length of time a piece of equipment operates without failure (mean time between failures).<\/div><\/div>\n
Formula<\/div>
MTBF = total operating time \/ number of failures<\/div><\/div>\n
Landmark<\/div>
Track performance on a machine-by-machine basis and by trend, never based on the fleet average.<\/div><\/div>\n<\/div>\n\n\n\n

A declining MTBF indicates aging equipment, insufficient preventive maintenance, or changes in operating conditions. When tracked on a machine-by-machine basis, it helps prioritize investments in replacement or refurbishment.<\/p>\n\n\n\n

4. MTTR (Mean Time to Repair)<\/h3>\n\n\n\n
\n
What it measures<\/div>
The average time required to get equipment back up and running after a failure (mean time to repair).<\/div><\/div>\n
Formula<\/div>
MTTR = total downtime for repairs \/ number of failures<\/div><\/div>\n
Landmark<\/div>
Analyze the distribution of durations: just a few very long procedures are enough to skew the average.<\/div><\/div>\n<\/div>\n\n\n\n

The typical causes of a high MTTR are well known: missing parts, lengthy diagnostics, and technicians who are not sufficiently trained on the equipment. Another cause is less obvious when looking at the average: the availability of the only person authorized to perform the repair.<\/p>\n\n\n\n

\n

What the data show<\/p>\n

Of 108 sites organized by sector, 105 have at least one qualification or certification held by a single active employee. In the aerospace sector, the median is 52 such exclusive holdings per site, including a median of 7 certifications.<\/p>\n<\/div>\n\n\n\n

When a task requires one of these authorizations and the authorized person is absent, the repair must wait until that person returns. Count the number of authorized technicians for each critical task: if there is only one, the MTTR for that task depends on the vacation schedule.<\/p>\n\n\n\n

When considered alongside the MTBF, it provides a complete picture of the fleet's reliability: one metric indicates how frequently failures occur, while the other shows how long they halt production.<\/p>\n\n\n\n

\n
\n
\n <\/span>\n Maintenance & reliability<\/span>\n <\/div>\n

Reduce your MTTR with authorized technicians<\/span> and available<\/h2>\n

Keep track of your maintenance teams' authorizations in real time, anticipate renewals and assign the right profiles to the right jobs to limit line stoppages.<\/p>\n \n Book a demo\n <\/line><\/polyline><\/svg>\n <\/a>\n

\n \n <\/polyline><\/svg>\n Centralized authorizations\n <\/span>\n \n <\/polyline><\/svg>\n Automatic alerts\n <\/span>\n \n <\/polyline><\/svg>\n No obligation\n <\/span>\n <\/div>\n <\/section>\n<\/div>\n\n\n\n

Productivity and Throughput Metrics<\/h2>\n\n\n\n

Labor productivity is the ratio of output to hours worked. Cycle time indicates whether this output is being produced at the rate required by demand, as expressed by takt time.<\/p>\n\n\n\n

5. Labor Productivity<\/h3>\n\n\n\n
\n
What it measures<\/div>
The volume produced per hour worked or per operator.<\/div><\/div>\n
Formula<\/div>
Productivity = quantity produced \/ number of hours worked<\/div><\/div>\n
Landmark<\/div>
Comparison from one period to the next, assuming a constant product mix and scope.<\/div><\/div>\n<\/div>\n\n\n\n

A decline is often interpreted as a shortage of staff. First, check whether the current staff can handle the most demanding positions: a site may be fully staffed in terms of numbers but understaffed in its most demanding positions.<\/p>\n\n\n\n

\n

What the data show<\/p>\n

Depending on the sector, the median number of positions held by fewer than three fully qualified operators ranges from 24.8 % in the pharmaceutical industry to 77.8 % in the automotive industry (see KPI 17 for details).<\/p>\n<\/div>\n\n\n\n

Next, cross-reference productivity with absenteeism and the staffing schedule to identify the root cause: bottlenecks, lack of training, or chronic understaffing.<\/p>\n\n\n\n

6. Cycle time and takt time<\/h3>\n\n\n\n
\n
What it measures<\/div>
The actual production time for a unit, as compared to the takt time\u2014the pace dictated by customer demand.<\/div><\/div>\n
Formula<\/div>
Cycle time = end of production \u2013 start of production; takt time = available production time \/ customer demand<\/div><\/div>\n
Landmark<\/div>
Cycle time less than or equal to the takt time.<\/div><\/div>\n<\/div>\n\n\n\n

With a takt time of 60 seconds, one part must come off the line every minute. A longer cycle time means that the production rate cannot be maintained without overtime or additional staff, while a much shorter cycle time risks creating excess inventory.<\/p>\n\n\n\n

When cycle time deteriorates at a workstation for reasons unrelated to the machine, examine the operator\u2019s recent practices. A skill that has been certified but not practiced remains recorded in the matrix, even though the operator may have lost the ability to perform the task.<\/p>\n\n\n\n

\n

What the data show<\/p>\n

We compared the skills validated at the operational level with the scheduled hours over a 12-month period, excluding cross-functional skills and considering only employees who all worked during that period.<\/p>\n

The median number of skills never assigned is 26.1 % in the pharmaceutical industry, 32.4 % in the cosmetics industry, 40.8 % in the agri-food industry, 44.9 % in the automotive industry, and 49.6 % in the aerospace industry.<\/p>\n<\/div>\n\n\n\n

Three Metrics for Quality Management<\/h2>\n\n\n\n

The scrap rate includes what ends up in the dumpster, the first-pass yield (RPP) includes what isn't good on the first try, and the cost of nonconformity converts both into euros.<\/p>\n\n\n\n

7. Reject rate<\/h3>\n\n\n\n
\n
What it measures<\/div>
The portion of production that fails inspection and cannot be salvaged.<\/div><\/div>\n
Formula<\/div>
Scrap rate = (quantity scrapped \/ total quantity produced) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
Track by position and by team, not just by line.<\/div><\/div>\n<\/div>\n\n\n\n

An increase can be attributed to three factors: setup, materials, and skill. The first two are documented in most MES systems; the third depends on whether the operator met all the job requirements at the time the batch was produced.<\/p>\n\n\n\n

\n

What the data show<\/p>\n

Of the 4,511 positions analyzed across 98 sites, 1,538 have no operator who meets 100 % of their requirements, representing 34.1 %.<\/p>\n

\n
\n\n\n
Sector<\/th>Positions Analyzed<\/th>No full-time player<\/th>Part<\/th><\/tr><\/thead>\n
Automotive<\/td>1 156<\/td>647<\/td>56,0 %<\/td><\/tr>\n
Cosmetics<\/td>255<\/td>88<\/td>34,5 %<\/td><\/tr>\n
Food industry<\/td>611<\/td>195<\/td>31,9 %<\/td><\/tr>\n
Aeronautics<\/td>2 112<\/td>533<\/td>25,2 %<\/td><\/tr>\n
Pharmaceuticals<\/td>377<\/td>75<\/td>19,9 %<\/td><\/tr>\n
Together<\/td>4 511<\/td>1 538<\/td>34,1 %<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Positions with at least one required skill or authorization configured, August 2025 through August 2026.<\/p>\n<\/div>\n<\/div>\n\n\n\n

A position without a fully qualified incumbent has no employee who possesses all of its required skills and authorizations. Either the requirement is genuinely missing from the team, or the matrix has not been filled out: in either case, no compliant assignment is possible.<\/p>\n\n\n\n

Compare this list with your rejects by station. If the quality issues are concentrated there, focus on skills first, before adjusting settings or changing materials.<\/p>\n\n\n\n

8. First-Pass Yield (FPY)<\/h3>\n\n\n\n
\n
What it measures<\/div>
The percentage of parts that meet specifications on the first pass, without rework (first-pass yield, FPY).<\/div><\/div>\n
Formula<\/div>
RPP = (first-pass conforming parts \/ parts produced) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
Always less than or equal to 100: % minus the scrap rate.<\/div><\/div>\n<\/div>\n\n\n\n

More stringent than the scrap rate, the RPP also includes successfully reworked parts. The difference between the two measures the amount of rework that the shop absorbs without it being counted as scrap.<\/p>\n\n\n\n

9. Cost of Non-Quality<\/h3>\n\n\n\n
\n
What it measures<\/div>
All expenses resulting from defects, both internal (scrap, rework) and external (returns, product recalls, claims, penalties, damage to reputation).<\/div><\/div>\n
Formula<\/div>
Cost of non-quality = scrap + rework + returns + claims and penalties<\/div><\/div>\n
Landmark<\/div>
Expressed in euros and as a percentage of revenue.<\/div><\/div>\n<\/div>\n\n\n\n

Internal costs are calculated based on production data. External costs require data from customer service and accounting, which is why they are often omitted from the calculation.<\/p>\n\n\n\n

In euros, this cost can be directly compared to a training or maintenance budget, which is not possible when using a percentage.<\/p>\n\n\n\n

How can you track production costs and deadlines?<\/h2>\n\n\n\n

The unit cost indicates how much a part costs, the lead time indicates how long the customer has to wait, and the OTD indicates whether the promised delivery date was met.<\/p>\n\n\n\n

10. Production cost per unit<\/h3>\n\n\n\n
\n
What it measures<\/div>
The total amount spent to manufacture one unit: materials, labor, energy, and depreciation.<\/div><\/div>\n
Formula<\/div>
Unit cost = total production costs \/ units produced<\/div><\/div>\n
Landmark<\/div>
Analyze on a constant-volume basis, cost center by cost center.<\/div><\/div>\n<\/div>\n\n\n\n

Increasing production rates lowers the unit cost without any real improvement, simply by spreading out fixed costs. Also track the labor cost per unit, which includes overtime and temporary replacement workers.<\/p>\n\n\n\n

When tracked over time, it measures the actual impact of continuous improvement initiatives on material waste, downtime, poorly calibrated processes, and energy consumption.<\/p>\n\n\n\n

11. Lead Time<\/h3>\n\n\n\n
\n
What it measures<\/div>
The total time from when an order is received until it is delivered to the customer.<\/div><\/div>\n
Formula<\/div>
Lead time = actual delivery date \u2013 order receipt date<\/div><\/div>\n
Landmark<\/div>
Broken down by stage: procurement, waiting, production, shipping.<\/div><\/div>\n<\/div>\n\n\n\n

When broken down, lead time generally shows that the waiting time between stages accounts for a larger portion than the production process itself. Reducing it without improving reliability in the upstream stages shifts the problem to quality.<\/p>\n\n\n\n

12. Customer Service Rate (OTD)<\/h3>\n\n\n\n
\n
What it measures<\/div>
The percentage of orders delivered on time.<\/div><\/div>\n
Formula<\/div>
OTD = (orders delivered on time \/ total orders) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
Threshold set by the client, often 95 % or higher: below this level, the client relationship becomes fragile.<\/div><\/div>\n<\/div>\n\n\n\n

As an output indicator, the OTD provides an overview of planning, production, quality control, and shipping. When it drops for no apparent reason in production, review the weeks when the assignment schedule<\/a> was the most heavily edited (KPI 15).<\/p>\n\n\n\n

HR Metrics That Drive Production<\/h2>\n\n\n\n

Absenteeism, turnover, and schedule stability measure whether the planned workforce is present and the cost of reorganizing it. These are the starting points for capacity indicators\u2014the ones for which the study provides the most insights.<\/p>\n\n\n\n

13. Absenteeism Rate<\/h3>\n\n\n\n
\n
What it measures<\/div>
The proportion of work time lost due to unplanned absences: illness, accident, or unexcused absence.<\/div><\/div>\n
Formula<\/div>
Absenteeism rate = (days absent \/ scheduled workdays) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
4.98 % in 2025 across all sectors, compared with 4.84 % in 2024 (Social Performance Observatory 2026<\/a>, Ipsos BVA for Oasys and Diot-Siaci).<\/div><\/div>\n<\/div>\n\n\n\n

A rate that consistently exceeds the national average should be cause for concern: it often indicates excessive workload, physically demanding conditions, or a lack of recognition. When tracked monthly, it highlights seasonal trends and the teams most affected.<\/p>\n\n\n\n

An overall rate combines long absences\u2014which are planned\u2014with short absences discovered when an employee starts their shift. Track them separately, because it\u2019s the short absences that require reassignment within the hour.<\/p>\n\n\n\n

Two sites with 5 % in absenteeism do not pay the same price for it. The difference stems from their ability to replace employees with qualified personnel, as measured by versatility and position coverage (KPIs 16 and 17).<\/p>\n\n\n\n

14. Turnover<\/h3>\n\n\n\n
\n
What it measures<\/div>
The rate of departures relative to the workforce.<\/div><\/div>\n
Formula<\/div>
Turnover = (departures during the period \/ average headcount) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
Break down the data by tenure and status before drawing any conclusions.<\/div><\/div>\n<\/div>\n\n\n\n

Every departure comes at a cost in terms of recruitment, onboarding, job training, and lost expertise\u2014not to mention the impact on the remaining teams. Exit interviews and performance reviews help us understand what motivates employees to leave.<\/p>\n\n\n\n

When an employee leaves, the company loses the skills that person possessed. However, versatility builds up with seniority during the first few years and then levels off, with different trajectories depending on the contract.<\/p>\n\n\n\n

\n

Percentage of employees who hold at least two positions, by length of service and contract type<\/p>\n

A position is considered mastered when the employee meets 100 % of the required competencies and authorizations.<\/p>\n

\n\n<\/line>\n0 %<\/text>\n<\/line>\n10 %<\/text>\n<\/line>\n20 %<\/text>\n<\/line>\n30 %<\/text>\n<\/line>\n40 %<\/text>\n<\/line>\n50 %<\/text>\nLess than 1 year<\/text>\n1 to 3 years<\/text>\nAges 3 to 10<\/text>\n10 years and older<\/text>\n<\/polyline>\n<\/circle>\n<\/circle>\n<\/circle>\n<\/circle>\nPermanent Contract<\/tspan> 32,9 %<\/text>\n<\/polyline>\n<\/circle>\n<\/circle>\n<\/circle>\n<\/circle>\nTemporary Work<\/tspan> 3,8 %<\/text>\n<\/polyline>\n<\/circle>\n<\/circle>\n<\/circle>\n<\/circle>\nFixed-Term Contract<\/tspan> 8,9 %<\/text>\nTemporary Employment 43.4 1Q3Q<\/text>\n<\/svg>\n<\/div>\n
<\/span>Permanent Contract<\/span><\/span>Temporary Work<\/span><\/span>Fixed-Term Contract<\/span><\/div>\n
Source: Mercateam study, active employees at the sites surveyed, August 2025 through August 2026. Employees on fixed-term contracts and temporary workers with more than ten years of service are rare; their percentage should be interpreted with caution.<\/figcaption>\n<\/figure>\n\n\n\n

For permanent employees, the percentage rises from 10.6 % in the first year to 23.5 % between one and three years, then to 31.8 % between three and ten years. After ten years, it increases by only one point.<\/p>\n\n\n\n

An employee who has been on a permanent contract for more than three years is therefore three times more likely to hold two positions than a new hire. Turnover concentrated among this group has a greater impact on flexibility than a higher turnover rate among new hires.<\/p>\n\n\n\n

The percentage of temporary workers who have been on the job for three to ten years stands at 43.4 %, higher than that of permanent employees. A likely explanation: since they are assigned to areas where there is a shortage of staff, they change jobs more often.<\/p>\n\n\n\n

15. Schedule Stability<\/h3>\n\n\n\n
\n
What it measures<\/div>
The percentage of allocations carried out as published.<\/div><\/div>\n
Formula<\/div>
Stability = (assignments unchanged between publication and execution \/ total assignments) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
To be updated weekly, with the date and time of the changes.<\/div><\/div>\n<\/div>\n\n\n\n

A schedule that is constantly being revised reflects unforeseen circumstances (absences, breakdowns, emergencies) or a plan that was drawn up without knowing who can fill which position.<\/p>\n\n\n\n

The time period during which the changes are concentrated helps distinguish between the two. This is the first piece of data to look at when trying to improve the staff scheduling management<\/a>.<\/p>\n\n\n\n

\n

Date of the last update to the schedule lines<\/p>\n

Percentage of total volume over 12 months, across all sites studied.<\/p>\n

\n\n<\/line>\n0 %<\/text>\n<\/line>\n5 %<\/text>\n<\/line>\n10 %<\/text>\n<\/line>\n15 %<\/text>\n<\/line>\n20 %<\/text>\n<\/line>\n25 %<\/text>\n<\/path><\/g>\n17,9 %<\/text>\nMon.<\/text>\n<\/path><\/g>\n18,4 %<\/text>\nTue.<\/text>\n<\/path><\/g>\n23,0 %<\/text>\nWed.<\/text>\n<\/path><\/g>\n22,1 %<\/text>\nThu.<\/text>\n<\/path><\/g>\n14,7 %<\/text>\nFri.<\/text>\n<\/path><\/g>\n2,6 %<\/text>\nSat.<\/text>\n<\/path><\/g>\n1,3 %<\/text>\nSun.<\/text>\n<\/svg>\n<\/div>\n
Source: Mercateam study, 104 locations, August 2025 through August 2026. Weekly hourly peak: Wednesday at 8 a.m.<\/figcaption>\n<\/figure>\n\n\n\n

Wednesday and Thursday account for 45 % of the most recent updates, with a peak on Wednesday at 8 a.m. On weekdays, 48 % of these updates occur between 6 a.m. and 12 p.m., when employees start their shifts.<\/p>\n\n\n\n

The schedule therefore stabilizes during the current week rather than before it begins. Friday accounts for only 14.7 % of the volume, and the weekend accounts for less than 4 %.<\/p>\n\n\n\n

\n

Scheduling lines created or modified by week, by site<\/p>\n

\n\n\n
Site Size<\/th>Sites<\/th>P25<\/th>Median<\/th>P75<\/th><\/tr><\/thead>\n
50 to 100 employees<\/td>9<\/td>119<\/td>212<\/td>344<\/td><\/tr>\n
100 to 300 employees<\/td>48<\/td>221<\/td>467<\/td>851<\/td><\/tr>\n
More than 300 employees<\/td>47<\/td>650<\/td>1 277<\/td>2 112<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Source: Mercateam study. Quartiles calculated based on all active weeks for the sites in each category.<\/p>\n<\/div>\n\n\n\n

Measurement limit.<\/strong> Successive versions of a schedule are not tracked, so the analysis does not calculate the stability rate itself. Instead, it tracks the number and timing of the most recent updates, including new creations.<\/p>\n\n\n\n

For sites of comparable size, the volume varies from one-third to three or even four times as much between the first and third quartiles. This discrepancy reflects a combination of differences between sites and week-to-week variations.<\/p>\n\n\n\n

What metrics should be used to manage production capabilities?<\/h2>\n\n\n\n

A position is considered to be properly staffed when the operator meets all its requirements: validated skills and valid certifications. Depending on the sector, a position has an average of 3.73 (pharmaceutical) to 7.20 (automotive) such requirements.<\/p>\n\n\n\n

The following three indicators are based on this strict definition. An operator who meets six out of seven requirements may work at the position every day, but is not counted as qualified.<\/p>\n\n\n\n

16. Versatility Rate<\/h3>\n\n\n\n
\n
What it measures<\/div>
The percentage of operators capable of filling at least two positions while meeting 100 % of their requirements.<\/div><\/div>\n
Formula<\/div>
Versatility rate = (operators proficient in at least 2 positions \/ total number of operators) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
Median by site ranging from 10.1 % in the agri-food industry to 20.1 % in the cosmetics industry. Mercateam study<\/span><\/div><\/div>\n<\/div>\n\n\n\n
\n

Versatility Rates by Sector<\/p>\n

Median per site and the middle half of the sites, from the first to the third quartile.<\/p>\n

\n\n<\/line>\n0 %<\/text>\n<\/line>\n10 %<\/text>\n<\/line>\n20 %<\/text>\n<\/line>\n30 %<\/text>\n<\/line>\n40 %<\/text>\n<\/line>\n50 %<\/text>\n<\/line>\n60 %<\/text>\n<\/line>\n70 %<\/text>\nCosmetics<\/text>\n<\/rect>\n<\/circle>\n20,1 %<\/text>\nAeronautics<\/text>\n<\/rect>\n<\/circle>\n16,8 %<\/text>\nAutomotive<\/text>\n<\/rect>\n<\/circle>\n14,9 %<\/text>\n0,8 %<\/text>\n67,4 %<\/text>\nPharmaceuticals<\/text>\n<\/rect>\n<\/circle>\n14,6 %<\/text>\nFood industry<\/text>\n<\/rect>\n<\/circle>\n10,1 %<\/text>\n<\/svg>\n<\/div>\n
<\/span>Median per site<\/span><\/span>From the first to the third quartile of sites<\/span><\/div>\n
Source: Mercateam study, 98 sector-specific sites and 38,282 employees, August 2025 through August 2026.<\/figcaption>\n<\/figure>\n\n\n\n

The industry classification is less important than the width of the bars. In the automotive sector, the top quartile of sites peaks at 0.8 %, while the bottom quartile reaches 67.4 %, under the same business constraints: site organization has a greater impact than the industry on the versatility<\/a>.<\/p>\n\n\n\n

Nor does the number of requirements explain the rate. The pharmaceutical industry (3.73 requirements per position) and the automotive industry (7.20) have nearly identical medians: 14.6 % and 14.9 %.<\/p>\n\n\n\n

The rate also fluctuates from year to year\u2014and not always for the better\u2014because an expired authorization removes coverage from a position even when the procedure remains under control.<\/p>\n\n\n\n

\n

Changes in Team Versatility Over 12 Months, by Site<\/p>\n

\n\n\n
Sector<\/th>Sites<\/th>Up<\/th>Stable<\/th>Down<\/th>Average change<\/th><\/tr><\/thead>\n
Aeronautics<\/td>29<\/td>34,5 %<\/td>41,4 %<\/td>24,1 %<\/td>+3.4 pts<\/td><\/tr>\n
Cosmetics<\/td>9<\/td>33,3 %<\/td>44,4 %<\/td>22,2 %<\/td>+3.3 pts<\/td><\/tr>\n
Food industry<\/td>12<\/td>41,7 %<\/td>25,0 %<\/td>33,3 %<\/td>+3.2 points<\/td><\/tr>\n
Pharmaceuticals<\/td>15<\/td>26,7 %<\/td>46,7 %<\/td>26,7 %<\/td>+0.6 pt<\/td><\/tr>\n
Automotive<\/td>25<\/td>8,0 %<\/td>72,0 %<\/td>20,0 %<\/td>-1.5 pt<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Source: Mercateam study. Up or down: a change of more than 2 points between the first four and the last four weekly readings for the period.<\/p>\n<\/div>\n\n\n\n

In each sector, between one in five and one in three sites saw their versatility decline over the course of a year. A survey limited to the\u2019annual personal interview<\/a> does not allow us to see these setbacks taking shape.<\/p>\n\n\n\n

\n

What level of versatility should you aim for?<\/p>\n

Instead of using the median for your industry, use the third quartile as a benchmark: between 32.6 % and 36.6 % depending on the industry, and 67.4 % in the automotive industry. Start with positions held by fewer than three people.<\/p>\n<\/div>\n\n\n\n

17. Position Coverage Rate<\/h3>\n\n\n\n
\n
What it measures<\/div>
The percentage of positions held by at least three fully qualified operators.<\/div><\/div>\n
Formula<\/div>
Coverage rate = (positions with at least 3 qualified incumbents \/ total positions) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
Median number of understaffed positions (with fewer than 3 employees) ranges from 24.8 in the pharmaceutical sector (Q1\u2013Q3) to 77.8 in the automotive sector (Q1\u2013Q3). Mercateam study<\/span><\/div><\/div>\n<\/div>\n\n\n\n

The three-staff member threshold is a standard rule for using cross-functional rosters: it allows for the simultaneous coverage of an absence and a time-off without assigning the task to an unqualified person.<\/p>\n\n\n\n

\n

Job Requirements and Position Coverage, by Sector<\/p>\n

Average number of requirements per position (horizontal) and median share of positions held by fewer than 3 qualified operators (vertical).<\/p>\n

\n\n<\/line>\n0 %<\/text>\n<\/line>\n25 %<\/text>\n<\/line>\n50 %<\/text>\n<\/line>\n75 %<\/text>\n<\/line>\n100 %<\/text>\n3<\/text>\n4<\/text>\n5<\/text>\n6<\/text>\n7<\/text>\n8<\/text>\nAverage Requirements per Position (Skills and Authorizations)<\/text>\n<\/circle>\nPharmaceuticals<\/text>\n3.73 required \u00b7 24.8 1Q3Q<\/text>\n<\/circle>\nCosmetics<\/text>\n4.57 required \u00b7 30.0 %<\/text>\n<\/circle>\nFood industry<\/text>\n5.12 required \u00b7 40.0 %<\/text>\n<\/circle>\nAeronautics<\/text>\n5.78 required \u00b7 52.6 1Q3Q<\/text>\n<\/circle>\nAutomotive<\/text>\n7.20 required \u00b7 77.8 1Q3Q<\/text>\n<\/svg>\n<\/div>\n
Source: Mercateam study, 98 sector-based sites, August 2025 through August 2026. Each dot represents a sector.<\/figcaption>\n<\/figure>\n\n\n\n

The five sectors are arranged in the same way along both axes. Five points are not enough to establish a pattern, but the mechanism is arithmetic: each additional requirement reduces the number of people who meet all of them.<\/p>\n\n\n\n

Be sure to measure coverage before and after any changes to the reference framework. Adding an authorization to a position may be justified, and the resulting loss of flexibility can be quantified at the same time.<\/p>\n\n\n\n

There remains significant variation from one site to another: in the automotive sector, the first quartile has 33.3 % understaffed positions, while the third quartile has 100 %. Before providing training, check for validated skills that have never been assigned (KPI 6), which is the primary source for filling vacancies.<\/p>\n\n\n\n

18. Rate of exclusive jurisdiction<\/h3>\n\n\n\n
\n
What it measures<\/div>
The number of certifications or authorizations held by a single active employee at the site.<\/div><\/div>\n
Formula<\/div>
Monopolies = exclusive authority and powers granted to a single entity; as a percentage of positions = (positions with a single incumbent \/ total positions under control) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
97 % of the sectorized sites have at least one monopoly. Mercateam study<\/span><\/div><\/div>\n<\/div>\n\n\n\n
\n

Exclusive Authority and Authorization by Sector<\/p>\n

\n\n\n
Sector<\/th>Sites<\/th>Locations with at least one monopoly<\/th>Monopolies per site (median)<\/th>Positions with a single incumbent (median)<\/th><\/tr><\/thead>\n
Aeronautics<\/td>34<\/td>100 %<\/td>52<\/td>14,3 %<\/td><\/tr>\n
Cosmetics<\/td>10<\/td>100 %<\/td>9<\/td>0 %<\/td><\/tr>\n
Automotive<\/td>31<\/td>93,5 %<\/td>8<\/td>8,3 %<\/td><\/tr>\n
Pharmaceuticals<\/td>15<\/td>93,3 %<\/td>7<\/td>5,7 %<\/td><\/tr>\n
Food industry<\/td>18<\/td>100 %<\/td>5<\/td>7,1 %<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Source: Mercateam study. Positions with a single incumbent were calculated based on sites with at least one position under control, ranging from 9 to 28 sites per sector.<\/p>\n<\/div>\n\n\n\n

The two measures do not overlap. In the field of cosmetics, the median number of positions with a single incumbent is zero, whereas the sites have a median of 9 competencies or authorizations with a single holder.<\/p>\n\n\n\n

A dual role may therefore conceal specific expertise possessed by only one person, and only the second person can bring that expertise to light.<\/p>\n\n\n\n

The aviation figure also reflects the sophistication of the standards: the more a site details its specialized processes and individual certifications, the more rare skills it identifies. In this sector, you should primarily compare yourself to your own track record.<\/p>\n\n\n\n

In the automotive sector, the average number of positions with a single incumbent (24.8 %) is three times the median (8.3 %), and the third quartile stands at 50 %. A few highly exposed sites are driving the average upward.<\/p>\n\n\n\n

These critical skills<\/a> are the first to be included in the training plan\u2014before the announced departures, rather than after.<\/p>\n\n\n\n

\n

Assess your website based on the study's criteria<\/p>\n

The Mercateam Skills Matrix calculates your versatility rate, position coverage, and skill monopolies based on your own data.<\/p>\nBook a demo<\/a>\n<\/div>\n\n\n\n

Security and Compliance Metrics<\/h2>\n\n\n\n

The frequency rate tracks reported accidents. The compliance rate and the up-to-date matrix rate verify in advance that individuals in each position possess the required qualifications and that this information is reliable.<\/p>\n\n\n\n

19. Workplace Accident Frequency Rate<\/h3>\n\n\n\n
\n
What it measures<\/div>
The number of work-related accidents resulting in lost time, relative to the number of hours worked.<\/div><\/div>\n
Formula<\/div>
TF = (lost-time accidents \u00d7 1,000,000) \/ hours worked<\/div><\/div>\n
Landmark<\/div>
Nationally, the frequency rate stood at 26.4 lost-time accidents per 1,000 employees in 2024, compared with 26.8 in 2023 (2024 Annual Report of the Health Insurance Program: Occupational Risks<\/a>).<\/div><\/div>\n<\/div>\n\n\n\n

Beyond the human aspect, every accident disrupts service, triggers an investigation, and requires teams to be reorganized.<\/p>\n\n\n\n

Do not confuse these two metrics. The frequency rate relates accidents to hours worked, while the Health Insurance frequency index relates accidents to the number of employees: only the former provides a fair comparison between sites with different work schedules.<\/p>\n\n\n\n

The TF remains a performance indicator that changes once an accident has occurred. Supplement it by tracking near-misses and using a leading indicator: assignment compliance.<\/p>\n\n\n\n

20. Compliance Rate<\/h3>\n\n\n\n
\n
What it measures<\/div>
The percentage of scheduled assignments in which the employee possesses all the skills and authorizations required for the position.<\/div><\/div>\n
Formula<\/div>
Compliance Rate = (Compliant Assignments \/ Total Assignments) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
85.7% in Q1-Q3 out of 3.65 million assignments, representing 14.3% of assignments that did not meet standards in Q1-Q3. Mercateam study<\/span><\/div><\/div>\n<\/div>\n\n\n\n
\n

Percentage of Non-Compliant Allocations, August 2025 through July 2026<\/p>\n

Assignments for which the employee does not meet all job requirements, as a percentage of the monthly total. The vertical axis starts at 10 %.<\/p>\n

\n\n<\/line>\n10 %<\/text>\n<\/line>\n12 %<\/text>\n<\/line>\n14 %<\/text>\n<\/line>\n16 %<\/text>\n<\/line>\n18 %<\/text>\nAugust<\/text>\nSept.<\/text>\nOct.<\/text>\nNov.<\/text>\nDec.<\/text>\nJan.<\/text>\nFeb.<\/text>\nMarch<\/text>\nApr.<\/text>\nMay<\/text>\nJune<\/text>\nJuly.<\/text>\n2025<\/text>\n2026<\/text>\n<\/line>\n<\/line>\n<\/line>\n<\/line>\n<\/circle>\n11,25 %<\/text>\n<\/circle>\n11,8 %<\/text>\n<\/circle>\n12,75 %<\/text>\n<\/circle>\n14,9 %<\/text>\n<\/circle>\n16,4 %<\/text>\n<\/svg>\n<\/div>\n
<\/span>Monthly Breakdown<\/span><\/span>Intermediate months not listed in detail<\/span><\/div>\n
Source: Mercateam study, 3,651,583 assignments over 12 months. Only the months marked with a dot are broken down in the data.<\/figcaption>\n<\/figure>\n\n\n\n

Over a 12-month period, 523,745 of the 3,651,583 assignments analyzed involved an employee who was missing at least one requirement, or approximately 1,435 per day. Per site, the median is 14.2 %, with the first quartile at 3.6 % and the third quartile at 31.2 %.<\/p>\n\n\n\n

The rate rose from 11.25 % in August 2025 to 16.4 % in July 2026. This increase could reflect a genuine deterioration, such as expanded reference sets or sites currently being configured; however, the aggregated data does not allow for a definitive conclusion.<\/p>\n\n\n\n

A non-compliant assignment does not necessarily pose a security risk: the missing requirement could be a quality competency or an authorization that has expired. Break down the rate by type of requirement before presenting it to the committee.<\/p>\n\n\n\n

21. Current Matrix Rates<\/h3>\n\n\n\n
\n
What it measures<\/div>
The percentage of workers for whom at least one skill has been assessed or updated within the last 12 months.<\/div><\/div>\n
Formula<\/div>
Current matrix rate = (operators evaluated within the last 12 months \/ total number of operators) \u00d7 100<\/div><\/div>\n
Landmark<\/div>
Median per site ranging from 11.1 % in the pharmaceutical sector to 54.1 % in the automotive sector. Mercateam study<\/span><\/div><\/div>\n<\/div>\n\n\n\n
\n

Percentage of operators evaluated within the last 12 months<\/p>\n

Median per site and the middle half of the sites, from the first to the third quartile.<\/p>\n

\n\n<\/line>\n0 %<\/text>\n<\/line>\n25 %<\/text>\n<\/line>\n50 %<\/text>\n<\/line>\n75 %<\/text>\n<\/line>\n100 %<\/text>\nAutomotive<\/text>\n<\/path><\/g>\n54,1 %<\/text>\n<\/line>\n<\/line>\n<\/line>\nAeronautics<\/text>\n<\/path><\/g>\n50,0 %<\/text>\n<\/line>\n<\/line>\n<\/line>\nFood industry<\/text>\n<\/path><\/g>\n42,4 %<\/text>\n<\/line>\n<\/line>\n<\/line>\nCosmetics<\/text>\n<\/path><\/g>\n24,8 %<\/text>\n<\/line>\n<\/line>\n<\/line>\nPharmaceuticals<\/text>\n<\/path><\/g>\n11,1 %<\/text>\n<\/line>\n<\/line>\n<\/line>\n<\/svg>\n<\/div>\n
<\/span>Median per site<\/span><\/line><\/line><\/line><\/svg>From the first to the third quartile<\/span><\/div>\n
Source: Mercateam study, sites with a configured skills matrix, August 2025 through August 2026.<\/figcaption>\n<\/figure>\n\n\n\n

This ratio determines the reliability of KPIs 16 through 20. The pharmaceutical and automotive sectors show similar versatility (14.6 % and 14.9 %), but the former is based on 11.1 % recent valuations, while the latter is based on 54.1 %.<\/p>\n\n\n\n

The frequency of updates is also measured in days. The median time between updates to an employee\u2019s record is 49 days, and it exceeds 118 days for one-quarter of employees.<\/p>\n\n\n\n

A skills matrix<\/a> A slightly adjusted figure masks the actual trends in both directions. Of the 23,269 employees tracked over a 12-month period, 12.8 % showed improvement and 20.2 % showed a decline.<\/p>\n\n\n\n

\n

Changes in Employees' Skills Over 12 Months<\/p>\n

Percentage of employees whose skills have improved, remained the same, or declined, by sector.<\/p>\n

\n\nAeronautics<\/text>\n9,501 employees<\/text>\n<\/rect>\n16,2 %<\/text>\n<\/rect>\n56,7 %<\/text>\n<\/path><\/g>\n27,1 %<\/text>\nFood industry<\/text>\n3,567 employees<\/text>\n<\/rect>\n14,5 %<\/text>\n<\/rect>\n69,2 %<\/text>\n<\/path><\/g>\n16,3 %<\/text>\nPharmaceuticals and Cosmetics<\/text>\n7,049 employees<\/text>\n<\/rect>\n10,8 %<\/text>\n<\/rect>\n75,2 %<\/text>\n<\/path><\/g>\n14,0 %<\/text>\nAutomotive<\/text>\n3,152 employees<\/text>\n<\/rect>\n<\/rect>\n77,4 %<\/text>\n<\/path><\/g>\n17,7 %<\/text>\n<\/svg>\n<\/div>\n
<\/span>Progress<\/span><\/span>Stagnation<\/span><\/span>Regression<\/span><\/div>\n
Source: Mercateam study, 23,269 employees tracked from August 2025 to August 2026. Automotive: 4.9 1Q3Q, up.<\/figcaption>\n<\/figure>\n\n\n\n

The aerospace sector has more downgrades (27.1 %) than upgrades (16.2 %). A downgrade can result from a downward revision of the rating or from a rating that has expired without renewal.<\/p>\n\n\n\n

Five complementary indicators, ranging from maintenance to finance<\/h2>\n\n\n\n

These five indicators are not included in the main list because they are typically reviewed by the executive committee or during the annual review. However, they do complement the performance dashboard for an industrial site.<\/p>\n\n\n\n

\n

Additional Indicators at a Glance<\/p>\n

\n\n\n
Indicator<\/th>Formula<\/th>What it illuminates<\/th><\/tr><\/thead>\n
Employee Satisfaction Index<\/td>Average score from internal surveys, out of 10 or on a % scale<\/td>Engagement, Risk of Absenteeism, and Turnover<\/td><\/tr>\n
Preventive Maintenance Ratio<\/td>(Preventive maintenance cost \/ total maintenance cost) \u00d7 100<\/td>Maturity of maintenance, reliability to come<\/td><\/tr>\n
Inventory Turnover Rate<\/td>Cost of Goods Sold \/ Average Inventory<\/td>Fixed assets, risk of obsolescence<\/td><\/tr>\n
Gross Profit Margin<\/td>((Revenue \u2013 cost of goods sold) \/ revenue) \u00d7 100<\/td>Profitability of Production Before Overhead Costs<\/td><\/tr>\n
ROI of Industrial Equipment<\/td>((Revenue generated \u2013 total cost of equipment) \/ total cost) \u00d7 100<\/td>Appropriateness of Machinery Investments<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n\n\n\n

Employee Satisfaction Index<\/h3>\n\n\n\n

Measured through quarterly or semiannual surveys, it assesses the well-being and engagement of teams. Satisfied employees are more engaged and take fewer days off\u2014provided that companies address the issues raised rather than simply collecting feedback.<\/p>\n\n\n\n

Fairness in work assignments is a key factor in this score. When only three operators are able to handle a demanding position, it is always the same ones who are assigned to it\u2014a situation that can be anticipated based on position coverage and monopolies (KPIs 17 and 18).<\/p>\n\n\n\n

Preventive Maintenance Ratio<\/h3>\n\n\n\n

The goal is to increase the proportion of preventive maintenance rather than to reduce spending, with an 80\/20 split often targeted. Every euro spent on preventive maintenance prevents emergency repairs, production line downtime, and production losses.<\/p>\n\n\n\n

A stagnant ratio may be hampered by the availability of technicians authorized to perform preventive maintenance: as soon as a breakdown occurs, they are dispatched to handle the corrective work, and the preventive maintenance round is skipped.<\/p>\n\n\n\n

Inventory Turnover Rate<\/h3>\n\n\n\n

A high inventory turnover rate means that inventory is moving and being converted into revenue. A low turnover rate ties up capital and increases the risk of obsolescence. The optimal level depends on the industry and lead times.<\/p>\n\n\n\n

Gross Profit Margin<\/h3>\n\n\n\n

As a financial barometer for the factory, the gross margin measures the profitability of production before indirect expenses. If it declines while volume remains stable, either costs are rising or selling prices are eroding.<\/p>\n\n\n\n

Track it month by month and product by product: a consolidated figure at the site level masks the unprofitable items among the profitable ones.<\/p>\n\n\n\n

ROI of Industrial Equipment<\/h3>\n\n\n\n

Calculated before the purchase, the projected ROI helps the investment committee prioritize projects. Calculated after implementation, the actual ROI confirms or refutes the initial assumptions.<\/p>\n\n\n\n

The difference between the two is also due to the teams: a new production line, whose settings few operators are familiar with, takes longer to reach its rated output. Include the training plan in your calculations from the start.<\/p>\n\n\n\n

How do you choose the metrics for your dashboard?<\/h2>\n\n\n\n

Select 5 to 10 metrics per management level, based on your 3 to 5 priority issues. A team leader doesn\u2019t need the same KPIs as a manufacturing director, and a 21-row table simply won\u2019t be read through.<\/p>\n\n\n\n

Each selected indicator must be SMART: specific, measurable, achievable, relevant to decision-making, and tracked over a defined period. Then, associate each outcome indicator with a capacity indicator that anticipates it.<\/p>\n\n\n\n

\n

Combining Performance Metrics and Capacity Metrics<\/p>\n

\n\n\n
Your Top Priority<\/th>Performance Indicators<\/th>Capacity Metrics to Monitor Concurrently<\/th><\/tr><\/thead>\n
Breakdowns and Line Outages<\/td>TRS (1), MTBF (3), MTTR (4)<\/td>Monopolies on Maintenance Certifications (18)<\/td><\/tr>\n
Non-quality<\/td>Scrap Rate (7), RPP (8), Cost of Nonconformity (9)<\/td>Positions Filled (17), Appropriate Assignments (20)<\/td><\/tr>\n
Delivery Delays<\/td>OTD (12), lead time (11)<\/td>Schedule stability (15), position coverage (17)<\/td><\/tr>\n
Absences and Substitutes<\/td>Absenteeism (13), labor productivity (5)<\/td>Versatility (16), Position Coverage (17)<\/td><\/tr>\n
Turnover and Employee Retention<\/td>Turnover (14)<\/td>Versatility by tenure (16), monopolies (18)<\/td><\/tr>\n
Audits and Security<\/td>Frequency (19)<\/td>Valid assignments (20), up-to-date matrix (21)<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n\n\n\n

A metric is only useful if it is understood by the field teams. When displayed at workstations, updated daily, and discussed during team meetings, it becomes a management tool; when displayed in an office that no one visits, it makes no difference.<\/p>\n\n\n\n

Before each review, also ask when the data used to calculate the indicator was collected. A rate calculated based on evaluations from more than a year ago reflects the site's history more than its current operations.<\/p>\n\n\n\n

Environmental indicators (energy or carbon per unit produced, water consumption, waste recovery) now complement these dashboards, driven by regulations and the requirements of contracting authorities.<\/p>\n\n\n\n

Summary table of the 21 indicators<\/h2>\n\n\n\n
\n
\n\n\n
No.<\/th>Indicator<\/th>Family<\/th>Summarized formula<\/th>Landmark<\/th><\/tr><\/thead>\n
1<\/td>TRS<\/td>Facilities<\/td>Availability \u00d7 Performance \u00d7 Quality<\/td>85 % world-class, approximately 60 % standard<\/td><\/tr>\n
2<\/td>TRG<\/td>Facilities<\/td>Effective time \/ open time<\/td>Less than or equal to the TRS<\/td><\/tr>\n
3<\/td>MTBF<\/td>Facilities<\/td>Operating time \/ number of failures<\/td>Trend by machine<\/td><\/tr>\n
4<\/td>MTTR<\/td>Facilities<\/td>Repair time \/ number of failures<\/td>Distribution of Durations<\/td><\/tr>\n
5<\/td>Labor productivity<\/td>Productivity<\/td>Quantity Produced \/ Hours Worked<\/td>With a constant product mix<\/td><\/tr>\n
6<\/td>Cycle time<\/td>Productivity<\/td>End of production \u2013 Start of production<\/td>Less than or equal to the takt time<\/td><\/tr>\n
7<\/td>Scrap rates<\/td>Quality<\/td>Rejected quantity \/ total quantity produced \u00d7 100<\/td>By position and by team<\/td><\/tr>\n
8<\/td>RPP (FPY)<\/td>Quality<\/td>Passed the first inspection \/ produced \u00d7 100<\/td>\u2264 100 % \u2013 scrap rate<\/td><\/tr>\n
9<\/td>Cost of non-quality<\/td>Quality<\/td>Scrap + rework + returns + complaints<\/td>In euros and as a percentage of revenue<\/td><\/tr>\n
10<\/td>Unit cost<\/td>Costs and Timelines<\/td>Total Costs \/ Units Produced<\/td>At constant volume<\/td><\/tr>\n
11<\/td>Lead time<\/td>Costs and Timelines<\/td>Delivery \u2013 Receipt of the Order<\/td>Step by step<\/td><\/tr>\n
12<\/td>OTD<\/td>Costs and Timelines<\/td>On-time deliveries \/ orders \u00d7 100<\/td>Often 95 % or more<\/td><\/tr>\n
13<\/td>Absenteeism<\/td>Teams<\/td>Days absent \/ total days \u00d7 100<\/td>4.98 % in 2025<\/td><\/tr>\n
14<\/td>Turnover<\/td>Teams<\/td>Departures \/ average headcount \u00d7 100<\/td>By seniority and status<\/td><\/tr>\n
15<\/td>Schedule Stability<\/td>Teams<\/td>Allocations unchanged \/ allocations \u00d7 100<\/td>45 % updates on Wednesdays and Thursdays<\/td><\/tr>\n
16<\/td>Versatility<\/td>Skills<\/td>Operators in 2 or more shifts \/ operators \u00d7 100<\/td>Median of 10.1 % to 20.1 %<\/td><\/tr>\n
17<\/td>Filling Open Positions<\/td>Skills<\/td>Positions with 3 or more incumbents \/ positions \u00d7 100<\/td>24.8 % to 77.8 % of understaffed positions<\/td><\/tr>\n
18<\/td>Monopolies on Expertise<\/td>Skills<\/td>Single-holder competencies<\/td>97 % of the affected sites<\/td><\/tr>\n
19<\/td>Frequency rate<\/td>Security<\/td>Accidents resulting in lost time \u00d7 1,000,000 \/ hours<\/td>National rate: 26.4 per 1,000 employees<\/td><\/tr>\n
20<\/td>Compliant Assignments<\/td>Compliance<\/td>Compliant assignments \/ assignments \u00d7 100<\/td>85.7 (average of Q1 and Q3)<\/td><\/tr>\n
21<\/td>Updated Matrix<\/td>Compliance<\/td>Rated within the last 12 months \/ operators \u00d7 100<\/td>Median of 11.1 % to 54.1 %<\/td><\/tr>\n
+<\/td>Employee Satisfaction<\/td>Supplement<\/td>Average score from internal surveys<\/td>Quarterly or Semiannual Survey<\/td><\/tr>\n
+<\/td>Preventive maintenance<\/td>Supplement<\/td>Preventive maintenance cost \/ total maintenance cost \u00d7 100<\/td>The 80\/20 rule is often cited<\/td><\/tr>\n
+<\/td>Stock rotation<\/td>Supplement<\/td>Cost of Goods Sold \/ Average Inventory<\/td>Depending on the sector and lead times<\/td><\/tr>\n
+<\/td>Gross margin<\/td>Supplement<\/td>(Revenue \u2013 cost of production) \/ Revenue \u00d7 100<\/td>By product and by month<\/td><\/tr>\n
+<\/td>Return on Investment (ROI) for Equipment<\/td>Supplement<\/td>(Profit \u2013 cost) \/ cost \u00d7 100<\/td>Forecast, then actual<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Benchmarks for KPIs 15 through 18, 20, and 21: Mercateam study. Other benchmarks: AFNOR, OEE.com, Ipsos BVA, Assurance Maladie. The lines marked \u00ab+\u00bb include additional indicators.<\/p>\n<\/div>\n\n\n\n

Most of these metrics are calculated using data from your ERP, MES, CMMS, or HRIS. Skills are an exception: although they are often spread across multiple files, they are used to calculate five of these metrics (KPIs 16 through 18, 20, and 21).<\/p>\n\n\n\n

To see where your site fits into the study's framework based on your own data, request a Mercateam demo<\/a>.<\/p>\n\n\n\n

\n

About the study.<\/strong> The figures labeled \u00abMercateam study\u00bb are derived from the anonymized and aggregated analysis of data from 210 industrial sites equipped with the platform\u2014representing 59,298 active employees\u2014over a 12-month rolling period (August 2025 to August 2026).<\/p>\n

Demo accounts and internal environments are excluded. A position is considered mastered when the employee meets 100 % of its configured requirements, validated skills, and valid authorizations.<\/p>\n

Each rate is calculated on a site-by-site basis and then summarized by sector using the median and quartiles. The sample size varies depending on the indicator: 98 to 108 sites grouped by sector for skills, 104 sites for scheduling, and 23,269 employees for skills development.<\/p>\n

Mercateam serves more than 300 industrial sites; the study included the 210 sites whose data met its inclusion criteria.<\/p>\n

These data describe the manufacturers that use Mercateam and are not intended to represent the industry as a whole.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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