Sell More of the Machine Hours You Already Pay For: An Auto-Component Owner's Guide (₹)
How do you increase auto-component productivity without buying machines? Sell more of the hours you already pay for. Indian auto-parts factories produced ₹10.9 lakh of value added per person in 2023-24, from ₹6.6 lakh (Karnataka) to ₹20.4 lakh (Rajasthan; MoSPI ASI, checked 20 Oct 2026). US audits of 658 auto-parts plants: productivity fixes paid back in a median 0.52 years.
Last checked: 20 Oct 2026
By the Mama Editorial Team. Where are the extra machine hours in a Tier-2 plant, and what is each worth? Public industry numbers, audit findings and six moves priced in rupees, each linked to a deeper page.
Key takeaways
- The industry benchmark is about ₹10.9 lakh of value added per person a year. That is the all-India figure for NIC 293 (parts and accessories for motor vehicles) in ASI 2023-24; among the 11 states with over 25,000 persons engaged it runs from ₹6.6 lakh in Karnataka to ₹20.4 lakh in Rajasthan.
- Your fixed costs do not stop when the spindle stops. Depreciation and interest took 19.3% of the industry's gross value added in 2023-24, per the same ASI figures.
- Productivity fixes are rare in audits but pay back fastest. In the US DOE ITAC database, they were 3.1% of recommendations to auto-parts plants but 14.1% of the savings identified, with a median payback of 0.52 years.
- Six moves cover most lost hours in a machine or press shop, from first-piece waits and die changes to breakdowns. Two, job set-up verification and TPM, sit in IATF's top-ten audit findings.
- In our worked example, a 40-CNC two-shift shop recovers about 8,800 cutting hours a year, worth roughly ₹88 lakh at an assumed ₹1,000 contribution per cutting hour, but only if there are orders to fill those hours.
What one person's year is worth in Indian auto components
MoSPI's Annual Survey of Industries (ASI) covers registered factories; 2023-24 is the latest year its API returned for NIC 293 (checked 20 Oct 2026).
Indian auto-component factories in the 11 states with over 25,000 persons engaged, ASI 2023-24, NIC 2008 group 293, combined rural and urban (MoSPI ASI API, checked 20 Oct 2026; GVA per person is our division).
| State | Factories (number) | Persons engaged (number) | Gross value added (₹ crore) | GVA per person engaged (₹ lakh/year) |
|---|---|---|---|---|
| Tamil Nadu | 1,361 | 2,49,527 | 26,761 | 10.7 |
| Maharashtra | 1,237 | 2,13,691 | 27,700 | 13.0 |
| Haryana | 850 | 2,04,584 | 22,284 | 10.9 |
| Karnataka | 443 | 81,449 | 5,367 | 6.6 |
| Uttar Pradesh | 250 | 44,180 | 5,997 | 13.6 |
| Uttarakhand | 285 | 43,173 | 3,930 | 9.1 |
| Gujarat | 292 | 37,306 | 3,810 | 10.2 |
| Madhya Pradesh | 129 | 30,181 | 2,561 | 8.5 |
| Punjab | 257 | 30,025 | 2,462 | 8.2 |
| Jharkhand | 135 | 28,596 | 2,816 | 9.8 |
| Rajasthan | 169 | 28,543 | 5,833 | 20.4 |
| All India | 5,753 | 10,37,635 | 1,13,359 | 10.9 |
Tamil Nadu, Maharashtra and Haryana together held 64% of the persons engaged in Indian auto-component factories in 2023-24, per the ASI figures above.
The average auto-component factory in operation engaged about 206 people in 2023-24 (ASI: 10,37,635 persons in 5,030 operating factories, our division).
The ASI API gives no factory-size or plant-level spreads (checked 20 Oct 2026), so state averages are the closest public benchmark.
To compare, divide your annual gross value added (sales minus materials, power and bought-in services) by everyone on the payroll, contractors included. If headcount per machine looks high, see two people per machine.
Why an idle hour still costs money
Indian auto-component factories carried ₹1,22,973 crore of fixed capital in 2023-24, per ASI. They booked ₹15,477 crore of depreciation and paid ₹6,439 crore of interest that year, per the same survey.
Together, ₹21,916 crore, they equal 19.3% of the industry's gross value added (our arithmetic) and accrue by the calendar, not by the cutting hour.
An unsold machine hour is a loss, not a saving. The costing sheet hides it when the divisor assumes hours that never happen; the machine hour rate page shows how, and the machine hour rate calculator does the sum on your own numbers.
What auditors found in comparable plants
India has no open plant-by-plant audit database; the US DOE Industrial Training and Assessment Centers (ITAC) programme publishes every recommendation from its free one-day plant audits (ITAC database, release of 19 Oct 2026) under a CC BY 4.0 licence.
Filtered to motor-vehicle parts plants (NAICS 3363, or SIC 3714 in older records), it holds 658 plants and 5,168 recommendations, 1982 to 2026 (our count, checked 20 Oct 2026).
The median plant in that sample had 230 employees, and 58% had between 200 and 1,000 (ITAC database, our count). That is the size band this page is written for.
What ITAC auditors recommended to 658 US auto-parts plants, by type of measure (ITAC database release of 19 Oct 2026, our count; dollars kept as shares, not converted to rupees).
| Type of measure (ITAC category) | Share of recommendations (%) | Share of identified annual savings (%) | Median simple payback (years) | Implemented by the plant (%) |
|---|---|---|---|---|
| Energy (2.xxxx) | 90.6 | 75.2 | 0.77 | 46.9 |
| Waste (3.xxxx) | 6.3 | 10.7 | 0.62 | 37.4 |
| Direct productivity (4.xxxx) | 3.1 | 14.1 | 0.52 | 52.9 |
Productivity measures were recommended in 110 of the 658 plants, or 16.7% (ITAC, our count). The median productivity recommendation was worth six times the median energy recommendation in annual savings (ITAC, our count).
"Implemented" means the plant reported the measure done on the follow-up call 6–9 months later (ITAC assessment protocol, checked 20 Oct 2026).
Leaving aside one office item (paying bills on time, 15 plants), the most frequent productivity measures were preventive or predictive maintenance (11 plants, implemented 80%), re-arranging the layout to cut handling (10 plants), replacing unsuitable equipment (9 plants) and adding equipment or operators at the bottleneck (8 plants), per the ITAC database (our count) and its recommendation code index. The counts are small: read them as direction.
US machine-shop and fastener plants (NAICS 3327, or SIC 3451 and 3452 in older records) show the same pattern: productivity measures appeared in 39 of 246 plants, with a median payback of 0.61 years, but only 26% were implemented (ITAC, our count).
Six moves that give hours back, priced
Each row links to a page with its own worked example; if machines idle while the job sheet is full, start with the four causes. The ₹ effect column comes from the worked example in the next section. The first-piece and maintenance rows are also IATF audit clauses; the last two rows would read the same in any factory, so they sit at the end.
| Move | Where the hours go | ₹ effect in the worked example (₹ lakh/year) | Cash cost | Evidence | Government support | Go deeper |
|---|---|---|---|---|---|---|
| Cut the first-piece approval wait | Machine stands while the first part queues at QC | 25 | Low: inspection at the machine, staggered setups | IATF 16949 clause 8.5.1.3, verification of job set-ups: 10th among major and 5th among minor audit findings worldwide (IATF data via VDA QMC, 23 Apr 2026) | None specific | First-piece approval waiting |
| Quick changeover (SMED) | Internal setup steps done while the machine is stopped | 24 | Low to medium: trolleys, pre-set tools, standard steps | Changeover time −43% to −45% in a forging study (Sundaramali et al., 2024) | Lean MSME scheme (see below) | Die change time, changeover calculator |
| Planned maintenance (TPM) | Repeat breakdowns on presses and CNCs | 9 | Medium: PM schedule, spares, operator checks | Breakdown time −23% and production +15.6% in a North Indian unit (Singh et al., 2018); IATF 16949 clause 8.5.1.5, total productive maintenance, tops the minor-findings chart (same IATF data) | Lean MSME scheme | OEE benchmarks, OEE calculator |
| Shorten waits between operations | Parts sitting in bins between turning, milling, heat treatment, grinding | Cash, not hours: frees working capital | Low: smaller transfer lots, visible queues | Practice | None specific | Parts waiting between operations |
| Break the setter queue | Machines waiting for one skilled setter | 10 | Training time for a second setter | Practice | Lean MSME scheme | One setter, five machines |
| Close the shift edges | Late first chip, early wash-up | 20 | Near zero: start-up routine, material staged the night before | Measured at the spindle, not the punch clock | None specific | Late start and early wash-up, shift-edge calculator |
A worked ₹ example: a 40-CNC Tier-2 shop
All figures here are assumptions; replace them with yours.
Assume 40 CNCs, two 8-hour shifts, 300 working days: 1,92,000 planned machine hours a year. Assume the spindle cuts 40% of that, about 76,800 hours, which sits inside the 25–50% band discussed on the spindle utilisation page.
Assume each cutting hour earns ₹1,000 of contribution (sale value minus material, power and tooling), and half of every recovered machine hour turns into cutting time.
| Move | Assumption (per machine) | Machine hours recovered (h/year) | Cutting hours at 50% (h/year) | Value at ₹1,000/h (₹ lakh/year) |
|---|---|---|---|---|
| First-piece wait | 1 setup a day; wait cut from 40 to 15 min | 5,000 | 2,500 | 25 |
| Changeover | 1 change a day; 60 min cut by 40% | 4,800 | 2,400 | 24 |
| Breakdowns | 4% of planned hours lost; cut by 23% | 1,766 | 883 | 9 |
| Setter queue | 20 min a day of waiting, halved | 2,000 | 1,000 | 10 |
| Shift edges | 10 avoidable min per shift recovered | 4,000 | 2,000 | 20 |
| Total | 17,566 | 8,783 | ≈ 88 |
The changeover row stops when the first part reaches QC, so it does not double-count the first-piece wait. Spindle utilisation moves from 40% to about 44.6%: modest on the floor, large in the accounts.
The ₹88 lakh is real only if there is work to put into the hours, and an OEM may want paperwork first. Ford's PPAP rules (January 2025) require a new submission for "any change in supplier operating pattern" and for required capacity "exceeding verified supplier capacity" (Ford PPAP requirements, applicability); see the real cost of a re-PPAP. If the order book is thin, the same hours are a reason to quote for new business; the contribution test for low-rate jobs shows when filling them pays. And before any capex, prove the capacity you already have.
Which move to start with
- Log every stop over 5 minutes on 5–10 machines, with the reason, for 10 working days.
- Sort the minutes into the six buckets above. Your IATF records may already hold part of it: if set-up verification (8.5.1.3) and maintenance (8.5.1.5) records carry times, the first-piece wait and breakdown buckets start there. If "other" is large, the logging is too coarse.
- Price the biggest bucket with the machine utilisation calculator and your own contribution per hour.
- Fix one bucket, then measure again for two weeks.
Ways to collect start-stop records are compared in logs vs sensors vs controller data vs cameras; for machines without a PLC, see tracking old CNCs.
Government support for the work
The Lean MSME scheme named in the table is the same for every industry. Under its 2022 guidelines, the government pays 90% of the consultant fee, so a plant pays at most ₹12,000 of the ₹1,20,000 intermediate level (Lean scheme guidelines, 7 Oct 2022). The guidelines print a validity to 31 March 2026; no extension order was found on msme.gov.in or dcmsme.gov.in (checked 17 Oct 2026). Status is on the ZED and Lean page.
What cameras can and cannot tell you here
A camera over a CNC bay can show whether a machine is running, standing with its door open, or waiting with nobody near it. It can time a changeover from the last part out to the first part in.
It cannot read cycle times, part programs or dimensions, tell a planned stop from an unplanned one without the plan, or price an hour.
Count machine states and aggregate patterns, never rank operators. If cameras watch work areas, give workers a written notice; a template is in the DPDP worker CCTV notice.
Where Mama fits
Mama reads the cameras a plant already has, or adds a few, and every morning sends the owner a short plain-language note: which machines stood yesterday, for how long, and in which of the buckets above. It watches machines and flows, not individuals. Leave an email or phone in the form below and Mama will get back to you with a camera plan.
FAQ
How can I increase productivity in an auto-component plant without new machines? Recover the hours your current machines lose to waiting: first-piece approval, changeovers, setter queues, shift edges and breakdowns. In US DOE ITAC audits of 658 auto-parts plants, productivity measures had a median payback of 0.52 years (database release of 19 Oct 2026, our count).
What is a good value added per worker for an Indian auto-component plant? The all-India figure for NIC 293 was ₹10.9 lakh of gross value added per person engaged in 2023-24, per MoSPI ASI (checked 20 Oct 2026). Among the 11 states with over 25,000 persons engaged, averages ranged from ₹6.6 lakh in Karnataka to ₹20.4 lakh in Rajasthan.
Which move usually pays back first? The cheapest: cutting the first-piece wait and staging die and tool changes need routines more than money, and both answer top-ten IATF audit findings. Measure two weeks of stops first; the biggest bucket differs by shop.
Is there government support for productivity work in an auto-component MSME? The MSME Competitive (LEAN) Scheme guidelines of 2022 cover 90% of the consultant fee. The guidelines print a validity to 31 March 2026 and no extension order was found on msme.gov.in or dcmsme.gov.in (checked 17 Oct 2026), so confirm with the portal before you plan on it.
Are US audit numbers relevant to an Indian plant? As direction, yes: they show which fixes pay back fastest and how often owners carry them out. Dollar values do not carry over.