Research libraryUtilisation Decides
Chapter 11 of 14 · Part III · Draft web edition
Defects consume capacity twice, first through lost good output and then through rework, queues and repeated interruption.
The cloth is woven from this chapter's 5 exhibits: each weft is one exhibit's own data, normalised; each warp group is a section. The probe reads the exhibit's own value where it crosses. Procedural visualisation, not a measurement.
Read-out
Derived with illustrative inputs using the OEE loss structure in S04 and SMV accounting in S13: 500 scheduled shift garment equivalents less 50 availability loss and 70 speed loss leaves 380 gross pieces completed; 57 defective pieces (15 per cent first-pass defect share) reduce first-pass good output to 323 pieces, while inline re-sewing of 48 repairable pieces at 0.5 garment-equivalent cycle each displaces 24 additional new-garment equivalents of station capacity, leaving 299 net first-pass equivalents.
Unit: good-garment equivalents per shift. Status: derived. Sources: S04, S13.
Quality at source is a capacity control before it is an inspection routine. A sewing line that measures its speed by gross pieces sewn while pushing defective seams into downstream repair is not achieving high utilisation; it is paying twice for the same garment. The first payment occurs when a station spends scheduled time, needle strokes, thread and operator attention producing a unit that cannot pass to the customer. The second payment occurs when that defective piece re-enters the line, interrupts a productive station, consumes additional standard minutes for unpicking and re-sewing, and inflates work in progress (WIP) while upstream bundles wait.[S04][S13][S22] In Green Future Tech Limited's (GFT's) Cameroon textile programme, prepared on 31 January 2024 with source refreshes retained in 2026, hourly tracking of defects alongside output and downtime is built into the first shift. Where Cameroon's cotton-to-garment sector seeks to move from an inherited baseline of roughly 95 per cent raw cotton fibre export and 5 per cent local processing towards a 50 per cent target by 2030, and where reported incumbent plant utilisation stood between 10.83 and 48.9 per cent in July 2023, factories cannot forfeit scarce effective capacity to hidden repair loops.[R06][R07][R12]
Treating quality as an end-of-line filter separates the people who create defects from the signal that a defect has occurred. When an inline seam fault waits in a progressive bundle until final inspection, the line continues to consume cut components and machine hours under the same faulty tension, worn needle or misunderstood method.[S02][S21][S32] Even at the final table, manual checking is an imperfect barrier: industry inspection literature records manual fabric and defect inspection at 60 to 70 per cent accuracy, missing 20 to 30 per cent of defects.[R29] This chapter shows why effective output must exclude reworked pieces until their repair burden is counted, how Total Productive Maintenance (TPM) links machine condition to seam integrity, why smaller transfer batches compress the time between defect creation and containment, how root-cause inquiry prevents repeat stoppages, why raw piece-rate incentives undermine first-pass yield, and how integrated garment programmes lower WIP and defects together.[S02][S04][S12][S28][S29][S32] Automated visual inspection hardware and computer-vision deployment remain with wp03.[R29]
11.1
Related · chapter 047 paragraphs step it
Read-out
Derived with illustrative inputs using the SMV and efficiency formulas in S13 and S0402: operation SMVs of 0.8 + 1.2 + 0.6 = 2.6 minutes; 200 good pieces earn 520 standard minutes; 8 operators x 480 minutes = 3,840 attended minutes (13.54 percent efficiency). Intermediate loss buckets are illustrative allocations of the 3,320 unrecovered minutes.
Unit: labour minutes per shift. Status: derived. Sources: S13, S0402.
Read this exhibit in its chapter ↗In the Overall Equipment Effectiveness (OEE) framework, quality is a multiplicative determinant of effective capacity, not a post-production deduction on a shipping report. Availability measures downtime losses from breakdowns or adjustments, performance captures speed losses from minor stoppages and slow cycles, and quality measures defect losses caused by process faults and reduced yield.[S04] Because the three ratios multiply together, a line operating at 85 per cent availability and 80 per cent performance in an illustrative calculation stands at 68 per cent before quality is considered; if only 85 per cent of completed garments pass right first time, effective output drops to 57.8 per cent of planned production time.[S04] Counting gross units off the last sewing machine as saleable output conceals that 10.2 percentage point capacity loss.[S01][S04]
Garment lines are vulnerable to false volume reporting because most sewing defects are repairable rather than total scrap. When a lockstitch seam skips, a collar point puckers, or oil stains a cotton panel, a helper or inline checker unpicks the stitches and returns the panel to the operator for repair.[S09] If the line board records only total pieces passing the finishing table at shift end, a repaired garment looks identical to a garment sewn right first time. Yet the repaired garment consumed the standard minute value (SMV) of the operation during initial assembly, consumed unrecorded labour minutes during unpicking, and returned to consume machine and operator time a second time during re-sewing.[S04][S13]
Because garment SMV is the sum of the SMVs of each required step, and line efficiency compares earned standard minutes of good output with attended labour minutes, a garment that passes through an operation twice earns its standard minute credit only once.[S13] Every minute spent unpicking and re-stitching reduces productive time for new garments while adding zero earned standard minutes.[S04][S13] Under Theory of Constraints principles and apparel bottleneck diagnostics, any rework time stolen from the highest-utilisation station or the station with the largest upstream WIP buffer permanently reduces finished throughput for the whole line.[S20][S30]
Exhibit 51 shows how this accounting boundary exposes the gap between gross production and net first-pass capacity in a derived shift reconciliation.
In Exhibit 51, a shift that appears on a gross tally sheet to have sewn 380 pieces (or 371 shipped pieces after 48 of the 57 defective garments are repaired and 9 are scrapped in this illustrative derivation) has generated only 323 first-pass good garments and sacrificed 24 new-garment equivalents of station time to re-sewing.[S04][S13] The exhibit is a derived illustration of the accounting mechanism rather than an empirical measurement of a Cameroon plant, and it requires shift-level observation of actual repair durations.
11.3
Read-out
Sourced from Company X sewing-line monitoring in S09. Values show cumulative recorded downtime minutes by fault category during the baseline monitoring period (total 12,476 minutes across these four fault categories). Single-company empirical observation, not a universal industry benchmark.
Unit: recorded downtime minutes. Status: sourced. Sources: S09.
Read this exhibit in its chapter ↗11.3 · Introduction
Treating sewing defects as purely human errors ignores stitch mechanics.
Exhibit 13, chapter 03: Technical malfunction
11.4 · Empirical defect Pareto distributions point back to machine care and operation design
Apparel monitoring confirms that a few machine- and handling-related categories dominate sewing quality losses.
Exhibit 13, chapter 03: Technical malfunction
11.5 · Seam mechanics and task complexity govern defect creation at the needle
Materials and industrial-engineering research shows why defect prevention must start in technical setup and work design before the first bundle enters the line.
Exhibit 13, chapter 03: Machine adjustment
11.3 · the bodyThe film above follows these sub-steps.
Treating sewing defects as purely human errors ignores stitch mechanics. In NIST's analysis of advanced maintenance, Total Productive Maintenance (TPM) aims for zero breakdowns and zero defects by eliminating six major losses: equipment breakdown, setup and adjustment slowdowns, idling and short-term stoppages, reduced capacity, quality-related losses, and startup or restart losses.[S02] In a sewing room, the boundary between an availability loss, a micro-stoppage and a quality defect is thin. A bent needle, an uncleaned rotary hook, an improperly set bobbin spring, or a misaligned folder first causes stitch tension irregularity or puckering, then triggers thread breakage, and finally forces a mechanical adjustment or needle change.[S02][S09]
Apparel monitoring confirms that a few machine- and handling-related categories dominate sewing quality losses. In the Wiley Engineering Reports study of Company X, Pareto analysis of recorded sewing defects showed that two categories, Lock Stitch Repair and Stain, accounted for 54.77 per cent of all product defects, while the top five categories accounted for 87.56 per cent of total defects.[S09] In the same factory, downtime monitoring recorded 4,923 minutes lost to technical malfunction, 3,694 minutes to machine adjustment, 2,432 minutes to faulty needles, and 1,427 minutes to thread breakage.[S09]
Exhibit 52 summarises the concentration of defect categories reported in that apparel case study.
The concentration shown in Exhibit 52 carries a direct operational lesson, even though Company X's percentages reflect one factory rather than a universal benchmark.[S09] Neither "Lock Stitch Repair" nor "Stain" can be eliminated by telling operators to be more careful. Oil and grease stains originate from over-lubricated needle bars, leaking head gaskets, oil-saturated lint beneath the throat plate, or dirty work surfaces, which are direct targets of TPM autonomous cleaning and seal inspection.[S02][S09] Similarly, lockstitch faults arise from the mechanical interaction of needle geometry, hook timing, presser-foot pressure, feed-dog height, and thread-to-fabric selection.[S09][S1102]
Materials and industrial-engineering research shows why defect prevention must start in technical setup and work design before the first bundle enters the line. Experimental testing of woven fabrics sewn with polyester threads of varying linear densities demonstrates that seam yielding force and ultimate seam strength increase with sewing thread strength and fabric compactness, and that structural degradation inside a seam begins at an identifiable Seam Yielding Point before complete rupture.[S1102] If an operation bulletin specifies an incompatible thread linear density, excessive dynamic tension, or a needle point that damages compact warp yarns, the seam is compromised at stitching even if it passes a casual glance.[S13][S1102]
An empirical study of 96 sewing operations in a garment workshop published in Heliyon evaluated physical action complexity, posture-keeping complexity and cognitive complexity against operation time variance and defect rates.[S1101] Action complexity and cognitive complexity were significantly correlated with both sewing operation time fluctuation and the rate of sewing defects.[S1101] Complex manual manipulation or high cognitive load without visual fixtures creates wider cycle-time swings, destabilising line balance while raising defect rates.[S15][S17][S1101]
A practical TPM and industrial-engineering programme therefore attacks quality losses at source through four pre-emptive controls:
11.6
Even with sound maintenance, abnormalities occur during a shift: a bobbin runs low, a thread cone hits a knot, a folder screw loosens, or a shade mismatch enters the line. What determines whether that abnormality ruins one garment or forty garments is the size of the transfer batch and the volume of WIP between adjacent operations.[S21][S28][S32]
In a progressive bundle system, cut parts move between stations in tied bundles of 20 to 50 pieces buffered by racks or carts.[S21] Each operator unties a bundle, performs one sewing step on all pieces, reties it, clips a ticket, and pushes the bundle into the downstream queue.[S21] Suppose an attachment slips at Operation 4 so the seam margin narrows beyond tolerance, preventing Operation 7 (three stations downstream) from catching the inner ply. Under progressive bundle flow with an illustrative two-bundle buffer (60 pieces) between stations, Operation 4 can complete up to 180 defective pieces across intermediate queues before Operation 7 unties the first affected bundle and spots the fault.[S21][S22]
In strict single-piece flow, there is no WIP between adjacent operators, and in small-lot pull cells, transfer batches are constrained to a single garment or two to five pieces authorised by a kanban limit.[S21][S24] When Operation 4 hands a garment directly to Operation 5, or into a three-piece buffer, the downstream operator handles the seam within minutes. Lean Enterprise Institute analysis emphasises that small-lot flow supports quality because abnormalities are exposed immediately rather than buried in inventory, and protective excess capacity at non-constraints is normal and beneficial because it gives operators time to detect, stop and resolve problems without starving the constraint.[S28][S30]
For long-run averages in a stable system that is not starting, stopping or surging, Little's Law states that average WIP equals throughput rate multiplied by throughput time (WIP = TH x TT).[S22] Across the line segment between defect creation (i) and detection (j), throughput time is the defect detection latency (T_detect), and intermediate WIP (WIP_ij) equals the maximum number of defective garments in transit before the alarm is raised (T_detect = WIP_ij / TH).[S22]
In an Ethiopian garment factory case study published in the Journal of Textile and Apparel, Technology and Management, replacing large-buffer push flow with kanban pull and single-piece flow reduced WIP by 48.5 per cent and throughput time by 80.3 per cent.[S31] Although those figures come from one factory and are not a Cameroon forecast, cutting inter-station queues and transfer time by half to four-fifths compresses the maximum population of hidden in-process defects by the same proportion.[S22][S31]
MIT's capacity analysis notes that the relationship between waiting time and capacity utilisation is strongly nonlinear.[S23] Flooding every non-constraint station to full occupancy swells queues nonlinearly, stretches detection latency from minutes to hours, and pressures operators to pass marginal work forward.[S23][S28][S32] Pull-capped WIP and protective capacity at non-constraints keep queues short so every handoff acts as an immediate seam check.[S24][S28]
Interlude · the cloth half-woven
Woven: sections 11.0 to 11.6Still to weave: 11.9 to 11.14
Read-out
Sourced from World Bank research and Enterprise Survey indicators: unconditional average revenue-based total factor productivity (TFP) gap for outage-exposed manufacturers across 84 developing countries, 2006 to 2019 (S36); share of surveyed Cameroon firms experiencing electrical outages in 2024 (S0301); and average value lost due to electrical outages as a share of sales for affected Cameroon firms in 2024 (S0302). Cross-country and sector-aggregate figures are context, not a single-factory garment forecast.
Unit: percent. Status: sourced. Sources: S36, S0301, S0302.
Read this exhibit in its chapter ↗11.9
Shortening detection latency improves utilisation only if detection triggers permanent prevention rather than faster re-sewing. In many garment plants, inline quality control stops at containment: a roaming checker spots a puckered placket or a skipped chainstitch, marks it with chalk, and hands the piece back to the operator, who repairs it in thirty seconds. Because no root-cause inquiry takes place, the fault recurs fifteen minutes later, creating a chronic drain on OEE performance and quality components.[S04][S29]
To break chronic rework cycles, Lean practice pairs containment with the Five Whys, the practice of asking "why" repeatedly whenever a problem is encountered in order to move beyond obvious symptoms to discover the root cause.[S29] In a sewing room, a Five Whys inquiry must take place at the workstation while the defective piece, thread, needle and attachment are still in place, and it must never stop at "operator carelessness", which describes a human state rather than a controllable process mechanism.[S26][S29][S1101]
Consider a trace on a garment line experiencing repeated skipped stitches on a trouser side-seam overlock operation:
Warning
Three failure modes that turn root-cause logs into administrative theatre
[^S02], [^S1101]) untouched.[^S09]) from separating a single maladjusted machine from a thread batch issue ([^S1102]).[^S26], [^S29]).Fixing only Step 1 saves one trouser while leaving the line certain to produce more skipped seams. Fixing Step 3 stops defects on the current line today. Fixing Steps 4 and 5 (updating the operation bulletin and adding first-piece mechanic verification to the SMED changeover checklist) prevents the defect class across all lines and future runs.[S13][S25][S26]
Exhibit 53 maps the six-step closed loop that connects inline abnormality detection to updated standard work.
Read-out
Illustrative operational decision flow synthesising one-piece flow abnormality exposure (S28), Five Whys root-cause inquiry (S29) and standardised work baseline updates (S26). Not a record of an executed factory trial.
Unit: . Status: illustrative. Sources: S26, S28, S29. Illustrative figures are teaching examples, not measured factory results.
As shown in Exhibit 53, the loop closes only when the verified physical fix is written into standardised work, which the Lean Enterprise Institute defines as the documented current process for all shifts that reduces variability, eases training, reduces strain, and establishes the baseline for improvement.[S26] Because Exhibit 53 is an illustrative design, a factory must set local escalation rules: an operator adjusts thread tension immediately, whereas a fault affecting three consecutive pieces triggers a flag requiring the supervisor and mechanic within five minutes.
11.11
Raw piece rate + overtime
Raw piece rate + overtime: Constraint flow alignment (0 to 10) 1.5, First-pass quality protection (0 to 10) 2. Quadrant: Raw individual piece rate · illustrative
A quality-at-source protocol will fail if the compensation system rewards operators for violating it.
Gross line-output bonus
Gross line-output bonus: Constraint flow alignment (0 to 10) 6.5, First-pass quality protection (0 to 10) 3.5. Quadrant: Uncapped gross line volume · illustrative
FPY-gated individual bonus
FPY-gated individual bonus: Constraint flow alignment (0 to 10) 3.5, First-pass quality protection (0 to 10) 7.5. Quadrant: Quality-gated individual pay · illustrative
Base wage + line FPY & skill bonus
Base wage + line FPY & skill bonus: Constraint flow alignment (0 to 10) 8.5, First-pass quality protection (0 to 10) 8.8. Quadrant: Line FPY and flow bonus · illustrative
Illustrative 0-to-10 design comparison of garment incentive models based on the piece-rate and batch quality risks in S32 and overtime productivity limits in S41. Scores are analytical design ratings, not empirical factory measurements.
Unit: illustrative design score (0 to 10). Status: illustrative. Sources: S32, S41. Illustrative figures are teaching examples, not measured factory results.
11.11 · the bodyExhibit 54 above; its figures are illustrative where marked.
A quality-at-source protocol will fail if the compensation system rewards operators for violating it. Across garment manufacturing, managers often rely on individual piece-rate incentives or gross-bundle targets in the belief that paying per piece sewn maximises labour effort and utilisation.[S14][S32] In practice, unadjusted piece-rate pressure interacts with progressive bundles to degrade both first-pass quality and flow.
Empirical research from the National Bureau of Economic Research (NBER) on international differences in lean production, productivity and employee attitudes shows how piece-rate and batch systems undermine performance.[S32] Examining component production prior to lean transformation, the NBER study notes that piece-rate payment combined with large batches led to long lead times and low quality, both because workers had an incentive to work as fast as possible and because large batches meant that many bad products could be made before inspectors caught them.[S32]
Protecting quality at source requires shifting incentives and supervisor evaluations from individual gross speed to right-first-time system flow. Better Work Ethiopia's 2023 annual report (covering 2022 data) records that factories reported improved productivity, quality and workplace organisation, alongside reduced absenteeism, in lines led by newly trained supervisors.[S40] Those outcomes confirm that supervisory skill (coaching standard methods, responding to flags, and balancing stations hourly) drives joint quality and productivity gains.[S12][S40]
Exhibit 54 compares four compensation architectures across constraint flow alignment and first-pass quality protection.
In Exhibit 54, the four points represent management choices rather than empirical measurements:
11.14
Read-out · sourced
Sourced from the ILO final report on the Factory Improvement Toolset in the Asian garment sector (S12). Programme-level aggregate results across participating factories; outcomes reflect combined balancing, WIP and quality interventions and are not a standalone forecast for Cameroon.
Unit: per cent change in participating Asian garment sewing rooms. Status: sourced. Sources: S12.
In its final report on the Factory Improvement Toolset (FIT) in the Asian garment sector, the ILO combined operation bulletins, SMV measurement, line planning, hourly balancing, bottleneck management, workplace organisation and inline quality tracking.[S12] Across participating sewing rooms, line achievement improved by 9.49 per cent, while work in progress and defects per hundred units (DHU) both fell by 22 per cent.[S12]
Exhibit 55 presents these three simultaneous programme outcomes.
Why did line achievement rise by 9.49 per cent while WIP and DHU each dropped by 22 per cent in Exhibit 55?[S12] First, cutting in-process bundles by 22 per cent meant stitch and alignment faults were caught after a few garments rather than after full racks had been sewn, driving the 22 per cent drop in DHU.[S12][S22][S28] Second, a 22 per cent drop in DHU freed operators and bottleneck machines from unpicking and re-stitching, converting rework time into first-pass good garments.[S04][S12] Third, with fewer stalled rework bundles clogging the racks, supervisors could see true station queues and rebalance work hourly around the constraint, lifting line achievement by 9.49 per cent.[S12][S20]
Complementary ILO evidence from Africa confirms that these mechanisms translate to emerging garment hubs when adapted to local skill baselines. Under the ILO SCORE and SIRAYE programme in Ethiopia, workplace cooperation combined with line balancing and quality improvement enabled Edget Garment to raise line-balance efficiency from 24 per cent to 75 per cent and increase daily line output from 50 to 120 pieces.[S37] These figures establish what integrated industrial engineering can achieve under specific factory conditions; they must not be copied into a Cameroon financial model without local baseline measurement and a line trial.[S12][S31][S37]
For a garment operation in Cameroon, whether a turnaround addressing the 10.83 to 48.9 per cent capacity utilisation gap recorded for incumbent plants in July 2023 or a new line processing domestic cotton fibre locally, quality at source must be engineered as a manual discipline before digital software is purchased.[R06][R12][S09] Clean station tally cards and disciplined supervisor huddles create the data hygiene that later digital tools require.[S07][S09]
| Control tier | Boundary and cadence | Primary signal captured | Immediate operational action | System link to OEE and flow |
|---|---|---|---|---|
| Tier 1: Needle self-check | Every bundle; 3-minute shift-start swatch test | Tension, stitch density, oil-free surface ([^S02], [^S1102]) | Wipe oil/lint, adjust tension or swap needle before bundle ([^S02], [^S09]) | Blocks micro-stops and defect creation at source ([^S04]) |
| Tier 2: Operator handoff | Small transfer batch (1 to 5 pieces) under WIP cap ([^S21], [^S24]) | Incoming seam completeness and margin ([^S28]) | Return faulty piece; flag if 3 consecutive pieces fail ([^S28], [^S29]) | Compresses detection latency (T_detect) ([^S22]) |
| Tier 3: Roving traffic-light | Every 2 hours at complex and constraint stations ([^S20], [^S1101]) | 5-piece sample scored green (0), amber (1), red (2+ defects) ([^S09]) | Red station triggers immediate Five Whys ([^S29]) | Protects constraint ([^S30]) and complex tasks ([^S1101]) |
| Tier 4: Hourly FPY board | Hourly tally; daily 10-minute shift huddle ([^S01], [^S12]) | N_start, N_fpy, N_rwk, N_scr, top 3 DHU codes ([^S04], [^S12]) | Assign standard-work or SMED update ([^S25], [^S26]) | Feeds honest Q_OEE and earned SMV ([^S04], [^S13]) |
When a factory governs quality at source through these four rules, OEE, line balancing and lean flow work as one system: good maintenance stabilises stitch formation, feasible balancing and guides lower task complexity, pull-capped buffers expose abnormalities rapidly, and first-pass accounting ensures that every earned standard minute represents a saleable garment.[S02][S04][S13][S24][S1101] The next chapter turns to the human and infrastructure envelope (lawful hours, supervision, retention, ergonomics and electrical power) that sets the attainable ceiling on this operating system.[S33][S35][S38][S39][S40]
Check the record
Every knot is a source this chapter cites; select one to read its record, or filter the register by grade. Publication dates and inherited-source limits stay explicit. Read the paper's full evidence summary.
37 sources · 227 citation marks on this pageMost cited: S04, 24 times
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R06 · Grade C · inherited record
Inherited evidence R06
About 95 percent of cotton fibre exported raw against 5 percent processed locally; industrialisation target of 50 percent local processing.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
R07 · Grade C · inherited record
Inherited evidence R07
SND30 restates the 50 percent local-processing target by 2030.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
R12 · Grade C · inherited record
Inherited evidence R12
CICAM capacity utilisation, July 2023 report: Garoua 33.7 percent, CICAM 1 48.9 percent, CICAM 2 10.83 percent; underutilisation cost about CFAF 2 billion in 2020.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
R29 · Grade C · inherited record
Inherited evidence R29
Manual defect inspection 60 to 70 percent accurate, missing 20 to 30 percent of defects; computer vision above 90 percent across more than 40 defect types; line-scan inspection up to 60 metres a minute.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
c11-S01 · Grade A
ISO 22400-2:2014: Automation systems and integration: Key performance indicators for manufacturing operations management, Part 2
The KPIs are presented by means of their formula and corresponding elements, their time behaviour, their unit/dimension and other characteristics.
2014. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S02 · Grade A
The Costs and Benefits of Advanced Maintenance in Manufacturing
Total productive maintenance (TPM) is a program that aims for zero breakdowns and zero defects and focuses on eliminating six losses: equipment breakdown, setup and adjustment slowdowns, idling and short-term stoppages, reduced capacity, quality-related losses, and startup/restart losses.
2018. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S04 · Grade B
Overall Equipment Effectiveness: Systematic Literature Review and Overview of Different Approaches
Availability measures downtime losses due to breakdowns or setup/adjustments; performance measures speed losses due to minor stoppages and reduced speed; and quality measures defect losses due to process defects or reduced yield [9].
2020-09-17. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S07 · Grade B
Overall equipment effectiveness when production speeds and stoppage durations are uncertain
In practice, components of OEE may consist of uncertainty due to manual or semi-automatic measurement systems.
2017-10-12. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S09 · Grade B
Achieving Sustainable Overall Equipment Effectiveness (OEE) in Apparel Industry With Lean and Digital Integration
Company X improved its OEE from 42.91% to 52.27% using PMDs and digital line balancing.
2025-07-26. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S12 · Grade A
Implementation of the Factory Improvement Toolset in the Asian garment sector: Final report
Sewing-room line achievements improved by 9.49 per cent and the work in progress and defect per hundred units of garments both reduced by 22 per cent.
2023. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S13 · Grade A
Creating Better Jobs for Women and Boosting Productivity in Bangladesh's Garment Factories: An assessment of the Gender Equality and Returns Program
The SMV for the garment is the sum of the SMVs for each of the steps required to produce the garment.
2025. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S14 · Grade C
How to Calculate OEE in the Garment Manufacturing Industry?
The sewing floor is labor intensive and overall production performance depends on the employees' performance instead of machine and equipment performance.
2024-11-14. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S15 · Grade B
Improvement of garment assembly line efficiency using line balancing technique
The cycle time is an amount of time for which a job remains in a workstation.
2020-04-03. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S17 · Grade B
Applying Meta-Heuristics Algorithm to Solve Assembly Line Balancing Problem with Labor Skill Level in Garment Industry
The workers' performances for each task are rated from 0 (no skill at all) to 1.5 (fully skilled) according to the workers' skill table provided by Chan et al. [2].
2021-04-26. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S18 · Grade B
Evaluation of Manual Skill Degradation Due to Automation in Apparel Manufacturing
The average capacity percent of the MMG increased from 60.6% to 79.4%, increasing learning by almost twenty percent in six weeks.
2021-11-23. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S20 · Grade B
Knowledge Graph-Embedded Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel Production Processes
Bottleneck workstations BNk are those with the highest long-term utilization or those whose work-in-process inventory in the upstream buffer is the largest of all buffers in the system.
2023-11-02. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S21 · Grade B
Apparel manufacturing systems
Single-piece flow means there is no WIP between any two operators in the sewing line.
2021. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S22 · Grade B
LAI Lean Academy Glossary
Little’s law strictly applies to long term averages of stable systems, i.e. ones which are not starting, stopping or surging.
2012. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S23 · Grade B
Class 3: Capacity Lecture
The relationship between waiting time and capacity utilization is strongly non-linear!
2004. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S24 · Grade B
Pull Production
Nothing is produced by the upstream supplier process until the downstream customer process signals a need.
2020-07-02. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S25 · Grade B
Single Minute Exchange of Die
SMED refers to the target of reducing changeover times to a single digit, or less than 10 minutes.
2020-07-02. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S26 · Grade B
Standardized Work
The benefits of standardized work include documentation of the current process for all shifts, reductions in variability, easier training of new operators, reductions in injuries and strain, and a baseline for improvement activities.
2020-07-02. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S27 · Grade B
Cell
A U shape (shown below) is common because it minimizes walking distance and allows different combinations of work tasks for operators.
2020-06-12. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S28 · Grade B
Ask Art: How One-Piece-Flow Supports Quality
Having excess capacity is in fact normal and actually a good thing.
2016-01-13. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S29 · Grade B
5 Whys - What is it?
5 Whys is the practice of asking why repeatedly whenever a problem is encountered in order to get beyond the obvious symptoms to discover the root cause.
2020-07-02. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S30 · Grade B
What is the Theory of Constraints, and How Does it Compare to Lean Thinking?
TOC advocates beginning with the constraint that most limits Throughput.
2006-09-08. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S31 · Grade B
Process-loss Optimization through the Implementation of Kanban & Single-piece Flow: A Case Study in an Ethiopian Garment Factory
The WIP and throughput time were reduced by 48.5% and 80.3% respectively.
2022-03-03. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S32 · Grade B
International Differences in Lean Production, Productivity and Employee Attitudes
This practice led to long lead times and low quality, both because of the incentive to work as fast as possible and because the large batches meant that many bad products could be made before they were caught by inspectors.
2007. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S33 · Grade A
Cameroon Labour Code, Law No. 92/007 of 14 August 1992
Statutory hours of work in all public and private non-agricultural establishments may not exceed forty hours per week.
1992-08-14. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S35 · Grade A
Creating Markets in Cameroon: Unleashing private sector growth
Access to electricity: energy supply is limited and unreliable.
2022. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S37 · Grade A
Advancing productivity, quality improvement and decent work in Ethiopia's garment industry: results so far
As a result, Edget Garment improved its line balance efficiency from 24 per cent to 75 per cent and increased its daily line production from 50 to 120 pieces.
2022-06-01. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S38 · Grade B
Workplace ergonomics: a comparative analysis of sitting and standing workstations in Ethiopian garment manufacturing industries
Sewing operator safety is vital in Ethiopia's clothing industry, but poor ergonomics cause long-term health risks.
2025-08-07. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S39 · Grade B
Labour turnover and workers' well-being in the Ethiopian manufacturing industry
Turnover in this study is lower than previous reports from the manufacturing industry in Ethiopia; almost one-third left before the first six months and half left within the first year.
2021. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S40 · Grade A
Better Work Ethiopia Annual Report 2023: Data from 2022
Factories also reported improved productivity, quality, and workplace organization, and a reduction of absenteeism in the lines led by the newly trained supervisors.
2023. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S41 · Grade A
Excessive Overtime, Workers and Productivity: Evidence and Implications for Better Work
However, in reality it has been proved that the extension of working hours does not in general yield proportionate increases in total output, and may even lead to an absolute reduction in total output in the long run (White, 1987).
2011. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S1101 · Grade B
Evaluating sewing operation complexity and its influence on sewing operation quality
The results show that action complexity and cognitive complexity are significantly correlated with the fluctuation of sewing operation time and the rate of sewing defects.
2023-02-20. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c11-S1102 · Grade B
Influence of Sewing Thread Properties on Seam Strength and Yielding Behavior in Woven Fabrics
Seam yielding force and ultimate seam strength increased with sewing thread strength and fabric compactness.
2026-09-10. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗S04 MDPI, Applied Sciences. 2020-09-17.
S13 International Finance Corporation and International Labour Organization, Better Work. 2025.
S22 Massachusetts Institute of Technology, MIT OpenCourseWare. 2012.
R06 Cameroon Trade Hub. Publication date unrecorded; inherited source-text check.
R07 Ecofin Agency. Publication date unrecorded; inherited source-text check.
R12 Business in Cameroon. Publication date unrecorded; inherited source-text check.
S02 National Institute of Standards and Technology. 2018.
S21 Elsevier, Lean Tools in Apparel Manufacturing. 2021.
S32 National Bureau of Economic Research. 2007.
R29 MDPI; industry inspection literature. Publication date unrecorded; inherited source-text check.
S12 International Labour Organization. 2023.
S28 Lean Enterprise Institute. 2016-01-13.
S29 Lean Enterprise Institute. 2020-07-02.
S01 International Organization for Standardization. 2014.
S09 Wiley, Engineering Reports. 2025-07-26.
S20 MDPI, Machines. 2023-11-02.
S30 Lean Enterprise Institute. 2006-09-08.
S24 Lean Enterprise Institute. 2020-07-02.
S33 Republic of Cameroon, English text mirrored by Ministry of Commerce of China. 1992-08-14.
S1102 MDPI, Materials. 2026-09-10.
S1101 Elsevier, Heliyon. 2023-02-20.
S15 Wiley, Engineering Reports. 2020-04-03.
S17 Atlantis Press, International Journal of Computational Intelligence Systems. 2021-04-26.
S26 Lean Enterprise Institute. 2020-07-02.
S25 Lean Enterprise Institute. 2020-07-02.
S31 Journal of Textile and Apparel, Technology and Management, North Carolina State University. 2022-03-03.
S23 Massachusetts Institute of Technology, MIT OpenCourseWare. 2004.
S14 Online Clothing Study. 2024-11-14.
S18 MDPI, Applied Sciences. 2021-11-23.
S41 Better Work, International Labour Organization and International Finance Corporation. 2011.
S40 Better Work, International Labour Organization and International Finance Corporation. 2023.
S27 Lean Enterprise Institute. 2020-06-12.
S37 International Labour Organization, SCORE and SIRAYE. 2022-06-01.
S07 Springer, International Journal of Advanced Manufacturing Technology. 2017-10-12.
S35 International Finance Corporation, World Bank Group. 2022.
S38 Inderscience, International Journal of Human Factors and Ergonomics. 2025-08-07.
S39 United Nations University World Institute for Development Economics Research. 2021.
Keep the thread · chapter 11 of 14
Move the shuttle along the rack to preview any chapter: its sections, exhibits and reading time. Draft web edition. This research is not investment, legal or tax advice; illustrative models are not measured factory results.
The paper's rack · 14 boltsBolt length: reading time
01Part I: Measure the factory that actually exists
A garment factory creates value when the whole line converts available time and skill into accepted output. Installed machines and busy stations are evidence of resources, not proof of delivery.
02Part I: Measure the factory that actually exists
A single OEE score can start a conversation, but only its components and loss codes can tell a supervisor what to fix.
03Part I: Measure the factory that actually exists
A factory cannot interpret downtime until it has separated legal hours, planned production, external interruptions and losses inside the line.
04Part I: Measure the factory that actually exists
SMV translates garment work content into earned minutes, but it must sit beside OEE and quality rather than replace them.
05Part II: Balance work to the constraint
Balancing begins with operation precedence and required output, then tests how work can be shared without violating machine, skill or quality constraints.
06Part II: Balance work to the constraint
A workstation assignment is feasible only when the operator can perform the method safely and repeatedly at the assumed cycle and quality.
07Part II: Balance work to the constraint
A balance that ignores machine availability, skill or precedence can look efficient on screen and fail on the floor.
08Part II: Balance work to the constraint
A balance calculated for one style and crew decays as absence, learning, defects and order mix change the work.
09Part III: Design flow instead of local busyness
Balanced work needs controlled release. A line shortens delivery time when it admits work that downstream operations can use, makes queues visible and protects good output at its constraint.
10Part III: Design flow instead of local busyness
Small batches become practical when style changes are short, methods are standard and the layout lets qualified operators share work around the constraint.
11Part III: Design flow instead of local busyness
Defects consume capacity twice, first through lost good output and then through rework, queues and repeated interruption.
12Part IV: Turn measurement into sustained performance
A technically balanced line still fails when learned operators are absent, hours are unsustainable or electricity interrupts production.
13Part IV: Turn measurement into sustained performance
The financial benefit is not a higher dashboard score; it is more good garments at acceptable labour, energy, capital and working-capital cost.
14Part IV: Turn measurement into sustained performance
Release the next commitment only when a measured trial shows that good throughput, safe work and controlled flow survive ordinary operating conditions.