Research libraryMeasure Before You Model
Chapter 02 of 14 · Part I · Draft web edition
Early targets should describe a controlled learning trajectory, with yield, rate and capability observed together rather than assumed away.
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
Illustrative GFT cohort trajectories over eight production weeks for one style and method revision. Good output, first-pass yield and demonstrated skill coverage move at different rates and dip at Week 5 when an unapproved speed push outruns skill and method stability. Rationale from S11 and S12; values are an illustrative model, not a GFT factory observation.
Unit: illustrative cohort index (0 to 100). Status: illustrative. Sources: S11, S12. Illustrative figures are teaching examples, not measured factory results.
A greenfield garment factory cannot manage its opening months by taking a mature factory's steady-state targets and scaling them down by an arbitrary percentage. Empirical research on production ramp-up demonstrates that a newly introduced manufacturing process is initially poorly understood, which causes low yields and low production rates to appear at the same time.[S11] Treating early shifts as a smaller version of steady-state production confuses two distinct management problems: learning how to execute a repeatable method without defects, and accelerating throughput once that method holds. Green Future Tech Limited (GFT), Hong Kong SAR, designs performance-management systems around a stricter premise. During ramp-up, the line is a structured learning system whose primary output is verified capability alongside saleable garments, and every target must link good output, first-pass yield and demonstrated competence inside a clearly bounded cohort.[S11][S12]
That distinction carries direct financial and industrial consequences where local processing is expanding from a narrow base. In Cameroon, inherited sector evidence indicates that about 95 percent of cotton fibre is exported raw while about 5 percent is processed locally, against a national target of 50 percent local processing by 2030.[R06][R07] Separate public reporting on the announced Camtext SA project cites planned processing of 12,000 tonnes a year, about 12,000 jobs at Dibamba and 3,000 at Garoua, an estimated return on investment near 11 years and optimal capacity around 2033, while July 2023 reports on CICAM recorded capacity utilisation of 33.7 percent at Garoua, 48.9 percent at CICAM 1 and 10.83 percent at CICAM 2.[R10][R12] Those figures describe sector context rather than a GFT operating result or demand forecast, yet they show why capital recovery in textiles depends on disciplined ramp control. When a new cut, make, trim and finishing plant mistakes early volume pressure for maturity, rework and turnover erode the capacity the investment case assumed.
2.1
Read-out
Sourced from the NIST MEP returned-material process case (S10): first-pass yield moved from 10 percent to 90 percent, and total processing time moved from 11.8 hours to 3.4 hours. Presented strictly as that external case, not as a garment benchmark or GFT target.
Unit: percent or processing hours (NIST MEP case S10). Status: sourced. Sources: S10.
Read this exhibit in its chapter ↗2.1 · Introduction
A ramp-up measurement system fails if a line can report rising piece counts while concealing the defects, repairs and unrecorded overtime used to produce them.
Exhibit 26, chapter 06: First-pass yield (%)
2.2 · Why gross piece counts distort early line behaviour
The arithmetic of rework explains why yield and speed cannot be separated.
Exhibit 26, chapter 06: First-pass yield (%)
2.3 · Reviewing the triad on one cohort axis
Before a plant director or line supervisor approves an increase in line speed, the performance record must show how output, quality and skill coverage are moving together.
Exhibit 26, chapter 06: Processing time (hours)
2.1 · the bodyThe film above follows these sub-steps.
A ramp-up measurement system fails if a line can report rising piece counts while concealing the defects, repairs and unrecorded overtime used to produce them. Terwiesch and Bohn show that early commercial manufacturing combines incomplete process knowledge, frequent disturbances and simultaneous pressure on production rate and yield.[S11] Because material handling, machine settings and operator motion are still stabilising, pushing speed alone routinely raises fault rates. A sewing line that runs faster by skipping alignment checks or bypassing in-line inspection does not create real capacity; it builds a hidden queue of defective bundles that must be unpicked, recut or downgraded later.
To prevent speed from masquerading as progress, GFT specifies a joint review rule from shift one: good output, first-pass yield and cycle time must be read together on the same board and in the same daily meeting. International Labour Organization (ILO) lean guidance for garment factories stresses balancing financial, behavioural and core-process metrics while combining in-process and end-of-process indicators.[S02] During ramp-up, gross pieces sewn can never stand alone as the headline metric. A line advances only when completed units pass their first boundary without repair, recut or reinspection-driven correction, and when the bottleneck station holds its pace within the approved method.[S02][S10]
The arithmetic of rework explains why yield and speed cannot be separated. A National Institute of Standards and Technology (NIST) Manufacturing Extension Partnership case documented a process where raising first-pass yield from 10 percent to 90 percent accompanied a reduction in total processing time from 11.8 hours to 3.4 hours.[S10] Although that sourced case comes from a specific manufacturing intervention rather than a garment benchmark, the mechanism applies directly to apparel assembly. Every bundle that fails first-pass inspection consumes sewing minutes twice: once when the fault is sewn, and again when a skilled operator unpicks the seam, re-sews the piece and returns it to inspection.[S10] Diverting scarce skilled operators to repair defects starves the main line of the competence needed to stabilise flow.
…where raising first-pass yield from 10 percent to 90 percent accompanied a reduction in…
S10
Before a plant director or line supervisor approves an increase in line speed, the performance record must show how output, quality and skill coverage are moving together. The illustrative trajectories in Exhibit 6 show how GFT structures this joint view across an eight-week cohort for a single garment style and method revision.[S11][S12]
In Exhibit 6, the illustrative series progress together from Week 1 to Week 4 as operators master the specified method and in-line quality stabilises, moving from an illustrative good-output index of 22 and first-pass yield of 64 percent at Week 1 to an illustrative index of 58 and yield of 81 percent at Week 4.[S11] At Week 5, the chart models a typical ramp failure mode: management demands a higher hourly quota before demonstrated skill coverage has expanded beyond an illustrative 65 percent of stations.[S12] First-pass yield drops from 81 percent to 72 percent in this illustrative sequence, rework absorbs station time, and net good output falls from 58 to 54 even though machines are running faster. Only when the line restores method discipline and completes cross-training in Weeks 6 to 8 does the illustrative good-output index reach 82 alongside a 92 percent first-pass yield. What this exhibit cannot establish is a universal weekly slope for all factories; every product family and workforce starts from its own empirical condition, which is why the underlying cohort baseline must be defined before any curve is plotted.
…an illustrative good-output index of 22 and first-pass yield of 64 percent at Week…
S11 · illustrative
2.4
Read-out · illustrative
Illustrative: GFT hour-by-hour line-board layout for one 8-hour sewing shift (40 operators, 24.0 SMV woven trouser, ramp cohort week 4 at 65 percent target efficiency). Values are illustrative design entries showing how balanced in-process and end-of-process measures (S02), daily line balancing (S04) and unmerged downtime causes (S07) appear with an explicit owner and response window. Not an observed GFT factory result.
Unit: illustrative shift-board control blocks. Status: illustrative. Sources: S02, S04, S07. Illustrative figures are teaching examples, not measured factory results.
Read this exhibit in chapter 11 ↗Credible factory improvement starts by establishing where performance stands before interventions begin, yet greenfield plants often reduce their baseline to a single undated average. In the ILO's Sustainable Competitive and Responsible Enterprises (SCORE) programme in Ethiopia's garment industry, trainers begin at step zero with a baseline assessment to identify operational gaps and measure change through key performance indicators.[S05] Statistical process control (SPC) likewise rests on comparing what is happening today with what happened previously.[S09] Neither approach works if "what happened previously" is an undocumented mix of different styles, shifting rosters, fabric substitutions and provisional sewing methods.
In a greenfield garment factory, the first shifts generate a dated, conditional population of observations rather than a static scalar. If Line A produces a basic crew-neck T-shirt during its first fortnight and switches to a woven work shirt in week three, pooling those two periods into one monthly "baseline efficiency" destroys comparability. Output changes between those windows reflect work content and method novelty, not worker effort. GFT therefore defines a baseline as a governed data object carrying a mandatory cohort header.[S05][S09]
No baseline figure may enter a management review or planning tool unless six boundary attributes are recorded alongside the numeric value:
The same discipline applies when sponsors compare early baselines with external benchmarks. In Cameroon, shared evidence records show a statutory minimum wage (SMIG) raised from XAF 41,875 a month in 2023 to XAF 43,969 a month from 23 February 2024 (about US$76, up 5 percent), a 40-hour standard week, a 48-hour maximum and overtime at 1.2 times base pay, with informal employment near 90 percent of the workforce.[R14][R16] A greenfield plant recruiting first-time industrial operators draws from a distinct labour-market population.[R16] Its initial two-week baseline measures that cohort's starting point; it cannot be compared directly with a mature plant in Cambodia (where the 2024 garment minimum wage was US$204 a month) without adjusting for tenure, style familiarity and supervision.[R17][S13]
2.7
Exhibit 075 paragraphs step it
Read-out
| Style and family | Style ID and construction class | Open new cohort; keep family link | Prevents mixing unlike garment complexity |
|---|---|---|---|
| Method and SMV | Approved breakdown and SMV version | Freeze prior series; start new cohort | Separates method redesign from operator speed |
| Line and shift | Line ID, shift and core roster share | Flag break if turnover exceeds rule | Isolates staffing churn from individual learning |
| Material and setup | Fabric batch class and machine setup | Split cohort on material spec change | Prevents fabric faults reading as operator error |
| Observation window | Start date, bundles and shift count | Require sample before narrowing band | Stops single-shift noise setting fixed targets |
Derived by GFT from baseline assessment principles in S05, method-based SMV conditions in S03, daily line-balancing practice in S04 and ramp process-learning constraints in S11. No factory values are assumed.
Unit: cohort governance attributes. Status: derived. Sources: S03, S04, S05, S11.
Industrial engineers rely on learning curves to plan capacity and pace line targets, yet learning curves are misapplied when factories ignore the assumptions behind standard work. As documented in the ILO's analysis of textiles and clothing in Ghana, the Standard Minute Value (SMV) is the time value arrived at for a task based on the average rate of output that qualified workers naturally achieve without over-exertion, provided they know and adhere to the specified method and are motivated to apply themselves.[S03] Similarly, the IFC and ILO Better Work assessment of Bangladesh garment factories notes that engineers break each style into steps, assign an SMV to each step for a fully efficient operator, multiply the garment SMV by completed pieces to obtain output minutes, and divide by available sewing minutes to calculate line efficiency, which in their factory sample typically ranged between 45 percent and 70 percent on any given day.[S14][S0204]
Every word in the SMV definition constrains how a learning curve may be read during ramp-up.[S03] When a greenfield line starts, operators are still becoming "qualified workers", the "specified method" is being refined on the floor, and supervisors are still learning daily balancing.[S03][S04] If an engineer alters the sewing method, adds a folder attachment, or changes seam construction, the task denominator has changed. Plotting output across that change as one uninterrupted learning curve mixes method engineering with human repetition.
To keep learning trajectories honest, GFT enforces explicit reset rules in master data. Exhibit 7 summarises how GFT derives cohort attributes and reset triggers from garment-engineering and ramp-up principles.[S03][S04][S05][S11]
As Exhibit 7 makes clear, a cohort reset is not an admission of failure; it is an accounting boundary that preserves analytical truth.[S03][S11] When a line moves to a new garment style, or when industrial engineering issues a revised operation breakdown that alters station work content, the system freezes the prior cohort series and opens a new cohort linked by product family. Because Exhibit 7 is a governance contract rather than an empirical dataset, it cannot tell a plant manager how many days a specific reset will cost; instead, it ensures that once the reset occurs, the factory measures the true recovery time rather than burying it inside a smoothed monthly average.
Interlude · the cloth half-woven
Woven: sections 2.0 to 2.7Still to weave: 2.9 to 2.18
Read-out
Illustrative worked example. 64 OTIF orders + 16 orders not OTIF = 80 orders due. OTIF rate = 64 / 80 x 100 = 80 percent. These are assumed values used to demonstrate the calculation.
Unit: illustrative orders and percent. Status: illustrative. Illustrative figures are teaching examples, not measured factory results.
Read this exhibit in its chapter ↗2.9
A greenfield ramp-up plan typically budgets for pre-launch induction training, yet treats operator competence as permanent once a course ends. Apparel research refutes that assumption. A study in Applied Sciences evaluating manual skill degradation in apparel manufacturing shows that sewing skills degrade when operators leave specific manual tasks or when task allocations shift, concluding that capacity-development drills are essential to maintain an efficient required skill set.[S12] During ramp-up, skill decay and partial mastery appear constantly: an operator trained on pocket setting during induction may spend three weeks on simple overlocking while early styles run, then struggle when rotated back to complex top-stitching.
Counting training hours or certificates measures an input, not line capability. Supervisors face daily absenteeism, uneven cycle times and style changeovers that require moving operators across operations.[S04][S18] If the skill matrix only records classroom attendance, the supervisor discovers too late that the operator cannot hold seam tolerance at line speed. Better Work research also shows that using excessive overtime to cover skill gaps leads to poorer worker health, reduced efficiency, higher accident rates, dissatisfaction and turnover, stripping learned skill from the line.[S18]
Every Level 2, Level 3 and Level 4 entry carries a last-demonstrated date and method revision tag. If an operator has not performed a complex operation within a defined recency window (such as an illustrative 30 to 45 days) or if the method revision changes, the matrix flags a refresher drill before scheduling that operator as full capacity.[S12]
…window (such as an illustrative 30 to 45 days) or if the method revision…
S12 · illustrative
2.12
Operators do not learn in a vacuum; the line supervisor controls the daily conditions under which operator learning consolidates or breaks down. In garment manufacturing, line balancing is performed systematically by line supervisors before production through planning and daily during production.[S04] When a bundle queue builds up, thread breaks recur, or a seam allowance drifts, the supervisor's response determines whether the event becomes a coaching moment or a confrontation that triggers defects and turnover.
Empirical garment-sector studies confirm both the value of supervisory training and the time required for classroom concepts to become floor habit. In a randomised evaluation of supervisory skills training in Cambodia published by the International Finance Corporation (IFC), training improved workers' perceptions of supervisor relationships and produced moderate improvements in firm productivity.[S13] Similarly, the 2025 IFC and ILO Better Work assessment of the Gender Equality and Returns (GEAR) programme in Bangladesh documented that trainees attended classroom sessions two days a week for five weeks while working as trainee supervisors on remaining days, and then shadowed a supervisor on a production line for another six to eight weeks.[S14]
Even when supervisor candidates hold strong technical skills, floor performance takes months to stabilise because supervision is both a social and an operational role. In a field experiment across 24 large Bangladesh garment factories published by the National Bureau of Economic Research (NBER), researchers randomly assigned marginal female and male supervisor candidates to manage production lines.[S0202] Baseline diagnostics showed few skill differences between them; initially, however, marginal female candidates had lower line productivity and subordinate evaluations due to negative initial worker beliefs, before those gaps disappeared after four to six months and female supervisor retention rose significantly above baseline.[S0202]
In a field experiment across 24 large Bangladesh garment factories published by the National…
S0202
That four-to-six-month convergence window in the NBER study and the six-to-eight-week shadowing period in GEAR carry a direct lesson for ramp planning.[S14][S0202] A factory cannot run a brief pre-launch workshop and expect mature line balancing on shift one, nor should it treat early transitional friction as proof that trainee supervisors lack capability.[S0202]
Rather than grading supervisors solely on raw output during early shifts, GFT gates supervisory readiness on four observable behaviours during shadowing and initial line assignment:
2.15
When sponsors prepare a greenfield business case or evaluate workforce training during ramp-up, it is tempting to paste headline percentage gains from published studies into financial spreadsheets as guaranteed productivity jumps. Rigorous performance management prohibits that shortcut. Published apparel evaluations owe their credibility to explicit counterfactual designs (randomised control groups, matched comparison lines and regression controls for style complexity), and their exact magnitudes belong to the populations studied.[S13][S15][S0204]
Consider the controlled apparel training evaluation reported by the World Bank's Development Impact group from a ready-made garment export firm in southern India, where female operators worked in lines of about 70 to 100 workers benchmarked by standard allowable minutes.[S15] Researchers randomised lines and workers within treatment lines to receive an 80-hour soft-skills programme (Personal Advancement and Career Enhancement) over 11 months.[S15] Controlling for garment style fixed effects and tenure, trained workers produced about 6 more garments per hour, or 10 percent more relative to the control group, while efficiency rose by 7 percentage points (12 percent relative to controls) and technical training requests rose by about 15 percentage points.[S15] Overall attrition across 26 months left about 25 to 30 percent of the initial sample, and the productivity gains materialised after training completed rather than during the course.[S15]
…worked in lines of about 70 to 100 workers benchmarked by standard allowable minutes
S15
A second controlled case comes from the 2025 IFC and ILO Better Work assessment of the GEAR supervisor programme in Bangladesh.[S14][S0204] Controlling for garment complexity and factory characteristics, lines managed by GEAR-trained supervisors were 2.5 percentage points more efficient than lines managed by all comparison supervisors (rising from 57.0 percent to 59.5 percent, not statistically significant in the pooled sample), and 4.3 percentage points more efficient than lines managed exclusively by male comparison supervisors (rising from 57.0 percent to 61.3 percent, significant at the 0.05 level).[S0204]
Read-out
Sourced from controlled evaluations in S15 (Indian apparel PACE study: output +10 percent and efficiency +12 percent relative to control indexed at 100) and S0204/S14 (Bangladesh GEAR study: line efficiency 57.0 percent vs 59.5 percent all comparison lines, and 57.0 percent vs 61.3 percent male-supervised comparison lines). Complemented by S13. Case results are non-transferable.
Unit: index (control = 100) or line efficiency (%). Status: sourced. Sources: S15, S13, S14, S0204.
Inspecting Exhibit 8 shows why the counterfactual is as important as the treatment outcome.[S15][S0204] In the Indian PACE study, the 10 percent output gain (100.0 to 110.0) and 12 percent relative efficiency gain (100.0 to 112.0) were identified only by tracking untreated control lines on comparable styles and controlling for tenure.[S15] In the Bangladesh GEAR study, the contrast between the 2.5 percentage-point gap against all comparison supervisors (57.0 percent to 59.5 percent) and the 4.3 percentage-point gap against male comparison supervisors (57.0 percent to 61.3 percent) depended on specifying the comparison group and controlling for style complexity.[S0204] What Exhibit 8 cannot establish is that a new factory in Cameroon will automatically gain 6 garments an hour or 4.3 percentage points of efficiency by running a similarly named course.
the 10 percent output gain (100.0 to 110.0) and 12 percent relative efficiency gain…
S15
A commercial factory does not need an academic team to apply counterfactual discipline during ramp-up, but it needs a structured comparison rule:
2.18
Method adherence
Method adherence: Review horizon (shift to monthly) 2, Direct line controllability during ramp 8.8. Quadrant: Shift learning controls · illustrative
The final requirement of a ramp-as-learning system governs how targets are set and tightened.
In-line FPY
In-line FPY: Review horizon (shift to monthly) 3, Direct line controllability during ramp 8. Quadrant: Shift learning controls · illustrative
Skill coverage
Skill coverage: Review horizon (shift to monthly) 6.2, Direct line controllability during ramp 7.8. Quadrant: Cohort capability gates · illustrative
Huddle quality
Huddle quality: Review horizon (shift to monthly) 3.8, Direct line controllability during ramp 7.2. Quadrant: Shift learning controls · illustrative
Cycle stability
Cycle stability: Review horizon (shift to monthly) 4.6, Direct line controllability during ramp 6.4. Quadrant: Shift learning controls · illustrative
Shift good output
Shift good output: Review horizon (shift to monthly) 6.8, Direct line controllability during ramp 4.2. Quadrant: Lagging commercial outcomes · illustrative
Cohort unit cost
Cohort unit cost: Review horizon (shift to monthly) 8.5, Direct line controllability during ramp 2.5. Quadrant: Lagging commercial outcomes · illustrative
The method behind it: GFT note.
Illustrative GFT classification of ramp indicators across review horizon (x-axis: 0 = immediate shift check to 10 = monthly cohort review) and line controllability (y-axis: 0 = lagging outcome to 10 = direct line control), applying S02, S05 and S45. Coordinates are conceptual placements, not empirical measurements.
Unit: illustrative horizon and controllability score (0 to 10). Status: illustrative. Sources: S02, S05, S45. Illustrative figures are teaching examples, not measured factory results.
2.18 · the bodyExhibit 09 above; its figures are illustrative where marked.
The final requirement of a ramp-as-learning system governs how targets are set and tightened. United States Office of Personnel Management guidance states that performance standards should be measurable, understandable, verifiable, equitable and achievable.[S45] Imposing a narrow steady-state efficiency quota on a greenfield line in Week 2 violates those criteria. When the process is poorly understood, standard minutes are provisional and inspection repeatability is unverified, a single-point quota is neither verifiable nor equitable.[S11][S40][S45]
To keep standards equitable and actionable during ramp-up, management must match each indicator to the horizon and controllability appropriate to the line's maturity. Following the ILO principle of combining in-process and end-of-process metrics across behavioural, process and financial dimensions, Exhibit 9 maps seven ramp indicators across review horizon and direct line controllability.[S02][S05][S45]
In Exhibit 9, the upper-left quadrant (Shift learning controls) holds measures that operators and supervisors influence immediately: method adherence (illustrative coordinates 2.0, 8.8), in-line first-pass yield (3.0, 8.0), supervisor huddle quality (3.8, 7.2) and cycle stability (4.6, 6.4).[S02][S04] The upper-right quadrant (Cohort capability gates) holds demonstrated skill coverage (6.2, 7.8), built through weekly drills.[S12] Shift good output (6.8, 4.2) and cohort unit cost variance (8.5, 2.5) sit in the lower-right quadrant (Lagging commercial outcomes) because early ramp output and cost depend heavily on cutting readiness, material batches, machine debugging and cohort age.[S11] Holding operators accountable for lagging unit cost variance in Week 2 is inequitable; holding line leadership accountable for method adherence, in-line defect capture and daily balancing is both equitable and verifiable.[S04][S45]
NIST statistical process control guidance explains that control limits are chosen so almost all points fall within them while the process remains in control, and that capability indices ($C_p$, $C_{pk}$) compare an in-control, stable process against specification limits using a sample size generally thought to be about 50 independent data values.[S09][S0201][S0203] Furthermore, NIST guidance on Gauge R&R studies emphasises characterising gauge and observer errors (repeatability, reproducibility and bias) so measurement error is not mistaken for process variation.[S40]
In the opening shifts of a garment cohort, the process is not yet in statistical control, completed shifts are far below 50 independent observations, and visual inspectors are still calibrating agreement on stitch faults.[S11][S40][S0201] Setting a tight point target under those conditions rewards noise and punishes honest defect reporting. Instead, GFT narrows review bands across four capability gates, as illustrated in Exhibit 10.[S09][S40][S45][S0201]
The four gates in Exhibit 10 translate statistical and managerial readiness into practical floor governance:
Again, the specific percentages in Exhibit 10 are illustrative planning bands rather than a universal norm: remember that even across established garment factories in the Bangladesh GEAR sample, typical daily line efficiency ranged widely between 45 percent and 70 percent.[S14][S0204] The transferable principle is the gating logic itself: a target earns the right to be narrow only when the underlying process, skill matrix, supervisory routine and measurement system have proved they can support it.
typical daily line efficiency ranged widely between 45 percent and 70 percent
S14, S0204 · illustrative
By treating ramp-up as a governed learning system, the factory protects its earliest shifts from theatrical targets and builds a clean, cohort-indexed record from day one. With that learning contract in place, the next chapter examines how strategic business outcomes cascade into the six KPI families and line-level measures without losing operational control.
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.
25 sources · 180 citation marks on this pageMost cited: S03, 17 times
Grade AGrade BGrade CKnot size: citations on this page
All 25 records shown
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.
R10 · Grade C · inherited record
Inherited evidence R10
Camtext SA: 12,000 t a year processed; about 12,000 jobs at the Dibamba hub and 3,000 at Garoua; return on investment near 11 years; optimal capacity around 2033.
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.
R14 · Grade C · inherited record
Inherited evidence R14
Cameroon minimum wage (SMIG) XAF 41,875 a month in 2023, raised to XAF 43,969 a month from 23 February 2024, about US$76, a rise of 5 percent.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
R16 · Grade C · inherited record
Inherited evidence R16
Informal share of employment about 90 percent; standard 40-hour week, 48-hour maximum, overtime at 1.2 times.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
R17 · Grade C · inherited record
Inherited evidence R17
Cambodia garment-sector minimum wage US$204 a month for 2024 (labour-cost benchmark).
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
c02-S02 · Grade A
Lean Manufacturing Techniques for Ready Made Garments Industry
The best approach is to balance the metrics you use among the three categories (financial, behavioral, and core-process) and to use a mix of in-process and end-of-process metrics.
2017-09-18. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S03 · Grade A
Sector systems analysis of textiles and clothing subsector in Ghana
A particularly important calculation is the Standard Minute Value (SMV), which is defined as “the time value arrived at for a task based on the average rate of output which qualified workers will naturally achieve without over exertion provided that they know and adhere to the specified method and provided that they are motivated to apply themselves to their work.“
2024-01-17. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S04 · Grade A
Implementation of the Factory Improvement Toolset in the Asian garment sector: Final report
Line balancing is done by line supervisors systematically, before production through planning, and during production, daily.
2023-03-21. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S05 · Grade A
Advancing productivity, quality improvement and decent work in Ethiopia's garment industry: results so far
As step zero, SCORE trainers conduct a baseline assessment to identify existing gaps and opportunities for improvement in SMEs, and measure change through Key Performance Indicators (KPIs).
2022-06-01. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S09 · Grade A
e-Handbook of Statistical Methods: What are Process Control Techniques?
The underlying concept of statistical process control is based on a comparison of what is happening today with what happened previously.
2003-06-01. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S10 · Grade A
First Pass Yield Improvement Creates Dramatic Reduction in Lead Time
In addition, total processing time went from 11.8 hours to 3.4 hours and first pass yield jumped from 10% to 90%.
2022-08-04. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S11 · Grade B
Learning and process improvement during production ramp-up
During that time, the new production process is ill understood, which causes low yields and low production rates.
2001-10-26. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S12 · Grade B
Evaluation of Manual Skill Degradation Due to Automation in Apparel Manufacturing
Therefore, capacity development drills of sewing operators are essential to maintain an efficient required skill set.
2021-11-23. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S13 · Grade B
Supervisory Skills Training in the Cambodian Garment Industry: A Randomized Impact Evaluation
The study showed that the training improved workers’ perceptions of relationships with their supervisors and led to moderate improvements in overall firm productivity.
2009-11-01. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S14 · Grade B
Creating Better Jobs for Women and Boosting Productivity in Bangladesh's Garment Factories: An Assessment of the Gender Equality and Returns Program
After completing classroom sessions, trainees continued to shadow a supervisor on a production line for another six to eight weeks.
2025-07-16. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S15 · Grade B
Worker productivity and soft skills
Production of the workers who get the training goes up, as they produce about 6 more garments per hours, or 10% more relative to the control group.
2017-09-07. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S18 · Grade B
Excessive Overtime, Workers and Productivity: Evidence and Implications for Better Work
It has been shown that excessive overtime can lead to poorer worker health, reduced efficiency, high rates of accidents, and worker dissatisfaction, resulting in high worker turnover.
2023-03-28. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S40 · Grade A
e-Handbook of Statistical Methods: Gauge R & R studies
The purpose of this section is to outline the steps that can be taken to characterize the performance of gauges and instruments used in a production setting in terms of errors that affect the measurements.
2003-06-01. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S45 · Grade A
Performance Management Cycle
Performance elements and standards should be measurable, understandable, verifiable, equitable, and achievable.
2019-11-04. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S0201 · Grade A
e-Handbook of Statistical Methods: What is Process Capability?
Most capability indices estimates are valid only if the sample size used is "large enough". Large enough is generally thought to be about 50 independent data values.
2003-06-01. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S0202 · Grade B
Challenges of Change: An Experiment Promoting Women to Managerial Roles in the Bangladeshi Garment Sector
Three sets of results emerge: (i) extensive diagnostic testing at baseline reveal few skill differences between marginal female and male supervisor candidates; (ii) initially, marginal female candidates have lower productivity and evaluations from sub-ordinate workers, though after four to six months, these gaps disappear; and (iii) the share of the female candidates retained as line supervisor after the trial is significantly higher than the share of female supervisors in the factories at baseline.
2020-08-03. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S0203 · Grade A
e-Handbook of Statistical Methods: What are Control Charts?
These control limits are chosen so that almost all of the data points will fall within these limits as long as the process remains in-control.
2003-06-01. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c02-S0204 · Grade B
Creating Better Jobs for Women and Boosting Productivity in Bangladesh's Garment Factories: An Assessment of the Gender Equality and Returns Program
When compared with lines managed by male comparison supervisors, lines managed by the female trainees were 4.3 percentage points more efficient, an increase from 57 percent to 61.3 percent, a difference that is significant at the 0.05 level.
2025-07-16. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗S11 International Journal of Production Economics, Elsevier. 2001-10-26.
S12 Applied Sciences, MDPI. 2021-11-23.
R06 Cameroon Trade Hub. Publication date unrecorded; inherited source-text check.
R07 Ecofin Agency. Publication date unrecorded; inherited source-text check.
R10 Ecofin Agency. Publication date unrecorded; inherited source-text check.
R12 Business in Cameroon. Publication date unrecorded; inherited source-text check.
S02 International Labour Organization. 2017-09-18.
S10 National Institute of Standards and Technology, Manufacturing Extension Partnership. 2022-08-04.
S05 International Labour Organization. 2022-06-01.
S09 NIST/SEMATECH. 2003-06-01.
S03 International Labour Organization. 2024-01-17.
S04 International Labour Organization. 2023-03-21.
S18 ILO and IFC, Better Work. 2023-03-28.
S40 NIST/SEMATECH. 2003-06-01.
R14 WageIndicator. Publication date unrecorded; inherited source-text check.
R16 WageIndicator. Publication date unrecorded; inherited source-text check.
R17 just-style. Publication date unrecorded; inherited source-text check.
S13 International Finance Corporation. 2009-11-01.
S0201 NIST/SEMATECH. 2003-06-01.
S14 IFC and ILO, Better Work. 2025-07-16.
S0204 IFC and ILO, Better Work. 2025-07-16.
S0202 National Bureau of Economic Research. 2020-08-03.
S15 World Bank. 2017-09-07.
S45 United States Office of Personnel Management. 2019-11-04.
S0203 NIST/SEMATECH. 2003-06-01.
Keep the thread · chapter 02 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: The measurement contract
A factory without an operating baseline cannot rely on hindsight, so GFT treats measurement as part of production from the outset.
02Part I: The measurement contract
Early targets should describe a controlled learning trajectory, with yield, rate and capability observed together rather than assumed away.
03Part I: The measurement contract
Strategic intent becomes an operating system when each line measure carries a decision, an owner, a feasible record and a guardrail.
04Part I: The measurement contract
Operational definitions, data-quality controls and measurement-system analysis must precede target comparisons and automated analytics.
05Part II: Measure the operating system
Good output is the result, while the production system must retain the component losses that explain why the result occurred.
06Part II: Measure the operating system
The quality system should connect first-pass yield, defect causes and variation so that inspection leads to prevention rather than sorting alone.
07Part II: Measure the operating system
Standard work, balanced capacity and order-level flow records must support the delivery promise before expediting becomes routine.
08Part II: Measure the operating system
A low nominal wage does not establish a low cost per good garment, so the people scorecard must include skill, supervision, attendance and working-time effects.
09Part II: Measure the operating system
Energy, water, waste and emissions become manageable only when each quantity has a declared boundary, source record and production denominator.
10Part III: Turn records into decisions
Source capture, master data and explicit stewardship turn daily logs into an operational asset without requiring a heavy platform on day one.
11Part III: Turn records into decisions
Daily correction, weekly problem solving and monthly reconciliation give each signal a forum, an authority level and a recorded response.
12Part III: Turn records into decisions
A corrective-action system should distinguish abnormal variation, test causes, verify effects and update standard work rather than count completed actions.
13Part IV: Scale with control
The factory should earn each digital step through reliable records, active management and verified improvement rather than buy a platform to compensate for their absence.
14Part IV: Scale with control
GFT recommends four gated stages: clean capture, active cadence, verified improvement and a data-driven floor, with value measured in the same system throughout.