Research libraryMeasure Before You Model
Chapter 14 of 14 · Part IV · Draft web edition
GFT recommends four gated stages: clean capture, active cadence, verified improvement and a data-driven floor, with value measured in the same system throughout.
The cloth is woven from this chapter's 4 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 programme roadmap sequencing pre-shift contract approval (month 0 to 1), Stage 1 clean capture (months 1 to 3), Stage 2 active cadence (months 3 to 8, covering months four to eight of full operation), Stage 3 verified improvement (months 8 to 12, covering months nine to twelve), Stage 4 connected digital uses (months 12 to 18, covering months thirteen to eighteen), and ongoing benefit verification (months 9 to 18). Timing is a planning design, not a guaranteed schedule.
Unit: planning months (0 to 18). Status: illustrative. Sources: S01, S05, S24, S41, S42, S43, S44, S27. Illustrative figures are teaching examples, not measured factory results.
The first eighteen months of a greenfield garment factory should install a governed performance-management system across four gated stages: clean capture in months one to three, an active management cadence in months four to eight, a verified improvement loop in months nine to twelve, and connected digital applications in months thirteen to eighteen, where the month boundaries define an illustrative planning design rather than a fixed calendar promise.[S01][S05][S27][S43][S44] Rather than treating performance management as a retrospective reporting layer added after production stabilises, GFT engineers the measurement contract into the physical operation before the first bundle of fabric is cut.[S01][S1401] Every stage produces an auditable operational asset that serves as the entry condition for the next stage: unambiguous metric definitions enable validated shift logs, validated logs feed daily and monthly decision forums, decision forums direct root-cause countermeasures, and verified historical records provide the training foundation for selective sensing and machine learning.[S23][S27][S41][S42] By measuring both factory output and system health inside the same governed architecture from the first shift, sponsors and plant leaders build a facility that can prove the causes of its own performance.[S02][S43]
This disciplined sequence is vital when a new cut, make, trim, and finishing plant operates inside an industrialising raw-material economy. As of the 31 January 2024 programme evidence cut-off, about 95 percent of Cameroon's cotton fibre is exported raw while about 5 percent is processed locally, against a national industrialisation target of 50 percent local processing by 2030.[R06][R07] Those sector figures define the strategic importance of competent domestic manufacturing, but they do not guarantee commercial margins or steady-state throughput for any individual factory.[R06][R07] Empirical ramp-up research shows that a newly launched production process is initially poorly understood, causing low yields and low production rates to occur together while operators, technicians, and supervisors learn the work.[S11] If a greenfield plant attempts to copy the software stack of a mature global apparel chain on day one, or chases headline volume targets without tracking rework, downtime causes, and working-time guardrails, it destroys the evidence needed to stabilise the floor.[S07][S10][S18] The eighteen-month roadmap resolves that tension by matching management complexity to demonstrated operational readiness.
14.1
Related · chapter 056 paragraphs step it
Read-out
Derived from an illustrative 480-minute shift (40-hour five-day week in R16) using OEE components in S06 and unmerged downtime in S07: 480 scheduled less 30 break minutes = 450 planned minutes; less 45 downtime minutes (20 failure, 15 changeover, 10 waiting) = 405 run minutes; less 81 speed-loss minutes (80 percent performance) = 324 standard minutes; less 16.2 quality-loss minutes (95 percent first-pass yield) = 307.8 good minutes (68.4 percent OEE).
Unit: productive-equivalent minutes per 480-minute shift. Status: derived. Sources: S06, S07, R16.
Read this exhibit in its chapter ↗A performance-management system begins before commissioning, when factory sponsors and operational leaders approve the metric contracts, master-data vocabularies, role accountabilities, and baseline rules as mandatory pre-shift deliverables.[S01][S22][S45] Public-sector digital delivery guidance states the governing rule plainly: teams should work out their measurements as they build the service, not afterwards, and establish a baseline against which subsequent changes are judged.[S1401] Waiting until month three of live sewing to debate whether first-pass yield excludes repaired garments or whether machine availability excludes material waiting time guarantees that the first quarter of ramp-up history will be irreconcilable.[S06][S07][S10]
GFT therefore places a formal pre-shift approval gate ahead of month one. Before production starts, the plant director, industrial-engineering lead, quality lead, human-resources lead, sustainability lead, and data steward sign off on the foundational operating charter across all six programme families: production, quality, delivery, cost, people, and sustainability.[S02][S22] In keeping with garment-sector lean guidance, this charter balances financial, behavioural, and core-process metrics while pairing in-process leading indicators with end-of-process lagging outcomes.[S02] Every approved key performance indicator (KPI) is documented using the structure of ISO 22400-2, stating its exact formula, constituent elements, unit of measure, time behaviour, counting boundary, and rounding convention.[S01] Simultaneously, every performance standard attached to those metrics is tested against public-administration performance criteria to verify that expectations are measurable, understandable, verifiable, equitable, and achievable under ramp-up conditions.[S45]
To prevent ambiguity during initial line commissioning, the pre-shift charter establishes four operational artefacts before the first shift clock starts:
To show how the pre-shift charter initiates the wider eighteen-month sequence, Exhibit 64 presents GFT's illustrative programme roadmap across the four gated stages and the parallel benefit verification stream.[S05][S27][S42][S43][S44]
The numeric planning window in Exhibit 64 illustrates how each stage builds on the previous stage rather than launching all workstreams in month one.[S27][S44] Pre-shift preparation occupies the initial commissioning window from point 0 to point 1 on the illustrative month scale, ensuring that Stage 1 capture begins immediately when the first sewing line starts. Subsequent stages advance only when their entry gate criteria are verified, and the benefits register runs continuously from month 9 through month 18 to satisfy post-implementation monitoring requirements.[S43] Because Exhibit 64 is an illustrative planning design, it cannot predict local construction delays, customs clearance times, or recruitment pace; if a factory requires an illustrative five months instead of three to pass measurement-error checks, the calendar shifts while the gate order remains fixed.
14.3
During the illustrative window of months one to three, the factory's priority in performance management is establishing clean, complete, and reconciled source capture at the point of work.[S05][S23][S24] Garment productivity programmes such as the International Labour Organization (ILO) SCORE methodology begin at step zero with a structured baseline assessment to identify operational gaps and measure subsequent change through KPIs.[S05] In a greenfield plant, months one to three construct that step-zero baseline across the initial production cohorts. Rather than deploying complex analytics, the operations and engineering teams launch controlled manual or lightweight local-form logs for line output, downtime, first-pass quality, attendance, skill demonstrations, and utility meters.[S12][S23][S39]
Every capture sheet introduced in this stage is engineered as a structured dataset governed by ISO/IEC 25012 and checked against the six core data-quality dimensions defined by DAMA UK and the UK Government Data Quality Framework: completeness, validity, consistency, timeliness, uniqueness, and accuracy.[S23][S24] Following agile alpha-phase practice, where teams test solutions to discovery problems on a bounded scope before full build, the data steward and industrial engineers test the recording forms on the first commissioned sewing line, remove ambiguous fields, and verify that line clerks and supervisors can complete entries within shift boundaries.[S1402]
To pass Gate One at the end of month three, the factory must demonstrate that its core operational logs satisfy five verification checks across the floor:
14.5
Pre-shift: KPI contracts & IDs
Pre-shift: KPI contracts & IDs. Starting point. Leads to Gate 1 (M3): Clean logs & MSA (Enables clean capture). · derived
Once Gate One confirms that source records are complete and measurement error is bounded, the illustrative window of months four to eight installs the management cadence that turns passive logs into owned operational decisions.
Gate 1 (M3): Clean logs & MSA
Gate 1 (M3): Clean logs & MSA. From Pre-shift: KPI contracts & IDs (Enables clean capture). Leads to Gate 2 (M8): Huddles & S&OP (Feeds trusted cadence). · derived
Gate 2 (M8): Huddles & S&OP
Gate 2 (M8): Huddles & S&OP. From Gate 1 (M3): Clean logs & MSA (Feeds trusted cadence). Leads to Gate 3 (M12): A3s & standards (Selects priority gaps). · derived
Gate 3 (M12): A3s & standards
Gate 3 (M12): A3s & standards. From Gate 2 (M8): Huddles & S&OP (Selects priority gaps). Leads to Gate 4 (M18): Edge & ML uses (Supplies clean history). · derived
Gate 4 (M18): Edge & ML uses
Gate 4 (M18): Edge & ML uses. From Gate 3 (M12): A3s & standards (Supplies clean history). Leads to Board: Audited benefits (Proves residual value). · derived
Board: Audited benefits
Board: Audited benefits. From Gate 4 (M18): Edge & ML uses (Proves residual value). End point. · derived
Derived GFT stage-gate handoff sequence combining pre-shift measurement design (S01, S1401), baseline and data-quality control (S05, S24), monthly S&OP cadence (S42), A3 problem solving and post-completion benefit monitoring (S41, S43), and data-gated machine learning (S27).
Unit: . Status: derived. Sources: S01, S05, S24, S41, S42, S43, S27, S1401.
14.5 · the bodyExhibit 65 above; its figures are illustrative where marked.
Once Gate One confirms that source records are complete and measurement error is bounded, the illustrative window of months four to eight installs the management cadence that turns passive logs into owned operational decisions.[S42][S44] A factory improves when each management tier reviews its assigned signals on a fixed schedule, diagnoses constraints within its authority, and escalates structural bottlenecks with intact evidence.[S08][S22] Research on sales and operations planning (S&OP) maturity emphasises that evolutionary progress requires advancing meetings and collaboration, organisational structure, and integrated planning alongside performance measures and information technology.[S44]
During this second stage, GFT requires every review forum in the plant to read from the single authoritative record established in Stage 1.[S22][S24] Private supervisor notebooks, unverified slide decks, and parallel finance spreadsheets are barred from management meetings. When the daily line huddle, the weekly plant problem-solving meeting, and the monthly S&OP review all query the same validated shift tables, discussions move immediately from arguing over whose piece count is correct to deciding how to remove the constraint on the floor.[S21][S22][S42]
A critical test during months four to eight is ensuring that the KPI cascade does not harden into an arbitrary top-down command structure. Scholarship on the balanced scorecard notes that a purely rational, top-down execution model can fail in decentralised or adaptive environments where front-line conditions evolve rapidly.[S46] In a greenfield garment factory, fabric batches vary, sewing methods undergo refinement, and provisional SMVs occasionally misstate the work content of a complex seam.[S03][S11] GFT therefore builds a formal bottom-up challenge route into the Stage 2 cadence: if a line supervisor or operator team demonstrates through clean shift logs that a missed target resulted from ungraded fabric defects, compressor pressure drops, or an unachievable provisional standard, the weekly review reclassifies the variance as an engineering or supply action rather than a line failure.[S16][S45][S46] This rule preserves the equity and verifiability of performance standards and aligns compensation with behaviours that workers actually control, notably defect reduction and attendance.[S16][S45]
The dependency chain in Exhibit 65 explains why GFT treats calendar shortcuts as an operational hazard.[S27][S44] Gate 2's monthly S&OP and daily huddles cannot function if Gate 1 has not eliminated competing piece counts and uncalibrated defect grading.[S24][S40][S42] Equally, Gate 3's structured improvement projects rely on Gate 2's reviews to select problems based on strategic consequence, and Gate 4's predictive models depend on Gate 3 having stabilised standard work and labelled historical exceptions.[S27][S41] While Exhibit 65 establishes the logical order of deliverables, it does not imply that earlier stages stop once passed; capture validation, daily huddles, and A3 problem solving continue permanently as the spine of the plant.
Interlude · the cloth half-woven
Woven: sections 14.0 to 14.5Still to weave: 14.8 to 14.14
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 ↗14.8
By month nine on the illustrative roadmap, the factory possesses six months of reconciled shift history and a stable rhythm of daily, weekly, and monthly reviews. Months nine to twelve use that foundation to prove the closed-loop improvement system, moving the plant from reactive containment to verified root-cause elimination and standard-work revision.[S08][S09][S41][S43] Factories often stall here because they confuse activity with improvement, logging corrective actions while line OEE, rework hours, and energy per piece remain unchanged.[S08] Stage 3 closes that loophole by requiring teams to complete end-to-end improvement cycles governed by statistical variation checks, reverse-tested A3 logic, guardrail verification, and formal benefits tracking.[S09][S41][S43]
Stage 3 closes that loophole by requiring teams to complete end-to-end improvement cycles governed…
S09, S41, S43
14.10
Only after the factory passes Gate Three at month twelve, having proven clean source capture, routine management cadence, and verified process improvement, does the illustrative roadmap enter Stage 4 (months thirteen to eighteen) to connect automated sensing, manufacturing execution modules, and machine-learning applications.[S27][S28][S44] This timing directly implements machine-learning engineering guidance: if machine learning is not absolutely required for an operation, teams should not use it until they have data, and they should benchmark complex models against simple heuristics first.[S27] By month thirteen, the greenfield plant possesses a year of governed, versioned, and reconciled operational history across styles, lines, operators, machines, and utility meters.[S01][S21][S23]
Stage 4 also follows public-sector beta-to-live service principles, where teams take solutions tested in earlier phases, build them for production scale, verify how they integrate with or replace existing workflows, and address technical constraints before live dependence.[S1403] Rather than purchasing a monolithic software suite, GFT introduces digital tools one approved use case at a time, using gradual MLOps automation and distributed edge computing so the floor remains resilient even when external telecommunications fail.[S26][S28]
14.12
A performance-management system proves its value only if it can distinguish genuine operational improvement from background shifts in product mix, order volume, material batches, or accounting rules.[S05][S43] The UK Department for Transport's benefits management and evaluation framework establishes the core governance principle: a formal framework ensures that expected benefits are defined upfront, assigned to owners, and managed and monitored beyond project completion.[S43] In a greenfield garment factory, raw KPI trajectories between month one and month eighteen reflect overlapping forces: operators climb learning curves, commercial teams shift from introductory T-shirts to higher-SMV woven shirts, utility baseloads spread across larger volumes, and inspection calibration tightens during Gauge R&R checks.[S03][S11][S36][S40]
GFT prevents false attribution by maintaining a governed benefits register as part of the system of record from Stage 3 onwards.[S22][S43] Every major intervention, whether an A3 line-balancing redesign, a supervisor coaching cohort, a compressor leak-repair campaign, or a Stage 4 RFID deployment, is entered into the register before implementation with a named benefit owner, a physical mechanism, a dated baseline population, adjustment rules, and cross-family guardrails.[S02][S41][S43]
Read-out
| Collar-attach folder and A3 method update (Quality lead) | Line 2 woven shirt cohort; reduce seam puckering and rework loop | Gross FPY rise adjusted for inspector Gauge R&R recalibration and style mix | Operation cycle time, operator ergonomics and zero overtime increase | Verified sustained at 8 weeks post-A3 |
|---|---|---|---|---|
| Supervisor 6 to 8 week line-shadowing cohort (HR and Production leads) | Lines 1 to 4 shift huddles; faster daily rebalancing and waiting reduction | Waiting-downtime drop adjusted for fabric-delivery delays on matched lines | Worker survey feedback, absence rate and first-pass yield stability | Verified sustained at 12 weeks post-coaching |
| Quick-changeover presser-foot kit and standard work (IE lead) | Small-batch style transitions; separate internal and external setup steps | Changeover minute reduction adjusted for batch-size variation across orders | First-bundle defect rate after style start and mechanic safety checks | Provisional: awaiting second monthly S&OP cycle |
| Compressor leak repair and header isolation (Facilities and Sustainability leads) | Plant compressed-air ring; eliminate off-shift leakage and pressure drops | kWh per 1,000 standard minutes adjusted for ambient load and shift volume | Pneumatic folder pressure stability and sewing availability | Verified sustained across 3 monthly meter closes |
Illustrative benefits-register table applying the post-completion monitoring discipline of S43, baseline KPI tracking from S05, A3 causal verification from S41, first-pass yield and flow linkage from S10, supervisor line shadowing from S14, and energy-intensity normalisation from S33. All entries describe governance structure and illustrative mechanisms, not realised GFT factory results.
Unit: . Status: illustrative. Sources: S43, S05, S10, S14, S33, S41. Illustrative figures are teaching examples, not measured factory results.
Each row in Exhibit 66 demonstrates how the benefits register prevents inflated performance claims.[S41][S43] The register binds the intervention to an accountable owner, names the physical mechanism tested, states which confounding variables were stripped out in the attribution bridge, and records the guardrail checks required for sign-off.[S02][S05][S43] Notice that the third entry in Exhibit 66 remains marked provisional until it completes its post-implementation monitoring window.[S43] Because Exhibit 66 is an illustrative governance template, it does not report empirical savings from an operating GFT facility; plant teams must populate every column with their own audited shift records.
To see how the benefits register arithmetic reconciles gross movement to verified residual value, consider an illustrative Stage 3 evaluation on a greenfield sewing line producing a standard woven workshirt over a four-week baseline cohort (Cohort A, month eight) and a four-week post-intervention cohort (Cohort B, month eleven) after an A3 collar-assembly method and supervisor line-balancing intervention.[S04][S41][S43] All figures in this worked example are explicitly illustrative and constructed solely to demonstrate the reconciliation method:[S01][S43]
14.14
Read-out
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 its chapter ↗Read-out
Illustrative design, not an implemented system claim. S01 supports explicit KPI definitions and S02 supports balanced process and outcome measures. The return path makes action verification part of the same chain.
Unit: illustrative GFT decision chain. Status: illustrative. Sources: S01, S02. Illustrative figures are teaching examples, not measured factory results.
Read this exhibit in its chapter ↗14.14 · Introduction
At the eighteen-month mark, the ultimate test of the performance-management system is how the sponsoring board and executive leadership evaluate the factory.
Exhibit 51, chapter 11: 1. Cumulative target vs actual
14.15 · Paired board indicators unite the six families with governance proof
In every quarterly board packet and at the month-eighteen Stage 4 review, plant leadership presents the governed indicators summarised in Exhibit 67 , keeping cohort denominators, range bounds, and benefit-register links open to inspection.
Exhibit 51, chapter 11: 1. Cumulative target vs actual
14.16 · Companion papers and interactive tools extend the operating system
This eighteen-month performance-management roadmap connects directly to the companion white papers and interactive decision tools in the GFT research library.
Exhibit 04, chapter 01: Work event
14.14 · the bodyThe film above follows these sub-steps.
At the eighteen-month mark, the ultimate test of the performance-management system is how the sponsoring board and executive leadership evaluate the factory. If board reviews examine only aggregate shipment volume and financial unit cost, leadership recreates at the governance level the blindness that OEE decomposition and first-pass yield rules prevented on the sewing floor.[S02][S06][S10] A factory can temporarily hit an eighteenth-month output target by deferring maintenance, burning out operators with excessive overtime, shipping reworked seconds as first-quality goods, or letting clerks fabricate missing meter reads.[S07][S18][S24] Conversely, a plant that reports honest ramp-up variance alongside high data completeness, rapid action closure, and audited benefit bridges gives its sponsors a trustworthy basis for capital allocation.[S43][S44]
GFT therefore structures the closing board scorecard around six paired tiles that unite the six operational KPI families with three explicit measures of performance-system health.[S02][S24][S43][S44] This dual view reflects both the ILO recommendation to balance financial, behavioural, and core-process metrics and the S&OP maturity principle that organisational, meeting, and measurement discipline must be evaluated alongside operational results.[S02][S44]
The six tiles in Exhibit 67 complete the argument of this white paper by showing how the six operating families and the three system-health gates lock together at the highest decision level of the enterprise.[S02][S43][S44] The first three tiles group the six operating families into three paired trade-off views (production with quality, delivery with effective unit cost, and people with resource intensity) so no single operational priority can advance by transferring harm to its partner family.[R16][S02][S06][S10][S33] The second three tiles tell the board whether the operating figures deserve to be believed: if data completeness falls below the gate threshold, or if action closure and audited benefit tiles show that management actions are closing on paper without verified residual effects on the floor, the board directs engineering effort back to capture and cadence discipline before authorising new capital or digital spend.[S24][S27][S43] Because Exhibit 67 defines a structural governance contract without fabricated factory numbers, it serves as an inspectable template that any greenfield sponsor can adopt prior to shift one.
This eighteen-month performance-management roadmap connects directly to the companion white papers and interactive decision tools in the GFT research library.[S01][S22][S44] Sponsors examining the macro-industrial context of Cameroon's cotton-to-garment value chain can pair this paper with wp01, while engineering teams preparing for Stage 4 digital deployments can consult wp03 on computer-vision defect inspection, wp04 on demand forecasting, wp07 and wp18 on resilient edge and reference architecture, wp14 on data governance, and wp15 on lean flow.[R06][R29][S26][S27] Similarly, human-resources and sustainability leaders can extend the people and resource contracts using wp06, wp09, wp12, and wp16.[S13][S30] Within the interactive toolset, the kpi-cascade, maturity, ramp-planner, oee-line, line-balancing, labour-cost, footprint, supervisor-pipeline, edge-architecture, build-or-buy, use-case-portfolio, and unit-economics tools implement the exact formulas, denominators, and gate criteria defined across these fourteen chapters.[S01][S44]
Finally, readers and sponsors should note the scope boundary of this work: the recommendations, metric contracts, worked examples, and eighteen-month roadmap presented in this paper constitute operational and industrial-engineering guidance. They do not constitute investment, legal, accounting, or tax advice. Every greenfield garment programme must validate its commercial assumptions, labour contracts, environmental permits, and capital budgets against current local statutes and site-specific engineering studies before committing funds. When sponsors combine that project-specific due diligence with a line-level measurement system installed from the very first shift, they equip the factory to learn rapidly, protect its workforce, and prove the value it creates.
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.
51 sources · 380 citation marks on this pageMost cited: S43, 34 times
Grade AGrade BGrade CKnot size: citations on this page
All 51 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.
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.
R27 · Grade B · inherited record
Inherited evidence R27
AI at scale in fashion: inventory cost down 35 to 40 percent; forecast accuracy up 20 to 30 percent; revenue per customer up 15 to 25 percent; forecast errors cut 20 to 50 percent; lost sales down up to 65 percent; 75 percent of executives prioritising; sector operating-profit potential US$150 to 275 billion over three to five years.
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.
R32 · Grade C · inherited record
Inherited evidence R32
CFA franc to US dollar reference rates; franc pegged to the euro at 655.957. The GFT study's method note places the franc near 590 to 610 per US dollar across the 2023 study window and near 563 to 577 more recently, and treats currency conversions as estimates.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
c14-S01 · Grade A
ISO 22400-2:2014 Automation systems and integration: Key performance indicators (KPIs) for manufacturing operations management, Part 2: Definitions and descriptions
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 ↗c14-S02 · Grade B
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. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S03 · Grade B
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.“
2023. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S04 · Grade B
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. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S05 · Grade B
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 ↗c14-S06 · Grade B
Comparison of OEE-Based Manufacturing Productivity Metrics
The OEE measurement aims to detect production losses and maximise equipment effectiveness.
2023. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S07 · Grade B
Uncovering the Influence of Operational Factors on Manufacturing Efficiency with Real Time Data
However, this loss categorization merges all downtime events, which can obstruct the identification of specific losses, representing a significant limitation of OEE.
2025. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S08 · Grade B
Overall equipment effectiveness: a review and development of an integrated improvement framework
The review found that there is a lack of improvement frameworks with systematic steps specifically developed for OEE implementation.
2020-05-11. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-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. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-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 ↗c14-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. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-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 ↗c14-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. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-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. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S16 · Grade B
Factory benefits to paying workers more: The critical role of compensation systems in apparel manufacturing
Workers focused their increased effort on reducing quality defects and tardiness, two behaviors which individual workers largely control.
2020-02-05. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S17 · Grade B
Piece rate pay brings mixed results for garment workers, new study shows
The research found that workers whose pay is jointly determined by hourly pay and a piece rate (described as partial piece rate pay) are more likely to report reduced emotional and physical health compared to workers paid by the hour.
2018-12-20. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-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.
2011. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S19 · Grade B
A method of garment factory workers' performance monitoring using control chart based on RFID system
Massive production data of front-line workers are recorded every day by RFID system in garment factory, which are mainly in multiple variety and small batch production.
2019-09-03. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S20 · Grade B
Remote Sensing of Sewing Work Levels Using a Power Monitoring System
In this study, a power monitoring system attached to the sewing machine was used to remotely collect power consumption data, which were then analyzed to extract the working times for a series of sewing tasks.
2020-04-29. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S21 · Grade A
NIST IR 8107: Current Standards Landscape for Smart Manufacturing Systems
The ability of disparate systems, however, to exchange, understand, and exploit product, production, and business data rests critically on information standards.
2016. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S22 · Grade A
Data Governance Playbook
Data governance is the process of setting and enforcing priorities for managing and using data as a strategic asset.
2020. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S23 · Grade A
ISO/IEC 25012:2008 Software engineering: Software product Quality Requirements and Evaluation (SQuaRE): Data quality model
This International Standard defines a general data quality model for data retained in a structured format within a computer system.
2008. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S24 · Grade A
The Government Data Quality Framework
There are six core data quality dimensions, as defined by DAMA UK.
2020-12-03. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S25 · Grade A
Data on the Web Best Practices
Finally, to promote the interoperability among datasets it is important to adopt data vocabularies and standards.
2017-01-31. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S26 · Grade A
NIST SP 500-325: Fog Computing Conceptual Model
One solution is to decentralize applications, management, and data analytics into the network itself using a distributed and federated compute model.
2018-03-14. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S27 · Grade B
Rules of Machine Learning: Best Practices for ML Engineering
If machine learning is not absolutely required for your product, don't use it until you have data.
2025-08-25. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S28 · Grade B
MLOps: Continuous delivery and automation pipelines in machine learning
You can gradually implement these practices to help improve the automation of your ML system development and production.
2024-08-28. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S29 · Grade A
ISO 50001:2018 Energy management systems: Requirements with guidance for use
EnPI(s) can be expressed by using a simple metric, ratio, or a model, depending on the nature of the activities being measured.
2018. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S30 · Grade A
The Greenhouse Gas Protocol: A Corporate Accounting and Reporting Standard, Revised Edition
Account for and report on all GHG emission sources and activities within the chosen inventory boundary.
2004. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S33 · Grade A
GRI 302: Energy 2016
These ratios express the energy required per unit of activity, output, or any other organization-specific metric.
2016. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S34 · Grade A
GRI 303: Water and Effluents 2018
Water consumption measures water used by an organization such that it is no longer available for use by the ecosystem or local community in the reporting period.
2018. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S35 · Grade A
GRI 306: Waste 2020
The processes that the organization has in place for collecting and monitoring waste-related data can reflect its commitment to managing waste-related impacts.
2020. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S36 · Grade A
RECP Experiences at INCALSA
The relative indicators provide a measurement of changes in resource use/pollution in relation to production output.
2015. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S39 · Grade A
Energy Efficiency Indicators: Fundamentals on Statistics
The main objectives of this manual are to identify the main sectoral indicators and the data needed to develop these indicators; and to make surveying, metering and modeling practices existing all around the world available to all.
2014. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-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. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S41 · Grade B
How to Test Your A3 Thinking
Reading an A3 in reverse helps A3 owners and their leaders confirm the use of a logical, fact-based thinking process.
2022-08-10. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S42 · Grade B
A case method for Sales and Operations Planning: a learning experience from Germany
It is performed at least once a month and is reviewed by management at an aggregate (product family) level.
2017. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S43 · Grade A
Local Authority Major Schemes: Benefits Management and Evaluation Framework
It also ensures those benefits are managed and monitored beyond the point of project completion.
2025. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S44 · Grade B
Proposal of a model for sales and operations planning (S&OP) maturity evaluation
The framework elaborated by Grimson & Pyke (2007), consists of five evolutionary stages, analyzed in the following categories: meetings and collaboration, organizational structure, measures of performance, information technology, and integrated planning.
2017-11-27. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-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 ↗c14-S46 · Grade B
Balanced Scorecard: History, Implementation, and Impact
Another criticism focuses on the BSC’s presumption of a rational, top-down approach to strategy execution, which may not align with decentralized or adaptive organizational structures.
2025-03-19. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S1401 · Grade A
How to set performance metrics for your service
Have a performance analyst on your team from the start of discovery and make them part of the process of building your service. Work out your measurements as you build the service, not afterwards.
2017-12-05. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S1402 · Grade A
How the alpha phase works
Alpha is where you try out different solutions to the problems you learnt about during discovery.
2019-05-08. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c14-S1403 · Grade A
How the beta phase works
The beta phase is where you take your best idea from alpha and start building it for real. It also involves thinking about how your service will integrate with or start to replace existing services, and preparing for the transition to live.
2021-02-19. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
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Keep the thread · chapter 14 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.