Rework Hours Share

Goal of the analysis:

Measure the proportion of total labor time spent on rework (fixing or reprocessing nonconforming product) and use insights to eliminate the root causes, recover capacity, and reduce unit cost. Rework Hours Share quantifies the “hidden factory” that consumes capacity without adding value, links rework to defect modes, materials, changeovers, and process stability, and translates hours into dollar impact and service risk (delays to OTIF). Executives use it to prioritize Quality and Operations programs (SPC, mistake-proofing, SMED, supplier quality, training), right-size buffers, and judge the ROI of automation and process controls.

Data required:

• Time and labor data (HRIS/MES):
  • Clock-in/out by employee/crew/shift; job codes (direct, indirect, rework/repair), line/work center assignments, overtime flags.
  • Labor reporting on work orders: hours booked to “rework,” “re-inspection,” “repair,” or non-conformance tickets.
• Production and quality records (MES/ERP/QMS):
  • Work orders and rework orders: SKU, lot, planned vs actual good units, scrap/rework quantities, rework routings/operations.
  • Nonconformance/defect logs with reason codes (dimension, cosmetic, functional, contamination, assembly error), severity, detection point (in-process, end-of-line, customer returns).
  • First-Pass Yield (FPY), DPMO/ppm, hold/release timestamps.
• Process and equipment telemetry:
  • Run/stop and changeover timestamps (SCADA/MES), parameter deviations, alarms; calibration and maintenance logs (MTBF/MTTR).
  • Environmental data where relevant (temperature/humidity) and their excursions.
• Materials and supplier context:
  • Incoming inspection results, CoA/COC data, supplier lots and yield, material substitutions, supplier OTIF.
• Standards and costing:
  • Standard hours per unit by operation; standard rework routings if used; labor and overhead rates; scrap/rework policy (what is counted as rework vs normal touch-up).
• Normalization and reference:
  • Shift calendars/breaks, product mix (SKU families), NPIs/ramp flags, engineering changes, rework coding taxonomy and capture rules.

Detailed step-by-step instruction on how to conduct the analysis:

1. Define the metrics and scope.
   • Rework Hours Share (%) = (Total labor hours booked to rework/repair/re-inspection) ÷ (Total direct production labor hours) × 100.
   • Rework Rate (units) = Reworked units ÷ Total produced units.
   • Rework Cost = Rework hours × fully loaded labor rate (+ variable overhead if applied).
   • Capacity Impact (constraint days) = Rework hours at constraint ÷ Effective daily capacity hours at constraint.
   • Define inclusions (internal rework only vs include customer returns/RMA rework) and time window (shift/day/week).
2. Assemble and reconcile data.
   • Extract 8–12 weeks of labor booking and production/quality data; ensure rework job codes and rework orders are consistently used.
   • Join rework labor to underlying lots/SKUs via nonconformance IDs or work order link; reconcile totals to payroll and MES hours.
   • Align timestamps to shifts; standardize reason codes and detection points.
3. Compute headline metrics.
   • Calculate Rework Hours Share, Rework Rate, and Rework Cost at plant, line, shift, and SKU family levels.
   • Overlay FPY and scrap to separate rework-heavy areas from scrap-heavy areas.
4. Normalize for mix and complexity.
   • Compute Rework Hours per 1,000 units by SKU family and per standard hour to account for different labor contents.
   • For high-mix sites, use Earned Standard Hours: Rework Hours ÷ Earned Standard Hours to compare apples-to-apples across products.
5. Attribute root causes.
   • Build a Pareto of rework hours by defect code, station/operation, SKU family, supplier lot, and shift/crew.
   • Trace detection points (in-process vs end-of-line vs customer return) and preceding events (changeover, PM, parameter drift) to form a “digital thread.”
   • Link spikes to material lots, specific tools/fixtures, or environmental excursions.
6. Translate to business impact.
   • Convert rework hours to capacity days at the bottleneck; estimate OTIF/backlog effect (orders delayed).
   • Compute Cost of Poor Quality (COPQ) slice: internal failure cost (rework labor, additional materials, re-inspection) and compare to external failure risk.
7. Trend and stability analysis.
   • Plot daily/weekly Rework Hours Share by line/shift with control limits; annotate NPIs, ECNs, supplier changes, SMED/TPM events.
   • Measure variability by crew; identify training or supervision gaps.
8. Scenario and what-if.
   • Model effect of FPY +2 pts, changeover standardization (−30% post-setup defects), supplier sort/containment, or parameter centerlining on rework hours and capacity.
   • Estimate payback for poka-yoke/automation vs expected rework hour reduction.
9. Integrity checks.
   • Ensure clean separation of “normal finishing” vs “rework” per policy; avoid double-counting time logged under both direct and rework.
   • Validate that customer-return rework is flagged distinctly; confirm labor booked to re-inspection is categorized consistently.

Format of the output of analysis:

• Executive scorecard: Rework Hours Share %, Rework Rate (units), Rework Cost $ (and per 1,000 units), Capacity impact at constraint (days), FPY and scrap overlays, trend vs target.
• Heatmaps: rework share by line/work center × shift and by SKU family; reason-code intensity by operation.
• Pareto charts: top defect modes, supplier lots, tools/fixtures driving rework hours and cost.
• Bridges: hours bridge from total direct hours → rework drivers (assembly error, material, setup, equipment, design) → remaining value-added hours.
• Linkage panel: rework-induced OTIF risk, backlog days, and expedite/overtime cost.
• Action tracker: prioritized countermeasures, owners, expected hour reduction, milestones, and verified savings.

How to interpret results:

• Rework concentrated at specific stations post-changeover: Setup/centering and SMED gaps; lock parameters, first-article checks, and quick-change error-proofing.
• Supplier-lot correlation: Incoming quality issue; put supplier on containment/sorting, tighten specs, or qualify alternates.
• Shift-to-shift spread with same mix: Skill/training or supervision gaps; standardize work, certify operators, and add leader coverage.
• High rework with low scrap and normal OEE: Hidden factory stealing capacity; target process capability and poka-yoke rather than just uptime.
• Customer-return rework elevated: External failure risk; prioritize corrective actions and feedback loop to design and suppliers; protect brand and warranty costs.
• Volatile rework trend: Process not under control; implement SPC and tighten change control (ECN discipline).

Steps a company can take to improve on this measure:

• Process control and quality fundamentals:
  • Deploy SPC on CTQs; set centerlines and visual limits; automate alarms for drift; conduct MSA to ensure measurement reliability.
  • Implement first-article inspection and layered process audits after changeovers; use checklists and digital sign-offs.
  • Install mistake-proofing (poka-yoke) for common assembly/pack errors; add vision or sensor checks where ROI-positive.
• Changeover and setup discipline:
  • Run SMED to externalize tasks and stabilize setup; lock recipes/parameters; use golden batch references; enforce recipe verification.
• Materials and supplier quality:
  • Strengthen incoming inspection on high-risk parts; require CoA/COC; implement supplier SPC and corrective actions; sort suspect lots.
  • Improve traceability: link rework to supplier lots to accelerate containment.
• Equipment reliability:
  • TPM and condition monitoring on stations with rework spikes from misfeeds/misalignments; calibrate tools/fixtures; standardize torque and clamping.
• People and training:
  • Skill matrices and certification for critical stations; buddy system for new/agency workers; refreshers after ECNs; visual work instructions.
• Design and engineering feedback:
  • Capture top defect modes and feed to DfM/DfA; adjust tolerances/fixtures; run PFMEA/Control Plan updates and verify risk reduction.
• Planning and governance:
  • Daily tiered meetings reviewing rework Pareto; root-cause (5-Why/Fishbone) on top issues; verify effectiveness within two cycles.
  • Incentivize FPY-adjusted productivity; avoid pressuring output at the expense of quality.
• Scenario guidance:
  • If Rework Hours Share is 9% with 40% at two post-setup stations, run SMED and parameter locks, add first-article checks; target ≤5% in 6–8 weeks and recover 2–3 capacity days/month.
  • If one supplier lot drives 30% of rework, enact containment and PPAP refresh; expect ≥70% reduction in related rework next month.
  • If night shift rework is 2× day shift, deploy cross-training and leader coverage, plus standardized work audits; close gap within two cycles.

Benchmark comparisons:

General benchmarks (directional):

• Discrete assembly: Rework Hours Share typically 2–6%; top quartile ≤2–3% with FPY ≥98%.
• Process/continuous industries: Internal rework often ≤1–3% of labor hours; spikes suggest parameter drift or contamination control issues.
• NPIs/high-mix: Temporarily higher (5–10%) during ramp; target to halve within 1–2 quarters as learning and controls mature.
• Customer returns rework: Aim for ≤0.5–1.0% of total labor hours in mature programs; higher rates warrant immediate corrective action.

Constructing internal benchmarks:

• Track 12–16 weeks of Rework Hours Share by line/shift/SKU family; publish quartiles and set guardrails (e.g., alert at >4% for assembly, >2% for process lines).
• Pair rework metrics with FPY, OEE, schedule adherence, and OTIF; prioritize cells with high rework and high customer impact.
• Reset targets post-SMED/TPM, ECNs, and supplier changes; codify best-performing cells’ practices as standards.
• Include rework in Cost of Quality dashboard (prevention, appraisal, internal/external failure) to ensure sustained investment in prevention.