🏭 Smart Factory Throughput Optimization — Monte Carlo Report

Generated: 15-Jun-2026 13:55 | Python engine

12000

Total Simulations

250

Replications/Combo

Production Stages

2400

Sim Duration [min]

272 s

Total Compute Time

Executive Summary

✅ Best labour strategy for throughput: Flexible achieves 62.3 units/shift on average (1.3 more than Lean).
✅ Predictive maintenance advantage: Reduces breakdowns by 43% vs reactive, saving €3541 per run in repair costs.
❌ Demand surge impact: Surge demand (4.0 min IAT) produces 91.2 units/shift vs 34.4 at low demand — but increases WIP by 46.5 units.
📈 Primary bottleneck: Final Assembly is the bottleneck in 100% of all runs (utilization: 58.9%).

Jump to full analysis ↓

▶ Watch the animated simulation

A frame-by-frame canvas animation of this scenario, side-by-side under contrasting operating policies.

1. Design of Experiments

A full-factorial design explores how labour strategy, maintenance policy, and demand level interact to shape factory performance. 4 × 3 × 4 = 48 combinations × 250 reps = 12000 runs.

Factors

Factor	Levels	Description
Labour Strategy	4	Balanced, Bottleneck-Focus, Flexible, Lean
Maintenance Policy	3	Reactive, Preventive, Predictive
Demand Level	4	Low, Normal, High, Surge

Stage Profiles

Stage	Name	Machines	Proc Time	CV	Defect%	Buffer
0	Raw Material Prep	2	4.0 min	0.15	0.5%	15
1	CNC Cutting	3	7.0 min	0.18	1.5%	12
2	Precision Machining	4	11.0 min	0.20	2.5%	10
3	Sub-Assembly	3	9.0 min	0.15	1.0%	15
4	Quality Inspection	2	3.0 min	0.10	0.2%	20
5	Surface Treatment	2	5.0 min	0.12	0.8%	18
6	Final Assembly	3	13.0 min	0.20	1.5%	10
7	Packaging	2	3.5 min	0.10	0.3%	25

1b. Strategy & Policy Definitions

Understanding each factor level is essential for interpreting the simulation results. Below are the practical implications of every labour strategy and maintenance policy modelled.

Labour Strategies

Strategy	Operators	Allocation	Description
Balanced	21	3-3-3-3-2-2-3-2	Equal staffing across stages — simple to manage, avoids bottleneck risk but may over-staff non-critical stages.
Bottleneck-Focus	21	2-2-5-2-2-2-4-2	Concentrates operators on historically bottleneck stages (Precision Machining & Final Assembly) — reduces cycle time at constraint points.
Flexible	18	Dynamic pool	Dynamic operator pool reassigned every minute based on queue lengths — maximizes utilization but requires cross-trained workforce. Senior operators are preferentially assigned to bottleneck stages.
Lean	15	2-2-3-2-1-1-2-2	Minimum staffing — lowest labor cost but risks starvation at high-demand stages.

Maintenance Policies

Policy	MTBF Mult	MTTR Mult	Approach	Description
Reactive	1.0×	1.0×	Fix when broken	No proactive action — lowest upfront cost but highest breakdown frequency and longest repair times. Baseline MTBF/MTTR.
Preventive	1.4×	0.7×	Scheduled service	Regular maintenance intervals extend MTBF by 1.4× and reduce MTTR by 0.7×. Scheduled cost per shift applies. Balances cost and reliability.
Predictive	1.8×	0.5×	Condition-based	IoT sensors monitor machine health, triggering maintenance before failure. MTBF ×1.8, MTTR ×0.5. Sensor cost per shift but fewest breakdowns and shortest repairs.

💡 Operator Skills: Each operator is randomly assigned a skill level between 0.8 (junior) and 1.2 (senior). Processing time is divided by the operator's skill level, so senior operators complete tasks ~33% faster than juniors. In the Flexible strategy, senior operators are preferentially assigned to bottleneck stages.

2. Key Performance Indicators (across all runs)

61.4

Throughput [units/shift] (±21.5)

96.0

Avg Cycle Time [min] (±78.9)

18.1

Avg WIP (±21.6)

38.3%

Machine Utilization (±14.5%)

35.7%

Operator Utilization (±14.3%)

484.9€

Unit Cost (±102.9€)

98.88%

Quality Yield (±0.22%)

53.1%

OEE (±18.4%)

25.7

Breakdowns/Run (±12.5)

29.7

Defects/Run (±12.3)

111.1k€

Total Cost (±21.3k€)

Final Assembly

Primary Bottleneck (100% of runs)

55.2k€

Energy Cost (±11.3k€)

8.9

Reworks/Run (±4.5)

151

Blocking Events (±318)

0.43×

Fatigue Impact Ratio

3. Monte Carlo Summary Dashboard

20-panel overview showing throughput distributions, utilization heatmaps, OEE comparisons, cost breakdowns, time series, bottleneck identification, and Pareto fronts.

3.1 Throughput Analysis

Production throughput distributions by labour strategy, maintenance policy, and demand level, plus cycle time characteristics.

3.2 Efficiency & Cost

WIP–throughput relationships, utilisation patterns, OEE comparison, and unit cost across strategies.

3.3 Quality & Operations

Quality yield, queue dynamics, bottleneck identification, and the throughput-vs-cost Pareto frontier.

3.4 Factor Effects & Maintenance

Utilisation drivers, main-effect decomposition, breakdown frequency, and operator utilisation by strategy.

3.5 Time Series & Cost Breakdown

WIP convergence diagnostics, throughput trajectories, cost component breakdown, and OEE interaction heatmap.

4. Key Insights

✅ Best labour strategy for throughput: Flexible achieves 62.3 units/shift on average (1.3 more than Lean).

✅ Predictive maintenance advantage: Reduces breakdowns by 43% vs reactive, saving €3541 per run in repair costs.

❌ Demand surge impact: Surge demand (4.0 min IAT) produces 91.2 units/shift vs 34.4 at low demand — but increases WIP by 46.5 units.

📈 Primary bottleneck: Final Assembly is the bottleneck in 100% of all runs (utilization: 58.9%).

🔗 Best OEE combo: Flexible + Predictive achieves 55.9% OEE (overall mean: 53.1%).

5. Stage-Level Analysis

Per-Stage Statistics

Stage	Name	Mean Util [%]	σ Util	Mean Queue	σ Queue	Proc Time [min]
0	Raw Material Prep	34.9	14.2	0.33	0.72	4.0
1	CNC Cutting	38.4	15.6	0.38	1.00	7.0
2	Precision Machining	42.9	17.2	0.57	1.25	11.0
3	Sub-Assembly	44.9	17.8	0.85	2.00	9.0
4	Quality Inspection	23.9	9.2	1.48	3.48	3.0
5	Surface Treatment	37.1	13.7	2.45	4.70	5.0
6	Final Assembly	58.9	20.8	2.25	3.31	13.0
7	Packaging	25.4	9.2	0.03	0.05	3.5

6. Maintenance Policy Impact

Policy Comparison

Policy	MTBF Mult	MTTR Mult	Mean Breakdowns	Mean Throughput	Mean Maint Cost [€]	Mean Unit Cost [€]
Reactive	1.0×	1.0×	33.5	59.4	16758	485.0
Preventive	1.4×	0.7×	24.3	61.7	25321	515.1
Predictive	1.8×	0.5×	19.3	62.9	13217	454.6

7. KPI Correlation Analysis

💡 Correlation Insights

What these manufacturing relationships reveal — with lean manufacturing and reliability engineering context.

KPI Pair	r	Strength	Actionable Insight
MachUtil ↔ EnergyCost	1.000	Strong	Running machines consume 3–5× the energy of idle machines. Higher utilization raises energy cost but improves per-unit energy efficiency — optimize for cost per unit, not absolute consumption.
Throughput ↔ OEE	0.999	Strong	Throughput and OEE show a positive relationship (r=1.00). Changes in one tends to reinforce the other.
CycleTime ↔ WIP	0.992	Strong	CycleTime and WIP show a positive relationship (r=0.99). Changes in one tends to reinforce the other.
CycleTime ↔ Blocked	0.992	Strong	CycleTime and Blocked show a positive relationship (r=0.99). Changes in one tends to reinforce the other.
WIP ↔ Blocked	0.984	Strong	WIP and Blocked show a positive relationship (r=0.98). Changes in one tends to reinforce the other.
Throughput ↔ EnergyCost	0.983	Strong	Throughput and EnergyCost show a positive relationship (r=0.98). Changes in one tends to reinforce the other.
Throughput ↔ MachUtil	0.982	Strong	Throughput and MachUtil show a positive relationship (r=0.98). Changes in one tends to reinforce the other.
OEE ↔ EnergyCost	0.980	Strong	OEE and EnergyCost show a positive relationship (r=0.98). Changes in one tends to reinforce the other.

8. Full Statistical Summary

KPI	Mean	Std	Min	Median	Max	95% CI
Throughput [u/shift]	61.35	21.53	24.25	59.50	110.00	[58.68, 64.02]
Cycle Time [min]	96.00	78.88	47.78	58.22	487.99	[86.23, 105.78]
WIP	18.08	21.56	3.10	8.20	102.73	[15.41, 20.76]
Machine Util [%]	38.31	14.47	14.41	36.02	70.36	[36.52, 40.11]
Operator Util [%]	35.67	14.35	12.00	33.22	74.24	[33.89, 37.45]
Unit Cost [€]	484.89	102.93	305.74	457.92	886.10	[472.13, 497.65]
Quality Yield [%]	98.88	0.22	97.87	98.89	99.74	[98.86, 98.91]
OEE [%]	53.13	18.38	21.08	51.57	96.24	[50.86, 55.41]
Availability [%]	97.22	1.18	90.26	97.54	99.48	[97.07, 97.36]
Performance [%]	55.39	19.44	21.89	53.72	99.31	[52.98, 57.80]
Quality Ratio [%]	98.88	0.22	97.87	98.89	99.74	[98.86, 98.91]
Breakdowns	25.71	12.54	1.00	23.00	77.00	[24.15, 27.26]
Defects	29.65	12.32	4.00	28.00	72.00	[28.13, 31.18]
Completed Units	245.41	86.13	97.00	238.00	440.00	[234.73, 256.08]
Maint Cost [€]	18431.79	6876.47	3000.00	17000.00	38500.00	[17579.37, 19284.20]
Labor Cost [€]	22500.00	2984.96	18000.00	23400.00	25200.00	[22129.98, 22870.02]
Total Cost [€]	111148.86	21329.00	68858.40	108957.65	161479.00	[108504.89, 113792.83]
Energy Cost [€]	55178.49	11320.89	36384.00	53388.15	80338.60	[53775.14, 56581.84]
Rework Count	8.94	4.48	0.00	8.00	27.00	[8.38, 9.50]
Blocked Events	151.37	317.75	0.00	0.00	1691.00	[111.98, 190.76]
Changeover Loss [mach-min]	2079.00	0.00	2079.00	2079.00	2079.00	[2079.00, 2079.00]
Fatigue Impact Ratio	0.43	0.22	0.00	0.39	4.00	[0.40, 0.46]
Material Starved [min]	1770.92	319.47	405.00	1869.00	2153.00	[1731.32, 1810.52]
Rework Proc Time [min]	116.39	61.51	0.00	107.00	363.00	[108.76, 124.01]
Multiple Rework Count	0.05	0.22	0.00	0.00	2.00	[0.02, 0.08]
Avg Operator Skill	1.00	0.03	0.90	1.00	1.10	[1.00, 1.00]
Avg Skill at Bottleneck	1.01	0.05	0.84	1.01	1.16	[1.00, 1.01]

9. Methodology & Model Description

9.1 Architecture

Discrete-time minute-by-minute simulation of an 8-stage serial manufacturing assembly line with finite inter-stage buffers, machine breakdowns, operator fatigue, shift changeovers, rework loops, and energy tracking. Blocked units are held (never lost). Breakdowns can occur mid-processing. Executed inside ProcessPoolExecutor for embarrassingly parallel Monte Carlo execution.

9.2 Stage Model

Each of the 8 stages has a configurable number of parallel machines (2–4). Processing times follow a log-normal distribution (non-negative, right-skewed) with stage-specific means (4.0–13.0 min) and CVs (0.15–0.2). Defect rates range from 0.2% to 2.5%.

9.3 Maintenance Model

Machines fail with exponentially distributed times-to-failure (MTBF 300–800 min). Three maintenance policies scale MTBF and MTTR: Reactive (baseline), Preventive (1.4× MTBF, 0.7× MTTR), Predictive (1.8× MTBF, 0.5× MTTR). Each has distinct cost structures including repair, scheduled, and sensor costs.

9.4 Cost Model

Total cost = Labour (€0.5/op/min) + Material (€50.0/unit) + Maintenance (repair + scheduled + sensor). Unit cost = Total cost / completed units.

9.5 Monte Carlo Design

4 labour strategies × 3 maintenance policies × 4 demand levels = 48 combinations × 250 reps = 12000 runs. Each run simulates 2400 minutes (5 shifts). Executed with 12 CPU cores in 271.7 seconds.

10. Interactive Analysis

11. Strategic Insights by Persona

Data-driven recommendations derived from the simulation results, tailored for four key stakeholder groups.

📈

Throughput Maximization

Flexible strategy delivers 62.3 units/shift, outperforming Lean by 1.3 units. Pair with predictive maintenance for optimal throughput.

⚡

Bottleneck Resolution

Final Assembly is the bottleneck in 100% of runs with 58.9% utilization. Adding capacity here yields the highest marginal throughput gain.

🕑

Shift Planning

Over 5 shifts (2400 min), mean cumulative output is 245 units. Surge demand increases throughput by 56.8 units/shift vs low demand.

🏆

Best Configuration

Flexible + Predictive achieves the highest OEE at 55.9% (overall mean: 53.1%). This is the recommended operating point.

🛠

Capacity Decisions

Mean machine utilization is 38.3%, with operator utilization at 35.7%. Flexible labour achieves comparable throughput with 18 vs 21 operators.

🔍

Overall Quality Yield

Mean quality yield: 98.88% with 29.7 defects per run. Precision Machining (stage 2) has the highest defect rate at 2.5%.

📉

Quality by Maintenance

Predictive maintenance yields 98.89% quality vs reactive at 98.88%. Fewer breakdowns mean fewer interrupted processes.

🚫

Defect Hotspots

Stage 2 (Precision Machining) and Stage 6 (Final Assembly) contribute the most defects at 2.5% and 1.5% respectively. Focus SPC efforts there.

💡

Inspection Effectiveness

Quality Inspection (stage 4) has only 0.2% reject rate with 3.0 min processing time. It acts as a gate but is not a throughput limiter.

🚀

Quality-Throughput Trade-off

Higher demand increases defects: surge demand averages 45.9 defects vs 16.1 at low demand. Quality holds above 97.9% even at surge.

📊

OEE Benchmark

Mean OEE: 53.1%. Best combo (Flexible + Predictive) reaches 55.9%. World-class target is 85% — gap analysis shows room for improvement in performance ratio.

📈

Utilization Deep Dive

Machine utilization ranges from 23.9% (Quality Inspection) to 58.9% (Final Assembly). Balancing these is key to flow efficiency.

🕑

Cycle Time Analysis

Mean cycle time: 96.0 min. Little's Law confirms: WIP (18.1) / throughput rate (0.102 u/min) ≈ cycle time.

⚖️

Strategy Comparison

Bottleneck-Focus strategy concentrates operators on stages 2 & 6, achieving 61.1 units/shift. Flexible strategy achieves 62.3 with 3 fewer operators.

🚀

Demand Scalability

Line scales from 34.4 (low) to 91.2 (surge) units/shift. Beyond normal demand, WIP explodes — indicating buffer saturation and need for capacity expansion.

💰

Unit Cost Optimization

Best unit cost: Flexible at €466.8 vs worst Balanced at €501.9. A €35.1 delta per unit at scale.

🔧

Maintenance ROI

Predictive maintenance reduces breakdowns by 43% vs reactive. Despite sensor costs (€100/shift/machine), net savings are €3541 per run.

👥

Labour Cost Analysis

Lean (15 ops): €18.0k vs Balanced (21 ops): €25.2k. Flexible (18 ops) offers the best cost-performance ratio at €21.6k.

📈

Cost Structure

Mean total cost: €111.1k. Split: Labour 20%, Material 14%, Maintenance 17%.

🎯

Break-Even Analysis

At mean unit cost €484.9 and 61.4 units/shift, revenue per shift must exceed €29749 to break even. Predictive maintenance reduces the breakeven point by 7%.

12. What-If Strategic Recommendations

All 48 DoE combinations ranked by unit cost, OEE, and quality yield. Use this analysis to identify the optimal operating configuration and quantify the improvement potential.

🏆 Best Overall Configuration: Flexible + Predictive + Surge achieves the lowest unit cost at €340.09.

❌ Worst Configuration: Bottleneck-Focus + Preventive + Low at €711.77 per unit.

📈 Switching from worst to best saves €371.68 per unit (52.2% reduction). At 10,000 units/month, this translates to €3,716,800/month in savings.

Top-3 Strategies by Objective

💲 Lowest Unit Cost

Rank	Strategy	Maintenance	Demand	Unit Cost [€]	OEE [%]
1	Flexible	Predictive	Surge	340.09	85.3
2	Lean	Predictive	Surge	347.56	81.4
3	Balanced	Predictive	Surge	366.88	81.4

⚡ Highest OEE

Rank	Strategy	Maintenance	Demand	OEE [%]	Unit Cost [€]
1	Flexible	Predictive	Surge	85.3	340.09
2	Flexible	Preventive	Surge	81.7	386.46
3	Lean	Predictive	Surge	81.4	347.56

📉 Highest Quality Yield

Rank	Strategy	Maintenance	Demand	Quality [%]	Unit Cost [€]	OEE [%]
1	Balanced	Reactive	Normal	98.918	508.79	43.4
2	Flexible	Predictive	High	98.914	382.40	61.8
3	Bottleneck-Focus	Predictive	Low	98.909	622.54	30.7

📊 Full Ranking Table (all 48 combinations)

Rank	Strategy	Maintenance	Demand	Unit Cost [€]	OEE [%]	Quality [%]	Throughput	Recommendation
1	Flexible	Predictive	Surge	340.09	85.3	98.881	97.8	✅ Top Pick
2	Lean	Predictive	Surge	347.56	81.4	98.866	93.4	✅ Top Pick
3	Balanced	Predictive	Surge	366.88	81.4	98.869	93.4	✅ Top Pick
4	Bottleneck-Focus	Predictive	Surge	368.35	80.8	98.881	92.7	✅ Top Pick
5	Flexible	Predictive	High	382.40	61.8	98.914	70.7	✅ Top Pick
6	Lean	Predictive	High	386.29	60.8	98.875	69.6	🔹 Good
7	Flexible	Preventive	Surge	386.46	81.7	98.885	94.8	🔹 Good
8	Flexible	Reactive	Surge	391.09	75.8	98.866	89.6	🔹 Good
9	Lean	Preventive	Surge	393.58	78.2	98.867	90.9	🔹 Good
10	Lean	Reactive	Surge	401.35	71.8	98.885	85.0	🔹 Good
11	Balanced	Predictive	High	411.15	61.0	98.892	69.8	🔹 Good
12	Bottleneck-Focus	Predictive	High	411.50	61.0	98.879	69.9	🔹 Good
13	Bottleneck-Focus	Preventive	Surge	412.43	78.5	98.879	91.2	🔹 Good
14	Flexible	Reactive	High	413.01	58.0	98.877	67.4	🔹 Good
15	Bottleneck-Focus	Reactive	Surge	413.90	73.8	98.885	87.4	🔹 Good
16	Balanced	Preventive	Surge	414.35	78.2	98.895	90.8	🔹 Good
17	Balanced	Reactive	Surge	416.01	73.2	98.876	86.7	🔸 Average
18	Lean	Reactive	High	420.92	57.1	98.883	66.5	🔸 Average
19	Flexible	Preventive	High	435.42	59.6	98.877	68.6	🔸 Average
20	Lean	Preventive	High	435.75	59.6	98.888	68.8	🔸 Average
21	Bottleneck-Focus	Reactive	High	448.14	57.0	98.882	66.4	🔸 Average
22	Lean	Predictive	Normal	448.26	45.9	98.868	52.5	🔸 Average
23	Balanced	Reactive	High	448.67	57.1	98.884	66.6	🔸 Average
24	Flexible	Predictive	Normal	450.64	45.9	98.878	52.4	🔸 Average
25	Balanced	Preventive	High	463.85	59.3	98.874	68.5	🔸 Average
26	Bottleneck-Focus	Preventive	High	464.70	59.2	98.856	68.4	🔸 Average
27	Flexible	Reactive	Normal	469.21	43.8	98.871	50.3	🔸 Average
28	Lean	Reactive	Normal	473.27	43.5	98.875	50.3	🔸 Average
29	Bottleneck-Focus	Predictive	Normal	478.78	46.2	98.861	52.8	🔸 Average
30	Balanced	Predictive	Normal	483.98	45.6	98.893	52.1	🔸 Average
31	Flexible	Preventive	Normal	508.34	45.2	98.901	52.0	🔸 Average
32	Balanced	Reactive	Normal	508.79	43.4	98.918	50.0	🔸 Average
33	Bottleneck-Focus	Reactive	Normal	508.89	43.5	98.884	50.2	🔴 Below Average
34	Lean	Preventive	Normal	512.39	44.6	98.872	51.3	🔴 Below Average
35	Balanced	Preventive	Normal	541.45	45.1	98.891	51.9	🔴 Below Average
36	Bottleneck-Focus	Preventive	Normal	541.74	45.1	98.889	51.9	🔴 Below Average
37	Lean	Predictive	Low	571.81	30.6	98.902	35.0	🔴 Below Average
38	Flexible	Predictive	Low	579.33	30.8	98.898	35.1	🔴 Below Average
39	Flexible	Reactive	Low	585.11	29.5	98.886	33.7	🔴 Below Average
40	Lean	Reactive	Low	587.06	29.1	98.871	33.3	🔴 Below Average
41	Bottleneck-Focus	Predictive	Low	622.54	30.7	98.909	35.0	🔴 Below Average
42	Balanced	Predictive	Low	623.79	30.6	98.906	35.0	🔴 Below Average
43	Balanced	Reactive	Low	636.09	29.4	98.898	33.6	🔴 Below Average
44	Bottleneck-Focus	Reactive	Low	637.87	29.2	98.905	33.3	🔴 Below Average
45	Lean	Preventive	Low	651.01	30.4	98.881	34.8	🔴 Below Average
46	Flexible	Preventive	Low	660.79	30.4	98.881	34.8	🔴 Below Average
47	Balanced	Preventive	Low	707.82	30.0	98.885	34.4	🔴 Below Average
48	Bottleneck-Focus	Preventive	Low	711.77	30.0	98.870	34.4	🔴 Below Average

💰 ROI Estimate: If monthly production is 10,000 units, switching from the current worst to the best configuration saves approximately €3,716,800/month (€44,601,600/year). Even a move from median to best saves €1,105,500/month.

13. Cost Waterfall — From Raw Material to Finished Product

This chart shows how the unit cost builds up from raw material through each cost category, broken down by labour strategy. Understanding the cost structure helps identify the largest cost drivers and where savings are possible.

Per-Unit Cost Breakdown

Strategy	Material [€]	Labour [€]	Energy [€]	Maintenance [€]	Rework [€]	Total [€]
Balanced	61.38	103.17	227.73	76.25	0.91	469.44
Bottleneck-Focus	61.41	103.06	227.70	76.07	0.91	469.15
Flexible	60.88	86.72	215.87	71.79	0.91	436.17
Lean	61.46	73.84	228.25	76.38	0.91	440.84

14. Supply Chain Impact

Upstream material supply variability directly affects throughput. The simulation models stochastic raw material arrivals with a supply reliability factor of 0.95 (normal). When reliability is below 1.0, inter-arrival times can randomly increase by 10–50%, simulating supplier delays, logistics disruptions, or inventory shortages.

1770.9

Avg Material Starved Minutes

73.8%

Starvation Rate

319.5

σ Material Starved Min

Material Starvation by Demand Level

Demand Level	Mean Starved [min]	Mean Throughput	Impact
Low	2078.0	34.4	High
Normal	1941.3	51.5	High
High	1782.2	68.4	High
Surge	1282.3	91.2	High

Interpretation

🚚 Supply Reliability at 0.95: 5% of inter-arrival intervals are extended by 10–50%. Low-demand scenarios show more starvation minutes because the natural inter-arrival time is already long. Surge demand saturates buffers quickly, masking supply variability.

💡 Recommendation: If the supply chain is unreliable (reliability < 0.90), consider increasing Stage 0 buffer capacity from 15 to 25, or negotiating shorter lead times with suppliers. Reliable supply chains (≥ 0.98) show negligible starvation.

15. Rework Efficiency Analysis

The enhanced rework model processes defective units at 1.5× the original processing time on the same machine (no re-queuing). Units that fail quality inspection after rework are counted as multiple reworks.

8.9

Avg Reworks per Run

116

Avg Rework Proc Time [min]

0.0

Avg Multiple Reworks

0.23%

Capacity Lost to Rework

Rework by Labour Strategy

Strategy	Reworks/Run	Rework Proc Time [min]	Rework Efficiency
Balanced	8.9	118	99.5% first-pass
Bottleneck-Focus	8.9	119	99.5% first-pass
Flexible	9.1	110	99.5% first-pass
Lean	8.9	118	99.4% first-pass

🔧 Rework Impact: Each rework cycle consumes 1.5× the normal processing time, holding the machine unavailable for new units. With an average of 8.9 reworks per run and 116 minutes of rework processing, approximately 0.23% of total machine capacity is consumed by rework. Reducing defect rates at Precision Machining and Final Assembly would yield the largest rework reduction.

16. Operator Skill Analysis

Each operator is assigned a random skill level between 0.8 (junior) and 1.2 (senior). Processing time is divided by the operator skill level. In the Flexible strategy, senior operators are preferentially assigned to bottleneck stages.

1.000

Avg Operator Skill

1.005

Avg Skill at Bottleneck

42%

Proc Time Range (Jr vs Sr)

Skill Distribution by Strategy

Strategy	Avg Skill	Avg Skill at Bottleneck	Throughput	Skill Advantage
Balanced	1.000	0.990	61.1	Uniform
Bottleneck-Focus	1.000	0.988	61.1	Uniform
Flexible	1.000	1.060	62.3	Senior at BN
Lean	1.000	0.984	60.9	Uniform

🎓 Skill Impact: A senior operator (skill 1.2) completes tasks ~33% faster than a junior (skill 0.8). The Flexible strategy places its most skilled operators at bottleneck stages, achieving an average bottleneck skill of 1.060 vs the overall average of 1.000. Cross-training investments that raise the minimum skill to 0.9 would reduce processing time variability by ~12%.

⚙ KPI Glossary & Definitions

Detailed explanation of every metric used in this analysis, with industry benchmarks and authoritative sources to help all stakeholders interpret results.

KPI	Definition	Why It Matters	Industry Benchmark
Throughput	Completed units per shift	Primary output measure — bounded by the bottleneck stage capacity.	Compare vs. theoretical max = bottleneck rate × time
Cycle Time	Mean time from stage-1 entry to final exit	Includes processing + waiting. Little's Law: CT = WIP / λ.	Hopp & Spearman, Factory Physics
WIP	Average units simultaneously in the system	Excess WIP signals congestion; lean target: minimize.	Toyota: ≤2× minimum (Ohno, 1988)
Machine Utilization	Processing time / available time × 100	70–85% is optimal. Above 90% causes queueing delays.	Factory Physics: 85% practical max
OEE	Availability × Performance × Quality	Gold standard for manufacturing efficiency. Combines three loss categories.	World-class: ≥85% (Nakajima, TPM, 1988)
Unit Cost	Total cost / completed units	Fully loaded cost per unit. Key for pricing and margin analysis.	Minimize while maintaining quality
Quality Yield	(1 − defects/total) × 100	First-pass yield. Six Sigma target: 99.9997%.	iSixSigma: ≥99.7% (3σ)
Operator Utilization	Active time / shift time × 100	Labour efficiency. Above 90% leads to fatigue and errors.	75–85% optimal (Givi et al., 2015)
Energy Cost	Σ machine-min × rate (run/idle/repair)	5–15% of manufacturing cost. State-dependent consumption model.	DOE AMO
Rework Count	Units re-entering a stage (30% of defects)	Consumes capacity that could produce new units — hidden cost of poor quality.	Best-in-class: <1% rework (ASQ)
Blocked Events	Minutes a unit cannot advance (full buffer)	Blocking propagates upstream; key signal of line imbalance.	Zero in a perfectly balanced line
Changeover Loss	Machine-min lost at shift boundaries	Non-productive transition time. SMED target: <10 min.	Shingo, 1985
Fatigue Impact	Avg fatigue-adjusted / normal proc-time ratio	Quantifies how fatigue in last 120 min degrades performance.	<1.05 = well-managed; >1.15 = concerning

📚 Sources & References

Industry standards, academic research, and data sources backing the model assumptions and benchmarks cited in this report.

#	Source	Used For
1	Hopp & Spearman — Factory Physics (3rd ed., 2018)	Little's Law, queueing theory, variability-throughput trade-off
2	Nakajima, S. (1988). Introduction to TPM	OEE definition, world-class benchmark (≥85%), six big losses
3	Lee et al. (2020). J. Manufacturing Systems	Predictive maintenance ROI: 30–50% breakdown reduction
4	Ohno, T. (1988). Toyota Production System	Lean manufacturing, WIP minimization, pull systems
5	ASQ — Statistical Process Control	SPC methodology, defect rate reduction (50–80% achievable)
6	Shingo, S. (1985). SMED	Single-Minute Exchange of Dies: changeover time reduction
7	U.S. DOE — Advanced Manufacturing Office	Energy consumption benchmarks in manufacturing
8	Givi et al. (2015). Ergonomics	Operator fatigue effects on productivity and error rates