Manufacturing Capacity & Production Bottleneck Analyzer

Manufacturing Capacity Planning & Bottleneck Optimization

The Problem: Hidden Production Constraints

Manufacturing managers face a critical challenge: Which constraint limits production? Is it a slow workstation? Frequent equipment breakdowns? High defect rates? Insufficient operators? Without clear visibility, managers waste resources upgrading the wrong bottleneck, implement ineffective scheduling, and fail to meet customer commitments.

The result is cascading problems: missed deadlines damage customer relationships, expedited production costs 20-30% more than planned production, inventory imbalances (either stockouts or overstock), and frustrated teams working overtime on non-critical tasks.

The Manufacturing Capacity & Bottleneck Analyzer solves this by identifying the exact constraint in your production system, calculating realistic capacity given all losses (downtime, quality, performance), and showing exactly what needs to change to increase output.

Core Concept: Overall Equipment Effectiveness (OEE)

OEE is the manufacturing industry standard for measuring production system health. It combines three factors that affect actual output:

Availability: The percentage of planned time the equipment actually runs. Losses include unplanned breakdowns, changeovers, maintenance, setup, and unscheduled stoppages. Industry standard: 90%+

Performance: The speed at which equipment runs relative to its theoretical maximum. Slower speeds (due to operator hesitation, minor stops, reduced speed for quality reasons) reduce this factor. Industry standard: 95%+

Quality: The percentage of units that meet quality standards without requiring rework or scrap. Industry standard: 99%+ (99.0% = 1% defect rate)

When multiplied together, these create a powerful metric. A system with 85% availability, 90% performance, and 95% quality has an OEE of 0.85 × 0.90 × 0.95 = 72.7%—meaning only 73% of theoretical capacity is actually delivered.

Bottleneck Theory (Theory of Constraints)

In any production system, one workstation is slower than others, creating a constraint that limits total system output. Improving non-bottleneck stations doesn't increase total capacity—only improving the bottleneck matters. This insight, known as the Theory of Constraints (TOC), is fundamental to manufacturing optimization.

Bottleneck identification is the first step to capacity improvement:

If you have 5 stations with cycle times of 2, 3, 2.5, 4, and 2.2 minutes per unit, Station 4 (4 minutes) is the bottleneck. The entire line can produce at most 15 units per hour (60 minutes / 4 minutes per unit), regardless of the other stations' capacity.

Realistic Capacity Calculation

Theoretical capacity assumes perfect conditions (no downtime, perfect speed, 100% quality, 100% schedule adherence). Real capacity is significantly lower:

For example, if theoretical capacity is 1,000 units/week with an OEE of 72% and schedule adherence of 92%, realistic capacity is 1,000 × 0.72 × 0.92 = 662 units/week.

Worked Example: Electronics Assembly Line

TechCorp manufactures circuit board assemblies with 5 workstations: Solder (40 sec/unit), Component Placement (35 sec/unit), Testing (60 sec/unit), Rework (25 sec/unit), Packaging (20 sec/unit). They operate 2 shifts, 5 days/week, 10 operators.

Step 1: Identify Bottleneck
Testing station: 60 seconds per unit is the longest time
Bottleneck: Testing (60 seconds = 1 minute per unit)

Step 2: Calculate Theoretical Capacity
Available time: 2 shifts × 8 hours × 60 minutes = 960 minutes/day
Operating days: 5/week = 4,800 minutes/week
Downtime (unplanned): 30 min/shift × 2 = 60 min/day = 300 min/week
Effective time: 4,800 - 300 = 4,500 minutes/week
Theoretical capacity: 4,500 minutes ÷ 1 minute/unit = 4,500 units/week

Step 3: Apply OEE Factors
Availability: 85% (300 min downtime out of 4,800 available)
Performance: 88% (slight speed reduction to maintain quality)
Quality: 96% (4% rework rate)
OEE: 0.85 × 0.88 × 0.96 = 71.8%

Step 4: Calculate Realistic Capacity
Realistic capacity: 4,500 × 0.718 × 0.95 (schedule adherence) = 3,063 units/week

Step 5: Compare to Target
If TechCorp's target is 3,500 units/week, they have a gap of 437 units/week.
Options to close gap:
(a) Reduce Testing time by 15% → 51 sec/unit capacity increases to 3,300 units
(b) Add 3rd shift → capacity increases to ~4,600 units
(c) Improve quality to 98% → OEE increases to 73.7%, capacity to 3,150 units
(d) Add 2nd testing station in parallel → bottleneck broken, capacity increases to 3,800 units

OEE Improvement Benchmarks by Industry

OEE Level	Manufacturing Performance	Typical Industry	Improvement Actions
<60%	Poor/Crisis	Struggling operations	Complete system overhaul needed; basic maintenance, training
60–70%	Below Average	Low-cost assembly	Preventive maintenance, operator training, quality systems
70–80%	Average	Most small–medium manufacturers	Lean Six Sigma, equipment upgrades, bottleneck focus
80–85%	Good	Well-managed operations	Advanced predictive maintenance, continuous improvement
>85%	Excellent/World-class	High-tech, automotive, pharma	Maintain systems, innovation focus

Five Focusing Steps (Theory of Constraints)

Step 1: Identify the Bottleneck - Find the constraint limiting capacity (typically the station with longest cycle time).

Step 2: Exploit the Bottleneck - Maximize output from the bottleneck with no new investment: eliminate small breaks, ensure optimal feed from upstream stations, prevent quality escapes that cause rework.

Step 3: Subordinate Non-Bottleneck Operations - Adjust non-bottleneck stations to feed the bottleneck optimally, even if they run below capacity. Prevent buildup upstream.

Step 4: Elevate the Bottleneck - If exploitation isn't sufficient, invest in bottleneck capacity: add equipment, hire operators, reduce cycle time through design changes.

Step 5: Repeat - Once the bottleneck is resolved, a new constraint emerges. Continue the cycle.

Limitations & Important Caveats

Variability & Queuing: This analysis assumes constant flow. Real systems have variability (some units process faster, some slower), creating queuing and bottleneck movement. Simulations may be needed for high-variability systems.

Setup/Changeover Assumptions: The model includes setup time per unit but assumes consistent batch sizes. Frequent product changes may dramatically reduce capacity.

Downtime Unpredictability: Estimated downtime varies; actual downtime may be higher during periods of equipment aging or operator inexperience.

Quality Rate Assumptions: Quality rates depend on process maturity and operator skill. New products or inexperienced teams may have lower quality rates than assumed.

Labor Constraint Not Modeled: This analysis assumes sufficient operator availability. In reality, labor may be the bottleneck (not equipment). Cross-training and scheduling are critical.

External Factors: Supply shortages, customer demand variability, and equipment failures beyond average assumptions can significantly impact actual capacity.