Recovering hidden capacity, preparing for rate increases

A complete Assessment + Simulation Study, built in full on a modeled six-operation fabrication area so the method can be shown end to end. Everything here, from inputs to validated model to sequenced moves, is what an engagement delivers on your area.

00 · The answer

Four no-capital moves take the modeled area from missing its 90-a-day commitment on permanent overtime to meeting demand at straight time. Deliverable capacity rises 46 percent and around $1.1M a year of waste reduced. When the build rate climbs, a lean second shift carries the area to about 157 a day, counted separately.

01 · The problem

Missing demand while paying overtime

The area commits to 90 parts a day and ships about 80, missing roughly 11 percent of demand while already running 2 hours of overtime every shift. The premium is paid and the area still falls short. Before it is a cost problem, it is an on-time-delivery problem.

80 of 90 good parts a day against committed demand, on 2 hours of overtime every shift
~$637K a year of overtime spend while still falling short of demand
~$742K a year of quality waste, the scrap and rework line items combined
92.4% of a part's lead time is waiting, 1134 minutes of a 1228 minute total
6.0 Op 10 3.0 Op 20 3.5 Op 30 4.0 Op 40 1.4 Op 50 3.3 Op 60 demand takt 4.5 min/part 11 min/part Rework (offline)
Each station's pace in minutes per part against the straight-shift pace needed to meet demand, with only Op 10 above it.

02 · The diagnosis

One operation caps the whole area

Every line has a drum, the one operation that sets the pace for everything else. Here the floor evidence points to Op 10, and the model confirms it, running at 100 percent while every other station shows headroom. The cart ahead of the area tells the same story, nearly two hundred parts deep and still building.

PRODUCTION CONTROL Master Schedule (MRP) daily release Component Supplier feeds Op 30 component ⇒ Op 30 Upstream Area lumpy cart / shift PUSH (lumpy, 95/d: 90 demand + scrap replacements) CART BUILDUP filled by push, emptied by Op 10 avg 193 · max 282 parts still climbing, not settled ▲ building +10.3/day in 95/d push ▸ out 85/d pull PULL Op 10 2 stations · 2 people cyc 8-12-16 min ◀ DRUM, system constraint Utilization 100% Op 20 2 stations · 1 person cyc 2-3-4 min Utilization 50% Op 30 17 machines · 3 people cyc 60 min wait 93 min Utilization 71% Op 40 3 stations · 2 people cyc 6-8-10 min Utilization 78% Op 50 5 stations · 5 people cyc 7 min Utilization 27% Op 60 1 station · 1 person cyc 2-3-5 min quality loss Utilization 65% avg 0 (0–1) ≈ 0/day avg 18 (12–25) ≈ 0/day avg 1 (0–6) ≈ 0/day avg 0 (0–0) ≈ 0/day avg 0 (0–1) ≈ 0/day ship Complete GOOD OUTPUT 79.8/day ✗ 10/day SHORT of 90 CUSTOMER 90/day demand 15% fail REWORK. OFFLINE (2 quick-fix bays) 2 × 22 min · cap 47/d · in 14/d backlog avg 0 · keeps up (WIP +0.0/day) utilization 30% rejoins the Op 30 queue Utilization gauge (bar fill = % of capacity in use, full bar = 100%): healthy warm at capacity parts waiting between stations, shown avg (range) and change per day cyc a-b-c = min-typical-max minutes
The current state at a glance: the drum at Op 10 caps good output at 79.8 a day against 90 committed. Open full size

The floor also shows what a static utilization report misses. Parts visibly stack up at Op 30 even though the report reads it at about 71 percent busy, and walking the machines explains why. They are dedicated by part type, one type pool is the true limit, and parts wait about 93 minutes there while the average shows headroom. The model quantifies that wait and confirms the pool as the next constraint in line once the drum is relieved.

100% of straight-shift capacity 103% Type A 35 / 34 per day 95% Type B 26 / 27 per day 108% Type C 22 / 20 per day 95% Type D 19 / 20 per day 105% Type E 14 / 14 per day
Each bar is a type pool's daily load against its straight-shift capacity once the drum is relieved, with Type C the binding pool.

03 · The moves

Four moves, no capital

Four floor-level changes, not model settings. A constraint does not disappear when it is relieved, it moves, and the model predicts where it lands next. Each move was tested in the model and aimed at that next constraint before it surfaced on the floor, which is why four small moves are enough and why the sequence looks simpler than it is.

1. Relieve the drum

A processing-time study found Op 10 running each part longer than quality requires. Standard work was requalified to the shorter cycle, real work done with operators and the quality org rather than a model setting, with no loss of quality. The overtime roughly halved, and the constraint moved to Op 40.

2. Balance the line

One operator moved from Op 50, which has slack, to Op 40, the new drum. No new headcount. Overtime fell to about 30 minutes a shift, and the constraint moved to Op 30.

3. Sequence the release

A repeating, demand-mix-based release sequence keeps Op 30's type-dedicated machine pools fed without letting one part type pile up. By design it adds no throughput. It cuts WIP and lead time.

4. Root-cause the quality loss

Finding and fixing the root cause of defects cuts rework from 15 to 8 percent and scrap from 5 to 2 percent, the largest single cost cut. The last of the overtime goes, and demand is met at straight time. This is the hardest move of the four, months of disciplined root-cause work on the floor rather than a knob in the model. The model sizes what that work is worth before it starts.

04 · The result

Demand met at straight time, nothing spent

On the same crew, with no new hires and no capital, deliverable capacity rises about 46 percent. The area goes from shipping about 80 a day on permanent overtime to the full 90 a day at straight time, and it holds headroom to about 117 a day on 2 hours of overtime when demand surges. The modeled recoverable annual waste falls from about $1.4M to about $0.3M, subject to validation against a client's actual labor, material, quality, and inventory costs.

release ~92/d CART BUILDUP now stable avg 36 · max 90 ≈ +0.0/day (was building) PULL Op 10 2 stations · 2 people cyc 6-7.5-8 min was drum → relieved Utilization 81% Op 20 2 stations · 1 person cyc 2-3-4 min Utilization 68% Op 30 17 machines · 3 people cyc 60 min wait 21m · headroom ◀ tightest (next lever) Utilization 90% Op 40 3 stations · 3 people cyc 6-8-10 min Utilization 65% Op 50 5 stations · 4 people cyc 7 min Utilization 43% Op 60 1 station · 1 person cyc 2-3-5 min QUALITY ROOT-CAUSE Utilization 81% avg 0 (0–1) avg 5 (0–15) avg 0 (0–3) avg 1 (0–4) ship Complete GOOD OUTPUT 90/day ✓ meets 90/day demand CUSTOMER 90/day demand 8% fail REWORK. OFFLINE (2 quick-fix bays) 2 × 22 min · cap 47/d was 15% fail rejoins the Op 30 queue Utilization gauge (bar fill = % of capacity in use, full bar = 100%): healthy warm at capacity parts waiting between stations, shown avg (range) and change per day cyc a-b-c = min-typical-max minutes
The same map after the four moves, the cart stable and demand met at straight time with the overtime gone. Open full size
~46% modeled rise in deliverable capacity, same crew, no capital
90 a day full demand met at straight time in the model, the permanent overtime gone
Demand 90/day straight time 64 80 2 h OT Baseline Crew 14 (12 needed) max 95 straight time 82 90 ~55 min OT Step 1, Op 10 relieved 14 (12 needed) max 104 straight time 87 90 ~30 min OT Step 2, balanced 14 (12 needed) max 104 straight time 87 90 ~30 min OT Step 3, sequenced 14 (12 needed) max 117 straight time 90, no OT 90 no overtime Step 4, quality fixed 14 (12 needed) max 170, second shift on 2 h OT straight time 157, no OT 157 no overtime Rate step, two shifts 18 (12 + 6)
Each move buys output until demand is met at straight time, before any second shift is counted.

05 · The payoff

What the recovery is worth

~$1.1M a year of modeled recoverable waste removed, from about $1.4M down to about $0.3M
NECESSARY FLOOR ~$1.6M (crew), stays RECOVERABLE WASTE ~$1.4M, cut to ~$0.3M by Steps 1 to 4 $1.62M Crew (straight time) $0.64M Overtime $0.67M Scrap ~$1.1M removed ~$0.3M remains Rework $0.07M WIP carry $0.03M
The recovery removes about $1.1M of the modeled waste and the crew cost floor stays.

06 · The rate path

Rate-readiness, counted separately

When the build rate climbs past what the recovered shift can hold, a lean second shift adds rate on the same equipment. Two shifts run back to back and carry the area to about 157 a day at straight time, for 4 net new hires, with 2 of the second shift's 6 operators redeployed from slack the recovery freed.

Op 10 2 stations · 2 people cyc 6-7.5-8 min ◀ shift-1 drum Utilization 100% Op 20 2 stations · 1 person cyc 2-3-4 min Utilization 82% Op 30 17 machines · 3 people cyc 60 min changeover operators 16% Utilization 75% Op 40 3 stations · 3 people cyc 6-8-10 min Utilization 81% Op 50 5 stations · 2 people cyc 7 min Utilization 90% Op 60 1 station · 1 person cyc 2-3-5 min Utilization 96% ship ▲ Op 30 buffer builds 23 → 57 across the shift feeds shift 2 Shift 1 of 2 FULL CREW 12 people 2 redeployed to shift 2 from today's crew of 14 Shift 1 ships THIS SHIFT ≈103/day to ~157/day across both shifts Utilization gauge (bar fill = % of capacity in use, full bar = 100%): healthy warm at capacity parts waiting between stations, the Op 30 buffer the handoff between shifts cyc a-b-c = min-typical-max minutes
The first shift of the two-shift day, 12 people on the full area, Op 10 its drum, building the Op 30 buffer for shift 2. Release, cart, rework, and customer flows stay on the daily maps above. These two maps isolate each crew and the buffer handoff. Open full size

It needs no capital and no new equipment, only added labor, so it is counted separately and never summed into the recovery. The model shows each shift hitting its own drum, Op 10 on the full first shift and Op 40 on the lean second, which is what makes the two-shift day work. At each rate the model shows the labor required, the stations and machines in use, the overtime needed, and the ceiling before the next constraint binds.

Op 10 1 station · 1 person cyc 6-7.5-8 min Utilization 82% Op 20 1 station · 1 person cyc 2-3-4 min Utilization 36% Op 30 17 machines · 1 person cyc 60 min changeover operator 52% Utilization 83% Op 40 1 station · 1 person cyc 6-8-10 min ◀ shift-2 drum Utilization 97% Op 50 1 station · 1 person cyc 7 min Utilization 89% Op 60 1 station · 1 person cyc 2-3-5 min Utilization 53% ship ▼ Op 30 buffer draws down 57 → 23 across the shift built on shift 1 ▲ builds behind Op 40 avg 17 (0–41) queue at the shift-2 drum Shift 2 of 2 LEAN CREW 6 people 2 redeployed from shift 1 4 net new hires Shift 2 ships THIS SHIFT ≈53/day to ~157/day across both shifts Utilization gauge (bar fill = % of capacity in use, full bar = 100%): healthy warm at capacity parts waiting between stations, the Op 30 buffer the handoff between shifts cyc a-b-c = min-typical-max minutes
The lean second shift, 6 operators on the same equipment, Op 40 its drum, running down the Op 30 buffer that shift 1 builds. Open full size

What each configuration holds as the build rate climbs, from the modeled rate ramp.

Configuration90/day (today)110/day120/day150/day
Current state~80 ceiling~80 ceiling~80 ceiling~80 ceiling
Step 1, relieve the drum~55 min OT~95 ceiling~95 ceiling~95 ceiling
Step 2, balance the line~30 min OT~104 ceiling~104 ceiling~104 ceiling
Step 3, sequence the release~30 min OT~104 ceiling~104 ceiling~104 ceiling
Step 4, root-cause the quality lossno overtime~1.4 h OT~117 ceiling~117 ceiling
Rate step, two shiftsno overtimeno overtimeno overtimeno overtime

Overtime entries are what it takes to meet that rate. Ceiling entries are the most that configuration can ship even on full overtime.

07 · The method

How an engagement runs

  1. The study starts on the floor. Walking the line, talking with operators, and measuring cycle times, yields, staffing, and the shift pattern. The constraint is found here, not in software.
  2. The line is then modeled in discrete-event simulation from its own measured inputs, and the model is validated against the line's measured behavior before any conclusion is drawn.
  3. Improvement moves come from floor experience. Each one is tested in the model before anything changes on the floor, so the costly decisions are made on evidence.
  4. The model also shows where the constraint migrates after each move, so the full sequence is known in advance and the next bottleneck is never a surprise.
  5. Findings land as a costed, sequenced recommendation the client's team can execute, with the evidence behind every step.

08 · The proof

How the model is validated

A model is only worth what it can prove, so this one has to earn trust twice. First against the area. The model is built from the area's own measured cycle times, yields, staffing, and shift pattern, tuned to match one measure, throughput, and then required to reproduce WIP and lead time on its own, measures it was never tuned to.

Second against itself. An accounting check confirms every part released is either shipped, scrapped, or still in process, so the model neither creates nor loses work. Every figure in the study is the average of 30 replications with a 95 percent confidence interval. In an engagement the same validation runs against the client's measured line before any future-state move is tested.

09 · The ask

The constraint is rarely the equipment

It hides in handoffs, scheduling, and flow, where no single number shows it. If your line is meeting demand on overtime, or falling behind with overtime not closing the gap, or facing a rate increase it may not hold, the locked-up capacity is likely already on your floor. I find it the same way it was found here.

This is the work the Assessment + Simulation Study delivers, a fixed fee over four weeks, on your area instead of a modeled one.

Find it on your line