£200,000/year Lost to Invisible Waiting Time

UK manufacturer · Lean & Six Sigma diagnostic

Results:

• Lead time cut from 5+ days to under 2
• Waiting time reduced ~60%
• ~£200,000/year of recoverable value unlocked
• £0 CAPEX — process-driven only

The operational challenge

A mid-sized UK manufacturing facility reported healthy machine utilisation (85% OEE) but faced margin compression, extended lead times and rising operational friction. Machines ran consistently and departmental KPIs were met, yet order-to-ship cycles regularly exceeded five days, and finished goods were moved multiple times before dispatch — driving damage, packaging failures and write-offs. Management blamed suppliers, forecasting and fatigue. No one had mapped the actual flow of material and information from intake to shipment.

The diagnostic approach

Gemba walk & single-part tracking

I followed one discrete unit from raw-material receipt to final dispatch — no dashboards, no historical reports — recording observation manually to avoid filtering reality through existing metrics.

Value stream mapping

Every touchpoint, queue, decision gate and transport move was logged, separating cycle time, wait time and handover frequency to isolate value-add from non-value-add activity.

Waste & decision-point analysis

The seven classic wastes were mapped against actual flow, with attention to transport, motion, waiting and defects. I tracked how long work waited for approvals and routing — bottlenecks traced to unclear ownership, not capacity.

The discovery

Actual processing time was 23 minutes per unit; total lead time was over four days. The time was consumed waiting for internal transport, queuing for batched quality checks, sitting idle between operations, and waiting for management decisions. As the production director pointed to the live OEE dashboard — "we’re running at 85% efficiency" — the machines were, but the system was not. High local efficiency pushed work downstream regardless of capacity, creating queues, intermediate storage and repeated re-handling. Damage and loss were flow failures, not quality failures.

The 12-week intervention

Changes were rolled out over 12 weeks with no new machinery, software or facility modifications — zero CAPEX.

• Weeks 1–3: replaced push scheduling with a pull system triggered by downstream capacity, standard work sequences and visual control boards with queue limits.
• Weeks 4–6: eliminated non-essential finished-goods transfers, defined fixed FIFO staging zones, standardised handling and removed redundant inspection steps.
• Weeks 7–9: clarified ownership for routing and quality holds, replaced verbal approvals with structured escalation, and cut decision latency from days to hours.
• Weeks 10–12: documented new SOPs, trained team leaders, and shifted primary metrics from machine utilisation to lead time, first-time-through rate and damage frequency.

Measured results

Lead time fell from 5+ days to under 2. Unnecessary re-handling was eliminated and damage dropped to baseline tolerance. Waiting time fell ~60%, freeing floor space and releasing working capital. Expedited freight and management firefighting were nearly eliminated — all with £0 CAPEX, unlocking roughly £200,000/year of recoverable value.

Why local efficiency metrics mislead

OEE and utilisation targets are valuable when the system is balanced. When flow is broken, they become vanity metrics: high local efficiency in a disconnected system creates queues, inflates lead time and traps cash in inventory. Customers do not pay for busy machines — they pay for reliable delivery.