£200,000/year Lost to Invisible Waiting Time

UK manufacturer

Lean & Six Sigma diagnostic

Results

• Lead time reduced from more than 5 days to under 2

• Waiting time cut by approximately 60%

• Around £200,000 in annual recoverable value identified

• £0 CAPEX required

The business

A mid-sized UK manufacturing facility with strong machine performance and an average OEE of 85%.

Despite meeting departmental targets, the business was experiencing margin pressure, extended lead times, product damage and increasing operational disruption.

The challenge

Order-to-ship lead time regularly exceeded five days, even though the actual production work required only minutes.

Finished products were moved several times before dispatch, increasing handling, packaging failures, damage and write-offs. Teams attributed the problems to suppliers, forecasting and workload, but the complete flow of materials and information had never been mapped.

Machine performance was being measured. System performance was not.

The diagnostic

Gemba walk and single-unit tracking

We followed one unit from raw-material receipt to final dispatch.

Instead of relying on dashboards or historical reports, every movement, delay, decision and handover was recorded directly on the shop floor.

Value stream mapping

The complete process was mapped to separate:

• processing time

• waiting time

• transport

• handovers

• approvals

• rework

This exposed where value was being created and where time was being lost.

Waste and decision analysis

The seven Lean wastes were assessed against the actual production flow, with particular attention to waiting, transport, motion and defects.

We also measured how long work remained idle while waiting for routing decisions, quality approval or management intervention.

The discovery

Actual processing time was just 23 minutes per unit.

Total lead time exceeded four days.

Most of that time was spent:

• waiting for internal transport

• queuing for batch quality checks

• sitting between operations

• waiting for approvals and routing decisions

• being moved into and out of temporary storage

The machines were operating efficiently, but the overall system was not.

Push scheduling kept production running regardless of downstream capacity. This created queues, excess work in progress, repeated handling and unnecessary storage.

Damage and write-offs were not isolated quality failures. They were consequences of poor flow.

The 12-week intervention

The process was redesigned without new machinery, software or building modifications.

Weeks 1-3: stabilising flow

Push scheduling was replaced with a pull system based on downstream capacity.

Standard work sequences, visual control boards and queue limits were introduced to prevent excess work from entering the process.

Weeks 4-6: reducing movement

Non-essential finished-goods transfers were removed.

Fixed FIFO staging areas were created, handling procedures were standardised and redundant inspection steps were eliminated.

Weeks 7-9: accelerating decisions

Ownership of routing decisions and quality holds was clarified.

Verbal approvals were replaced with a structured escalation process, reducing decision delays from days to hours.

Weeks 10-12: sustaining the changes

New procedures were documented and team leaders were trained to manage the revised process.

Performance measurement shifted from machine utilisation alone to metrics that reflected system performance:

• total lead time

• first-time-through rate

• waiting time

• damage frequency

Measured results

Lead time fell from more than five days to under two.

Waiting time was reduced by approximately 60%, unnecessary handling was eliminated and product damage returned to normal tolerance levels.

The changes released floor space, reduced work in progress and freed working capital. Expedited freight, operational firefighting and management intervention were nearly eliminated.

The intervention required no capital investment and unlocked approximately £200,000 in annual recoverable value.

Why local efficiency can be misleading

OEE and utilisation remain valuable measures, but only when the wider system is balanced.

When flow is poorly controlled, high machine utilisation can create more inventory, longer queues and greater pressure downstream.

Customers do not pay for busy machines. They pay for reliable delivery.