Example 1: Automotive supplier – production line optimised

Industry: Mechanical engineering / automotive supply

Initial situation:

A medium-sized supplier produces precision parts across three production stations. The lead time for a single component was 8 hours – significantly longer than that of competing suppliers. Production was chaotic: queues in front of Station 2, high stock levels between stations.

Identified bottlenecks (value stream analysis):

• Station 2: Long set-up times (45 minutes per variant change)
• Between Stations 1 and 2: Large buffer stock (120 parts)
• Information flow: Shift supervisors received production plans with a delay (manually from the end of one shift to the start of the next)
• Capacity utilisation: Station 3 was utilised at an average of only 60% (upstream bottleneck)

Solution developed:

• Set-up times halved through standardisation (quick changeover method)
• Buffer between Stations 1 and 2 reduced to 30 parts (just-in-sequence)
• Production planning digitised (automatic notifications for shift supervisors)
• Staff flexibility increased: an employee can now switch between workstations

Results after 6 months:

• Lead time: 8 hours → 5.5 hours (31% reduction)
• Throughput: +30% (more parts per shift)
• Working stock: 40% reduction
• Delivery reliability: Improved thanks to a predictable production flow

Key findings:

Often, it is not the machines that are the problem, but the organisation surrounding them. Set-up times and buffers take up more time than the actual processing.

Example 2: Logistics distribution centre – optimised flow of goods

Industry: Distribution logistics / e-commerce

Initial situation:

A distribution centre stores consumer goods and dispatches them daily to retail outlets. The dispatch process was a bottleneck: at the end of the day, large quantities of goods were ready for dispatch but were not all loaded in time. Next-day deliveries were difficult.

Identified bottlenecks (value stream analysis):

• Long buffer time between order picking and palletising
• Insufficient loading bays (bottleneck in outbound transport)
• Information delay: the warehouse management system updated with a delay
• Stock management: some items were picked several times a day, whilst others remained in storage for longer periods

Solution developed:

• Staggered picking times (several times a day, not just in the morning)
• Increased loading capacity through optimisation of shift schedules
• Real-time warehouse management (barcode scanning instead of manual lists)
• Zone-based logistics: Fast-moving items located close to the dispatch zone

Results after 4 weeks:

• Dispatch rate: 85% → 98% (same-day dispatch)
• Order turnaround time: 24 hours → 4 hours (at the centre)
• Staff efficiency: Same volume dispatched with 10% fewer staff
• Stock levels: -15% (improved stock management)

Key findings:

In logistics, speed is key. The value stream analysis helped to reduce batch sizes and increase frequency instead.

Example 3: Mechanical engineering – accelerated order processing

Industry: Mechanical engineering / design and manufacturing

Initial situation:

A mechanical engineering firm has long lead times: it takes 12 weeks from the customer order to delivery. Competitors deliver in 8 weeks. The bottleneck lies not only in production, but also in design and work planning.

Identified bottlenecks (value stream mapping):

• Design: Customer drawings have to be revised several times (iterations)
• Work planning: Manual creation of production documents (1–2 weeks’ waiting time)
• Production planning: Material requirements are identified too late
• Warehouse logistics: Standard parts must be available quickly

Solution developed:

• Introduce a design checklist (identify common errors earlier)
• Partially automate work planning (CAD → production plans generated by software)
• Material requirements planning linked to design (procurement runs in parallel with production)
• Keep standard parts available at all times (smaller stock levels, but no delivery risk)

Results after 3 months:

• Lead time: 12 weeks → 8 weeks (33 per cent faster)
• Redesigns: -60 per cent (better planning)
• Delivery reliability: 75% → 95%
• Costs: Reduced due to faster turnaround times and lower holding costs for materials

Key takeaways:

For complex processes (design + manufacturing + logistics), a holistic value stream analysis is required, not just a focus on manufacturing.

What these examples show

Regardless of the sector: value stream mapping works because it makes the obvious visible. Often, the bottlenecks aren’t hidden – they simply go unnoticed in day-to-day operations.

Furthermore: the most effective improvements aren’t expensive. Reducing set-up times, optimising stock levels, speeding up the flow of information – you don’t need expensive machinery for this, just good planning.

Is your sector affected too?

Do you recognise similarities with your own processes? A digital value stream map helps you to identify specific bottlenecks and simulate improvements.