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Case Study — Manufacturing Transformation

From Hidden Production Queues to Predictable Manufacturing Flow

How a packaging and printing manufacturer transformed delayed, people-dependent workflows into a measurable planning and execution system.

Implemented and operationalVerified quantified impactReal anonymised engagement

This is a real anonymised engagement. The client identity and confidential business information have been withheld.

  • Manufacturing Flow
  • Work-in-Progress
  • Production Planning
  • Multi-Stage Routing
  • Delivery Control
At a glance

The engagement in summary.

Industry
Packaging and printing
Operational scope
Pre-press, printing and multi-stage finishing
Workflow complexity
3–21 processing stages per job
Primary challenge
Delayed customer orders, hidden work-in-progress and unpredictable completion
Solution
Manufacturing Flow Control Tower with real-time allocation, ageing, routing, planning and execution analytics
Transformation status
Implemented and operational
Headline results

Measured outcomes, stated precisely.

4.3×
Increase in pre-press output

This result relates specifically to pre-press productivity and must not be described as a 4.3× increase in total factory output.

36% → 82%
On-time delivery
12–15 → 6–8 days
Average turnaround time
76%
Production-plan adherence, measured objectively for the first time

The 76% figure is the organisation's first measured baseline. It is not presented as a before-versus-after improvement.

3–21 steps
Digitally routed, planned and tracked for each job
The challenge

Orders were moving, but nobody could see the complete flow.

Customer jobs entered a multi-stage operation involving pre-press, approvals, printing and different combinations of finishing processes.

Depending on the product, a single job could require between three and twenty-one processing stages.

The company knew orders were being delayed, but it could not reliably identify where each job was located, how long it had remained at a stage, what had become the bottleneck or when the customer order was likely to be completed.

Where is each job now?

How long has it remained at the current stage?

Which jobs are approaching their customer commitment?

Which stage has become the bottleneck?

Who is responsible for the next action?

When will this order realistically complete?

Each of these questions is answered directly in the interactive replica below:

The operating system

Eight connected capabilities inside the Manufacturing Flow Control Tower.

01 · Live work-in-progress visibility

  • Jobs pending at every stage
  • Age distribution
  • Jobs within standard time
  • Jobs approaching delay
  • Critically ageing jobs
  • Responsible user or machine
  • Customer and order details

Managers could click any stage or ageing category to see the exact jobs contributing to the number.

02 · Digital routing across 3–21 processes

  • Required processing steps
  • Correct sequence
  • Parallel processes
  • Eligible machines and teams
  • Expected time at each stage
  • Stage dependencies
  • Required next action

Jobs could no longer disappear between departments because the complete route remained digitally visible.

03 · Real-time job allocation

  • Assigned jobs
  • Job priority
  • Target completion time
  • Standard processing time
  • Current workload
  • Completed output
  • Urgent jobs

Supervisors could compare workloads and reallocate work when an employee, stage or machine became overloaded.

04 · Standard time and objective performance

  • Standard versus actual time
  • Expected versus completed output
  • Work assigned versus completed
  • First-time-right output
  • Rework
  • Waiting time
  • Active processing time

The purpose was not simply to rank employees. It was to identify whether lost output resulted from workload, skill, rework, unclear priority or poor process design.

05 · Exception management

  • Jobs exceeding stage-level standard time
  • Jobs approaching customer commitments
  • Jobs awaiting approval
  • Jobs blocked by material or information
  • Jobs deviating from route
  • High-value or priority orders
  • Bottleneck accumulation

Daily management shifted from reviewing every job to managing the exceptions most likely to affect delivery.

06 · Production planning and load balancing

  • Customer due date
  • Job priority
  • Required route
  • Machine eligibility
  • Available capacity
  • Existing load
  • Material availability
  • Changeover requirement
  • Process dependency

Jobs could be grouped and sequenced to improve utilisation and reduce unnecessary material or production changeovers.

07 · Plan-versus-actual execution

  • Jobs planned
  • Jobs started
  • Jobs completed
  • Jobs executed outside the plan
  • Stage delays
  • Sequence changes
  • Actual processing time
  • Reasons for deviation

Current production-plan adherence: 76%. The remaining deviation can now be classified into causes such as urgent customer requirements, planning error, execution deviation, material unavailability, breakdown, quality issue or approval delay.

08 · Customer-service visibility

  • Current job stage
  • Time spent at stage
  • Completed processes
  • Pending processes
  • Reason for delay
  • Expected next action
  • Estimated completion

Customer-facing teams could retrieve status from the operating system instead of repeatedly contacting production.

Open the matching capability in the interactive replica below:

Interactive product walkthrough

Explore the Manufacturing Flow Control Tower

Move from a live view of every workflow stage to the ageing behind a queue, the delivery risk it creates and the exact process route of a single job.

This interactive replica reproduces the analytical structure of a real Xylm implementation. Every customer, order number, job, owner and date shown below is fictional and anonymised.

  • Real implementation logic
  • Fictional anonymised data
  • Interactive product replica
Loading the interactive replica…

This is an interactive illustrative replica based on a real Xylm implementation. It is not connected to a live client environment. Customers, jobs, order numbers, owners and dates are fictional.

Measured impact

The measured operating impact

Pre-press output increased 4.3×

Real-time allocation, ageing visibility, standard times and structured intervention materially increased processing output inside pre-press.

On-time delivery improved from 36% to 82%

The company moved from delivering approximately one-third of orders on time to delivering more than four-fifths on time.

Average turnaround reduced from 12–15 days to 6–8 days

Average turnaround was approximately halved, while the company also gained visibility into the full distribution of job ageing.

Production-plan adherence became measurable

The company established its first objective plan-versus-actual baseline at 76%.

Before and after

What changed in daily operation.

Before
  • Work-in-progress hidden within departments
  • Status dependent on calls and individual knowledge
  • Jobs ageing for 20–30 days without clear escalation
  • No consolidated view across pre-press, printing and finishing
  • Work allocation communicated manually
  • Limited standard-time measurement
  • Delayed or uncertain customer updates
  • Reactive production priorities
  • No measurable production-plan adherence
After
  • Every job visible by stage, age and owner
  • Complete routing across 3–21 processes
  • Real-time job allocation
  • Standard versus actual performance visible
  • Bottlenecks and ageing jobs highlighted
  • Production load balanced across people and machines
  • Changeovers considered during sequencing
  • Customer-facing teams able to retrieve live status
  • On-time delivery improved to 82%
  • Turnaround reduced to 6–8 days
  • Pre-press output increased 4.3×
  • Plan adherence established at 76%
Strategic outcome

From retrospective delay management to proactive flow control.

This was not merely a dashboard implementation.

Xylm created a closed-loop manufacturing management system in which every job has a defined route, every stage has an expected time, every delay becomes visible, every production decision can be compared with the plan and every customer commitment can be traced to shop-floor execution.

Where this model applies

Relevant for multi-stage, job-based and make-to-order manufacturing.

  • Packaging and printing
  • Fabrication
  • Precision engineering
  • Electronics manufacturing
  • Contract manufacturing
  • Industrial components
  • Furniture and interior manufacturing
  • Plastics and converted products
  • Multi-stage finishing operations

These are potential application areas for the operating model. They are not presented as additional client references.

Can you see every order, stage, bottleneck and delivery risk?