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textile manufacturer · Process improvement

Production Waste & Lead-Time Improvement

A process-led improvement programme that combined operational analysis and reporting to reduce production lead time and deliver a documented net saving.

Waste reductionSQL analyticsTableauIoT machine data
A textile worker operating a loom as fabric moves through the machine.
Waste, production and demand in one view

Waste reduction needed more than a process map

SQL analysis brought production and waste records together with machine data from IoT devices. Tableau helped the team see where inefficiencies appeared across materials, production conditions and the wider operating picture.

Procurement trends and customer buying classifications added context around material use and demand. The main focus remained waste reduction, while end-to-end production lead time showed whether the broader process was improving.

Connect the evidence

Use more than one lens on waste

Machine signals showed operating conditions around production events. Waste and rework records showed where the cost appeared, while procurement data added material price and purchase-volume context.

Customer buying classifications helped explain changes in product and order mix. SQL supported the analysis and Tableau made the combined patterns easier to review before improvement decisions were made.

Read the result correctly

Less elapsed time, with savings reported separately

Reported lead time fell from seven days to three: four fewer days from start to completion. That is 57.1% shorter lead time, not an equivalent increase in throughput.

The programme also recorded a client-reported AUD 350,000 net saving over 18 months. Lead time describes how quickly work finished; the net saving describes the financial result over that period.

Process → Architecture → Delivery

Our methodology

  1. 01DiscoverAgree the outcome, the decisions and the requirements.
  2. 02Current stateMap how the work and data flow today, with evidence.
  3. 03Root causesFind what drives the delay, rework or disagreeing numbers.
  4. 04ApproachAgree the target design, scope and measures of success.
  5. 05BuildBuild the process change, model or report the design calls for.
  6. 06OptimiseTest at real volumes; tune speed, cost and usability.
  7. 07ProductioniseRelease with managed deployment, monitoring and support.
  8. 08GovernSet owners, access and controls so it keeps working.
Value-stream map: Value stream before: reported 7-day production lead time. The steps are listed below the diagram.
Illustrative reference process 1 of 2Value stream before: reported 7-day production lead time The 7-day total is the reported figure. The split across steps is illustrative; it shows where waiting typically sits in a print-and-finish flow.
Steps in this diagram
  • Order + artwork
  • Material issue
  • Print
  • Finish
  • Pack + dispatch
Full size
Value-stream map: Value stream after: reported 3-day production lead time. The steps are listed below the diagram.
Illustrative reference process 2 of 2Value stream after: reported 3-day production lead time The 3-day total is the reported figure; the step split is illustrative.
Steps in this diagram
  • Order + artwork
  • Material issue
  • Print
  • Finish
  • Pack + dispatch
Full size
Architecture diagram: Reference design on Microsoft Fabric: machine, production and commercial data. The components are listed below the diagram.
Reference architectureReference design on Microsoft Fabric: machine, production and commercial data Machine signals stream through IoT Hub into a Fabric Eventhouse and are aggregated to run level. Production and waste records, procurement and customer classifications land in a lakehouse, and one semantic model joins them for the waste-intelligence report. The original engagement used SQL and Tableau.
Components in this design
  • Machine sensors (IoT devices)
  • Production + waste (runs, scrap, rework)
  • Procurement + sales (lots, orders)
  • IoT Hub (device telemetry)
  • Pipelines (daily batch)
  • Eventstream (route + window)
  • Eventhouse (run-level aggregates)
  • Lakehouse (production + commercial)
  • Semantic model (run grain)
  • Waste intelligence (sample report on this page)
  • Line monitor (live conditions)
Full size
First page of the Production Waste Intelligence sample report
Delivery: interactive Power BI sample reportProduction Waste Intelligence Rank materials and conditions linked to waste and rework, and add procurement and demand-mix context, so improvement work targets the right investigation. Delivered with named owners, role-based access, release pipelines and a controlled way to change governed measures.
Full screen
Who it is for
Production manager, CI lead, procurement.
Decision it supports
Which material / machine condition to investigate first; whether purchasing lots or demand mix shift the pattern.
Report pages
Waste intelligence

Reference design: how this pattern is typically built on Microsoft services. Component choices for a specific engagement depend on the client's environment and existing licences.

Microsoft icons are used under Microsoft's terms. Icon notices

Synthetic sample data, not client data.

Results in view

Fewer days to completion

Reported result

Production lead time

57.1%shorter lead time

Days from start to completion · lower is better

Before7 days
After3 days

A four-day reduction in elapsed production time.

Client-reported result

Reported saving

AUD 350,000over 18 months

Saving amount · AUD · axis starts at zero

Saving reportedAUD 350,000

Reported net saving for the full 18-month period. No monthly series or individual cost allocation was supplied.

Next step

Is waste or lead time hiding in the process?

Tell us what is happening. We agree priorities and scope before proposing any work.

Talk through your projectThe service: Lean Six Sigma & Process Improvement