← All case studies
consumer-products manufacturer · Quality management

Quality Management & Quality Control System Redesign

More than 150 spreadsheets became one shared quality database, connected to SAP® software and SharePoint. The redesign brought inspection records, traceability, export documentation, automation and production analysis into one coordinated quality-management process.

Six SigmaAzure SQLPower BIPower Automate
Quality inspector examining a manufactured product
Beyond consolidation

Make the quality record useful.

More than 150 spreadsheets held inspection results and production history. Investigating an issue meant finding the right files, reconciling records and piecing together what had happened.

One shared database changed the starting point. The wider redesign connected quality assurance, production systems, follow-up and analysis—so the record could support a decision, not just store a result.

Trace the issue. Follow the response.

Keep the whole quality story together.

Nonconformance reports (NCRs) were issued and tracked alongside rework and product recalls. The team could connect a quality concern with its production context and the action taken to address it.

That history supported earlier root-cause analysis. It also gave ongoing performance and formulation reviews a stronger basis: what happened, what changed, and what needed another look.

Assurance beyond the factory floor

Connect the evidence to the paperwork.

The work extended to quality-assurance documentation and issuing Certificates of Conformance for international exports. SharePoint integration made assurance part of the connected quality process.

Power Automate handled notifications, alerts and reminders. Desktop RPA bridged legacy applications, helping keep data and systems in sync without replacing every application the manufacturer relied on.

Export documentation was a delivered capability.

Analysis that feeds back into production

Watch quality and productivity together.

Power BI

Power BI reporting brought the cost of poor quality and productivity into view. Process control charts helped monitor variation and identify signals for investigation, keeping attention on both product quality and production performance.

QualityWhere are issues recurring?

NCRs, recalls and rework history.

CostWhat is poor quality costing?

The impact of defects and corrective work.

ProductivityHow is production performing?

Performance alongside quality variation.

Machine-learning model development

Another way to investigate production issues.

Machine-learning model generation complemented the reporting and process-control work, supporting earlier root-cause investigation. It formed part of the wider effort to improve performance and formulations.

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.
Swimlane process map: As-is: investigating a quality issue across 150+ spreadsheets. The steps are listed below the diagram.
Illustrative reference process 1 of 3As-is: investigating a quality issue across 150+ spreadsheets The investigator hunts for files, reconciles versions and rebuilds the batch history before any root-cause work starts.
Steps in this diagram

Lanes: Production, QA inspector, Quality engineer, Export / customer.

  • Defect observed
  • Record in a spreadsheet
  • Find the right files
  • Reconcile versions
  • Late root-cause analysis
  • Certificate assembled by hand
  • Shipment delayed
Full size
Swimlane process map: To-be: record once, trace the response, release with evidence. The steps are listed below the diagram.
Illustrative reference process 2 of 3To-be: record once, trace the response, release with evidence Inspection is captured once into the shared database, NCRs carry production context, alerts keep follow-up moving, and certificates are issued from the evidence trail.
Steps in this diagram

Lanes: Production, Quality system (automated), Quality engineer, Export / customer.

  • Batch inspected
  • Capture once (Access form)
  • Shared quality record
  • In spec?
  • NCR with SAP context
  • Alerts + reminders
  • Corrective action / rework / recall
  • Issue CoC from evidence
  • Released with evidence
Full size
Value-stream map: Value stream: from defect to closed NCR (current state). The steps are listed below the diagram.
Illustrative reference process 3 of 3Value stream: from defect to closed NCR (current state) Illustrative. Finding and reconciling records is the constraint, which is why the shared database comes before analytics.
Steps in this diagram
  • Record defect
  • Gather records
  • Root cause
  • Corrective action
  • Close + certify
Full size
Architecture diagram: Connected quality system (logical view). The components are listed below the diagram.
Reference architectureConnected quality system (logical view) Access captures inspection data into the shared Azure SQL database. SAP manufacturing context is integrated, Power Automate cloud flows send notifications, alerts and reminders, Desktop RPA keeps legacy applications in sync, SharePoint holds QA documents, and Power BI analyses quality, cost and productivity.
Components in this design
  • Microsoft Access (inspection capture)
  • SAP® software (manufacturing context)
  • Legacy applications (production systems)
  • Power Automate Desktop (keeps legacy apps in sync)
  • Azure SQL Database (one quality database)
  • Power Automate (notifications, alerts, reminders)
  • SharePoint (QA documents, CoCs)
  • Power BI (quality, cost, productivity)
  • Model development (runtime not established)
Full size
First page of the Quality & Process Control sample report
Delivery: interactive Power BI sample reportQuality & Process Control Review OEE, first-pass yield, control charts and the cost of quality on one record. Delivered with named owners, role-based access, release pipelines and a controlled way to change governed measures.
Full screen
Who it is for
Quality and production managers
Decision it supports
Which machine, shift or defect type to investigate first, from OEE and control charts to the records.
Report pages
Overview · Process control (SPC) · Cost of quality

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

Sample data is synthetic and shows how the reporting is structured; it is not client data.

The work behind the system

Process redesign, integration and follow-through.

The value came from connecting these disciplines. Consolidation made the information usable; workflow, traceability and analysis made it part of daily quality management.

Shared quality records

An Access capture interface and Azure SQL database replaced spreadsheet-based capture, connecting inspection information with the wider manufacturing environment.

Assurance and export documents

SharePoint integration supported quality assurance. Certificates of Conformance were issued for international exports as part of the documentation process.

NCRs, rework and recalls

Traceable issue and response records supported investigation, corrective action and ongoing review of quality performance.

Cloud workflows and Desktop RPA

Power Automate supported synchronisation and follow-up. Desktop automation connected legacy systems, while notifications, alerts and reminders kept work visible.

Power BI and process control

Reports connected the cost of poor quality with productivity. Process control charts supported variation monitoring and investigation of production issues.

Models and continuous improvement

Model-development work added analytical capability. Investigation findings, rework and recall history informed ongoing performance and formulation review.

Technology icon sources and notices

Next step

Is quality history spread across spreadsheets?

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

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