Operational Data
Quality reports, complaints, NCRs, programme data, cost exports and project reviews.
Dasher Quality Intelligence Demo
Transform Operational Data into Continuous Improvement
Move beyond dashboards. Understand why problems happen, which causes matter and what leadership should do next.
This deterministic demonstrator uses synthetic records for Northbridge Clinical Engineering Ltd: Power BI exports, NCRs, quality reports, complaints, project reviews, supplier scorecards and lessons learned.
Active AI finding
Hospital overruns are concentrated in late-stage clinical environments
The strongest overrun pattern is not general poor delivery. It is a cluster of late design change, supplier evidence failures and constrained access on clinical projects after package release.
GBP 559kBusiness value at riskWorsening12-month signal3Related projects
Overspend
Defects
Productivity
Business DataQuality EventsPattern RecognitionRoot Cause AnalysisAI InsightsImprovement RecommendationsBusiness Outcomes
Pattern Detection
Recurring defects, margin leakage, delays and supplier signals are connected across records.
Root Cause Analysis
The engine links outcomes to probable causes, supporting evidence and confidence.
Improvement Actions
Leadership gets prioritised actions with value, effort, ROI and known gaps.
Quality dashboard
Operational picture, ready for investigation
KPIs, projects, margins, customer satisfaction, defects, NCRs, overspend and programme delay are visible as connected operational signals.
Filtered portfolio value
Average filtered forecast
Average project overspend
Open quality signal volume
Average delay exposure
Average customer score
Project performance
ProjectManagerMarginDelayQualityStatus
12-month operating trend
Overspend %
NCRs
Customer satisfaction
Rework cost
AI investigation
Prompt-led analysis without a chatbot shell
The demonstrator answers leadership questions with patterns, correlations, root causes, confidence and supporting evidence.
AI finding
Hospital overruns are concentrated in late-stage clinical environments
The strongest overrun pattern is not general poor delivery. It is a cluster of late design change, supplier evidence failures and constrained access on clinical projects after package release.
Forecast margin leakage of GBP 559k across St Cuthbert, Hartswell and Norwood.
Patterns
Projects with clinical access constraints average 7.0 weeks delay versus 0.8 weeks elsewhere.
Late design changes appear in 3 of the 4 highest-rework records.
Supplier evidence failures occur after procurement freeze, not at initial tender review.
Correlations
Supplier risk above 75 aligns with average overspend of 19.7%.
Projects with weekend recovery labour show lower productivity and higher defect recurrence.
Customer satisfaction falls below 80 when rework explanations are not evidence-backed.
Root cause explorer
Issue to evidence to action
Every root cause connects to source records, related projects, lessons learned and recommended actions.
Power BI integration
Explain the chart, then prove the explanation
Existing dashboards remain useful, but the intelligence layer turns a chart movement into causes, confidence and action.
Project overspend
JulAugSepOctNovDecJanFebMarAprMayJun
Continuous improvement
Prioritised opportunities by value, effort and confidence
Recommendations become an improvement portfolio with estimated business value, implementation effort, confidence, priority and ROI.
Synthetic annualised opportunity
Ready for leadership action
Evidence-weighted recommendations
Selected initiative
Healthcare quality gate before procurement release
Actions
Make room data, clinical physics comments and commissioning prerequisites mandatory before package release.
Block procurement exceptions unless risk is accepted by Operations and Commercial.
Reuse Alderley and Kingswater gateway evidence as the pilot template.
Evidence
Evidence panel
Every recommendation is traceable
Source records remain visible: quality reports, Power BI data, project reviews, lessons learned, complaints and supplier scorecards.
Trust layer
Visible reasoning and human review
Confidence: 89%Sources: 5 evidence recordsEvaluation: consistent with project, supplier and monthly trend dataKnown gaps: Access constraints are partly captured in email threads and not always coded in PMO exports.Human review: Operations Director and Quality Manager should validate the design gate threshold before rollout.
Azure architecture
Designed as a governed intelligence layer
No live Azure or OpenAI services are used in this demo; the architecture shows how the production pattern would connect customer-owned data to trusted recommendations.
ERP
Project, cost, variation and labour records.
Financial source of truthPower BI
Existing KPI exports and monthly portfolio measures.
No dashboard replacement requiredBlob Storage
Quality reports, emails, complaints, reviews and lessons learned.
Customer-owned storageAzure AI Search
Indexes evidence, metadata, themes and linked records.
Source citation and retrievalAzure AI Foundry
Evaluation, prompt orchestration and governed workflows.
Traceable reasoningAzure OpenAI
Summarisation, pattern explanation and recommendation drafting.
Private governed inferenceQuality Intelligence Engine
Connects trends, root causes, confidence and improvement value.
Human review requiredExecutive Dashboard
Evidence-backed decisions, priorities and outcomes.
Decision support, not autopilotBusiness outcomes
Reduce rework, improve margins and make operational decisions easier to defend.
Reduce defectsImprove forecastingIncrease customer satisfactionPrioritise recurring issuesSupport quality managers
Primary causes are supplier delays, late design changes, weekend recovery labour and poor planning controls. Supporting evidence has 89% confidence.