A public-service authority needed to direct limited inspection capacity towards the situations that created the greatest public risk. The work had to distinguish genuine risk from gaps in reporting and avoid turning incomplete data into automatic enforcement. Marketways connected inspection priorities, shared management information and continuity planning in one regulatory programme.
The engagement objective
Through the initial discovery, Marketways defined the objective: improve how a regulator detects, prioritises and learns from risk.
How Marketways translated the problem
We began with a practical question: Which facilities, transactions or cases need attention first? The first analysis used inspection history, incidents, complaints, facility profile and exposure.
That evidence could not be read in isolation. A prioritisation model must not turn incomplete data into an unchallengeable enforcement decision. Different agencies may use similar words for different populations, events and completion states.
More reported incidents did not automatically mean greater underlying risk. We examined reporting exposure, inspection history and missing information before allowing a score to redirect enforcement capacity.
We did not judge each component by its isolated KPI. We examined how people, assets, decisions and constraints affected one another, then used the evidence to test whether an apparent improvement would strengthen the complete system or merely move cost, pressure or risk elsewhere.
How the engagement developed
The initial work on inspection and regulatory risk prioritisation exposed dependencies with government data and management-information design, emergency demand and continuity simulation. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Inspection and regulatory risk prioritisation: Direct limited inspection capacity towards higher-risk situations.
- Government data and management-information design: Create a shared view of outcomes, operations and emerging risk.
- Emergency demand and continuity simulation: Expose fragile dependencies before a disruption.
Evidence we examined
- Inspection history, incidents, complaints, facility profile and exposure.
- Definitions, source systems, ownership, quality checks and decision cadence.
- Demand scenarios, capacity, dependencies, response times and recovery assumptions.
Industry conditions we accounted for
- A prioritisation model must not turn incomplete data into an unchallengeable enforcement decision.
- Different agencies may use similar words for different populations, events and completion states.
- Plans may look adequate until queues, supplier constraints and staff availability interact.
How our engagement contributed to business impact
We connected every method to a decision and a business measure. The organisation could assess the engagement through operating results as well as model performance.
- Inspection and regulatory risk prioritisation
- Method: Machine Learning & Predictive Analytics, Forecasting, Risk & Optimisation.
- Evidence: Inspection history, incidents, complaints, facility profile and exposure.
- Decision supported: Direct limited inspection capacity towards higher-risk situations.
- Impact measure: Detection yield, missed-risk rate, review burden and fairness.
- Government data and management-information design
- Method: Data Foundations & Business Intelligence, Process, Workflow & Systems.
- Evidence: Definitions, source systems, ownership, quality checks and decision cadence.
- Decision supported: Create a shared view of outcomes, operations and emerging risk.
- Impact measure: Measure consistency, decision lead time and data exceptions.
- Emergency demand and continuity simulation
- Method: Forecasting, Risk & Optimisation, Process, Workflow & Systems.
- Evidence: Demand scenarios, capacity, dependencies, response times and recovery assumptions.
- Decision supported: Expose fragile dependencies before a disruption.
- Impact measure: Critical-service continuity, recovery time and unserved demand.
Implementation
The engagement was structured as pilot followed by controlled scale-up. We connected the analysis to the decisions, operating constraints and measures that the organisation would continue to use.
How success was assessed
The overall assessment considered higher detection value with explainable, proportionate intervention. The supporting measures included:
- Detection yield, missed-risk rate, review burden and fairness.
- Measure consistency, decision lead time and data exceptions.
- Critical-service continuity, recovery time and unserved demand.
Services and methods used
Services: Risk Detection, AI & Model Risk, Business Systems Design & Architecture, Operational Performance Diagnostic, Risk & Uncertainty Modelling.
Methods: Machine Learning & Predictive Analytics, Forecasting, Risk & Optimisation, Data Foundations & Business Intelligence, Process, Workflow & Systems.