A regional utilities company needed to compare efficiency, renewable-energy, storage and capital options without weakening reliability. Average performance measures concealed important differences in utilisation, weather, operating state and asset condition. Marketways developed a common analytical basis for comparing transition choices, detecting material losses and challenging investment assumptions.
The engagement objective
Through the initial discovery, Marketways defined the objective: compare decarbonisation opportunities and investment pathways.
How Marketways translated the problem
We began with a practical question: Which efficiency measures warrant engineering and investment attention? The first analysis used energy, production, operating state, tariffs, equipment and interventions.
That evidence could not be read in isolation. A lower energy intensity can reflect product mix or utilisation rather than genuine improvement. Intermittency and correlated weather make average generation an inadequate planning measure.
Headline efficiency ratios were not comparable across assets with different loads, weather and operating states. We normalised the conditions before allowing an apparent leader or laggard to shape the investment case.
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 energy-efficiency opportunity portfolio exposed dependencies with renewables and storage integration scenarios, capital project scenario and decision assurance, emissions and leakage detection. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Energy-efficiency opportunity portfolio: Prioritise practical reductions without weakening output or reliability.
- Renewables and storage integration scenarios: Compare transition pathways against reliability, cost and emissions goals.
- Capital project scenario and decision assurance: Challenge assumptions before capital is committed.
- Emissions and leakage detection: Find material sources earlier and verify whether interventions work.
Evidence we examined
- Energy, production, operating state, tariffs, equipment and interventions.
- Generation profiles, weather, demand, storage, network constraints and costs.
- Demand cases, cost estimates, schedule risks, technical options and constraints.
- Sensors, inspections, production, maintenance, weather and verified events.
Industry conditions we accounted for
- A lower energy intensity can reflect product mix or utilisation rather than genuine improvement.
- Intermittency and correlated weather make average generation an inadequate planning measure.
- Large projects combine demand, schedule, cost, technical, regulatory and market uncertainty.
- Sensor coverage, operating state and environmental conditions affect what can be detected.
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.
- Energy-efficiency opportunity portfolio
- Method: Statistics & Econometrics, Data Foundations & Business Intelligence.
- Evidence: Energy, production, operating state, tariffs, equipment and interventions.
- Decision supported: Prioritise practical reductions without weakening output or reliability.
- Impact measure: Normalised consumption, cost, reliability and payback confidence.
- Renewables and storage integration scenarios
- Method: Forecasting, Risk & Optimisation, Statistics & Econometrics.
- Evidence: Generation profiles, weather, demand, storage, network constraints and costs.
- Decision supported: Compare transition pathways against reliability, cost and emissions goals.
- Impact measure: Reliability, system cost, curtailment and emissions.
- Capital project scenario and decision assurance
- Method: Forecasting, Risk & Optimisation, Statistics & Econometrics.
- Evidence: Demand cases, cost estimates, schedule risks, technical options and constraints.
- Decision supported: Challenge assumptions before capital is committed.
- Impact measure: Decision robustness, contingency adequacy and assumption closure.
- Emissions and leakage detection
- Method: Machine Learning & Predictive Analytics, Data Foundations & Business Intelligence.
- Evidence: Sensors, inspections, production, maintenance, weather and verified events.
- Decision supported: Find material sources earlier and verify whether interventions work.
- Impact measure: Detection time, verified reduction, false alerts and coverage.
Implementation
The engagement was structured as portfolio design followed by investment gates. 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 credible emissions reduction, reliability and investment robustness. The supporting measures included:
- Normalised consumption, cost, reliability and payback confidence.
- Reliability, system cost, curtailment and emissions.
- Decision robustness, contingency adequacy and assumption closure.
- Detection time, verified reduction, false alerts and coverage.
Services and methods used
Services: Opportunity Discovery, Business Feasibility Study, Risk & Uncertainty Modelling, Decision Assurance, Risk Detection, Operational Performance Diagnostic.
Methods: Statistics & Econometrics, Data Foundations & Business Intelligence, Forecasting, Risk & Optimisation, Machine Learning & Predictive Analytics.