A regional property operator needed a consistent way to compare maintenance, energy, tenant experience and capital needs across dissimilar assets. Raw costs and ratings were misleading because utilisation, redundancy, occupancy and asset condition differed. A low-cost asset could be creating complaints, service risk or deferred failure elsewhere. Marketways built the programme around fair comparisons, connected consequences and the timing of intervention.
The engagement objective
Through the initial discovery, Marketways defined the objective: coordinate maintenance, energy, tenant experience and capital priorities.
How Marketways translated the problem
We began with a practical question: Which building systems need attention before service failure? The first analysis used bms, alarms, condition, work orders, utilisation and service impact.
That evidence could not be read in isolation. Different assets have different redundancy, criticality and sensor coverage. Occupancy, weather, operating hours and tenant mix can resemble inefficiency.
Raw maintenance cost made lightly used and intensively used assets look comparable. Occupancy, redundancy, condition and service consequence were needed before cost could support a capital priority.
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 predictive facilities maintenance exposed dependencies with building energy and operational performance, portfolio capital and refurbishment prioritisation, tenant, resident and community experience. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Predictive facilities maintenance: Reduce disruption and use maintenance windows more effectively.
- Building energy and operational performance: Prioritise operational and retrofit opportunities on a fair baseline.
- Portfolio capital and refurbishment prioritisation: Compare risk, value and timing across the portfolio.
- Tenant, resident and community experience: Connect experience evidence to specific asset and service improvements.
Evidence we examined
- BMS, alarms, condition, work orders, utilisation and service impact.
- Meters, weather, occupancy, equipment, tariffs and interventions.
- Condition, income, occupancy, maintenance, compliance, capex and scenarios.
- Surveys, complaints, work orders, amenities, lease events and property context.
Industry conditions we accounted for
- Different assets have different redundancy, criticality and sensor coverage.
- Occupancy, weather, operating hours and tenant mix can resemble inefficiency.
- A high maintenance cost can indicate poor condition, high utilisation or deliberate preventive work.
- Location and tenant mix affect ratings, so raw comparisons can misdirect action.
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.
- Predictive facilities maintenance
- Method: Machine Learning & Predictive Analytics, Forecasting, Risk & Optimisation.
- Evidence: BMS, alarms, condition, work orders, utilisation and service impact.
- Decision supported: Reduce disruption and use maintenance windows more effectively.
- Impact measure: Availability, emergency work, tenant disruption and maintenance value.
- Building energy and operational performance
- Method: Statistics & Econometrics, Data Foundations & Business Intelligence.
- Evidence: Meters, weather, occupancy, equipment, tariffs and interventions.
- Decision supported: Prioritise operational and retrofit opportunities on a fair baseline.
- Impact measure: Normalised energy, comfort, cost and verified savings.
- Portfolio capital and refurbishment prioritisation
- Method: Forecasting, Risk & Optimisation, Statistics & Econometrics.
- Evidence: Condition, income, occupancy, maintenance, compliance, capex and scenarios.
- Decision supported: Compare risk, value and timing across the portfolio.
- Impact measure: Risk reduction, value protected and capital-plan stability.
- Tenant, resident and community experience
- Method: Market, Customer & Behavioural Analytics, Statistics & Econometrics.
- Evidence: Surveys, complaints, work orders, amenities, lease events and property context.
- Decision supported: Connect experience evidence to specific asset and service improvements.
- Impact measure: Resolution, renewal, service consistency and issue recurrence.
Implementation
The engagement was structured as portfolio diagnostic and staged implementation. 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 availability, cost, risk, renewal and capital value. The supporting measures included:
- Availability, emergency work, tenant disruption and maintenance value.
- Normalised energy, comfort, cost and verified savings.
- Risk reduction, value protected and capital-plan stability.
- Resolution, renewal, service consistency and issue recurrence.
Services and methods used
Services: Predictive Maintenance & Reliability, Risk Detection, Operational Performance Diagnostic, Opportunity Discovery, Decision Assurance, Risk & Uncertainty Modelling, Customer Satisfaction & Experience Research, Mystery Shopping & Service Quality Audit.
Methods: Machine Learning & Predictive Analytics, Forecasting, Risk & Optimisation, Statistics & Econometrics, Data Foundations & Business Intelligence, Market, Customer & Behavioural Analytics.