A global industrial company wanted to improve reliability and resource efficiency while introducing greater automation. Asset condition, spare availability, utility use, operator knowledge and new exception-handling work could not be optimised independently. Marketways connected maintenance, resource performance and workforce design, updating the view as operating evidence accumulated so that efficiency gains remained safe, workable and sustainable.
The engagement objective
Through the initial discovery, Marketways defined the objective: coordinate assets, spares, utilities and workforce change.
How Marketways translated the problem
We began with a practical question: Which failure risk warrants planned action and which spare protects continuity? The first analysis used condition, alarms, failure, maintenance, utilisation, spares and consequence.
That evidence could not be read in isolation. Sensor patterns, duty cycle, failure mode and spare lead time must be linked. Resource use must be normalised for product, volume, quality and operating state.
A lower unit-energy figure could reflect product mix or operating load rather than a real improvement. We separated those effects and kept reliability, operator knowledge and exception handling in the interpretation.
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 maintenance and spares exposed dependencies with energy, water and emissions performance, industrial workforce and automation design. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Predictive maintenance and spares: Reduce critical downtime without excessive preventive work or inventory.
- Energy, water and emissions performance: Turn sustainability goals into verified operating improvements.
- Industrial workforce and automation design: Realise automation value while maintaining safe exception handling.
Evidence we examined
- Condition, alarms, failure, maintenance, utilisation, spares and consequence.
- Meters, production, product mix, equipment, tariffs, weather and interventions.
- Task analysis, skills, incidents, workload, automation events and performance.
Industry conditions we accounted for
- Sensor patterns, duty cycle, failure mode and spare lead time must be linked.
- Resource use must be normalised for product, volume, quality and operating state.
- Removing manual tasks can create new monitoring, maintenance and recovery work.
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 maintenance and spares
- Method: Machine Learning & Predictive Analytics, Forecasting, Risk & Optimisation.
- Evidence: Condition, alarms, failure, maintenance, utilisation, spares and consequence.
- Decision supported: Reduce critical downtime without excessive preventive work or inventory.
- Impact measure: Critical downtime, lead time, maintenance value and spare availability.
- Energy, water and emissions performance
- Method: Statistics & Econometrics, Data Foundations & Business Intelligence.
- Evidence: Meters, production, product mix, equipment, tariffs, weather and interventions.
- Decision supported: Turn sustainability goals into verified operating improvements.
- Impact measure: Normalised use, cost, yield and verified reduction.
- Industrial workforce and automation design
- Method: Process, Workflow & Systems, Research & Evidence Collection.
- Evidence: Task analysis, skills, incidents, workload, automation events and performance.
- Decision supported: Realise automation value while maintaining safe exception handling.
- Impact measure: Productivity, safe recovery, skill readiness and adoption.
Implementation
The engagement was structured as pilot assets followed by site 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 availability, resource intensity, safety and capability. The supporting measures included:
- Critical downtime, lead time, maintenance value and spare availability.
- Normalised use, cost, yield and verified reduction.
- Productivity, safe recovery, skill readiness and adoption.
Services and methods used
Services: Predictive Maintenance & Reliability, Risk Detection, Operational Performance Diagnostic, Opportunity Discovery, Organisation Design & Operating Model, Workforce Performance & Capability Assessment.
Methods: Machine Learning & Predictive Analytics, Forecasting, Risk & Optimisation, Statistics & Econometrics, Data Foundations & Business Intelligence, Process, Workflow & Systems, Research & Evidence Collection.