A specialist fleet operator needed to prepare for changing volumes and disruption while managing ageing assets and driver performance. Driver scores alone did not explain delays, fuel use or service variation because duty cycle, vehicle condition, route difficulty and recovery options shaped the result. We combined forecasting, reliability, reward, workforce and contingency decisions in one control programme.
The engagement objective
Through the initial discovery, Marketways defined the objective: combine capacity forecasts, asset reliability, workforce and disruption choices.
How Marketways translated the problem
We began with a practical question: What cargo, parcel or passenger demand must each node serve? The first analysis used bookings, manifests, customers, lanes, seasonality, events and capacity.
That evidence could not be read in isolation. Customer wins, trade lanes, events and economic conditions create step changes. Age alone is a weak replacement rule because duty cycle and maintenance history vary.
A driver score could attribute route difficulty, vehicle condition or schedule pressure to the individual. We separated those influences before connecting performance evidence to reward, training or intervention.
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 volume and capacity forecasting exposed dependencies with fleet reliability and replacement, driver performance, safety and reward, disruption and corridor resilience. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Volume and capacity forecasting: Prepare vehicles, labour and facilities for plausible demand.
- Fleet reliability and replacement: Improve availability and total cost over the asset life.
- Driver performance, safety and reward: Support coaching and fairer recognition without rewarding unsafe speed.
- Disruption and corridor resilience: Predefine workable alternatives and decision triggers.
Evidence we examined
- Bookings, manifests, customers, lanes, seasonality, events and capacity.
- Telematics, maintenance, failures, utilisation, fuel, routes and costs.
- Telematics, incidents, routes, loads, service, hours and coaching.
- Flows, routes, capacity, inventory, lead time, disruption and alternatives.
Industry conditions we accounted for
- Customer wins, trade lanes, events and economic conditions create step changes.
- Age alone is a weak replacement rule because duty cycle and maintenance history vary.
- Route, vehicle and load affect performance; raw league tables can punish difficult assignments.
- Diversion decisions move congestion and may create new customs, capacity or shelf-life constraints.
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.
- Volume and capacity forecasting
- Method: Forecasting, Risk & Optimisation, Statistics & Econometrics.
- Evidence: Bookings, manifests, customers, lanes, seasonality, events and capacity.
- Decision supported: Prepare vehicles, labour and facilities for plausible demand.
- Impact measure: Capacity fit, peak performance, overtime and forecast reliability.
- Fleet reliability and replacement
- Method: Machine Learning & Predictive Analytics, Statistics & Econometrics.
- Evidence: Telematics, maintenance, failures, utilisation, fuel, routes and costs.
- Decision supported: Improve availability and total cost over the asset life.
- Impact measure: Availability, lifecycle cost, failure and replacement timing.
- Driver performance, safety and reward
- Method: Statistics & Econometrics, Data Foundations & Business Intelligence.
- Evidence: Telematics, incidents, routes, loads, service, hours and coaching.
- Decision supported: Support coaching and fairer recognition without rewarding unsafe speed.
- Impact measure: Safety, service, fuel, fairness and sustained improvement.
- Disruption and corridor resilience
- Method: Forecasting, Risk & Optimisation, Process, Workflow & Systems.
- Evidence: Flows, routes, capacity, inventory, lead time, disruption and alternatives.
- Decision supported: Predefine workable alternatives and decision triggers.
- Impact measure: Recovery time, unserved demand, cost and critical-flow continuity.
Implementation
The engagement was structured as annual planning with operational control loops. 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, safety, capacity and recovery. The supporting measures included:
- Capacity fit, peak performance, overtime and forecast reliability.
- Availability, lifecycle cost, failure and replacement timing.
- Safety, service, fuel, fairness and sustained improvement.
- Recovery time, unserved demand, cost and critical-flow continuity.
Services and methods used
Services: Sales Forecasting & Demand Planning, Workforce Planning & Capacity, Predictive Maintenance & Reliability, Decision Assurance, Workforce Performance & Capability Assessment, Reward & Performance Systems, Risk & Uncertainty Modelling.
Methods: Forecasting, Risk & Optimisation, Statistics & Econometrics, Machine Learning & Predictive Analytics, Data Foundations & Business Intelligence, Process, Workflow & Systems.