A regional logistics company wanted to improve distribution without treating every product and delivery promise as equivalent. Early analysis showed that product condition, vehicle compatibility, depot design and recovery options changed the value of a route. Marketways connected those constraints so that route efficiency did not come at the expense of saleable life, safety or service.
The engagement objective
Through the initial discovery, Marketways defined the objective: connect product condition, routes, vehicles, depots and customer promises.
How Marketways translated the problem
We began with a practical question: How should product-specific deterioration change cooling, loading and routing? The first analysis used manual decay tests, time-temperature history, product, route, cooling and rejection.
That evidence could not be read in isolation. Decay must be estimated by product and exposure; milk and fresh food cannot be treated like non-decaying parcels. Vehicle compatibility, driver rules, service windows and depot processes determine feasibility.
A kilometre or utilisation target could improve while product condition and service deteriorated. We modelled compatibility, recovery options and delivery promises inside the same distribution decision.
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 perishable distribution and decay model exposed dependencies with fleet and route optimisation, network, depot and warehouse design, last-mile promise and customer recovery. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Perishable distribution and decay model: Protect saleable life and safety rather than optimising kilometres alone.
- Fleet and route optimisation: Reduce distance, delay and overtime while respecting real constraints.
- Network, depot and warehouse design: Balance service reach, resilience, facility cost and working capital.
- Last-mile promise and customer recovery: Improve trust by aligning promise, dispatch and recovery.
Evidence we examined
- Manual decay tests, time-temperature history, product, route, cooling and rejection.
- Orders, locations, time windows, fleet, shifts, travel and costs.
- Demand geography, flows, facilities, inventory, handling, lead time and disruption.
- Promise, route, attempt, contact, location, exception and recovery.
Industry conditions we accounted for
- Decay must be estimated by product and exposure; milk and fresh food cannot be treated like non-decaying parcels.
- Vehicle compatibility, driver rules, service windows and depot processes determine feasibility.
- A central network may lower inventory while increasing last-mile exposure and recovery time.
- Aggressive slots can increase failed deliveries, redelivery and contact cost.
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.
- Perishable distribution and decay model
- Method: Statistics & Econometrics, Forecasting, Risk & Optimisation.
- Evidence: Manual decay tests, time-temperature history, product, route, cooling and rejection.
- Decision supported: Protect saleable life and safety rather than optimising kilometres alone.
- Impact measure: Remaining shelf life, spoilage, cooling cost and service.
- Fleet and route optimisation
- Method: Forecasting, Risk & Optimisation, Process, Workflow & Systems.
- Evidence: Orders, locations, time windows, fleet, shifts, travel and costs.
- Decision supported: Reduce distance, delay and overtime while respecting real constraints.
- Impact measure: On-time service, distance, utilisation, overtime and plan adherence.
- Network, depot and warehouse design
- Method: Forecasting, Risk & Optimisation, Data Foundations & Business Intelligence.
- Evidence: Demand geography, flows, facilities, inventory, handling, lead time and disruption.
- Decision supported: Balance service reach, resilience, facility cost and working capital.
- Impact measure: Delivered cost, service, inventory and disruption recovery.
- Last-mile promise and customer recovery
- Method: Process, Workflow & Systems, Market, Customer & Behavioural Analytics.
- Evidence: Promise, route, attempt, contact, location, exception and recovery.
- Decision supported: Improve trust by aligning promise, dispatch and recovery.
- Impact measure: First-attempt success, on-time delivery, recovery and customer effort.
Implementation
The engagement was structured as controlled lane pilots followed by network expansion. 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 saleable quality, service, cost and operational adoption. The supporting measures included:
- Remaining shelf life, spoilage, cooling cost and service.
- On-time service, distance, utilisation, overtime and plan adherence.
- Delivered cost, service, inventory and disruption recovery.
- First-attempt success, on-time delivery, recovery and customer effort.
Services and methods used
Services: Fleet & Logistics Optimisation, Operational Performance Diagnostic, Process & Workflow Analysis & Redesign, Business Feasibility Study, Business Systems Design & Architecture, Customer Satisfaction & Experience Research.
Methods: Statistics & Econometrics, Forecasting, Risk & Optimisation, Process, Workflow & Systems, Data Foundations & Business Intelligence, Market, Customer & Behavioural Analytics.