Summary

Deecon partnered with Cadent to undertake a comprehensive fleet and logistics optimisation programme, combining advanced data analysis, forecasting and operational modelling to assess transport routes, driver utilisation, inventory management and secondary store strategy. By consolidating and analysing historic workload, material consumption patterns and operational performance data, Deecon identified opportunities to optimise resource deployment and reduce inventory holdings.

The Requirement

Deecon were engaged in a Fleet & Logistics data analysis and modelling exercise to support three main deliverables: transport routes & optimisation, min-max material forecast, and secondary stores strategy.

The Solution

The project consisted of two key phases: Data Analysis and Insights and Forecast Modelling.

Phase 1 – Data Analysis

Deecon consolidated and analysed multiple data sources, including historic workload data, driver utilisation, route structures, warehouse activity and material consumption patterns. Using Power BI and Azure-based modelling, spatial and operational data were integrated to provide a clear, visual understanding of current performance. This enabled detailed assessment of route efficiency, driver utilisation against contracted hours, and workload distribution across regions and secondary stores. 

Building on this foundation, Deecon applied a data-led methodology to identify inefficiencies and opportunities across the network. This included mapping transport routes to highlight optimisation potential, conducting utilisation analysis to align driver capacity with demand, and developing heatmaps to assess secondary store coverage relative to workload density. In parallel, inventory analysis was undertaken using statistical techniques, including Pareto analysis and percentile distribution, to understand demand variability and key material drivers. These insights informed the development of a dynamic Min-Max stock model designed to better align inventory levels with demand and reduce excess stock. 

Phase 2 – Forecast Modelling

Deecon developed a forecasting model to link historic demand patterns with future workload projections, enabling more accurate material planning. The model incorporated historical consumption, planned works and material grouping to forecast future demand at a material level. Alongside this, Deecon translated the analysis into practical tools and recommendations, including visual route planning dashboards, driver and resource optimisation recommendations, and training materials to support adoption. 

This structured approach ensured that recommendations were both analytically robust and operationally actionable. This provided Cadent with a clear understanding of the opportunities available across transport, network strategy and inventory management, alongside the tools required to support implementation and ongoing decision-making. 

The Results

Identified a 14% inventory reduction opportunity, reducing indicative stock holding from £8.8m to £7.6m through demand-led Min-Max stock levels
Identified a 22% reduction opportunity in driver resource, optimising deployment against actual route demand and contracted hours while maintaining capacity for unplanned requirements
Identified a 33% reduction opportunity in transport administration, through optimisation of Transport Manager requirements and administrative activity 
Developed 2 Power BI models and 5 dashboards to provide visibility of transport, driver utilisation, workload and inventory performance
Developed 4 training manuals and 2 application tasks to support knowledge transfer and adoption of the new tools and approaches
Produced 12 actionable recommendations across transport optimisation, resource deployment, inventory management and secondary stores strategy