FMCG supply chainUplifting planning and replenishment capability for a leading regional fmcg group
Trusted Data were asked to support a regional FMCG group with a major supply chain improvement programme, developing an AI planning & replenishment solution to reduce waste in a direct to store distribution environment
How we recovered 4% net profit for the Client in year one
Trusted Data supported a well established food and dairy group to overcome a long-standing issue that had impacted bottom line performance.
Supplying over 35,000 retail outlets across several countries, in a highly competitive regional market, supply chain and sales needed to find common ground to ensure that retail outlets were not under-supplied and that waste, brought about through store-level overstocking, was minimised.
Several global supply chain solutions had already failed to successfully overcome this problem for the Client.
Trusted Data were therefore tasked to develop a solution, which could forecast demand by outlet, by SKU, on any given delivery day.
Machine Learning fuelling more accurate forecasts and a dynamic replenishment approach
The Client was supplying (direct-to-store) fresh produce to a huge network of multi-format retail outlets and was liable for overstocking (i.e. unsold, expired goods on-shelf).
Highly competitive shelf space and so cutting supply to reduce waste would impact market share.
SKU’s had a varying shelf life and Customer accounts, based upon format / size, had different expiry terms with our Client.
Replenishment schedules, which had pre-determined delivery frequency to each outlet, were not effectively aligned with store demand, often leading to unprofitable store deliveries.
The Client wanted to reduce on-shelf waste by over 50%, yet maintain a competitive market share.
Trusted Data developed a specific solution methodology based upon the Clients’ business requirements and internal forecasting and replenishment processes.
A 6-week process to transform missing and anomalous data, which had not been adequately addressed during prior solution implementations.
SKU-level forecasts with the predicitve ability to adjust to actual store-level demand and replenishment patterns, achieving upwards of 94% accuracy.
Replenishment schedules, by store, re-engineered to align with store demand, to eliminate non-value add deliveries that did not meet a specified revenue threshold.
Deployment of forecasting and replenishment models, into the Clients’ sales & planning module, which then connected with the Distribution team and Salesman handheld devices.
Active monitoring for a brief period, post implementation, to ensure shelf replenishment compliance with the forecast models to eliminate stock dumping.
Annualised Savings in year one
Reduction in aggregate waste level
SKU / Store forecast accuracy
94 – 98%
Annual Planning ‘man-hours’ saved