Cohort company SEA, together with TTP plc, has been selected to develop technology designed to improve the efficiency and timely delivery of supplies to frontline battlefield assets.
The technology forms part of the UK MoD’s Defence and Security Accelerator Autonomous Last Mile Re-Supply programme and seeks to provide military logistics specialists with a better understanding of future supply and demand at the frontline, identifying which supplies will be required, where and when.
Such information is crucial in helping to deliver vital supplies quickly and efficiently to maintain operational tempo and enable successful mission outcomes.
The solution proposed by SEA and TTP is based on provision of near real-time usage information of Combat Supplies (CSUPS) through exploitation of the opportunities provided by platforms with an integrated, open architecture and use of a neural net based Demand Forecast expert system to provide a forecast of supply and demand based on the received usage information.
Laurence Bedford, SEA Head of Research, explained: “This information enables the supply chain to pre-empt demands, ensuring supplies are readily available when needed and thus improving operational tempo. Additionally, prior knowledge of the expected demand also facilitates optimisation of the supply chain and maximises utilization of the available distribution assets.”
Sam Hyde, Managing Director of TTP added:
“We are delighted to be working with SEA on this project. This draws upon our strengths in machine learning and mathematical modelling, and will enable proactive logistical support for troops in challenging situations.”
The technology will be based around the MoD Land Open System Architecture (LOSA) initiative, developing open platform system architectures, namely the Generic Soldier Architecture (GSA) and the Generic Vehicle Architecture (GVA), through the Common Open Interface (Land) (COIL). These enable soldiers at the frontline to be connected to other, wider systems, through an open interface. This in-turn enables supply status information, such as battery power levels in a soldier’s radio, to be fed back through the open architecture to feed into a logistic supply prediction capability
Demand forecasting will be provided by a neural network based mathematical model, that can formulate a future prediction based on ‘trained’ historical data – it is able to predict what will happen, based on what it has observed happen in the past. This will be tailored to allow specific operational information to be incorporated into supply predictions, such as movement routes, climatic information or likely threats faced. Collectively these will enable the expert system to inform Commanders when and where supplies will be required by forward forces to maintain operational tempo.