ASCO sets out £18.3m logistics AI investment programme

ASCO has committed £18.3 million over the next six years to further develop logistics software, artificial intelligence and automation across its industrial materials and transport operations. The Aberdeen-headquartered group plans to extend systems already used to track cargo, coordinate quayside activity and control movement between storage and transport. The announcement is a prospective six-year investment commitment, not an amount already spent on new technology.

Part of the investment will go towards ASCO’s Integrated Logistics Management System (iLMS), which incorporates material, cargo, quayside and transport management. Those functions record different stages of a shipment: receipt into storage, allocation against a work programme, preparation for loading, movement to a quayside and onward carriage by vehicle or vessel. Linking the records can expose a missing item or inconsistent status before a loading window closes, provided that physical movements are entered accurately into the systems.

The software works alongside ASCO’s around-the-clock operational control centres in the UK and Norway. These centres coordinate transport, shipping, marine and aviation activity, drawing on personnel, live operating records and existing planning tools. The new programme is intended to improve connections between operational information and the systems used to act on it. The company has not given a year-by-year spending profile or dates when individual new modules will be available to customers.

ASCO also intends to develop its Waste Management System to record waste type, composition, location and disposal route in greater detail. For regulated industrial materials, information must remain associated with a consignment as it passes between collection, handling, transport and treatment. A digital record can assist with reconciliation and environmental reporting only if classifications and transfer information are captured correctly; automation cannot rectify an incorrectly identified waste stream without further checks.

The group already uses AI-supported load optimisation, automated data capture, image recognition, hazard identification and barcode- or RFID-enabled materials control. Their functions are distinct. Barcode and radio-frequency identification connect objects to inventory records; load optimisation examines competing use of vehicles, lifts or available capacity; and image analysis may flag a condition for review. ASCO intends to extend these capabilities, but the release does not identify a newly deployed robot model, a new client installation or a quantified improvement attributable to this six-year programme.

Logistics exceptions are an important test for integrated systems. A missing certificate, late vessel arrival or materials discrepancy can require several teams to change a plan, rather than merely update one database. Better connections between data and operational workflows could help identify those conflicts sooner, but decisions about lifting, dangerous goods and safe transfer still require established controls and qualified personnel. ASCO has not specified whether particular decisions will be automated or which human approvals will remain at each stage.

ASCO cites past customer engagements as evidence of experience in changing materials workflows. It reports an 80% reduction in materials processing time for an energy client and £36 million of inventory savings for a North Sea operator. A separate pulp and paper project identified around £10 million in inventory optimisation opportunities. The last figure is described as identified savings, not necessarily realised cash; all three outcomes relate to earlier work and cannot be used to calculate the return on the newly announced £18.3 million commitment.

For offshore wind planning, ASCO says it has simulated more than two million vessel movements to explore logistics bottlenecks and possible infrastructure requirements. Simulations allow planners to vary assumptions about fleet availability, port handling or offshore activity and study where queues might develop. The model’s conclusions still depend on the traffic patterns and operational constraints supplied to it. ASCO has not stated that the two million movements occurred physically or that the work was funded by the newly announced programme.

Steve Shearman has joined ASCO as chief AI officer and will lead adoption and the search for new technology-enabled services. ASCO credits him with more than 15 years of technology product management and strategy experience and more than 25 years in wider business management. His appointment gives the programme a named lead, although the company has not specified how the overall budget will be distributed between software development, operational equipment, recruitment and deployment.

Chief executive Mike Pettigrew says the group aims to apply its digital logistics capabilities in more regions and sectors. ASCO operates at about 70 locations worldwide and employs approximately 1,500 people. Processes developed for offshore energy supply chains may be transferable to other industrial settings, but system interfaces, local transport rules and customers’ handling requirements would still need to be configured for each operation. No additional customer contracts have been announced as a direct consequence of this investment.

The immediate confirmed steps are the £18.3 million six-year commitment, continued development of iLMS and waste systems, and Shearman’s appointment. Evidence of delivery will come from specific implementations and independently attributable operating measures, rather than the historic savings cases cited in the launch material. ASCO has yet to publish milestones against which the new spending can be assessed or the portion committed to each component of the programme.