Proving the Business Case for the Internet of Things

XPO Logistics pilots smart productivity tools

Steve Rogerson
July 9, 2019

Connecticut-based supply chain company XPO Logistics is piloting its XPO Smart workforce productivity tools in its less-than-truckload (LTL) network in North America.
LTL terminal managers in Massachusetts, Michigan and North Carolina are using the proprietary analytics to make dock operations more efficient.
The optimisation tools compare real-time productivity rates with the number of active dock workers, using machine learning to predict how adjustments in labour levels affect productivity. The tools track motor moves (goods moved from dock to truck) against production targets and analyse productivity gaps to improve performance.
“While the dynamics of labour productivity differ from service to service, the goal is the same: the greatest possible efficiency for our customers,” said Mario Harik, chief information officer of XPO Logistics. “With XPO Smart, we have the infrastructure in place to achieve this. Our pilot LTL operators are acting on insights gained from site-specific machine learning and our predictive analytics.”
The company expects to conclude the LTL pilot programme in the third quarter and begin a phased roll-out of the new tools to its 290 terminals.
The LTL pilot follows the company’s introduction of workforce planning analytics in its warehouse operations in March. XPO deployed the system in 20 logistics facilities, including dedicated customer warehouses and hubs for XPO Direct, its shared-space distribution network. Approximately 200 implementations are planned by the end of the year.
The proprietary labour productivity system puts warehouse operations under a technological microscope, providing managers with insights at many different levels, from total facility output to the productivity of individual workers and teams. The software analyses three key drivers in combination: labour output, including gaps that need resolution; fast-moving inventory SKUs for optimal placement within the warehouse; and outbound production measured against on-time targets.
“We’re putting new technology in the hands of our logistics operators to realise efficiencies for our customers,” said Harik. “Our labour productivity system uses proprietary algorithms and site-specific machine learning to determine how individual output contributes to collective goals. Very quickly, the system has had positive impacts with cost control and fulfilment speed.”
The productivity system interfaces with the company’s warehouse management system and third-party workforce management applications. It delivers data through a single dashboard, using machine learning to predict how a decision in one area will affect total operations. Managers gain deep visibility into workforce operations, including the number of scheduled versus active workers by job role in real time.