< Project Overview >
Efficiencies in warehouse and logistics companies are often hampered when static operational data from different constituencies are combined to plan operations, for example in warehousing and logistics companies. This mini-project aims to demonstrate the benefits of combining real-time data, from the Internet of Things (IoT), with static operational data in an integrated platform, enabling up-to-date informed decisions to be made.
The project will build a demonstration platform which integrates IoT and operational data taken from the warehouse and logistics industry.
For example real-time location of warehouse order pickers (from IoT data) will be integrated with static (operational) information of warehouse picklists, warehouse layout. The project will then quantitatively analyse the potential benefits on picking efficiency of such a data integration.
The demonstration Integration Platform, developed by the project, ingests and integrates data coming from both IoT-based devices (e.g., hand-held device used by pickers) and Industrial Systems (e.g., picklists from a Warehouse Management System). The Integration Platform can then serve the integrated IoT+operational data to any data consumer.
The project proposes to carry out several industrial studies to demonstrate the application of the Integration Platform to actual logistics data.
The case studies will aim to show that IoT data can be seamlessly integrated with existing industrial data, and this in turn delivers operational benefits. Benefits from the use of integrated IoT+operational information may be reflected into enhanced/new decision-making and reporting, or even new business models.
Project lead: Professor Duncan McFarlane
Duncan leads the Distributed Information and Automation Laboratory (DIAL) at the Institute for Manufacturing (IfM) part of the Engineering Department at the University of Cambridge.