Innovative Use Cases of IoT in the Transport and Logistics Sector

While the level of interest in the Internet of Things (IoT) has been on the rise among enterprises on a global scale in the transport and logistics sector, many of them face challenges in defining use cases that enable them to generate new sources of revenue. There is a need to gain new capabilities in terms of domain expertise and technical know-how. Some enterprises have invested substantially in analytics platform and vertical expertise, others have entered into cross industry collaboration with enterprises outside their industry.

At the same time, if enterprises decide to rapidly implement IoT without changing their operating model, they face challenges in delivering the new solution. Regardless of how they go about doing it, IoT has enabled them to gain new insights that they did not have in the past to bring about new service offering that differentiate them from their competitors.

This report looks at how enterprises in the Transport and Logistics sector have found 10 use cases and have gained from IoT in the following- ways:

• Enhancing customers’ experience while using sensors installed in cars to collecting real time information on traffic flow and drivers’ behaviour
• Diversifies between selling cars and renting cars on members’ usage
• Aiding the tourism sector and generating revenue by providing tourists with hotspot heat maps with a smartphone app.
• Using small data to make incremental changes in their operational processes.
• Gives authorized mechanics access to vehicle data to support servicing and components requirements.
• The E-bike Management system was developed in an effort to reduce the number of cars on the road.
• Using big data analytics to understand consumers’ purchasing behaviour as they no longer go for the cheapest tickets.
• Adapting ways to alleviate traffic condition by resolving congestion together with the private sector companies.
• Adopting a variety of ways to collect data on real time traffic patterns, developing capabilities rapidly through acquisitions to deliver more data.
• The use of big data analytics to determine the lowest costs of truck configuration for fleet operators.