Simplify Supply Chain with Technology
Internet of Things (IoT) for Logistics and Supply Chain Industry
According to research and advisory firm Gartner, five billion devices are connected to the internet today. By the year 2020, that number is projected to grow to twenty billion. The majority of these devices exist to meet a particular use case – GPS location, temperature and weather, and various other device states and conditions – that create vast amounts of data.
With Artificial Intelligence leveraging both machine learning – meaning pattern recognition within these systems themselves – and real-time feedback from the physical world, we don’t even have to directly interact with these devices. IoT provides a physical interface with those connected devices. Take, for example, a self-driving vehicle. In order for it to navigate, it must have algorithms that both guide its decisions and permit its devices to locate it within a space relative to other physical objects.
Having the ability to monitor these conditions for our business partners and customers in real-time improves performance, communication, and quality of service during each step in the process throughout the life of a shipment. When changes are required, in routing or due to other circumstances, being able to use real-time sensors and environment data allow for more confident, dynamic decision-making.
It is the consistency in transit times that helps us build the trust necessary to reduce buffers and create additional value for our customers
The ability to consume and analyze the data afterwards helps us understand what happened to the shipment. Beyond determining why the shipment was early or late, it also provides direction in how we can respond to fix, improve, or replicate positive outcomes and results across all shipping engagements. Overall, the use of IoT data allows us to become more intelligent in our execution, resulting in optimized inventory levels, lower cost, and better service to our customers.
Technology to Mitigate Rising Supply Chain Costs
Transportation Management Systems (TMS) leverage network modeling, mode and carrier selection optimization, routing, driver/load assignment, and load consolidation to drive costs out of a supply chain. Large shippers have been using TMS for some time; however, many cloud-based TMS options are now available. As a result, more and more small- and mid-sized shippers are benefitting from the low barrier to entry they offer, adopting them in order to leverage these same cost savings once only available to larger operations.
As part of our mission to transform J.B. Hunt from an asset company with technology into a technology company with assets, we have deployed a TMS with proprietary capabilities – JBHunt 360. Customers who use the JBHunt 360 e-commerce platform can leverage an already large number of capabilities, but we are currently expanding those offerings. Expansion will allow us to offer more sophisticated tools, including collaborative demand sensing as well as forecasting tools.
These tools will process vast amounts of data in real time, recognize complex patterns, and identify actionable steps in response. Utilizing a combination of data and predictive analytics, gives us the ability to develop more intelligent planning and delivery execution that allows us to reduce variability of transit times. It is the consistency in transit times that helps us build the trust necessary to reduce buffers of inventory and create additional value for our customers.
Big Data Analytics in Supply Chain and Challenges
The challenge with managing a big data analytics effort is that it’s not bounded. To be successful with big data, technology companies have to be comfortable with a research and development approach. They have to hire smart people and fund them to innovate or experiment.
In the case of traditional modeling efforts, companies use data that resides within their immediate computing or organizational infrastructure. In logistics, an example of this would be exploring the relationships between hard-breaking or roll-stability events to predict driver safety. Big data analytics goes beyond data that exists within the confines of an organization. It pulls data from sources well outside of those limits.
For example, in the case of driver safety, is weather a factor? The challenge in determining this comes in finding a global source for weather that ties directly to a point in time when an accident did or did not occur. Much of this work is left up to the data scientists to theorize and explore.
Finding data architects and business intelligence professionals who can augment this work by bringing large amounts of external data into the same environment for analysis can also be daunting and impede realization of big data analytics.