How Machine Learning and IoT Are Changing Supply Chain Management

Technology has become a key driver of change within today’s complex supply chain. The Internet of Things (IoT) is here to stay, and with it comes machine learning and machine-to-machine communications. These technologies have the potential to bring increased visibility and predictability, improved customer service, and cost savings to the enterprise supply chain. Although machines are communicating and adding efficiencies to the process, human workers must be able to access the data being generated and have the ability to make real-time decisions to ensure the workflow remains efficient.

The driving force behind the change in how the supply chain is managed are IoT-based sensors which can track multiple aspects of the shipping process — inventory, finished products, shipments in route, or assets in the field (think trucks, machinery and containers). Sensors communicate status updates to backend systems, and field workers combine that information with data from outside sources to develop smarter transportation and supply chain decisions. Mobile devices play an integral role in maintaining the connection between workers and machines across all phases of the supply chain.

Machine learning is a form of artificial intelligence (AI) which allows software applications to gain accuracy in predicting outcomes without being explicitly programmed. Machine learning leverages predictive analytics and big data to transform information into insights, giving managers the information they need to better manage their supply chains. Reliable connectivity in the hands of supply chain workers is the key to ensuring on-time delivery and the development of ongoing process improvements.

Beyond Location Tracking

For the supply chain, the emerging use of IoT is to affix tracking devices onto inventory or assets to track not only the location of an item, but numerous conditions such as temperature, humidity, shock, and other environmental conditions. Sensors located in the device track this information, all in real-time, and transmit data back to an application. The application relays data to transportation managers who can be alerted to changes in arrival times or if a pharmaceutical or other perishable shipment has breached temperature conditions outside of pre-set parameters. Managers can also be alerted to route deviations, non-permitted movement or other disruptive conditions. Rugged mobile devices help to ensure all of this real-time data is captured and communicated to other partners within the supply chain.

Machine learning takes supply chain intelligence even further by combining real-time sensor data with third-party data streams, such as weather, traffic, carrier data such as schedule information, or even disaster-related information to make predictions and detect problems about the state of goods and assets in real-time. Fraud detection and prevention, improved carrier management, and SLA compliance are just a few ways companies and their customers benefit from these systems.

For example, managing high-value shipments along truck routes or at shipping ports where routes or locations may pose higher risk of theft or loss than others, machine learning can help managers identify routes to avoid and relay route changes to the crew. Contingency planning for peak or seasonal operations is another improvement that can be made using this technology. Outfitting workers and managers within the supply chain equipped with mobile devices helps ensure valuable data is collected and related to the next team in line – effective, timely and secure communications adds up to process improvements that can be implemented with the next shipment.

And IoT tracking devices placed on assets such as construction forklifts, cranes, chassis equipment, 40-foot containers in a shipping yard or trucks of any size, can assist managers across industries to locate assets in real time and manage them appropriately via their mobile devices. Machine learning can then turn the data into actionable information that can help managers foresee events, such as storms, to make better risk assessments. It also supports more accurate ETA reporting and improved inventory management with data such as average expected detention times at a port or a facility.

Mobility, the IoT and Machine Learning Together

The IoT, aided by machine learning, shows great potential for bringing actionable information to transportation and logistics managers throughout the enterprise. The real value however, is found when data is delivered in real time to the supply chain or transportation managers utilizing enterprise mobile devices, ideally a rugged touch-screen tablet or 2-in-1 convertible.   The result is driving a highly visible supply chain that allows managers to make real-time decisions to maximize efficiencies and identify business value. Just as supply chains are always moving so are the managers.  And having enterprise mobile devices purpose-built for the rugged work conditions in warehouses, distribution centers, ports or intermodal terminals is essential.

The integration of IoT supply chain and machine learning solutions into mobile applications is the next frontier of real-time decision making. Panasonic is an industry expert in the mobile technology requirements of transportation and supply chain companies and stands ready to bring these mobile solutions to users with the Toughbook line of rugged laptops, tablets, 2-in-1’s and handhelds.

For more information about how mobility solutions can increase efficiency in your supply chain, visit Panasonic online.