Supply chain management is facing its own pandemic of outdated processes. As many as 85 percent of workers state that they could handle their duties more efficiently with enhanced workplace tech. Fortunately, this tech is available and already transforming supply chain management in the fight against Covid-19 challenges.
From enhanced tracking and logistics capable from Internet of Things (IoT) devices to the insights gained from telematics, supply chain technology offers powerful solutions. In the pandemic stricken world, the race is on to adopt this new tech and reap all the associated benefits.
The concept of supply chain management itself is being redefined by new tech. Here’s what you should know.
Tracking supply chains
Supply chains rely upon the flow of trade goods and information. This entails not only the physical transportation of products but the direct and constant communication with vendors, executives, customers, and more as inventories move along global trade routes.
Covid-19 created problems in this process. With shutdowns in common from suppliers to vendors, the need for real-time data and communication flow became more essential than ever. Luckily, new tech was there to help things along.
Telematics is one of these technologies. Defined as systems used to gather data and monitor information, telematics offers insights and communication opportunities to supply chains that ease the management process. Telematics systems paired with IoT sensors and monitors can track a wide variety of metrics, giving unprecedented transparency within supply chains. These metrics include:
- Driver behavior
- Vehicle location
- Engine diagnostics
- Vehicle performance
- Shipment status
The implications of this information are far-reaching, extending even to rail freight management. Rail Pulse, for example, is a joint venture dedicated to the widespread utilization of telematics for roughly 20 percent of the North American rail fleet. The program is under development and seeks to provide a platform for seamlessly monitoring everything from the status of doors and hatches to when the unloading process has begun.
With this level of awareness over the supply chain process, managers can prepare for every inevitability.
Anticipating shortages and maintenance
Perhaps the greatest supply chain problem of the pandemic has been adjusting supply to suit fluctuating demand. Telematics and IoT devices are smoothing this process by giving real-time information and communication platforms to supply chain managers and their operators.
With smart data dashboards that showcase all the necessary metrics of a functioning supply chain, managers can quickly anticipate gaps and reach out to plug them. Drivers are even being outfitted with in-vehicle dashboards that can instantaneously provide them updated route and dispatch info, making it possible to redirect at a moment’s notice. This means that when a shortage occurs or a supply chain is disrupted, drivers can be informed at once.
Supply chain management is further improved by the use of artificial intelligence tools that use big data to make predictive maintenance recommendations. In this fashion, AI is transforming fleet management, cutting costs, and improving transportation safety.
The AI gathers telematic data and assembles predictive models based on data from other vehicles. This gives supply chain managers greater insight into when maintenance is needed, in turn reducing the risks of a tire blowout or engine malfunction that can endanger the lives of everyone in the proximity of a transport vehicle as well as lead to gaps in the supply chain.
Long before the pandemic, telematics and AI were burgeoning aspects of the supply chain industry. However, the challenges of the pandemic have broadened the application of this tech. Now, supply chain managers look for ways to shift their technology usage to have as much information and communication potential at all times.
The pandemic may have caught us by surprise, but with new technology, future disruptions can be better avoided through real-time data and predictive modeling.