The past three years have seen supply chains coping with the dislocations brought on by the COVID-19 pandemic. Those problems may not be completely behind us — and more have been added to the menu, notably, the supply chain implications of the war in Ukraine. But a shift in demand from goods to services and the ramping down of stress levels on transportation and logistics systems show that supply chain conditions are improving.
That means that in 2023 supply chain leaders will have the opportunity to pick up supply chain initiatives that may have been sidelined during the crisis years — including data and digitization programs that precisely orchestrate supply chain activities, as well as sustainability efforts that reduce companies’ carbon footprints. The connective tissue that ties these efforts together is the integration of location data with supply chain operational data.
Companies are sitting on ever-growing volumes of data generated by their logistics operations and supply chains, but, especially when it comes to coordinating supply chain activities, that data lacks context if location data is not included. Companies that endeavor to blend logistics and operations data with location data, on the other hand, will find improvements in the efficiency, accuracy and reliability of their supply chains. According to Bart Coppelmans, director and global head of industry solutions at HERE Technologies, “Machine learning in the context of location analytics can identify transportation patterns and provide a confidence level with regard to, for example, the likelihood of delays, and apply those to future planning.”
In an ever-changing and volatile supply chain environment, the speed of decision-making becomes a key factor in supply chain success — and the use of location data is important to that effort. “The faster you can get access to data and use it to sense what’s going on in your supply chain, the faster you’re able to make decisions,” says Manish Govil, global segment lead, supply chain at Amazon Web Services (AWS). “Resiliency in the face of supply chain disruption requires ingesting a large amount of data in real time and being able to analyze that data in near-real time to come up with predictions that inform supply chain leaders of the implications downstream.”
The application of location data and its integration with other enterprise data to derive supply chain insights is the touchstone that ties together all of these developments at the technology and data levels. Integration of data sets from multiple sources, together with data from commercial vehicles and positioning devices, yield real-time intelligence on current supply chain conditions and insights into future supply chain planning.
“This information can provide insights on different transport lanes and around distribution centers,” says Coppelmans. “Location intelligence will play a role in providing context on the current state of the supply chain and for predicting the future, to the benefit of planning processes.”
Predictive Supply Chains That Improve Decision-making
Predictive analytics, one key to effective planning, has a hefty appetite for large volumes of disparate categories of data. “Getting more accurate predictions requires learning from the current state,” says Coppelmans. “Understanding how, when and why certain supply chain problems occur is important in designing better networks.” Tying location data with logistics data provides more accurate and predictive estimated times of arrival adding a confidence level that benefits logistics operators, shippers and their customers by promoting certainty and inspiring confidence.
While logistics delays can have cascading effects on supply chains, the analysis of data reveals implications for downstream operations. “If a truck is going to be delayed,” says Govil, “they need to know what’s being carried on the truck and what operations are going to be impacted. It’s also going to have an impact on labor schedules.”
At a fulfillment center, a message indicating a late truck arrival might spur managers to look at worker schedules. “Do they need to adjust their labor numbers?” asks Govil. “Managers will also need to consider how the delay will impact inventory positioning.”
In the case of a manufacturer, an inbound shipment, perhaps containing raw materials, might be delayed an hour or two if it’s on a truck, or a week or two if it’s on an oceangoing vessel. “Manufacturers need to know the impact on their bills of materials and on manufacturing activities,” says Govil. Understanding the precise position of materials and products, by analyzing location data, allows manufacturers to sharpen their contingency plans.
From a planning perspective, artificial intelligence plays a crucial role in mitigating potential delays with better route planning. “It’s possible to learn from an analysis of location data,” says Govil, “that a truckload leaving at 9 a.m. will take longer to arrive than one leaving at 11 a.m. because the earlier shipment can get held up in rush-hour traffic. AI and machine learning play a part by predicting future conditions based on past behavior.”
The same capabilities can also help supply chain operators manage in the moment, by taking into account changing conditions in real time. For example, current weather information can be integrated to understand the possibility of delay. “By using AI,” says Govil, “you can create models which will come up with predictions based on the interaction of all this data.” The integration of location provides clarity to these predictive processes.
Building More Sustainable Supply Chains
Sustainability considerations in transportation and logistics hit the big time only about two years before the pandemic struck. That’s when many shippers began to require carriers to provide information on their carbon efficiency as part of their bidding processes.
Many companies put sustainability issues on hold during the pandemic and will now want to catch up. “After a short break during the pandemic, where securing transportation capacity was the top issue keeping everyone busy, sustainability is coming back to the top of the agenda,” says Tomas Robenek, transportation and logistics industry solutions manager at HERE.
“Now that the pandemic is subsiding, we’re moving away from spending mainly on goods and starting to spend on services again,” Robenek added, “thus easing the pressure on the supply chains and leaving some space to think again about sustainability.”
The resurrection of sustainability programs will also serve to enhance company efforts to increase supply chain efficiency and customer service. As will be the case with other supply chain initiatives, location intelligence will play an important role in these efforts.
The Paris Agreement aims at getting the globe to a state of carbon net zero by 2050, but many companies have their own accelerated schedules as their customers, shareholders, employees and suppliers become more aware of the implications of climate change. A 2019 study of U.S. consumers showed that 70% considered environmental impacts when shopping. A more recent study showed similarly high levels of concern among consumers around the globe.
Location intelligence can play a key role in getting supply chains to their sustainability goals by supporting navigation applications, which can be used by companies to audit their carbon footprints. Among other things, supply chain managers can use location data to plan routes to minimize truck idling, thereby reducing emissions. Technology tools have already been introduced that perform just those tasks.
The move to electric vehicles will also require access to location intelligence when it comes to route planning and battery recharging. In these early stages, since the recharging infrastructure is sparse, routes need to be planned so that delivery vans can return to depots to recharge.
“In the future, when the density is higher, you will still need to know where the recharging stations are located,” says Robenek. “Even then, it’s expected that many delivery vans will charge at the depots by default, rather than somewhere on the road, because in many cases it won’t make sense for a delivery driver to sit for two hours while the van recharges.”
The more accurate ETA predictions afforded by location intelligence will also contribute to sustainability goals. “You can more efficiently optimize your operations by combining loads to burn less fuel and to make decisions about modes of transportation,” says Coppelmans. “Many supply chains want to shift from road to rail, which is more sustainable, but you need to know when and where you can do that and for which products, and how to manage customers’ expectations.”
Organizations that don’t focus on sustainability risk being left behind, according to Robenek. “Demand for clean services is going up,” he says, “and those that aren’t climate neutral will receive fewer orders. They also risk higher costs, because governments are considering emissions pricing and increasing fossil fuel taxation, and they risk becoming non-compliant with regulations as they evolve.” A growing body of regulations also deals with the sustainable sourcing of raw materials, and location data is important to verify their points of origin.
“Some countries are already introducing bans on the production of internal combustion engines,” Robenek adds, “so being proactive about the transition is the more sensible thing to do to be prepared.”
Digital Twins That Deliver Value
Supply chains in 2023 will become ever more flexible, thanks to the increasing use of predictive analytics, according to Coppelmans. One use case is the digital twin, which applies AI and machine learning to create virtual supply chain models that analyze problems, predict future impacts and suggest reaction plans.
“Logistics operators can use digital twins to provide a controlled overview of what’s happening with their shipments,” says Coppelmans. “Location intelligence is introduced to provide shipment tracking and predictions about when certain shipments will be arriving. With the control-tower approach provided by digital twins, shipments may be redirected or moved to other transportation options if problems arise.”
Integrating enterprise data with location intelligence will help enterprises more efficiently manage their operations. ETA accuracy and efficiency can specifically improve last-mile operations, according to Coppelmans. “Incorporating location intelligence into last-mile delivery operations is going to create huge operational efficiencies for companies in the logistics space,” he says.
Besides ETAs, actual times of arrival are also gaining importance. Location systems provide electronic timestamps for shipment arrivals, which streamlines the processes surrounding penalties and claims for late deliveries, and for defending against them. “A lot of this is now being done by e-mail and with manual processes,” says Coppelmans.
The optimization of production and transportation processes brought about by merging operational and location data will ultimately generate higher levels of customer satisfaction for logistics providers and their customers. “It’s all about delivering a confidence level to the customer base,” says Coppelmans. “Optimizing production, operations and logistics processes is going to create additional revenue streams for manufacturers and logistics operators alike. Combining operational data with location data will deliver a most valuable impact to efficiency and sustainability in the future.”
Source: Supply Chain Brain