Where is my package? With the expansion of ecommerce, customers are increasingly demanding fast, transparent delivery. While this seems like a simple question, it actually uncovers a very complex web of infrastructure and data. Deutsche Post (DHL) is a global logistics company that has taken up the task of warehouse fulfillment and shipping for many companies. As DHL strengthens its supply chain network, it is turning to machine learning to improve processes and answer customer’s demands. From the warehouse to the doorstep, AI helps process tons of data on a global level. Connecting the dots and parsing through the data is difficult, yet machine learning is proving to be a quicker and more reliable way to do so.
DHL uses machine learning for creating more optimized routes (predictive network management), adjusting bad addresses, processing customs paperwork, predicting demand, and capacity planning. Utilizing machine learning can help minimize the constant tradeoffs between cost and speed that come with the supply chain. For instance, AI can help remove inefficient steps from the supply chain. When a package is shipped from New York to Nebraska, DHL pools all orders from all their customers to create efficient economies of scale. If the truck doesn’t need to make a stop in Cleveland, then they can send it straight through to Nebraska and save on both time and cost. Currently, they route trucks using historical data; however, with machine learning, they can do this dynamically and route trucks as they receive packages. In the next 10 years, DHL is pushing towards what they call “seeing, speaking and thinking logistics operations”, looking to implement all AI powered operations [1]. This would include autonomous delivery fleets, vision-based intelligent sorting, and vision-based inventory management. While there’s still a long way to go to get to fully autonomous operations, DHL’s investment in AI puts it in a good position for the future.
Recently, machine learning has helped DHL provide more transparency to their business customers. DHL launched its Resilience360 platform which uses machine learning to analyze data and predict possible supply chain issues [2]. The platform includes a Supply Watch module which combs through 8 million online posts and 300,000 online and social media sources every day looking for significant events that might disrupt the supply chain. [3] Through analyzing this data, “the machine learning model is able to predict if the average daily transit time for a given lane is expected to rise or fall up to a week in advance” [1]. Companies can then tailor their copy to each individual customer and alert them if they anticipate any delays. This transparency goes a long way towards establishing goodwill with customers. Ultimately, this ties into DHL’s goal to provide an end-to-end solution that will help businesses be competitive. [4]
Additional to DHL’s current focus, they could be exploring how to provide real time feedback to end customers. Rather than delivery windows that are a few hours, they could predict package delivery down to a 5 or 10-minute window. They could also look to partner with Google or Waze to incorporate traffic patterns into their delivery estimates. Furthermore, one possibility is that DHL could allow customers to share their location through their app and, similar to how one calls for an Uber, the system could direct the delivery driver to a new spot if it’s more efficient for the driver’s route. This would improve routes automatically and help cut down on driving distance.
DHL still has a long way to go though. They estimate that “95% of companies are yet to realize the full benefits of digitalization technologies for their supply chains”. [5] As we look to the future and DHL tries to bridge the gap, this leaves a few questions. Will DHL be able be able to maintain a competitive advantage in the marketplace or will the abundance of technology allow for companies to handle logistics themselves? Currently, we see this already with Amazon saving on costs by delivering packages themselves, but technology could possibly make it easier for companies of a smaller scale to do the same. [6] Additionally, what will be the downsides of using machine learning in the supply chain? Driverless cars are likely to replace trucking jobs and it seems likely that there will be other unintended consequences that haven’t been thought through.
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[1] DHL and IBM Whitepaper: Artificial Intelligence in Logistics. (2018). https://www.logistics.dhl/content/dam/dhl/global/core/documents/pdf/glo-ai-in-logistics-white-paper.pdf
[2] DHL supply watch: Machine learning to mitigate supplier risks. (2017, May 24). Business Wire. http://search.proquest.com.ezp-prod1.hul.harvard.edu/docview/1901774955?accountid=11311
[3] Jeffery Burt, DHL GETS LOGICAL – AND LOGISTICAL – ABOUT MACHINE LEARNING. (2018, April 18). The Next Platform. https://www.nextplatform.com/2018/04/18/dhl-gets-logical-and-logistical-about-machine-learning/
[4] Louis Columbus, 10 Ways Machine Learning Is Revolutionizing Supply Chain Management. (2018, June 11). Forbes. https://www.forbes.com/sites/louiscolumbus/2018/06/11/10-ways-machine-learning-is-revolutionizing-supply-chain-management/#5f9133343e37
[5] DHL Press Release. (2018, May 3). http://www.dhl.com/en/press/releases/releases_2017/all/logistics/95_percent_of_companies_are_yet_to_realize_the_full_benefits_of_digitalization_technologies_for_their_supply_chains.html
[6] Petersen, C. Will the future of retail be place, purple fairy dust or prime? (2015, Sep 25). Retail Customer Experience: News Features. http://search.proquest.com.ezp-prod1.hul.harvard.edu/docview/1716379290?accountid=11311
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