{"id":36064,"date":"2018-11-13T19:39:18","date_gmt":"2018-11-14T00:39:18","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/dhl-cracking-logistics-with-the-help-of-machine-learning\/"},"modified":"2018-11-13T19:40:34","modified_gmt":"2018-11-14T00:40:34","slug":"dhl-cracking-logistics-with-the-help-of-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/dhl-cracking-logistics-with-the-help-of-machine-learning\/","title":{"rendered":"DHL: Cracking Logistics with the help of Machine Learning"},"content":{"rendered":"

DHL and applications of Machine Learning in the logistics industry<\/u><\/strong><\/p>\n

Innovations born out of machine learning and artificial intelligence have significant implications and uses for logistics companies and supply chain companies.\u00a0 A clear example of this is leading logistics provider, DHL, and it plans to augment its logistics platform through the use of Artificial Intelligence and Machine Learning.<\/p>\n

Issues facing the Logistics Industry<\/strong><\/p>\n

Logistics and supply chain management have significant implications for every industry.\u00a0 A large-scale logistics company such as DHL therefore, has, in some form, a hand in every industry possible.\u00a0 Such companies need to be able to forecast and manage spikes and drops in demand for key clients and multiple routes, process, weather and other transport delays and process orders as they come in to reduce wait time.<\/p>\n

Logistics companies also face significant potential supplier failures such as unpredicted quality issues, failure to produce adequate product, ect.\u00a0 Due to the fact that logistics companies such as DHL only handle the transport of goods and services, they are usually hamstrung to handling such issues reactively resulting in both financial losses and reputational damage.[1]<\/a>\u00a0 In addition, there are a number of functions which could benefit from automation.\u00a0 These include short term examples as customer service interactions to longer term plays such as fleet automation.<\/p>\n

Steps Taken<\/strong><\/p>\n

DHL recently published a report in partnership with IBM Watson outlining the key applications of Machine Learning in the logistics space.[2]<\/a> The report included steps to address supply chain issues and augment its current capabilities in the short term and long term.\u00a0 Key amongst these has been the introduction of DHL Supply Watch to the company\u2019s Resilience 360 risk management system.<\/p>\n

Supply Watch uses a combination of machine learning and natural language processing for early identification of any potential disruptions to DHLs supply chain. The system monitors over \u201c140 different risk categories including financial, environmental and social factors among risks resulting from crime, labor breaches, quality defects and supply chain perils such as shortages, capacity constraints and delays\u201d[3]<\/a> to help the company proactively inform customers and suppliers of such issues rather than informing customers retroactively after the issue has arisen.<\/p>\n

Such enhancements should also allow the company to provide a more personalized experience for its customers through tracking of past orders current activity potentially giving them an edge on competitors.\u00a0 DHL has also suggested it will begin to use chat bots to automate some of the easier customer service engagements.[4]<\/a>\u00a0 In its joint report, DHL goes so far as to suggest that its predictive models could one day enable them to deliver packages to customers before they have even ordered them.<\/p>\n

DHL has taken steps both in the medium and short term to improve its physical fleet management as well.\u00a0 In the short term it is using machine learning to track customer demands improve its route optimization. In the medium term it\u2019s begun to implement automation testing for its ground services.\u00a0 In 2019 it plans to begin testing \u201ctruck platooning\u201d or the intelligent caravanning of semi-trucks on a highway- enabling groups of two to five trucks (led by a human driven truck in the front) to follow each other on highways.[5]<\/a>\u00a0 They are also looking into Autonomous fleets as a more long-term solution.\u00a0 [6]<\/a><\/p>\n

Further Recommendations<\/strong><\/p>\n

Moving forward, DHL can expect to see machine learning become more a part of its day to day operations from automation of its fleet to the providing risk management information.\u00a0 DHL and other logistics companies in general should then focus their efforts on other parts of the value chain that could benefit from machine learning applications.\u00a0 Two such instances include their suppliers and clearing customs.\u00a0 Even with additional forecasting provided by AI and machine learning, supplier interruptions will remain an issue for any logistics company.\u00a0 By extending its machine learning capabilities on the supply chain management side to its suppliers, DLH may be able to prevent further interruptions from suppliers and develop a competitive advantage beyond its own logistics platform.<\/p>\n

In addition, using machine learning to better predict delays in customs clearances will also benefit the company.\u00a0 Customs clearance can often impose significant delays on even the most simple packages.\u00a0 Utilizing machine learning capabilities to predict such delays may allow DHL to further inform customers proactively of wait times, or better help prevent such delays by giving customers the chance to proactively engage customs authorities before a package arrives.<\/p>\n

Remaining Questions<\/strong><\/p>\n

Two more important questions regarding DHL\u2019s efforts in machine learning include:<\/p>\n