DHL Uses Big Data to Optimize Last-Mile Delivery
How big data creates tremendous value in the last mile delivery for both customers and DHL.
Imagine it is 5 p.m., Valentine’s Day. You’re waiting at home to receive the flower you booked earlier on-line for your spouse. You know someone from DHL is sending the flower who is supposed to arrive by 6 p.m. As time passes, your anxiety increases exponentially. It is now only 15min before the scheduled dinner time, but the flower is still on the way…
Last mile delivery enjoys a significant growth both in developed and developing countries largely driven by e-commerce. In 2015, the growth rate of last mile delivery ranges “between 7 and 10 percent in mature markets, such as Germany or the US, and almost 300 percent in developing markets such as India” [1]. While the business potential is huge, last mile delivery has always been a headache for the logistics industry-delivery delayed, parcel damaged, pollutants emitted etc. Can we totally blame the unsatisfactory delivery on the 3rd-party logistics company? Probably not. After all, the truck drivers of DHL work just as hard as people of any other occupation, if not more. The truck can be caught in an unexpected traffic jam, delayed by an abrupt weather change or simply runs on a poorly maintained road. Besides, last mile delivery is expensive, the cost of which “often reaching or even exceeding 50%” [2] in total parcel delivery cost. Big Data technology provides the possibility of collecting and processing a large quantity of logistics data and optimize the operation of last mile delivery in a real-time manner. Smart Truck and MyWays are two initiatives of DHL to boost the efficiency of its last mile delivery with big data technology.
In 2010, DHL first launched the Smart Truck with the route planning system lying at the core. Before the shipment, the system processes a great deal of data collected from roads and GPS (Global Positioning System) and comes up with the ideal shipment sequence and route. When the truck is running, the telematics databases are tapped to automatically adjust the route based on real-time data of latest order and of the traffic, weather and environment condition. In this way, the Smart Truck can avoid jams. With the big data technology, DHL can achieve better customer service and a more cost-effective operation because of the optimum use of vehicles with less wasted miles. By using the Smart Truck, the number of miles was reduced by 15%. Fuel consumption and CO2 also decreased remarkably [3].
Apart from the Smart Truck, DHL also launched a crowdsourcing platform for parcel delivery named MyWay in 2013. The idea is a form of sharing economy and very much resembles Uber. MyWay matches those who are willing to transport parcels on their daily routes earn some additional money and shippers who seek flexible last mile delivery. The idea requires big data technology such as complex event processing and geo-correlation[4].The whole process is done through a mobile application that enables the crowd affiliates to publish their current location and get assigned the delivery task. The idea was first tested in Stockholm and received positive feedback[5].
While the importance of technology breakthrough cannot be emphasized too much, it’s worthwhile for DHL to think about improving its existing customer service without major R&D investment. The practice of Chinese logistics company can be a good reference: All the delivery person are required to call the recipient of the parcel to inform the arrival of the parcel and get the signature of the customer as an evidence of successful delivery. If the customer is away from the designated delivery address, both parties will align a proper place to put the parcel; otherwise, the delivery person will keep the parcel and deliver it again when the customer is available. What’s more, once a delivery order is placed, the customer can check through mobile application on the name, phone number of who is delivering his or her parcel. The location of the delivery person is updated on a real-time basis so customers can expect the time to receive the parcel. If there is any problem, the customer can reach the delivery person through phone call. Obviously, these practices are of low or no additional cost, but they offer convenience to the customer for sure.
Two questions for further thought are: How does DHL further deploy the usage of big data with other breakthrough technologies to come up with innovative last mile delivery solutions such as drone, autonomous ground vehicles with lockers or droid (small autonomous vehicles)? What is the competitive strategy of DHL to defend the potential disruption from logistics tech start-ups?
[1][2] “How customer demands are reshaping last-mile delivery”, McKinsey & Company, Oct, 2016, https://www.mckinsey.com/industries/travel-transport-and-logistics/our-insights/how-customer-demands-are-reshaping-last-mile-delivery, accessed November 2017
[3] “Big Data in Logistics-A DHL perspective on how to move beyond the hype”, DHL, December, 2013 http://www.dhl.com/content/dam/downloads/g0/about_us/innovation/CSI_Studie_BIG_DATA.pdf,accessed November 2017
[4] “Logistics Trend Radar”, DHL Customer Solutions and Innovation, 2016
http://www.dhl.com/content/dam/downloads/g0/about_us/logistics_insights/dhl_logistics_trend_radar_2016.pdf, accessed November 2017
[5] “Extending last-mile delivery to meet customers’ on-demand needs”, World Economic Forum
http://reports.weforum.org/digital-transformation/dhl/,accessed November 2017
Great and insightful essay! In response to the 2nd question, I believe it is to the advantage of DHL to leverage its large scale in terms of number of deliveries and the existing fleet to ensure enough deliveries to build a community and enough vehicles to fulfil delivery promise even when the unofficial network is unavailable.
To your first question, perhaps there is a way to use Big Data to make deliveries more proactive rather than reactive. It seems like DHL is using big data to figure out route planning once all orders have been placed. I believe that Amazon is using big data to predict what customers will order and shipping to local warehouses in advance of a local order, which takes this a step further.
I am also wondering how a company like DHL balances short-term and long-term investments in technology. For example, is it worth spending a lot of money now to optimize routes when drones may make the route optimization system obsolete? How do they determine how much money to invest in these technologies?
Finally, the crowdsourcing platform is interesting but I’m worried about what happens to customer service. Will these new delivery agents wear DHL uniforms? Will they know the proper way to interact with customers?
Very relevant topic, and I think DHL has to add technology and services to keep a competitive advantage. My question would be are these added services enough to differentiate them from low cost providers? In shipping, I feel a lot of customers will choose a lower price over added communication services when recieving their flowers. The innovations will help as a way to differentiate themselves from other major shippers, but DHL will need to still keep costs down as these are added or customers may choose to go with lower priced competitors.
Very relevant topic and discussion!
To your second question: I absolutely agree that DHL must have a solid strategy to defend itself from logistics tech start-ups. This is a real threat for DHL. There is, for instance, an HBS start-up called Veho that is doing exactly what DHL seeks to do through MyWay. They deliver packages through crowdsourced drivers and offer many of the services that you mention are being offered by Chinese players (e.g. real-time tracking of packages and direct communication with the person making a delivery). Veho has already started operations in Boston quite successfully. (See: http://veho-technologies.com/)
Having said that, I believe that, in addition to effectively leveraging its fleet size to outcompete potential entrants in terms of delivery promise (as suggested by ChS in her comment), DHL should consider two additional elements as part of its competitive strategy:
1. Invest more in R&D and/or design thinking: I disagree with your point that DHL should try to improve its existing customer service without major R&D investment. I actually believe they should go much further, proactively exploring cutting edge digital technologies, and implementing those that enable the company to either increase the efficiency of its supply chain or make DHL more “sticky” for clients.
2. Invest more in marketing: Although – as you point out – there is low brand loyalty and relatively high price sensitivity in this sector, there might be marketing initiatives that might make DHL more “sticky” for clients (e.g. loyalty programs, etc.). DHL should come up with such initiatives and implement them fast.
It would be very difficult for start-ups to replicate these two elements, as it would very hard for them to match DHL’s potential levels of investments in these matters. DHL is, after all, a 57 billion euro company.
Thank you very much for this article, I enjoyed getting to know how DHL is using Big Data to improve its efficiency and profits. However, and addressing your first question, I believe that Big Data will open the door to more impactful -and complex- opportunities that companies as DHL are still not focusing in. As an example, the airline industry is trying to use Big Data to forecast future demand and to target each customer individually. With enough information and data processing capabilities, an airline would be able to proactively send you an email offering a trip that aligns with your customer behavior and preferences including the right price, destination, purchase anticipation time and stay length. I think that DHL could also use Big Data in a similar way, trying to forecast future demand and individually target customers. This would allow DHL to increase profits and also influence demand, trying to spread it over a convenient period of time, thus further minimizing demand peaks, traffic jams, and other externalities affecting delivery times.