Updated: Jul 6, 2018
Last week, #DHL released its New Logistics Trend Radar 2018/19 providing in-depth analysis of 28 significant social, business and technology trends transforming the future of logistics (http://www.dhl.com/en/press/releases/releases_2017/all/logistics/the_future_of_logistics_depends_on_four_key_elements_customer-centricity_sustainability_technology_and_people.html). Key trends cover Artificial Intelligence and Robotics; new trends include ‘Smart Containerization’ which highlights the need for new container formats to enhance urban logistics, and ‘Connected Life’ which is the integration of logistics services with smart home environments
Data analytics, high short-term impact
At #Innitium, we were excited because Big Data Analytics, Digital Work and IoT were classified as high, short term impact, right in the middle of the “radar”. DHL “short term” is a horizon of less than 5 years, and classifies "high impact" events that create new (potentially disruptive) ways of doing business . Specifically, in the area of Data Analytics, DHL highlights that:
“Logistics is being transformed through the power of data-driven insights. Thanks to the vast degree of digitalization, unprecedented amounts of data can be captured from various sources along the supply chain. Capitalizing on the value of big data offers massive potential to optimize capacity utilization, improve customer experience, reduce risk, and create new business models in logistics.”
Dynamic, real-time route optimization
As we have at #Innitium, DHL identifies dynamic, real-time route optimization, smart forecasting (demand, capacity and labour), anticipatory shipping and end-to-end supply chain risk management and key areas. We also believe in the huge potential of this areas, currently suboptimal not only in SME but also in larger companies with important fleets.
As key challenges, the article discusses the strong business and IT alignment required for implementation, coordination between the elements of the supply chain (data exchange, privacy), data quality and sourcing data science skills.
Do you agree?