BIG DATA ANALYTICS
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.
Key Developments & Implications
Big data has already begun to make inroads in the logistics industry by turning large-scale data volumes into a valuable asset. Moving forward, harnessing the full potential of big data will require mastering the integration of structured and unstructured data (social, images, video, etc.) from multiple data streams. Here, data lakes will play an important role in enabling easy access of enterprise data especially in fragmented IT landscapes. Furthermore, the advancement of analytics and computing technologies (see Artificial Intelligence) will unlock exciting new ways to monetize data-driven operating and business models.
Dynamic, real-time route optimization through the intelligent correlation of data streams (shipment information, weather, traffic, etc.) can enable more efficient scheduling of assignments, optimization of load sequences, and ‘down-to-the-minute’ prediction of the estimated time of arrival (ETA).
Smarter forecasting of demand, capacity, and labor through big data analytics can significantly optimize planning and resource utilization, process quality and performance, and can reduce unnecessary costs in the supply chain.
Anticipatory shipping can be used by online retailers who have analyzed their customers’ purchasing behaviors to predict an order before it occurs. This can then be used to move goods to distribution centers that are closer to a customer who is likely to purchase the products. It can enable retailers to offer same-day or even one-hour deliveries.
End-to-end supply chain risk management can be improved by evaluating current conditions with existing data pools. Big data can be used to mitigate risks by detecting, evaluating, and alerting all potential disruptions on key trade lanes, caused by unexpected events such as growing port congestion or high flood risks. This can be further enhanced through the integration of data from IoT devices.
This Latest Trend Report Proposes and Explores Three Different Categories of Information Exploitation:
- Operational efficiency: real-time route optimization, crowd-based pickup and delivery, strategic network planning, and operational capacity planning
- Customer experience: customer loyalty management, continuous service improvement and product innovation, and risk evaluation and resilience planning
- New business models: market intelligence for small and medium-sized enterprises, financial demand and supply chain analytics, address verification, and environmental intelligence
Talk to an Expert
Pang Mei Yee
Innovation, Asia Pacific
Mei Pang leads the DHL Asia Pacific Innovation Center (APIC). APIC is part of a DHL global innovation platform with a mission to inspire, connect and engage industries with the future of logistics. Under Mei’s leadership, a team of innovators seed ideas to link businesses and relevant partners; bringing innovative solutions to life.
An entrepreneur in a global company, Mei is known to bring innovation and entrepreneurship to the teams she leads. As Partner at DHL Consulting and head of office for Asia-Pacific until 2015, Mei doubled the business size of DHL Consulting by spearheading a new service portfolio. DHL Consulting advises senior management of Deutsche Post DHL Group on management topics and customers on supply chain strategy. She led a team of consultants across Asia Pacific and has a keen focus on improving supply chain effectiveness for DHL’s customers in Asia.