BIG DATA ANALYTICS
Logistics is being transformed through the power of data-driven insights. Thanks to the vast degree of digital transformation and the Internet of Things, unprecedented amounts of data can be captured from various supply chain sources. Capitalizing on its value offers massive potential to increase operational efficiency, improve customer experience, reduce risk, and create new business models.
Key Developments & Implications
Since its arrival in the first edition of the DHL Logistics Trend Radar in 2013, Big Data Analytics has developed and today is increasingly becoming part of the de-facto operating model for the logistics industry. Surging demand for personalized and context-based services has driven development of artificial intelligence (AI) and machine learning applications which, in turn, have upped the need for larger datasets in the industry for better results.
Additionally, the rapid migration of enterprise data storage from traditional datacenters to the cloud has provided more flexibility in effectively scaling storage and processing power for all collected data. The need for visibility and prediction is ever-more pressing. COVID-19 has caused unprecedented uncertainty in supply chains globally, affecting how goods are moved and altering consumer demand and behavior.
Big data analytics holds the key to uncovering hidden issues across entire supply chains and surfacing trends that are not so obvious. As companies around the world recover, demand is growing for promising features of data analytics, such as mitigating disaster risks, simulating operations, and improving customer service.
Real-time process optimization and simulation are becoming increasingly important tools for supply chain management. As worldwide complexity grows, the ability to run global supply chains at peak efficiency becomes more and more challenging. Warehouse operators and supply chain managers can make better decisions with granular visibility of processes like order management, and inventory levels and resource utilization become transparent in live dashboards.
By uncovering patterns and anomalies in real-time data, operators can do things like allocate the optimal number of staff to certain tasks within a warehouse, group similar orders into the most efficient pick routes, and determine the optimal number of staff and assets between a group of warehouses.
Simulation models take optimization one step further by allowing logistics planners to test the impact of various levers that could be costly to execute on the ground. From exploring the consolidation of distribution centers to testing new delivery routes, simulations help answer service, cost, and risk questions in different scenarios. Building complex supply chain models with hundreds to millions of entities and activities is no trivial task, but companies can leverage analytics to help fill in the various variables as input and derive forecast models. Output can then inform future strategies and policies.
Graph analytics is an emerging technical form of data analysis that goes beyond having data in a pure tabular form to map visual structures of nodes and connecting lines. It focuses on the relationship between objects in a dataset. Early applications have been in social network analysis in, for example, LinkedIn, Facebook and Twitter.
For the logistics industry, graph analytics can help answer difficult questions that other data structures may not, for example identifying longest lead times, the weakest connection in a supply chain and relationship and community among industry players and customers.
This is a powerful method to identify the most prominent sources of failure and analyze the likelihood of downstream problems. Supply chains can similarly increase transparency and identify common points of failure with graph analytics, as well as assess the impact if a certain supplier, facility, or component fails.
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
Senior Data Scientist
DHL Asia Pacific Innovation Center, Singapore
Prerit is a Senior Data Scientist and the Lead of the DHL Advanced Analytics Team. He is involved in developing cutting-edge analytics solutions for DHL’s customers. The team’s projects include solutions for inventory and supply chain optimization, sales growth, strategic modeling, and end-to-end traceability. Prerit has over 9 years of experience in data science, software and data design/architecture, enterprise data analytics & integration across multiple industries.