Utilizing Big Data to Optimize 3PL Operations

By Annie Khor, IT Director, DB Schenker Logistics

Annie Khor, IT Director, DB Schenker Logistics

The concept of big data started in the 1990s when someone realized the increasing abundance of data that was being generated daily from traditional methods (structured data) to data generated through social media (unstructured data), and the potential value that can be harnessed from these data. The value in big data is in its potential to transform processes and business models. Despite its potential, in the 90s, big data analytics were in its infancy, there were limited tools available, and due to the breadth of data available, there was a lack of clarity on how to harvest, interpret, and leverage these data to bring value to the organization.

Fast forward to today, advances in tools and technology coupled with improved clarity on how we can leverage on big data, many organizations including 3PLs have embarked on big data analytics to harness the intelligence from the data and to utilize this intelligence to optimize their operations through predictive and prescriptive analysis. big data analytics is enabling 3PLs to make predictions, optimize the logistics network, and automate business decisions. 3PLs are building their own data ecosystem to leverage on big data analytics.

In DB Schenker, we see data as a driver for growth and change. We see the value of (big) data in its potential to transform processes and business models. In summer of 2016, DB Schenker set up a Data Strategy and Analytics department as part of our Digitization strategy.

Big data analytics are being used to bring value especially in 3 categories:

• To improve operational efficiency
• To improve customers’ supply chain
• To create and expand revenue stream

In the area of operational efficiency, we use big data analytics to make predictions such as shipment/ volume forecast to optimize resource utilization in operations, traffic condition forecast to prescribe optimum routes to be used. On operational process optimization, we can use big data to predict product mix in a warehouse and to prescribe product layout and optimize picking routes and storage utilization. On resource utilization optimization, big data is being used to predict patterns and productivity in operation needs and prescribe when, what, and how there source can be deployed.

In the area of customer’s Supply Chain improvement, apart from the operational efficiency, which potentially reduces the cost of customers’ supply chain, big data is being used to generate transparency in providing the right data at the right time to customers through the entire supply chain with no blind spots. This enables customers to plan better and react to issues more effectively. Using big data analytics, we are able to identify these data and transmit the alert to our customer timely that enables them to work on the corrective action proactively. In addition, we are also able to predict if a shipment will be delayed if a certain route is being used and proactively prescribe an alternative solution.

The amount and type of data that is available in the supply chain provide challenges and opportunities for new data-driven products to be developed to create and expand our revenue stream. In addition, big data analytics are enabling machine learning and artificial intelligence. This not only optimizes 3PL operations but also potentially can be packaged as data-driven products to create and expand the organization’s revenue stream.

In conclusion, big data analytics as part of Logistics 4.0 is transforming the way a 3PL operates today. It is a prerequisite for a 3PL to transition to become a 4PL and for 3PLs to stay relevant for today and the future.

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