How Big Data and New Tools are Changing Transportation Solution Design

Most transportation managers suspect there are opportunities to improve efficiency and reduce costs hidden in their transportation networks—and they’re usually right. But, until recently, the scale and complexity of transportation networks have resisted the ability to quickly identify and capitalize on these opportunities.

Time Intensive, Data Limited

Sophisticated transportation solution design and analysis is a complex undertaking. There are not only the major strategic decisions to be considered, but dozens of other factors that impact transportation costs. For example, can frequency be reduced without impacting service levels? Are there opportunities for consolidation? What is the impact of the comparative costs of different methods of ground transportation?

The way transportation specialists have addressed this complexity is to make strategic assumptions based on their experience and understanding of the network and then test the efficiency of these assumptions using multiple discrete tools, such as route and consolidation optimizers and modeling tools, to determine the cost savings that could be realized through the proposed strategy. The strategy is then modified based on the initial analysis and the cycle is repeated through multiple iterations until the solution has comprehensively analyzed.

The process is not only time intensive, but also data limited. The dedicated tools used to optimize routes or uncover consolidation opportunities can only accommodate a subset of freight data in their analysis. Even when control data is carefully selected to reflect the seasonal fluctuations of a particular shipper, it may not capture all the nuances or opportunities that might be uncovered with a larger data set. In addition, with the multiple iterations required to achieve optimization, the process can take weeks to complete.

The Missing Link

The data is available for more comprehensive analysis through various supply chain platforms. But what’s been missing is a macro transportation decision support tool that could work with big data – a tool that could enable transportation engineers to quickly develop flexible optimization strategies without multiple iterations.

Since that solution wasn’t available in the market, DHL Supply Chain decided to develop it. The result of that development effort is our Transportation Network Optimizer. This solution combines network, freight, and routing optimization with various costing engines (parcel, LTL, truckload, intermodal and fleet) to optimize on cost for historical or daily shipments.

Working with data sets ten times larger than is possible using the current generation of discrete tools, the Transportation Network Optimizer allows transportation engineers to evaluate the impact of strategic changes across eight value levers and refine the solution strategy within a single tool.

The groundbreaking tool includes four modules—Freight, Fleet, Round Trip and Pool Point – that can be run independently or sequentially to optimize on origin, destination, shipment date, delivery date or equipment size.

Using the Fleet module in concert with the Round Trip module, for example, enables transportation engineers to quickly right-size a dedicated fleet and reduce empty miles. Similarly, the Freight module working with the Pool Point module can identify opportunities to consolidate shipments into lower cost truckload lanes with deconsolidation in the final mile. Carbon dioxide optimization is included as one of the tool’s value levers, allowing engineers to quickly see the impact of transportation changes on CO2 emissions to help shippers achieve their sustainability goals.

The ability to refine the strategy without working through multiple optimization cycles significantly streamlines the process, enabling projects that would take weeks using the previous method to be completed in days. It also enables more comprehensive and fully optimized solutions that were not possible using the previous method. When we’ve used the solution to analyze existing transportation networks, we typically uncover cost savings of 5-15%.

A key capability of this new solution is the ability to optimize based on ship or delivery date and on origin or destination. This allows the solution to be used to quickly perform what-if calculations to determine the impact of specific changes on how products are shipped.

For example, we recently conducted an analysis for a manufacturer to determine which shipments should use its dedicated fleet and which could go on other carriers. The analysis uncovered the potential to reduce transportation costs by 12% by optimizing on ship date and reducing reliance on the dedicated fleet. In another case, we were able to identify $2 million in savings through freight and fleet optimization. We were also able to identify $3.5 million in savings for a large manufacturer using their data to identify optimization opportunities across their network.

In virtually every case, the combination of big data and the ability to optimize for multiple variables is identifying significant opportunities for cost savings that can be realized without impacting, and in some cases improving, service levels.

Transportation management isn’t getting any simpler and the pressure to keep rising transportation costs under control will continue to grow. Through the use of new solutions, such as DHL’s Transportation Network Optimizer, that enable dynamic decision-making using large data sets, we can help shippers identify the potential savings that exist in their networks and respond more quickly and efficiently to market changes that affect the network.

To learn how DHL Supply Chain can leverage data and technology to identify opportunities in your transportation network, visit: http://app.supplychain.dhl.com/e/er?s=1897772577&lid=8359.

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