Big Data Analytics in Logistics: Best Practices

IN Big Data General — 29 August, 2014

"Big Data in Logistics," a current trend report from DHL, looks at the question of whether the huge data volumes in the industry can be turned into better knowledge for improved decision-making. Katrin Zeiler coordinated the creation of the report. In a guest post, she summarizes the most important aspects.

 

© (2013 DHL / Detecon International GmbH) © (2013 DHL / Detecon International GmbH)

 

Big data and the logistics industry should actually be a dream team. Logistics service providers move masses of goods and there is constantly more and more of it. At the same time, they gather data sets that are just waiting to be turned into information that supports decisions. They capture and track the origin and the destination of the shipments, their size, weight, and their current location. However, now and again, enterprises in the industry often lack the insight into how they might gain value from the data material that collects from all that tracking.

 With the "Big Data in Logistics" trend report, a DHL report on how to move beyond the hype, we want to get the discussion going. We have identified three fields (value dimensions) that have an effect on the analysis of big data:

  • Increasing operational efficiency, for example, by increasing transparency, using resources optimally, and increasing process quality and performance.
  • Improving the customer experience with the result that customer loyalty and customer retention rise, customer groups can be segmented exceptionally well, and customers can be individually spoken to. The enterprise thus optimizes the entire communication with the customer and service.
  • New business models are made possible. Enterprises gain additional sources of revenue.

 We have brought together best practices from the sector that cover more than one industry, including two Blue Yonder use cases. DM illustrates how employee use planning can be made considerably more efficient. At the same time, sales forecasts based on the Blue Yonder algorithm, NeuroBayes, have helped prevent out-of-stock situations and unprofitable over-stock situations at German multi-channel retailer OTTO.

 These and other successful examples from the world of industry show that investments in big data analytics pay off. There is enough "fodder" for forecasts especially in logistics with its immense data-intensive processes, that make daily decision-making in enterprises more secure, faster, and — most of all —better.  To illustrate the breadth of possibilities in our industry, we have devised the vision of the data-driven logistics business (data-driven logistics provider, please see the graphic). One area of use is DHL Resilience360, a cloud-based supply chain risk management solution that visualizes weak-points along the entire value creation chain, evaluates them, and thus helps develop the corresponding plan of measures to be carried out. By means of the holistic monitoring in real time, relevant incidents are recognized early on and thus, production stoppages, loss of image, and other harms are prevented. Altogether, we have identified eleven different use fields, right up to automatic weather data capture when driving through a city. Of course, logistics enterprises are not going to become data power-houses in a single day. But with a bit of entrepreneurial spirit, many approaches can be discovered and developed. Let's discover the future potential together that is slumbering inside of data.

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