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Big Data can solve logistics' biggest problems

Steve Walker, CIO, Asia Pacific, DHL Supply Chain | June 2, 2015
In the logistics industry, "data science" technologies are already being applied to optimise every stage of the supply chain. But for these Big Data solutions to reach their full potential, they need to be scalable, flexible, and responsive in real-time.

This vendor-written piece has been edited by Executive Networks Media to eliminate product promotion, but readers should note it will likely favour the submitter's approach.

Big Data is set to transform the logistics industry not just because of the depth and relevance of insights, but because of its ability to decipher organisational complexity on a vast scale. Most CIOs already recognise the core value of Big Data and analytics, which is to provide rich and relevant insights into how their businesses are operating in reality and real-time. However, few have applied Big Data beyond verifying or testing existing assumptions in familiar areas of their business.

In the logistics industry, "data science" technologies are already being applied to optimise every stage of the supply chain. Doing so helps decision-makers to understand patterns and correlations which would've otherwise been invisible amidst a range of highly complex processes. But for these Big Data solutions to reach their full potential, they need to be scalable, flexible, and responsive in real-time.

Don't invest in "confirmation bias"
Big Data should be used first and foremost as a source of new insights, not a means by which to verify or test existing assumptions. There are two reasons for this. The first is that as humans, we have a tendency to fixate on evidence which supports our pre-existing beliefs, exercising "confirmation bias" in how we evaluate information that's available. This can potentially nullify the value of Big Data to an organisation, reducing its ability to optimise processes and enhance – rather than simply justify – business decisions.

The second is that we're not harnessing the full potential of Big Data if we restrict its application to areas of business that we already believe we understand. Big Data technologies have advanced to the point where they can analyse both structured and unstructured data sources accurately and at scale, from sensor data to audiovisual content, driver behaviours, and even macro trends in areas like demand and labor productivity. In other words, almost any aspect of business process and operation can now be quantified, evaluated, and optimised. Big Data's greatest value for CIOs will come from tackling previously-insoluble problems – the sort where information was either too vast, or too scarce, to draw valid conclusions.

Tackling these sorts of problems can not only improve operational efficiency but also generate long-term competitive advantages. For example, DHL recently worked with a leading passenger airline to collect and analyse data about beverage consumption across key flight routes. Armed with this data, the airline was able to refresh its inventory management system to provide the optimal volume and variety of in-flight beverages based on any flight's point of origin or destination, travel time, and a range of other seasonal factors. Apart from significant efficiency dividends, the solution also enhances the airline's ability to deliver on its customer service promise in a manner which could easily scale to many other aspects of onboard inventory and route management.

 

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