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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.

Tackling supply-chain complexity
Supply chain operations are extremely complex because of the number of variables that they encompass: everything from exchange rates to weather conditions can impact the ability to make deliveries profitably and on time. Two of the largest challenges for supply chain operators, for example, are shifts in demand that elude effective management (cited by 40% of operators), and disruptions in the flow of physical products (cited by 36%). Both these challenges are caused by highly complex and interrelated factors where data has not been traditionally available or easily accessible.

Big Data solutions offer a means for supply chain operators to gain insight not just into each of these factors, but how they correlate with one another and impact entire supply chains. To minimise disruptions in the flow of physical products to its consumers, a major retail chain now gathers and analyses data about supplier delivery schedules, weather forecasts, and road conditions, allowing it to better identify which of its stores will have greater demand at any given time. The insights from this Big Data then allow the retailer to more efficiently allocate store personnel and extra resources where needed most, dramatically improving customer satisfaction and sales efficiency.

This sort of insight, and the consistently elevated levels of customer satisfaction which it allows for, would be impossible to gain or act on manually. As alluded to above, it can also be instrumental in converting disruptions into opportunities: risk-management software platforms, for example, can visualise data from these vehicles and supply chain centres on a single global map, allowing decision-makers to quickly see where bottlenecks might be emerging and take steps to rapidly adjust their processes. So Big Data can be a powerful enabler of business agility – but only when its insights can be accessed in real time.

Big Data also allows supply chain operators to draw upon far greater volumes of data than previously available. To better understand and plan for shifts in demand, one of the world's largest mail-order and e-commerce companies now tracks over 300 million transaction records a week, each of which holds up to 200 item attributes, with an analytics system that then feeds into the business' sales forecasts. So far, the system has reduced the gap between procured and sold quantities by more than 30 percent – allowing the business to eliminate significant inefficiencies thanks to far more accurate predictions about demand.

Three keys to successful Big Data
To tackle large-scale levels of complexity, Big Data solutions need to be scalable, flexible, and responsive in real-time. To provide these "three keys", businesses need to invest strongly in data transparency and governance: as much information as possible should be both consistently structured and freely available within the organisation. Data science skills are increasingly essential, both for supply chain operators and a range of other industries, but they need to be applied to specific business problems – the larger and more complex, the better.


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