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Quality Assurance Using Big Data in Logistics

By ESOutlook | Wednesday, October 31, 2018

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Today, supply chain and big data have become a revolutionary topic in the field of business and logistics applications. The general idea behind the deployment of the supply chain in logistics is to monitor the activities involved from the starting stage of procurements of raw materials to the final delivery to the consumer. The bottlenecks that arise in the supply chain are in handling big data that is collected amid day to day activities and optimized routing.

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Big data comprises of a huge amount of data, which should be pre-processed to extract quality amount of resources effectively. These data will be collected from numerous industries and financial sectors through sensors. In a report predicted by Gartner in 2017, it is seen that the rise of SaaS-based business intelligence tools can be a solution with its advanced online solution techniques. Through the case studies on logistics, approximately over 28 percent of the total delivery cost is spent during the last mile of the supply chain. Last delivery state is termed as the black box state in which there is no clue about where most of that time is lost; it could be due to challenging road tracks or unavailability of customers or in handling goods with extra care. The interview with the wall street journal suggested that through advanced GPS enabled devices and IoT, shippers can easily track the delivery process, even in the last mile.

With sensors in traveling vehicles, the data gathered should be accurate and efficient. Transparency will be highly valuable for shippers, carriers, and customers. This kind of data transparency can help reduce the bottleneck during the shipment process and change the way information is exchanged in the logistics world.

Optimization parameters also influence the process of logistics in achieving better output. The parameters that may leverage the process of optimization is due to the fuel cost, temporary shutdown of roads on the path of designation, breakdown of vehicles due to repairs, and changing weather conditions. It can be minimized by employing different types of sensors, weather data capturing system, and real-time fleet status indicators through which efficient road schedule can be prepared.

At present, we are at the cusp of leveraging efficient big data techniques to transfer the nature of logistics. Big data techniques can minimize the inefficiencies, but still, several concepts such as data transparency and optimized delivery are at risk. Automation through blockchain technology can reduce the limitation and help in achieving better deliver services in the future.

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