How Data Analytics Can Streamline the process of logistics management

With the growing awareness about big data and data analytics, it has revolutionized many industries. Logistic is another field which is going to see transformations with the application of big data analytics in its process.

Logistics management is becoming more complex with the increasing complexities in supply chain management. Big data can be used to streamline the process of logistics. Using logistics analytics organisation can optimize its routing process and avoids the unnecessary delays. It can also ensure transparency in the whole supply chain management process.

Using data analytics in logistics management can improve the decision taken by third party logistics and can benefit both logistics and shipping companies. Researchers believe that logistics analytics will be used by companies as competitive edge in near future.

The ways BIG Data can improve Logistics management

In current logistics management, truck drivers are faced with the problem of standing in a queue for unloading at the destination. Sometimes huge traffic cause delay in delivery. These problems can be resolved through big data analytics. Analyzing the transportation problem and the requirements at the destination and estimating the distance from origin to destination can lead to more informed decisions. Using GPS censor in UPS delivery truck can guide the driver with the distance left to reach the destination, the possible issues that may occur.

Using censor in transportation and shipping process provide companies with more reliable data and this ensures transparency. These data can be used by logistics companies to build trust on new customer and deliver predicts within the given time. So, big data help shipping companies and logistics companies to meet its commitment as per customer expectation on time.

Big data can also be used to optimize logistics process and supply chain management as well. Doing this organisation can save inventory and warehouse costs by delivering the components and products at the right time and also prevent scarcity of resources which can cause loss of sales or interruption in production process. So, logistics analytics can reduce the overall cost of production and waiting time as well.

Using temperature sensor in trucks integrated with traffic and road work computer system can improve the delivery of perishable goods before it got perished. However, this field still need more research to better understand the applicability of data analytics to optimize its efficiency.

One of the biggest challenge for the organisation is implementing big data analytics in logistics management requires adequate amount of labelled data. Organisations have huge amount of data but mining of labelled data is difficult. To overcome this organisation can optimize its traditional data collection method and it can use some other source like driving pattern, vehicle diagnostic, location information, social media, financial forecasting and so on.

Optimizing logistic analytics will result in better inventory management, reduced warehouse costs, reduce delivery time, improved manufacturing process and the most important efficient supply chain management.

Source: HOB

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