Role of data analytics in logistics & supply chain
Technology is becoming a vital element in logistics & warehousing. There is an evident emergence of software that helps with the optimal route, warehouse utilization, and supply chain management. The logistics industry might be the very sector that could make the most out of big data and business intelligence, as long as it knows how to take the best advantage out of them. The hugeness of the flows handled every day with all the shipments, their weights, sizes, contact details, or returns is generating an incredible amount of data that has to be managed.
Logistical data can be used to develop actionable insights to improve your business performance. It is imperative to identify & manage the sources of data in order to generate streamlined insights.
Following are the major data sources in logistics:
A recent survey found that more than one-third of C-level executives were engaged in serious conversations to implement Big Data Analytics in the supply chain, and 3 out of 10 already have an initiative in place to implement advanced analytics.
Data analytics in the logistics industry helps typically in the following areas
Suppliers analyze shipment data to know the average delivery time, reasons for shipment delays, etc. to make improvements to their shipment processes. The active documentation of all shipper operations helps the data to be used to make the process more efficient. Any potential problems with carriers can be immediately intervened and solved.
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2. Route Optimization
Companies can create a personalized, optimal route & drive schedule for each driver so shipments arrive on time. Understanding the pervasiveness of data in transport management helps illustrate the great potential that analytics have in improving shipment route optimization processes. In the past, route optimization was a tedious process involving hours of employee time spent on analyzing the pros and cons of using different routes.
With data technology, you can simplify the shipment route optimization process and make it more accurate. Route and fleet managers can pull data from an array of sources like vehicles, active drivers, trackers, sensors, scanners, real-time traffic updates, weather reports, and more.
3. Warehouse Management
Analytics can be used for inventory management. Impeccable inventory management is a must. And this is where data science comes into play. More often retail companies look for intelligent solutions to improve their inventory management processes. The vast majority of these smart solutions are based on data science and artificial intelligence.
Companies use Business analytics to improve their efficiency in logistics. Business Analytics is used to find the best means for transport to reduce the cost of transport, the best routes for shipping, and the best way to reduce time. Shipment of goods day to day produces a huge amount of data such as contact details, weight, size, address, and return.
Business Analytics in Logistics is to establish a value-driven Logistic network aligning the supply & demand. Managing logistic analytics and optimizing business processes will help the supply chain to create more values at the same optimum cost by making better business decisions. Hence business analytics is very useful for this sector to get a competitive advantage.