Managing supply chains is becoming easier as companies are getting a chance to leverage logistics analytics to gain insights from operational data.
FREMONT, CA: From the past few years, data and analytics continue to transform different industries by boosting intelligence and business decision-making. Logistics analytics offers several opportunities to handle complex supply chains. Handling logistics in the supply chain includes methods dedicated to making the distribution of products from the warehouses to consumers or clients efficient. A report states that 93 percent of shippers and 98 percent of third-party logistics companies consider data analytics integral as it helps them make intelligent business decisions. Business professionals recognize logistics analytics as one of the most effective approaches that holds the potential to transform operational practices across organizations.
Logistics analytics supports analytical procedures to be carried out by organizations and streamlines the logistical operations in a precise and cost-effective manner. Combining other technologies such as Business Intelligence (BI) and big data helps generate invaluable insights into the operations related to logistics. Here is how logistics analytics contributes to tackling the operational challenges of the supply chain.
· Inventory Optimization
As the stocking up of inventory is a vital process of the supply chain cycle, inventory managers are required to maintain reports updated with real-time insights into their transportation systems and manage the process adequately. Professionals are deploying logistics analytics to process the supply chains’ operational data and provide invaluable insights. Such in-depth insights enable the professionals to manage the supply chain workforce more efficiently for inventory, supply chain, and overstocks and spoilage.
· Route Planning
Professionals of the supply chain can utilize the integrated data from scanners, vehicles, sensors, and traffic reports to manage and deploy assets more effectively. The fleet managers can also use machine learning (ML) and predictive analytics for saving significant amounts of time and fuel by personally scheduling, rerouting, and route planning, resulting in supply chain optimization.
· Improved Transportation Analytics
In the transportation phase, there are various vehicles and means involved. The organizations implant multiple sensors dedicated to individual vehicles and integrate the sensors through a common platform. The common platform plays the role of providing significant assistance in numerous distribution activities and procedures. Some of these procedures include managing transportation networks, reducing operational costs, planning future operations, and serving the customers better.
Combining the logistics insights from aircraft, vehicles, ships, and other static data can boost the effectiveness of in-depth analysis and help the organizations to gain an overall understanding of the supply chain metrics. Logistics analytics can ultimately allow organizations to save money, time, as well as effort for their logistics departments.