For a business, making logistics a highly efficient process is a step taken in the direction of increasing the profitability, growth and flexibility of the enterprise. It helps the business to attain a new level of efficiency and productivity. It increases the quality of the final customer’s experience and the value they derive at the end of a transaction.
It is for this reason that a digital ecosystem has been created for logistics processes. Such solutions make the connections between entities involved more integrated and also improve factors like reach, scale, predictability, and consistency of operations.
Big data is one of the most promising solutions that has been introduced into this digital ecosystem for logistics. With Big data solutions, no amount of data has ever again been an impediment for businesses and their growth.
To understand how Big Data and its ability to handle complex functions of tracking operations, assessing performance, predicting outcomes, and delivering competent service to the end customer, we must understand the core competencies that a logistics operation will want to achieve. These include distribution design, sourcing and management, supply chain and supplier management, supply chain continuity planning, and transportation sourcing and management.
For any core competency to be achieved, an initial process of evaluation of the existing process, review of operations, and projections for the future become important functions. These make logistics perfectly competitive in its quality of execution. Big Data not only offers the possibility of storing a huge amount of information related to logistics from various sources, but also the tools to do decisive activities like data analysis, statistical report creation, and creating data-based customized predictive models.
Some noteworthy benefits of applying Big Data in business logistics are as follows:
Route Optimization And Last-Mile Efficiency
Route optimization involves choosing the most effective and cost-effective route and mode of transporting logistics. Using AI algorithms, old trip-sheets as well as real-time GPS data and information like weather forecasts, holidays, and delivery sequence can be leveraged to estimate the optimal time of delivery for each shipment.
AI platforms that use Big Data can optimize the delivery route of each delivery vehicle in real-time. The cost and time savings and multiplied efficiency of logistics operations come as the obvious results. Data-laden dashboards enable logistics facility managers to make informed decisions as they have a tab on even information like the performance of drivers and facilities.
Optimization Of Warehouse Networks
The warehouses involved in a particular logistics operation are equipped with real-time data from automated systems that handle materials and smart-equipments. With such extensive data available, they can decide the optimal route for forklifts and clamp trucks that handle inbound and outbound cargo. This results in faster movement of materials and savings in fuel along with safe transportation of goods.
Even predictive AI algorithms and analytics can help logistics companies improve resource utilization and productivity at warehouses as well as distribution centres. Other benefits of Big Data in warehouse networking optimization include:
- Aggregation of customer demand
- Management of inventory
- Simplification of distribution networks
- Prompt allocation of manpower
- Mapping warehouse and equipment capacity and planning distribution accordingly
Consolidation Of Freight
As Big Data involves AI models that help make data-driven decisions and insights into logistic operations from several angles, there is scope for consolidation of shipments that reduces cost, saves transport time, and helps to deliver improved customer service. This is possible as AI models offer insights into:
- Volume and number of shipments by location
- Preferred time frames for delivery
- Pre-transport requisites to consider like season and climate
Big Data systems integrated with AI can maximize capacity utilization. This can be seen in how the system decides shipment types by the size and weight of the objects of the consignments. Even damage claims can be analyzed across routes for delivery and transportation modes. Rules-based AI can detect errors and frauds as it tracks the events in the supply-chain and the documents involved.
So, in all, Big Data’s big role in optimizing logistics and streamlining logistic business operations is only going to be more significant and even inevitable in the days to come.