Transformation of logistics sector with AI and other modern technologies

Sumit Sharma, co-founder, GoBOLT writes about the challenges in the logistics sector and how artificial intelligence and machine learning could solve them.

Rapid technological development in the fields of big data, algorithmic development, connectivity, cloud computing and processing power have made the performance, accessibility, and costs of AI more favourable than ever before. The emergence of technologies such as AI (AI), machine learning and blockchain has transformed the chaotic and fragmented logistics market.AI is now following a similar path. It has now become an integral part of every future software system. In an increasingly complex and competitive business world, companies that operate global supply chains are under unprecedented pressure to deliver higher service levels at lower costs.

Role of AI in logistics sector
AI play a significant role to save time, reduce costs and increase productivity and accuracy with cognitive automation. AI affects warehousing operations such as collecting and analyzing information or inventory processing. As a result, AI helps in increasing efficiency and getting profit. AI is profitable for transportation. Due to IoT and AI, Self-driving vehicles bring changes to the supply chain and help reduce expenses in logistics. The capabilities of AI are seriously ramping up company efficiencies in the areas of predictive demand and network planning. Having a technology for accurate demand forecasting and capacity planning allows companies to be more proactive. Industry to modify how resources are used for maximum benefit and AI can do these equations much faster and more accurate than ever before.

The impact of Big Data is allowing logistics companies to forecast highly accurate outlooks and optimize future performance better than ever before. Providing clean data has become an important step for AI in logistics companies as many simply do not have usable figures to implement. It is very difficult to measure Efficiency gains as some companies generate their data from multiple points and multiple people. These data and figures cannot be easily improved at the source, so algorithms are being used to analyze historical data, identify issues and improve data quality to the level where significant transparency on the business is gained.

Factors which affect the logistics industries to use AI
Logistics service provider companies depended on third parties logistics including common carriers, subcontracted staff, charter airlines, and other third-party vendors to operate core functions of their business. This puts an increased burden on logistics accounting teams to process millions of invoices annually from thousands of vendors, partners, or providers. AI technologies can access information such as billing amounts, account information, dates, addresses, and parties involved from the sea of unstructured invoice forms received by the company. Global logistics and supply chain operators manage large fleets of vehicles and networks of facilities worldwide. In the logistics industry, keeping address information complete and current is critical for the successful delivery of shipments.

Often, large teams of data analysts are tasked with CRM cleanup activities, eliminating duplicate entries, standardizing data formats, and removing outdated contacts.AI and machine learning are used by many companies to inform and fine-tune core strategies, such as warehouse locations, as well as to enhance real-time decision making like availability, costs, inventories, carriers, vehicles and personnel. The main focus are on IoT and myriad other data feeds on achieving greater optimization and responsiveness across the whole of their logistics, supply chain and transportation footprint.

These new technologies bring truckloads of data, the transportation industry has been capturing data for years. A few years ago, trucking, rail and sea cargo began being tracked by satellite via telematics. AI will be able to maintain data platforms and create datasets to regulate patterns and anomalies. The data patterns are based on predictive analysis. Due to rapid growth of digitization more and, more companies are adding AI to their supply chain in order to maximize their resources by reducing the time and money spent on track how, where and when to send a package to a certain place.

The current technologies active in the sector exist in functional silos, having created information and execution troughs. Stand-alone technology solutions have restricted functionality and productivity by being completely human dependent, giving rise to redundant process coordination, increasing the transaction lifecycle itself, ultimately reducing efficiency and increasing costs. As the supply chains become complex supply nets, the variables and number of stakeholders change dynamically. The entire arrangement of transfer of data between systems is managed by technologies.

By the time such technologies are implemented, the set of variables change making the entire implementations redundant. Technologies provide an opportunity for different levels of optimization in manufacturing, logistics, warehousing and last-mile delivery that could become a reality in less than a year with the high set-up costs deterring early adoption in logistics. Demand delivery will help consumers to have their goods delivered where and when they need them by using flexible courier services. These providers customer experiences through conversational engagement and even deliver articles before the customer has even ordered them.

Sumit Sharma is the co-founder of GoBOLT, one of the leading technology-driven end-to-end logistics service provider based out of New Delhi.