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The relationship between supply chain and logistics has long been characterized by mixing, the use of the two as synonyms. For many today, their relationship to each other is not clear either. Although the theoretical definition makes it clear that Supply Chain Management is an activity that integrates key processes from the manufacturer to the end-user, creating a product, service, and information of value to the consumer.
In contrast, logistics is the supply chain management that part which plans, implements, and controls the efficient and effective flow and storage of products, storage, services, and related information, from the place of origin to the consumer, in order to meet consumer needs. Separation often causes difficulties. It follows from the definitions that supply chain management is more than traditional and more than modern flexible logistics, as its content significantly exceeds that of logistics. The supply chain is built on information systems and includes manufacturing, marketing and financing, strategic resource planning, business process relationships, risk-sharing, and the involvement of supply providers in product development.
Of course, the global direction of digitization and data analysis known as Industry 4.0 has a dramatic impact not only on production but also on services. By analogy, the literature uses Logistics 4.0 to identify changes that affect the logistics services sector. The transformation affecting the whole supply chain management is characterized by network connection, integration of Internet solutions into processes, targeted data collection at all levels, their online and batch processing and analysis, and the decisions made by artificial intelligence (AI) application.
Continuously collected data is the key to reviewing the big data effect. The new - data mining - methods are based on finding non-trivial correlations on large amounts of data. The starting point for this is the existence of properly collected, stored, and structured data. The first challenge is to structure and record data collected from logistics applications and tools online. With the logistics management software development of analytical tools and platforms, the integration of AI capabilities into the system results in more and more sophisticated solutions, and the systems are able to find answers to the increasingly complex problems of logistics service providers.
More and more businesses are committed to data-driven decision making. Another benefit of big data analysis is the optimization of resource consumption and efficiency. Some key areas that are also characteristic of logistics are worth highlighting.
AI already plays an important role in the supply chain. The impact of AI on standard logistics is to change the way goods are transported worldwide, either by predictive analytics or by large volumes of data, robotics, or autonomous vehicles.
Among the areas in which AI is revolutionizing logistics are the following:
AI in combination with the power of large volumes of data can significantly improve the transparency and efficiency with which logistics managers forecast demand and make capacity planning, allowing them to be proactive in allocating resources. Using AI to analyze historical and current tiles and to create predictive data, managers can use the information collected to anticipate the requirements for the number of semi-trailers and can mobilize the semi-trailers where they are most needed. This reduces operating costs. Also, AI can assess a resource situation and recommend allocations much faster than human operators can.
An interesting case is that of UPS, which used AI data to improve the route planning of its vehicles. They found that, in general, 10% of turns are to the left. Eliminating the left turns made by its drivers in right-hand traffic, it saved 38 million liters of fuel and delivered 350,000 more packages a year. This happened even though the drivers had longer routes planned.
Due to the use of AI, robots are increasingly replacing humans in the operations of finding, tracking, and transporting stocks in warehouses. For example, Ocado in the United Kingdom has developed a complex robot-based storage solution for supermarket chains. Its automated order fulfillment center in Andover, near London, uses robots to store and lift food along an aluminum grid or "hive" structure, with human employees overseeing it. Ocado has reduced its order fulfillment time from two hours in the old warehouse to five to 15 minutes in the new one.
AI can generate valuable operational data on product movements along a supply chain to dramatically increase operational efficiency. This knowledge helps organizations reduce operating and inventory costs while responding faster to customers and providing improved customer service standards.
Examples of AI-based technology that can improve the operations presented in the "Artificial Intelligence in Logistics" report by DHL and IBM are a robot that quickly sorts letters, parcels, and pallet deliveries, and a robot that performs visual inspections of goods using special cameras to identify any damage or repairs required.
Thanks to the analysis, all high-traffic, difficult periods can be predicted for the employees working in the supply chain, and the phenomenon of overtime and exhaustion can be prevented with proper work organization.
The integration of online GPS, weather, route information, fleet, and historical data into the system enables applications to create optimal transportation routes based on what the business is aiming for. One interesting implementation of this is the “no turn left rule” used by UPS. Based on the big data analysis, it has become clear that turning left in a large arc will cause the company an otherwise avoidable surplus in time, fuel, and emissions.
The automation that can be implemented in production, trade, and warehousing also increases the speed and accuracy of the processes, therefore the implementation of the logistics must respond to changes both inside and outside the plant.
As the spread of data analysis and data-based decision-making mechanisms permeates the entire business process, they are able to identify seasonality, stronger or weaker periods in turnover and utilization, and changes in production programs in the process of enterprises. This knowledge can also make logistics systems integrated into corporate processes more transparent, automated, and optimized.
The Supply Chain Management and modern, flexible, or "lean" logistics to each other are not synonymous. The logistics are only one component of the supply chain. The most important difference between logistics and supply chain management is the shift from a functional mindset to a process-based approach.
According to a report by Cerasis analysts, AI-based solutions in logistics and supply chains are becoming more numerous. The technology is available today and can significantly improve data quality compared to current low levels. The challenge is now the shortage of human personnel with the right skills to use and capitalize on this new and extremely interesting technology. But that is likely to change, according to technology solutions company Salient, as 64% of supply chain executives see data analytics as a top priority for their organizations.
There are several actions that can help you join the AI revolution.
The last 4-5 years have shown that collecting and analyzing large amounts of data from football to logistics offers incredible opportunities and previously unimaginable potential. Incorporating or omitting the big data analytics capability into production and/or service reinforces the well-known old rule: lagging behind is left out.
Post your project requirement with us and we will assist you to churn out the benefits of Artificial Intelligence and Big Data in your logistics business.