- Artificial Intelligence
- Machine learning
- Artificial Intelligence
- Machine learning
On March 23, 2021, the global shipping industry faced an unusual and challenging situation. One of the world’s largest container ships, the Ever Given, got jammed in the Suez Canal, blocking the passage of cargo ships for six days, affecting countless businesses around the world. Suppliers were caught by surprise, as the blockage significantly slowed down supply chains. However, some issues could have been avoided If more companies were using AI in their supply chain management (SCM). The use of AI in supply chain technologies can bring order to all this chaos. Advanced AI and machine learning (ML) solutions are designed to make sense of information and transform it into valuable insights for the industry.
In general, the Covid-19 pandemic has revealed the need for agility and flexibility among suppliers more than ever and the environmental impact of supply chains has also been drawing a lot of media attention. These challenges require an urgent optimization of supply chain management (SCM) flows.
Forecasts from Markets & Markets research indicate that the supply chain sector will increasingly adopt AI and ML technologies, with the projected value of AI in the supply chain exceeding $10.1 billion by 2025, up from $527.5 million in 2017. AI is expected to play an influential role in solving problems within SCM, from procurement to sales simply because AI outperforms humans in the speed of processing huge amounts of data, understanding interconnections, ensuring transparency of operations, and making effective decisions.
A supply chain is a complex network that combines multiple functions. It includes logistics, manufacturing, purchasing, marketing, and sales. In many organizations, supply chain management has already shifted to dynamically and comprehensively optimizing the company’s global value rather than simply improving the performance of various local functions.
Today’s customers want personalization — they expect a supplier to predict their next purchase before they even understand they might want it themselves. So, an organization with a successful supply chain must be well connected to its customers.
Companies must comprehensively solve several problems to thrive and reach new buyers. First, suppliers of goods and brands must forecast demand across different product segments and geographic regions. The ability to dynamically identify trade-offs with thousands of interrelated variables and technical constraints is also essential.
Companies must ensure that their plan is completed on time and that it adapts, for example, to changes in demand, transport disruption, or production interruption. Finally, companies need to integrate AI solutions into the supply chain to deal with the constraints Covid-19 has handed to manufacturing.
According to market research done by McKinsey, 63% of CEOs report increased revenues from AI in their supply chains. AI enables companies to make intelligent predictions about a wide variety of topics. For example, forecasting the demand for a specific product or the replenishment of raw materials used in production processes. In addition, AI can be used to develop strategies using business intelligence tools.
Proper SCM can ensure the correct flow of goods in and out of the warehouse. Inventory is closely related to order processing, picking, and packaging of parcels and can also help prevent overstocking, understocking, and unexpected shortages. AI algorithms can predict and detect new consumption habits and predict seasonal demand which helps anticipate future trends in consumer demand while minimizing the cost of overstocking unnecessary inventory.
Warehouse automation is an integral part of the supply chain. It assists in the timely retrieval of goods from the warehouse and provides an unobstructed path to customer delivery. AI systems can solve multiple warehouse tasks faster and more accurately than humans and can simplify complex procedures and speed up work. In addition, AI-assisted warehouse automation significantly reduces the need for warehouse personnel, and therefore labor costs.
AI algorithms can also be used to improve workplace safety in terms of both workers and materials. The technology can analyze data on safety in the workplace and inform manufacturers of any potential risks and AI can record storage parameters and update operations with feedback for proactive maintenance. This helps manufacturers respond quickly and decisively to keep warehouses and employees safe.
For example, computer vision is able to identify pallet wear from wood texture and estimate when pallets have reached the end of their service life. This is especially useful given that there are around 5 billion wood pallets in use in the EU and the US.
The air freight industry is hampered by outdated data processing technologies and non-automated workflows. However, the pandemic saw the process of digitization of the industry kick up a notch. More and more initiatives began to emerge to improve logistics operations through ML. A group of researchers from the Qatar Institute has proposed an AI-based system designed to predict the weight received and the air traffic received. The study identified some systemic reporting gaps in the airline industry, indicating the need for better data for machine analysis.
Several companies are already working with AI to improve the way the air freight industry works. For example, American Airlines built a machine learning model that analyzed half a million air cargo records over a year to help predict which shipments were most likely to be no-shows. The system, which is set to run three days before each flight, allows American Airlines customer agents to identify shipments at high risk and to directly contact the shipper to confirm the shipment.
Sea transport might not be the fastest but is certainly the most cost-effective cargo transportation option. OECD data for 2021 shows that 90% of goods are transported by sea. However, the industry loses billions annually due to the slow delivery speed. Therefore, sea transportation can benefit greatly from the optimization of transportation and shorter and more productive stops at ports.
Hamburger Hafen und Logistik, a port operator in Germany, has introduced an ML-powered solution to predict dwell time for containers to reduce downtime in order to reduce the dwell time of each container arriving on site. This contributes to the more efficient operation of terminals and lower inventory storage costs. The database is constantly being updated, which gives developers a basis to expect long-term improvements in outbound traffic prediction accuracy and reduced latency.
Another world-famous port, Port of Montreal, has implemented AI to optimize freight planning. Among other projects, AI is used to evaluate how warehouse capacity, staff availability, and ship arrival times affect port turnover.
Another world-famous port, the Port of Montreal, has used AI to optimize the identification of vital cargo. This will ensure the rapid distribution of essential goods such as medical equipment and food. Among other projects, AI is used to evaluate how warehouse capacity, staff availability, and ship arrival times affect port turnover. The AI-based solution also aims to optimize the freight train schedules based on variables such as ship arrivals, wagon arrivals, warehouse capacity, and labor availability.
Today, many companies are already using AI-driven initiatives to optimize the ‘last mile’ delivery of goods. Amazon Flex calculates the number of anticipated deliveries to a given area and the AI-based technology calculates and allocates space in the delivery vehicle based on the weight of the goods and the volume of the parcels. The algorithm also takes into account the driver’s workload, traffic, and the type of building for which the delivery is intended.
Another logistics platform, LaMP, uses AI to combine shipments from multiple companies into the most convenient vehicle. LaMP, which covers Southeast Asia, makes it possible to define the means of transport available for the delivery of parcels.
Today, AI algorithms are improving logistics and supply chain management in many companies. Here are just a few well-known examples.
Have you heard of Flexible Vision? ML engineers from California have developed an AI-enabled hardware and software platform to spot product defects and identify items. The company proves that deploying AI solutions does not necessarily require advanced technological know-how. Any specialist can work with it, regardless of their qualification level.
Can you believe that AI helps even with the delivery of hot meals? Uber Eats feeds geolocation and traffic data to the AI system. The system, in turn, simulates various fields that allow predicting delivery times and calculating optimal delivery routes.
Geek+’s Smart Warehouse uses AI to represent warehouse operations in real-time. First of all, the goods arrive at the logistics center, where they are monitored and analyzed. The received data is compared with historical inventory data to determine the best method and location for storing items. The system then selects which robots are to be used, where, and at what time. Smart Warehouse also makes sure to get the firm’s employees involved. Technology assigns tasks to humans, enabling collaboration and communication between humans and machines.
AI will be able to provide teams with more detailed information with much greater frequency and detail than ever before. However, this detailing alone will not be enough to get more value out of AI supply chain solutions. Any significant investment in technology must be accompanied by organizational change, business process updates, and continuing education efforts. Only then will companies receive the expected return on investment.
AI can help facilitate timely delivery to customers by reducing reliance on manual efforts. Automated systems speed up traditional warehouse procedures, thereby removing bottlenecks in the SCM with minimal effort to meet delivery goals.
At Postindustria, we are able to empower you with advanced AI development solutions and build an AI system that will fit your technical requirements and specifications. Leave us your contact details in the form or reach out to our team and we’ll get in touch to discuss your custom solution.
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