In recent years, the manufacturing and logistics sectors have faced significant challenges, including disruptions to the supply chain and high energy costs. The 2021 invasion of Ukraine by Russia put additional strain on global logistics businesses, which were still in recovery from the COVID-19 pandemic.
However, despite these challenges, there have also been some positive developments, exacerbated by the digital acceleration in the logistics sector. AI is gradually transforming our industries and transport isn’t an exception. A recent study carried out by Accenture of 600+ logistics executives and industry experts indicated that a vast majority agrees that failing to digitally transform the business is detrimental for the business and that, overall, the industry still lacks a clear plan for implementing it.
Source: Accenture.
Meanwhile, many companies still struggle with building the resiliency necessary to prepare for potential future difficulties and events that may further challenge the sector. Gartner predicts that most companies will fail to enable that resiliency by 2026. One of the reasons behind it is the lack of clarity as to which technologies will proliferate and remain in use.
The application of AI in logistics and other industries isn’t a new phenomenon. Multiple businesses are already benefiting from various, AI powered solutions to bring greater efficiency, accuracy, and cost savings to business. In this white paper, we offer an introduction into the multifaceted advantages of applying AI in logistics.
Increasing efficiency
There are multitude of ways in which applying AI to logistics operations can bring a significant increase in efficiency. AI can be used to optimize routes, allowing companies to choose the best delivery path for every order while reducing delivery time and fuel consumption. Accenture predicts that AI will increase business productivity by 40% by 2035.
AI algorithms are able to analyze real-time data related to traffic conditions, weather, and vehicle capacity and determine the best route for a delivery truck to make multiple stops in a single trip taking all of these factors into account. With that, logistics managers can avoid making unnecessary trips and use vehicles to their full capacity.
Moreover, when combined with computer vision systems, AI can track the inventory and assets in real-time, which can be vital to reducing the risk of errors in order processing, or inventory losses and theft.
Finally, repetitive tasks related to logistics management can also be automated with AI. This not only means you’ll see a significant drop in human-made errors, but will also have more human resources available to handle more complex tasks.
Improving operational accuracy
Errors in order fulfillment may occur due to different reasons, i.e. human error, lack of verification process, mistakes made by the drivers or poor route planning. Companies should invest any means necessary to eliminate such errors, and that concerns retail companies specifically, as a recent study found that 85% of customers would stop shopping at a retailer after poor delivery experience.
AI can significantly improve the accuracy of logistics operations and eliminate errors. The technology can be applied to automate data entry, reducing the risk of errors that can occur on manual input, ensuring that inventory levels, shipment information, and other data are up-to-date and reliable.
With real-time tracking, managers can see data on the movement of goods, which allows them to track shipments in real-time and identify potential delays or issues before they occur.
Finally, AI can also be used for quality control which is also vital for accurate deliveries. AI mechanisms can help identify potential issues with shipments, such as damage or missing items, allowing managers to take corrective action before the shipment is delivered.
Enhancing predictive analytics
The logistics industry is already collecting vast amounts of data and, with AI, you can integrate that data to create a comprehensive view of your company’s supply chain.
When combined with machine learning algorithms, AI can identify patterns in the data and offer predictions for future demand. Examining historical data on sales and market trends is extremely helpful in forecasting and adjusting inventory levels accordingly. This can help logistics companies optimize their supply chain and reduce costs associated with overstocking or stockouts. Ultimately, increasing order fulfillment rates.
Improving predictive maintenance
Predictive maintenance is a technique that allows to predict when maintenance is required before an equipment failure occurs. In logistics, it is used to monitor the condition of vehicles, warehouse equipment and other machinery to prevent unexpected breakdowns. This typically involves the use of IoT sensors, which can be installed on equipment to collect telemetry like overall performance, temperature, vibration, and other parameters.
Using AI to monitor performance of machines gives managers visibility into the condition of assets (including anomalies such as overheating, vibration), allowing them to detect potential issues before downtime or failures occur. What’s more, AI and ML algorithms can learn from past equipment failures and predict when similar issues are likely to occur.
This knowledge helps to plan maintenance schedules in a way that prevents unexpected breakdowns. Improved maintenance system also translates to eliminating shipping delays and inefficiencies that may be caused by equipment downtime, not to mention the costs related to repair or replacement. as well as improved safety for warehouse staff.
Real-time asset-tracking
AI-powered real-time tracking is a vital asset for any business that involves logistics operations. Above all, it offers accurate and actual information about the location and status of shipments, which is crucial for monitoring the progress of order fulfillment and making better-informed decisions.
Real-time tracking systems are also capable of sending automated alerts to management when shipments are delayed or encounter other issues. With that, companies can take corrective actions quickly so that the impact on operations is minimal.
Finally, real-time tracking can help logistics companies maintain better communication with their clients. AI-powered tracking systems can send automated messages when shipments are delivered, delayed, or when delivery attempts were made unsuccessfully, due to e.g. absence of the recipient at the address.
Automating repetitive tasks
Application of AI to automate repetitive tasks isn’t a novelty. Businesses across various industries are already benefiting from this technology to process rules-based activities, such as order processing, invoice processing or data entry.
In the logistics industry, automation can enhance operations in numerous ways. AI-powered robotics can automate warehouse tasks such as inventory management, order picking, and package handling. This reduces the need for human intervention, improving efficiency, and reducing the risk of errors. Additionally, robots can work 24/7, without breaks, increasing throughput and reducing labor costs.
Natural Language Processing is increasingly being used to automate tasks such as data extraction and document processing. For example, NLP can be used to extract data from shipping documents and input it into a logistics management system. Chatbots can be used to automate customer service tasks, such as answering frequently asked questions and providing shipment status updates.
Image recognition mechanisms powered with AI can be applied to automate product control and inspection, speeding up the process and performing it more accurately than a human being would.
Finally, AI-powered autonomous vehicles are increasingly being applied to transportation and delivery (especially last-mile). For example, self-driving trucks can be used to transport goods between warehouses and distribution centers. According to PwC, this technology is likely to have the largest impact on the logistics industry, as long as it overcomes the challenges resulting from the lack of efficient autonomous fleet management software.
Greater profitability
All of the advantages stemming from applying AI to logistics operations help to increase business profitability. For example, AI-powered systems can optimize delivery routes to reduce the distance traveled and the time required to complete deliveries, leading to lower fuel costs and increased efficiency. A great example is the UPS Orion system, which the company uses to optimize its routes following a strict ‘no-turning-left’ policy. More on this in our article on building a GIS-based Decision Support System for logistics.
AI can also reduce processing times and increase throughput. Automated order processing reduces the time required to process orders and improves the speed of deliveries, allowing businesses to process more deals. Using AI to manage the inventory and forecast demand helps to reduce excess inventory and associated holding costs, and prevents stockouts and associated costs related to e.g. speeding up deliveries.
Enhanced customer experience
AI can also be applied to improve customer experience in logistics in a number of ways. First of all, AI-powered chatbots and voice assistants can improve communication and customer service, offering customers a solid mechanism for self-resolving issues. AI mechanisms that incorporate NLP (which we have mentioned when discussing automation) can understand customer queries and respond appropriately, providing real-time updates on the status of shipments, for example.
Competitive advantage
The use of AI-powered technologies and systems in logistics is growing rapidly. According to a report by ResearchAndMarkets, the global AI in logistics market is expected to grow with a CAGR of 24% between 2021 and 2027. Many large logistics companies, such as DHL, UPS, and FedEx, are already using AI-powered systems to improve their operations
Many startups and smaller logistics companies leverage AI to gain a competitive advantage in the marketplace while multiple companies continue to stay behind.
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