The Trend of AI in Logistics and Supply Chains - Applications, Advantages, and Challenges

Artificial intelligence (AI) is one of the most transformative technologies in modern history. It helps businesses around the world, improving efficiency and optimizing resources. AI has also found its way into logistics and supply chains, where it offers many benefits to companies willing to adopt emerging technologies. Artificial intelligence in the logistics industry is a developing field that can change how companies operate. Artificial intelligence in supply chains is vast, with many different applications used by various businesses worldwide.

Some small-scale machine learning solutions are being used to improve operations for smaller companies that want to stay ahead of the competition. Industry leaders are working on state-of-the-art solutions for autonomous vehicles and other impressive solutions. These innovations offer benefits such as increased efficiency of management tasks like order fulfillment, improved inventory accuracy, decreased delivery times, and more accurate forecasting models.

Let’s take a look at how AI can be applied in logistics and supply chain management!

TL;DR

• AI applications in logistics include supply chain management, smart freight matching, route optimization, last-mile delivery, dynamic pricing, autonomous vehicles, smart roads, automated warehouses, and inventory management.
• Implementing Artificial intelligence have many benefits on various levels like planning, procurement, production, warehousing, logistics and distribution, marketing and sales, back-office operations.
• To achieve a mature level of digital transformation, companies should take into account the following aspects: IT infrastructure, data management, AI skilled personnel and advisors
• AI can optimize routes, improve last-mile deliveries, and enable dynamic pricing strategies.
• Contact nexocode AI experts for AI solutions in logistics and supply chain management.

What Does AI Mean for Logistics Companies?

Applying AI in the production environment is a final step on the company’s road from digital transformation to AI maturity. It is not uncommon, though, that some companies are still struggling on their way towards digital transformation, not to mention the usage of complex technologies such as AI, machine learning (ML), or deep learning. To achieve a mature level of digital transformation, companies should consider the following:

• IT Infrastructure - The company’s IT system must be flexible enough and able to incorporate new technologies;
• Data Management - Data sources need standardization and proper pipelines applied for real-time data;
• AI Skilled Personnel and Advisors - AI usage requires data scientists to interpret results and communicate them to relevant stakeholders responsible for managerial decision-making.

Recent studies found that the logistics sector is one of the sectors with the most significant number of companies already making active use of machine learning processes. The main reason behind this is that logistics companies are well aware of their need to innovate and transform to remain competitive.

Over half of the logistics companies worldwide have begun digital transformation initiatives, with much more planning within two years. AI can be a powerful tool for businesses as it offers tremendous advantages over traditional methods. Machine learning technologies allow supervisors to automate time-consuming processes such as forecasting demand or optimizing routes. These automated solutions often lead to better results than those achieved by human employees due to machines’ ability for high-speed processing, relative objectivity, access to vast amounts of data sources, or even lack of subjective bias toward specific alternatives.

How Can AI Solve Logistics Problems and Generate Value? Applications of Artificial Intelligence in the Logistics Industry

AI is a powerful technology that can be used for various purposes. One of the main fields offers tremendous value in logistics and supply chains, which deal with complex processes such as managing inventories, distributing goods to warehouses, or controlling transportation routes in real-time.

This section will take a look at some machine learning applications with immense potential for logistics, transportation, and supply chain sectors.

Supply Chain Management

AI can be used in SCM for various purposes. First, AI systems can manage big data at high speeds, thus making them perfect tools to optimize operations based on large quantities of relevant information. As mentioned previously, machine learning solutions can process vast data sets and produce accurate predictive models that enable businesses to be more efficient and precise when forecasting sales, allocating inventory or managing transportation routes.

Another way in which AI can be applied to supply chains is through real-time 360 visualizations and supervision. This kind of automation allows companies to monitor the progress made by employees, machines closely, or transportation fleet, for example, detect problems before they arise or take action immediately when a critical event occurs. These are perfect tools, particularly for quality control purposes as well as resource management and risk assessment.

The use of AI in SCM can contribute to multiple benefits for companies willing to adopt innovative technologies. Applying AI in logistics allows businesses to contain costs by improving efficiency and reducing product or service lead times.

Related case study: Developing a logistics platform offering real-time visibility and integrations with different carriersOne of our clients was seeking to improve the global supply chain optimization productOur challenge? Providing visibility and data transmission for maximum efficiency and control. We supported solution development for end-to-end execution of logistics activities in Supply Chain Management at the PO/SKU level, including PO creation, stock management, suppliers and distributors management, consolidation and load planning, carrier allocation, documentation, and final delivery. Read more about this case study.

Smart Freight Matching

Shippers use various business networks to find the best carrier for their goods. These platforms allow shippers and carriers alike to post information about available transportation routes, capacity, rates, or other aspects that might be of interest to potential partners. The matching process between a given set of parameters posted by shippers and those provided by different freight companies can be done through algorithms involving machine learning abilities to recommend the most suitable match based on historical data collected from previous transactions.

By leveraging ML technologies, Transportation Management Systems (TMS) can provide accurate recommendations tailored to customers’ needs and preferences. This saves time spent on searching manually while ensuring better results than traditional methods used today.

A similar approach can be utilized within a single company planning shipments with their fleet or subcontractors’ fleet.  In this case, the company might be interested in finding out which fleet units and carriers would match best, considering capacity availability. This is a complex task due to various variables such as routes, type of goods, or even time windows that should not overlap with other shipments already planned for a certain period.

Using ML algorithms enables TMS systems to recommend optimal combinations based on available data. The system can learn from previous results and use them for future planning purposes while also identifying possible errors made by human employees when matching one given set of parameters with others during the manual selection process.

Related case study: Delivering a dedicated IT system to manage and sell freight deals and plan transportationA major logistics company approached us to create a dedicated IT system to handle their core business process – managing and selling logistics deals.Our challenge? The key challenge in the logistics sector is cutting the time of concluding deals to an absolute minimum. The tool has to be very responsive and help in the smart matching of carriers and freight, fleet management, and other logistics operations. The platform helps shipping agents minimize fuel consumption, maximize operational efficiency, and optimize fleet performance by matching multiple loadings on a similar route with a single carrier.  Read more about this case study.

Route Optimization

Companies can optimize routes through artificial intelligence, which directly impacts the trucking industry, allowing better utilization time and fuel usage due to shorter driving distances between destinations while avoiding congestions on roads by adjusting delivery times more precisely based on demand fluctuations across different regions. Advanced systems also allow logistics companies to manage and coordinate thousands of vehicles simultaneously, which means they can better allocate resources, decrease fuel consumption, and improve vehicle utilization.

AI technology in transportation management systems uses machine learning models to predict customer demand to match it with available transport capacity and join some of the deliveries together. This also allows logistics companies to plan their deliveries more accurately by considering deviations from historical trends or sensing traffic volumes based on unplanned events such as road closures due to accidents. Read more about parcel delivery optimization here.

Related case study: Implementing AI model to optimize routes and timelines of deliveriesA company from the logistics sector approached us to create a custom AI model that optimizes routes and the scheduling of deliveries.Our challenge? The key challenge here was to prepare a dedicated AI-based system designed for carriers to optimize delivery time depending on the destination address. Thanks to the model we managed to reduce failed and late delivery rates by 30%. Read our detailed case study of this project.

Efficient and Sustainable Last-mile Logistics

AI is also being used to improve last-mile deliveries. This sector offers a high level of potential for growth as it requires an effective and efficient operation that can reduce costs while increasing customer satisfaction rates. AI-based systems are especially useful in urban areas where traffic congestion is a significant issue impacting final delivery times negatively.

To cut down on transportation costs and improve customer experience, companies responsible for last-mile logistics are heavily investing in innovative solutions for route and scheduling optimization and autonomous logistic units. Several startups worldwide are offering new solutions for the final mile delivery market, such as autonomous drones capable of delivering small packages over short distances with a single click or without having any human intervention at all, which represents a significant advantage compared to traditional transportation methods that require an individual onboard during the shipment processing stage. These machines don’t need rest, and they’re always ready to do another trip saving time and money.

Dynamic Pricing Models

Supply chains are becoming increasingly complex, and as a result, logistics companies are under pressure to find ways to optimize their operations. One way to do this is by using a dynamic pricing strategy.

A dynamic pricing strategy in logistics can help companies to save money by reducing the need for manual price adjustments. It can also help businesses to be more strategic in their pricing by taking into account factors such as competitor prices, supply and demand, and customer psychology.

AI-based dynamic pricing software can be used to monitor these factors and make changes to prices accordingly. This will allow businesses to respond quickly to changes in the market and maximize profits. In addition, AI can also help businesses to identify opportunities for price discrimination and optimize their pricing strategies accordingly.

When used correctly, AI-based dynamic pricing can be a powerful tool for increasing profits and improving efficiency in the logistics industry. Read more on dynamic pricing models and their use in logistics here.

Autonomous Vehicles

The most impressive and hyped use of AI in the transportation industry is automated vehicles or self-driving cars. The concept is already 100 years old, and since the beginning of 2016, autonomous vehicles have been tested on public roads worldwide, with notable examples from Waymo, Tesla, and Google driverless cars. Firms such as Starsky Robotics and Otto have focused on autonomous trucks. Automation of trucks is critical due to the improved safety aspects (truck accidents make up about 6.5% of total accidents, with twice the percentage of fatal crashes) of these heavy vehicles and the possibility of fuel savings through fuel savings platooning.

AI technologies are used for perception (i.e., using sensors and computer vision to capture information about surroundings) as well as path planning where artificial intelligence algorithms are needed to process the incoming data and make decisions based on it while taking into account various parameters such as other road users, weather conditions, etc. This enables autonomous vehicles to recognize objects and plan optimal paths considering all possible scenarios that might occur during a specific journey. The idea is to build self-driving cars that can be taken manually by drivers and self-driving trucks that can operate unmanned with built-in route optimization.

Smart Roads and Pavements Systems

Artificial intelligence and IoT technologies power the design and development of integrated roadways based on smart pavement systems. Embedded sensors turn roads into digital networks, allowing automatically to collect and analyze data from road surfaces. Data from smart roads is then sent wirelessly to the cloud, where AI algorithms can process it. This enables companies to enhance their logistics management capabilities and monitor road conditions in real-time, which means they can better prepare for an emergency event such as snowfall or flooding, reducing the risks of accidents and other dangerous situations that might affect traffic.

Automated Warehouses

Autonomous vehicles, like the ones mentioned in the previous paragraph, are not the only places for automation. Nowadays, warehouses are heavily impacted by impressive AI systems and devices. Autonomous robotics for warehouse order fulfillment and other warehousing operations is already used by several companies like Amazon and Ocado.

At the same time, AI is used for automated classification and identification of goods stored in warehouses and for monitoring current inventory levels. The task requires artificial intelligence models to be trained with data collected from previous pickings by employees based on barcodes or RFID tags attached to products. Algorithms can identify these items using computer vision while taking into account information such as product dimensions, weight, etc. The automated classification of goods and labeling where products are stored can be trained with data collected from employees describing different types of goods. This way, AI can recognize and locate these items within the warehouse without human assistance. Warehouse automation for inventory processes reduces costs related to human errors and increases speed and accuracy when picking orders, increasing customer satisfaction rates that directly impact business revenue.

AI solutions used in warehouses can also improve employee safety. It’s less likely that an accident will occur if AI robots are in charge of operating hazardous equipment and storing inventory in hard-to-find places. Even if it does happen, a human being will not be in danger. Automated warehouse and robotic equipment is not the only way to make a warehouse working environment less dangerous. AI technology based on computer vision systems can track employees working and monitor various safety issues. Warehouse automation coupled with image recognition can identify suspicious acts such as unauthorized entry or work interruption. They can alert when an employee is not wearing a helmet, when a cart or dolly is left behind, or when no supervisor is in a specified area. The ability to identify postures without continuous monitoring and human supervision may help identify when an employee’s position that isn’t seen in typical work happens, such as when a worker has fallen or is hurt.

Inventory Management

Intelligent inventory management can guarantee that the right amount of items enters and leaves a warehouse. Order processing, picking and packing, and inventory monitoring are variables with which inventory management may become time-consuming and prone to error. Accurate inventory management can also assist in minimizing overstocking, undersupply, and unexpected stock shortages.

Artificial intelligence systems can organize products. These devices can scan and report a shipment as soon as it enters the warehouse, keeping track of your inventory. This operation might be tedious and time-consuming for workers in warehouses. Still, when machines replace human workers, the process can be completed faster, and employees may have more time to do activities that only a person can perform.

An effective warehouse is an essential element of the supply chain. Automation may help in the timely storing and retrieval of goods from a warehouse while also ensuring a trouble-free delivery to the consumer. AI systems can also speed up complex procedures and simplify complicated processes faster than humans, saving time and operational costs thanks to lowering workforce requirements.

Planning Distribution and Securing Supply Chains

Inventory processes are a crucial task in supply chains as they can determine the success of a business. To manage inventories, companies need accurate forecasting models that will allow them to make decisions based on demand and market fluctuations. Companies can apply artificial intelligence to procurement, production, distribution, sales, and other massive supply chain data lakes to control the shipping process, supply constraints, production scheduling, and dynamic allocation.

Highly accurate demand forecasting is not a novelty anymore in various industries, but what about forecasting distribution flows for supply chain optimization? Artificial intelligence applications allow supply chain organizations to perform deep analysis on their existing customers’ behavior by utilizing insights in past demand trends to create forecasts. With the help of machine learning, companies can make better decisions when it comes to distribution planning and securing supply chains.

For instance, machine learning methods allow adjusting stocking levels depending on actual customer demand and avoiding all forms of overstocking or shortages. To achieve that, AI-based solutions utilize both supervised and unsupervised ML algorithms.

AI-based stock management and distribution solutions allow real-time predictions through unsupervised learning methods such as predictive analytics. Predictive modeling enables analysis of past data points to identify signals and historical trends, enabling more accurate demand forecasting than those achieved using traditional statistical methods. This approach’s advantage lies in its ability to detect patterns even if they are hard or impossible to describe by human employees due to high complexity. The method brings numerous benefits, including higher efficiency and lower costs.

Inventory optimization based on predictive analytics also falls into the category of AI applications that can bring significant benefits by predicting and preventing supply chain disruptions. Predictive analytics solutions help manage demand volatility and have shown promising results for optimizing stocking levels depending on demand prediction across all warehouses involved in a distribution network.

Related case study: Optimizing drug distribution activities to hospital pharmacies networkTo improve current large-scale procurement processes, a pharma company approached us to use applied analytics to stock and distribute drugs among US hospitals.Our challenge? Maximizing savings by streamlining the procurement of medication across the hospital network and their pharmacies. Read more about this case study.

Predictive Maintenance

Predictive analytics can also be applied to predictive maintenance of equipment, thus reducing the costs associated with downtime. Companies that are struggling make their workers accountable for faulty machines and pay heavy fines when deadlines or safety regulations aren’t respected. AI makes it possible to understand what’s happening inside a device by using data collected from different sensors without being directly connected. Artificial intelligence algorithms are trained with this information and provide an alert if there is something wrong before any severe damage occurs, preventing unnecessary expenses related to repairing broken parts or other hazardous situations that may endanger human lives.

In addition, predictive maintenance is used not only on equipment but also on warehousing robots and transport units such as ships, trucks, forklifts, etc.

Quality Assurance to Improve Customer Experience

Logistic companies are also using AI technology for automated visual inspection of products before and after packaging. This way, algorithms can recognize if a product has been damaged in transit or when workers stored it. The damage may vary from minor scratches to broken parts, which must be replaced with new ones so that customers receive undamaged products they paid for. Artificial intelligence models trained with data collected during the whole process help improve business processes while increasing customer satisfaction rates resulting in higher revenue numbers at the end of each month/year.

With the use of computer vision algorithms, fully automated visual inspection is possible, thus reducing costs related to manual labor and data processing time needed when employees analyze images looking for defects manually. It’s less likely that human mistakes will occur when machines are in charge of looking for and identifying any issues.

Automating Routine Tasks - AI-Powered Robotic Process Automation

By using a technology called cognitive automation, companies are able to automate back-office operations that would otherwise require human supervision. Human employees can be assigned more interesting tasks or even replaced with AI-based solutions as they offer higher efficiency and objectivity when compared to humans. While automating back-office tasks may bring benefits for several years, it is crucial to focus on individual applications and consider the entire business process to find areas where AI could improve results.

Robotic Process Automation (RPA), also known as intelligent automation, is all about the use of software to automate back-office tasks and other company processes that are repetitive in nature. With RPA, companies can benefit from increased accuracy and speed while reducing human errors. This technology uses ML algorithms capable of understanding instructions given by humans via a graphical interface or any other type of electronic data processing system, including CRMs, ERPs, etc.

Natural language processing (NLP) is another solution that has the potential to automate routine tasks in the logistics industry and supply chain management by understanding, processing, and generating written or spoken text. NLP is a branch of artificial intelligence that aims to emulate human interactions with machines by using advanced data mining, text processing, and cognitive automation capabilities. As logistics sector is filled with various documents like bills of lading, CMRs, proofs of delivery, purchase orders, invoices, and other unstructured and structured data, creating a need for seamless processing.

For more information on the subject, head over to Document Processing Automation for Logistics Data with AI-based Solutions.

Advantages of Artificial Intelligence in Logistics and Supply Chains

Let’s go back to our list with potential business benefits that AI brings into play: improved inventory accuracy, decreased delivery times, increased customer service levels thanks to better forecasts - these are only a few examples of how companies worldwide have begun integrating artificial intelligence technology into their systems resulting in higher profits and market share growths. The benefits of implementing AI in logistics and supply chains are vast, and they can be listed along the supply chain operation flow:

• Planning - Predictive analytics are successfully used in demand planning to identify signals and historical trends that enable more accurate forecasting. It allows for full transparency and risk adjustment through end-to-end margin optimization.
• Procurement - Digital transformation allows for complete data integration with suppliers. Raw material recipes based on forecast process. Predictive analytics and neural networks provide advanced automated bidding capabilities to improve supplier selection.
• Production - The use of ML algorithms allows companies to produce better forecasts that can decrease overstocking or shortages which significantly increases the effectiveness of production planning and scheduling systems.
• Warehousing - ML solutions in warehousing and packing provide benefits through automation, increasing productivity, efficiency, and level of quality control, and reducing costs, time, and recruitment needs. Some solutions also bring additional safety benefits by making warehouses more automated through robotics and unsupervised safety surveillance. AI-based solutions can make predictions on future demand patterns and optimize stocking levels to ensure that the products will be available in time.
• Logistics and Distribution - Companies that implement artificial intelligence in logistics and distribution areas can expect multifunctional advantages, including dynamic routing optimization based on historical data for a better allocation of vehicles and minimized fuel usage. An example of such a solution can be seen through deep learning algorithms that help optimize load balancing among different trucks considering multiple factors such as delivery times, distance, number of deliveries, etc. AI models help in the intelligent pricing of transportation and carrier services. Driverless trucks and other autonomous vehicles are a significant and impressive part of the AI technology that, together with a global smart road system, will definitely revolutionize the logistics industry.
• Marketing and Sales - Besides the supply chain optimization, AI application in marketing and sales has brought significant improvements through various methods. AI-based solutions can be found across marketing and sales operations to obtain benefits such as increased customer experience through better logistics services and help chatbots, improved operational efficiency, higher profitability, etc. ML algorithms give retailers an opportunity for real-time predictions improving sales forecasts significantly compared to traditional statistical methods resulting in a significant reduction of operation cost due to lower stocks requirements (reduce carrying costs). Dynamic pricing models enable to price the logistics services properly and get even better margins. Demand prediction is also used for marketing products that are on the rise and need an additional boost to improve sales.
• Back-office Operations - Although not visible at first glance, back-office operations constitute a significant part of a logistics company’s operations costs. AI brings huge benefits to back-office automation. There is a substantial improvement in the efficiency of invoicing, order processing, and accounting that can be achieved through automation. All transactions will be done automatically without human specialists’ intervention or with minimum supervision.

How to Become AI-Ready and Kickstart AI Adoption?

To become AI-ready, companies should have a clear vision of how they want their business operations to be improved by machine learning and data science algorithms. This can be achieved through gaining insights from the overwhelming amount of data that is currently available.

To minimize the risk of not reaching the desired ROI on your AI investment, we suggest starting small with AI Design Sprint workshops and Proof of AI development. AI Design Sprint is a hands-on experience where, together, we identify potential AI use-cases for your business and explore the business opportunities available to you. We have created a set of tools for each step of the design-thinking process to help our clients look deeper into their data and processes and turn AI into social, user, and business value. With Proof of AI development, you don’t need to develop the whole AI solution. Still, you verify already available data and the idea possibilities on a tight budget and in a short time. For AI development, it is crucial to embrace the culture of iterative experiments. Each experiment focuses on evaluating data haves and the creation of an AI model. AI Proof of Concept helps consider the benchmarks and the opportunities of a full-grown model working with new data at scale. It lets you decide at an early stage whether AI on production would give you the desired value and is worth the investment.

The Future of AI in Logistics and Supply Chain

We expect that AI systems based on deep learning algorithms will be widely used in the near future by companies willing to optimize their business operations. These ML models should help companies process big data and make decisions with greater accuracy.

The use of AI-based applications is expected to increase significantly, primarily due to the increasingly competitive environment and the everchanging global economy forcing businesses to seek new ways to achieve better results. These technologies provide an opportunity for faster decision making improving overall efficiency. The most significant benefit of deep learning algorithms is the level of automation it enables, which has a substantial impact on reducing costs involved across all areas within logistics and supply chains (transportation cost reduction). We believe that there are endless possibilities where this technology can be applied among different domains providing benefits to society. Read more about AI in chemical supply chain here.

The most important thing here is that logistics companies should focus on redefining their business strategy to take advantage of this emerging technology and use its capabilities for achieving better results (reduction of operational costs, increase in customer satisfaction). The future of AI lies in how both individuals and businesses will use it - there are many opportunities provided through these technologies which can be easily realized when adopted efficiently.

AI Design Sprint workshops and Proof of AI development provide clients with an opportunity for kickstarting their AI journey without significant investments in time or money while minimizing the risks involved in developing entirely new solutions from scratch. These tools enable analysis within pre-defined parameters to achieve your desired goals at each stage before moving forward with more significant investments.

I hope this article was helpful. If you’re interested in discovering how AI can help your company contact us here at nexocode today! Our experts would love to answer any questions or concerns about Artificial Intelligence.

References

How AI Changes the Logistic Industry - Towards Data Science

Succeeding in the AI supply-chain revolution - McKinsey 

Designing Resilience into Global Supply Chains - BCG