As logistics businesses are required to fulfill more deliveries within a shorter timeframe and at lower cost, it becomes obvious that they need a complete change of supply chain management. Luckily, artificial intelligence can offer such a transformation.

According to Meticulous Research, the demand for AI technology in supply chain management will grow, with a projected market size of $58.55 billion by 2031 and a CAGR of 40.4% from 2024. The statistics prove that AI in logistics will soon be an irreplaceable tool. It’s even more fair to say that implementing AI is already a primary goal for many companies to achieve. With this statement in mind, let’s explore what capabilities and benefits AI brings to logistics and transportation and what can be significant roadblocks for adopting this new technology in the industry.

Understanding the Role of Artificial Intelligence in Logistics

Artificial intelligence feeds on big data collected from logistics processes and, thus, can automate tasks, unlock new functionalities, ultimately streamlining processes and reducing expenses. The McKinsey report shows that businesses, which quickly adopted AI-driven supply chain management, experienced operational enhancements. Those are a 15% cost reduction, a 35% decrease in inventory levels, and a 65% increase in service levels.

McKinsey further highlights the improvements possible with AI in logistics. For example, thanks to forecasting with AI, it is possible to reduce supply chain’s errors to 20-50%. At the same time, warehouse and administration costs can be decreased by 5% to 10% and 25% to 40%, respectively.

Beyond cost savings and reduced error, AI helps: 

  • Optimize route planning, minimizing expediting deliveries;
  • Improve inventory management, reducing wasted resources;
  • Automate manual efforts, allowing employees to focus on strategic activities;
  • Elevate customer experience, streamlining deliveries.

AI capabilities used in logistics also impacts other industries with one of the prominent examples being manufacturing. As long as supply chain management driven by AI can supply manufacturers with real-time insights, factories can optimize inventory management, reduce storage costs, and ensure there are no potential material shortages. 

Willing to learn about the impact of AI and ML in manufacturing?

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Applications of AI in the Logistics Industry

Artificial intelligence brings significant advancements across a wide range of logistics operations. Many companies have already adopted AI to cover specific needs. Let’s delve into the most standout examples of how AI is being used in this industry.

Applications of AI in Logistics

Route Optimization

DHL, a global logistics service provider, uses AI-powered route planning software to improve its shipping process. The company’s system analyzes all delivery points on a route, prioritizing them based on urgency like critical medical deliveries or those needed before 9 am. It also factors in distance between stops to create the most efficient route sequence. DHL highlights that AI enables faster deliveries with less fuel consumption, while their customers benefit from more accurate delivery time windows.

Indeed, traffic analysis and optimization can lead to customer satisfaction. We know that for sure, as we leveraged AI for a transportation construction company. By integrating AI into the system, we made it analyze traffic patterns across construction areas, providing insights into how construction work impacts road traffic before, during, and after construction. Our efforts resulted in efficient route optimization for more than 500 contractors in the US.  

Proactive Maintenance Predictions

Paccar, the American company that designs and manufactures large trucks used in the transportation industry, leverages machine learning for predictive maintenance, forecasting truck repairs before they create disruptions on the road.

The company equips trucks with a combination of hardware and software that unlocks the power of their data for quality control. This translates into proactive maintenance, allowing them to predict and address potential issues. Now, companies can make informed operational decisions, ensuring their trucks stay on the road and their deliveries arrive on time.

Demand Forecasting

Amazon employs deep learning, image recognition and natural language processing (NLP) capabilities to achieve accurate demand forecasting with machine learning. Predictions of demands are achieved by analyzing large volumes of historical sales data to identify complex patterns, extracting insights from product images, and analyzing customer reviews and search queries to understand evolving customer preferences.

Furthermore, Amazon is exploring the application of deep reinforcement learning (DRL) to optimize logistics and supply chain operations based on the enhanced demand forecasts. The company states that DRL offers the potential to translate forecasting precision into cost savings.

Automated Warehousing Processes 

Honeywell, a company that develops safety and productivity solutions, came up with the AI-powered robotic system for order picking and sorting. The solution helps logistics service providers to increase efficiency and accuracy in warehouse processes as well as reduce human error. This is achieved by maximizing floor space and managing over 20,000 SKUs using shuttles.

The company emphasizes that this technology delivers a 40% increase in throughput, enabling warehouses to optimize labor allocation. This contributes to minimization of manual processes and significantly lower operational costs.

Supply Chain Management

Unilever uses AI to ensure a smarter and sustainable supply chain. The company’s data scientists and ML engineers fine-tune AI algorithms to check real-time data from suppliers, manufacturers, and retailers. Hence, this global FMCG company gains a holistic understanding of their supply network. 

One of the major benefits of this AI-driven approach is supplying alternative ingredients for products, mitigating potential disruptions, or stabilizing prices. This strengthens the overall resilience of the company’s supply chain. Additionally, integrating AI into Unilever’s system enables the development of more sustainable product formulations. By identifying unnecessary ingredients, this personal care and food production company reduced environmental footprint and streamlined manufacturing processes.

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Fraudulent Activity Detection

One of the most prominent examples of fraud prevention in logistics is UPS and their DeliveryDefense system. The global delivery company uses machine learning algorithms that analyze historical data and assign each location a delivery confidence score. This score takes into account various factors, such as past delivery attempts, time of day, location characteristics, and even weather patterns.

Such a fraud detection system is equipped with anomaly detection powered by machine learning. This means the system continuously monitors incoming data for deviations from the established patterns. 

Customer Support and Chatbot Implementation

A retail company Lowe’s developed a chatbot that can answer customer questions and assess their preferences. In such a way, chatbot generates personalized product recommendations. This not only enhances the customer experience but also increases the potential for relevant upselling and cross-selling opportunities.

From our experience with HaulHub, we know how to ensure fluency in the chatbot’s generated responses. We integrated a Retrieval-Augmented Generation (RAG) approach for chatbot conversations for providing contextual information. This approach allows us to achieve a seamless retrieval of the information from the company’s data that is later used to elaborate clear answers to inquiries. 

Real-Time Vehicle Monitoring

One of the most popular logistics service providers FedEx combines AI/ML with sensors for capturing autonomous cargo shipping data and leverage predictive analytics for harnessing data-driven insights. Their AI system tracks shipments by fetching data every two seconds to provide real-time information. 

By processing a vast amount of data with machine learning, FedEx enables a more transparent and predictable supply chain, bringing advantages to the company and its customers.  

Advantages of Integrating AI in Logistics

In logistics, AI completely transforms how companies operate. By integrating artificial intelligence into their operations, logistics service providers are unlocking multiple benefits that change how the way goods move around the world. Below is a closer look at some of the key advantages.

Advantages of AI in Logistics

Enhanced Operational Efficiency

According to Statista, a 2022 survey revealed that 60% of companies worldwide expect robotic process automation (RPA) to transform supply chains by 2025. Logistics companies can benefit from this technology that allows them to imitate human actions and automates repetitive, rule-based tasks. AI-powered robots can free up human employees by handling such processes as:

  • Inventory management since robots equipped with sensors and AI can monitor inventory levels, identify low stock, and generate automatic purchase orders;
  • Warehouse management as robots pick and pack orders, transport goods, and optimize storage layouts based on product characteristics and access frequency;
  • Quality control as vision systems can perform automated quality checks the accuracy of completing different processes;
  • Predictive maintenance since robots’ sensors can continuously monitor equipment health. 
Serhii Leleko: AI&ML Engineer at SPD Technology

Serhii Leleko

AI&ML Engineer at SPD Technology

“Besides RPA, AI algorithms can be also used for intelligent automation. AI performs data entry, order fulfillment, and shipment document processing faster and more accurately compared to humans.”

Expense Minimization

As said above, artificial intelligence can automate routine processes. That means logistics companies can streamline their operations and, as a result, achieve significant cost savings. The McKinsey Global Survey confirms this, as it is revealed that 44% of executives whose companies have adopted AI claimed that this technology has reduced costs. 

To guarantee costs reduction, AI helps companies to achieve:

  • Minimized fuel consumption by optimizing delivery routes based on historical traffic patterns, weather conditions, and real-time road congestion analysis;
  • Timely equipment maintenance by predicting potential supply chain problems based on vehicles sensor data analysis;
  • Supplying according to demand by gaining insights into historical sales data, customer behavior patterns, and market trends;
  • Reduced issues by automating repetitive tasks prone to human errors.

Improved Eco-Friendliness

Transportation of products entails huge fuel consumption. However, Accenture states that AI-driven data analysis can be used to minimize greenhouse gasses emission and air pollution. To be more precise, harnessing data from supply chains, weather patterns, and even consumer habits allows companies to optimize routes and supply processes to reduce the usage of fuel within the transportation and logistics industry. 

The companies can optimize fuel consumption and consequently improve their eco-friendliness with:

  • Forecasting demand to have enough stock to meet customer needs and avoid unnecessary resupply.
  • Predicting disruptions due to weather conditions since AI can suggest alternative routes or adjust delivery schedules to minimize delays caused by disruptions.
  • Eliminating unnecessary shipments as accurate demand forecasts minimize the need for last-minute expedited deliveries.

Elevated Customer Service

One of the most powerful functionalities offered to customer service by AI is personalization. Particularly, NLP and machine learning improve communication between businesses and customers by understanding contextual data, defining customer sentiment, and generating natural conversations. Equipped with these technologies, businesses can create chatbots that respond to customer queries 24/7 with appropriate information.

Additionally, artificial intelligence transforms customer service for logistics companies with: 

  • Real-time tracking with accurate estimated time of arrival (ETA) to keep customers informed;
  • Automated notifications about potential delays or disruptions;
  • Predictive issue resolution based on data about customers, shipments, delays, etc.

Optimized Labor Schedules

AI doesn’t replace human workers in logistics. Instead, it empowers them. McKinsey reports that AI has enhanced the productivity of field workers by 20% to 30% and schedulers by 10% to 20%. Smart scheduling promotes effective and fair labor planning. With AI systems, logistics businesses receive a consistent approach to addressing productivity of personnel. 

This can be achieved thanks to:

  • Elimination of human bias, when AI creates objective schedules that distribute workloads according to responsibilities;
  • Automated analysis of such factors as employee skills, workload demands, and even traffic patterns for delivery drivers.
  • Proactive response to disruptions as AI analyzes real-time data on traffic delays, weather events, or even equipment issues for allocating additional human resources. 

Obstacles in Embracing AI for the Logistics Industry

Artificial intelligence presents a powerful toolkit for transforming logistics companies, but significant challenges can impede its adoption. Let’s explore some of the possible roadblocks.

The Challenges of AI in Logistics

Challenges Regarding Data Quality and Accessibility

AI feeds on high-quality data. However, it is not always possible to get one. It is reported that only 35% of CDOs feel they have enough resources for acquiring relevant and clean data. Logistics businesses may struggle with inconsistent data formats, where information from suppliers, warehouses, or transportation providers, may not be standardized, hindering seamless integration. Another challenge is incomplete datasets like missing information that can lead to inaccurate AI models and unreliable results.

Serhii Leleko: AI&ML Engineer at SPD Technology

Serhii Leleko

AI&ML Engineer at SPD Technology

“To ensure AI thrives on high-quality data, logistics companies can collaborate with industry partners to develop standardized data formats across the board. Additionally, implementing processes to scrub existing data sets for inconsistencies and missing information can help to ensure its cleanliness.”

Significant Implementation Expenses

In 2023, worldwide spending on AI systems was estimated at 154 billion U.S. dollars across all industries. This statistics indicates that AI technology and infrastructure require substantial upfront investment. Smaller logistics companies may find it as the greatest challenge as they operate with tighter budgets.

Phased implementation can help to overcome this problem. Companies can start with smaller, less complex AI projects to demonstrate value before scaling up. Also, it is important to leverage cloud-based platforms that offer pay-as-you-go options, reducing initial costs.

Integrating with Current Systems and Scalability Issues 

Legacy systems can be difficult to integrate with AI. Not only will it be time-consuming, but also complex because of technology incompatibilities. Plus, there is a risk that the system won’t scale to accommodate the growing company’s operations. Such a shift may require seeking AI solutions designed for modular integration or transferring your system to cloud platforms for ensuring enough capacity to process data. Our expertise involves designing scalable systems with AI capabilities for companies that seek expansion. SPD Technology delivered a solution that handles growing volumes of data without sacrificing performance.

Proficient Talent Shortage

Implementing and managing AI in logistics requires skilled professionals with expertise in data science, machine learning, and industry-specific operations. Yet, it is reported that 68% of executives find it difficult to hire qualified personnel.

Therefore, training programs are a must for logistics companies to help their employees gain AI/ML skills. Another solution to this challenge is hiring external expertise. At SPD Technology, we completed a number of projects for logistics and can combine our industry experience with AI/ML skills to help you address specific complexities of your system.

How Logistics Companies Can Start Embracing AI Innovation Step-by-Step

Challenges may scare businesses to adopt AI in logistics. However, there is an effective approach to integrate business systems with AI capabilities. So, if you are ready to undergo this digital transformation, the following steps can make it more simple. 

4 Steps to AI Adoption in Logistics

Recognizing Primary Obstacles

A logistics company embracing AI innovation must first recognize potential roadblocks. Those can be insufficient budget, legacy systems, lack of talent or inflexible approach to change. 

If financial constraints are your main concern, explore cost-effective solutions or start with a partial digital transformation instead of changing all the processes to AI-driven at once. To cover skill gaps, invest in AI training for existing staff or hire external experts for AI development tasks

In case your staff is not willing to embrace the change towards AI, clear communication and outlining of benefits can be helpful. If you mention how AI can optimize manual processes and reduce workload, the transition to AI-powered operations can be easier. Additionally, if your main obstacle is a legacy system, try either upgrading IT systems to ensure compatibility with AI or explore cloud-based AI solutions that minimize infrastructure dependence.

Assessing and Choosing Resolutions

Once you know what problem to address, try and investigate your logistics operations to identify areas where AI can bring tangible results. For example, some of the most common starting points for logistics AI transformation are inventory management, warehouse optimization, or implementing predictive capabilities. 

When you know what part of operations you are willing to change, research and compare different AI solutions, considering factors like functionality, features, cost, and scalability. After, develop a proof-of-concept (POC) as your pilot AI project. This will help you assess the solution’s effectiveness and suitability as well as correspondence to your specific needs.

Planning AI Integration Strategies

A successful AI implementation relies on a well-defined plan. Here, a phased approach proves to be the most efficient and risk-conscious. As mentioned above, you would better start with a pilot project in a specific area to test the AI functionalities and their impact on business before scaling up the solution across the entire company processes.

Another important aspect to consider is data. This is where you need to take care of data governance to ensure data quality and security for AI algorithms.

Serhii Leleko: AI&ML Engineer at SPD Technology

Serhii Leleko

AI&ML Engineer at SPD Technology

“Developing a comprehensive change management strategy should be also an integral part of your roadmap. It will allow you to address employee concerns, providing relevant training and fostering a culture of continuous learning within the company.”

Gradual Implementation with Vendor Support 

Based on the results from the POC, you need to evaluate the performance of the chosen AI solution. This will help you to understand what areas need improvements or work as expected. On top of that, to know what enhancements are needed to be made, ensure monitoring processes and establish key metrics (e.g. efficiency gains, customer satisfaction, cost savings)  for evaluation. And remember, if you are new to AI, a support from an experienced team won’t hurt. Seek collaboration with experienced AI solution providers who can offer implementation expertise and can navigate the knowledge of the logistics industry.

SPD Technology Expertise in AI for Logistics Industry

Our team quickly embraced AI and ML from the onset of these digital technologies and has a track record of projects in logistics gained over 18 years in the market. So, in case you need a hand, we combine our tech expertise with industry-specific knowledge to deliver AI solutions for unique business requirements.

For our client HaulHub, the company operating in the transportation niche, we delivered a business intelligence application capable of robust data analysis. Our team worked on:

  • Centralized Data Lake: We established a data lake to serve as a central repository for all of HaulHub’s data. This allowed for seamless data analysis across the organization.
  • Enhanced Data Availability: Our team utilized techniques like data denormalization, efficient caching mechanisms, and optimized database indexing to ensure HaulHub’s data was readily available for analysis.
  • Seamless Data Integration: We utilized AWS Database Migration Service (DMS) to streamline data integration from various sources. Moreover, custom Extract, Transform, Load (ETL) processes were implemented to transform raw data into a structured, relational format readily accessible for analysis.

Thanks to our efforts, HaulHub obtained a robust data processing solution capable of processing millions of analytical data points, delivering smooth performance with a response time of less than 2 seconds.

# Business Intelligence, # Data Analytics, # Data Lake, # Insights, # Logistics and Sypply Chaine, # OLAP
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Another project for HaulHub we completed was centered on AI/ML development. Our team integrated several AI models into the company’s system to ensure such functionalities as image processing, report generation, data extraction and classification, and chatbot functionalities. 

During the project, we leveraged:

  • OpenAI ChatGPT for tasks involving NLP for generating a chatbot’s responses to user queries and understanding contextual data.
  • WS Bedrock Claude for tasks like summarization and document comparison for drafting reports.
  • AWS Bedrock Titan for image, multimodal, and text models providing capabilities of content generation, image processing, search, and recommendations.
  • AWS Bedrock Jurassic for text generation tasks, including question answering, text generation, and summarization.
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Conclusion

In logistics, AI can help save money while increasing operational efficiency. This relatively new yet extremely powerful technology optimizes multiple logistics processes, including route planning, inventory management, maintenance predictions, demand forecasting, warehouse automation, supply chain management, fraud detection, customer support, and real-time vehicle monitoring. With all that optimization, businesses receive better operational efficiency, reduce negative impact on the environment, minimize expenses, and elevate customer satisfaction. 

Despite the number of benefits that present AI in logistics, there are still challenges that prevent businesses from implementing AI. Data quality and accessibility, high implementation costs, lack of skilled personnel, and complex integration with existing systems are some of the most pressing obstacles for using AI in this domain. However, to overcome these problems, businesses can apply a phased approach to adoption, leveraging cloud-based solutions, investing in training, and potentially partnering with AI experts.

Ultimately, AI can transform logistics businesses for the better and that is why this technology is worth trying despite challenges. You can always reach out to us to leverage our industry expertise and deep AI/ML knowledge to support your AI adoption.

FAQ

  • How Does the Integration of AI Technologies Help in Reducing Costs within Logistics Operations?

    In logistics, AI cuts costs through smarter operations. It optimizes routes for fuel savings, predicts equipment failures for reduced downtime, and streamlines warehouses with robots, minimizing labor costs. AI also forecasts demand for perfect inventory levels, preventing stockouts and wasted space.