5 Ways to Use Artificial Intelligence in Logistics

The landscape of logistics has undergone a significant transformation in recent years, with the explosive growth of ecommerce in the 2000s, followed by a dip in 2022. In the aftermath of the ecommerce frenzy, the need for streamlined operations and optimised processes becomes even more crucial as consumers' expectations evolve and businesses look to navigate seasonal peaks and troughs. 

This is where Artificial Intelligence (AI) emerges as a game-changer, offering innovative solutions to the challenges faced by the logistics industry. 

In this article, we explore 5 compelling ways to leverage AI in logistics, highlighting its significance in boosting efficiency and meeting the evolving demands of an era of ecommerce.

What is AI in logistics?

Artificial Intelligence (AI) is a technology that is rapidly transforming the logistics industry. 

It refers to the use of algorithms and machine learning to automate and optimise various logistics processes and is already being used to automate routine tasks and provide insights that were previously impossible to achieve!

Artificial intelligence is particularly well-suited for logistics due to the industry’s extensive networks. By analysing data, AI can predict future production and transportation volumes, leading to more efficient resource utilisation. Consequently, tasks are being delegated to self-learning digital systems at an increasing pace.

The role of artificial intelligence in logistics

The role of AI in logistics is ultimately to streamline operations across order processing, inventory management, supply chain and distribution in order to offer an enhanced customer experience. 

It can be used to automate routine tasks in order to reduce costs, improve efficiency and provide better customer service. AI in ecommerce logistics also provides real-time tracking and monitoring of parcels, which enhances the overall customer experience. Additionally, AI accelerates data analysis, allowing businesses to make smart decisions and improve operations. 

This is due to the fact that AI is able to process and filter large amounts of data, which then can be used to create forecasts for future developments in intralogistics. Placing certain items in a certain order or selecting picking-routes that minimise travel time are just two of many ways to optimise warehouse operations. 

Overall, the use of AI in ecommerce logistics is becoming increasingly essential in today's fast-paced business environment, however, research shows that not everyone in the logistics space is ready to adopt AI based strategies.

Challenges in AI adoption

Despite clear benefits of implementing AI throughout the logistics journey; from demand forecasting and supply chain management through to fulfilment and last mile, many businesses face several challenges that can be barriers to adopting AI technology. 

These challenges present themselves across strategic and operational hurdles, process clarity and governance structures, deciding what to focus on using AI and ensuring necessary data types and interfaces (APIs) are established.

Cost of implementation and maintenance

Firstly, implementing AI technology often requires significant investment in hardware, software and infrastructure, which can be costly. Research shows that in 2023, companies will spend an average of 267 euros per employee on software, meaning businesses must make careful considerations around employee expenses and company overheads. 

Additionally, ongoing maintenance, updates and training may be required to ensure that the AI system is functioning properly and providing accurate results, which can also add to the overall cost. For smaller companies or those with limited budgets, these expenses may be prohibitive and prevent them from fully adopting AI technology. 

Lack of resources

Since AI is a relatively new technological phenomenon, many businesses lack the necessary technical expertise or resources to implement and manage AI systems effectively. 

All the way back in 2011, a study by McKinsey & Company estimated that by 2018, the United States alone would face a shortage of 140,000 to 190,000 people with deep analytical skills, rendering aspects of AI pointless if the big data it yields is incomprehensible. They also estimated that 1.5 million managers and analysts would lack the know-how to use the analysis of big data to make effective decisions. 

Clearly, education is a core barrier to entry since the cost of hiring and training employees who are skilled in AI technology, further increases the overall cost of implementation and maintenance mentioned above. 

“People must therefore be trained on how to use the respective solution in everyday work.” - Digital Business

Concerns around privacy and security

When using AI systems to handle sensitive customer data, historical cybersecurity breaches have left businesses lacking in confidence when it comes to technology solutions. In this case, it’s important to consider if AI is appropriate to embed - some processes may lend themselves to other forms of automation.

For example, in April 2021, drinks giant C&C Group subsidiary shut down IT systems following a security incident. Despite Bulmers’ IT team reacting quickly, shutting down all IT systems, as per the cybersecurity response plan, the operational impact was astounding. The company was left fulfilling orders manually over phone calls whilst it restored its online capabilities! 

Addressing these challenges is crucial for businesses to fully realise the potential benefits of AI technology in their operations.

That said, there are plenty of use cases already for the use of AI in logistics, fulfilment and last mile - let's explore the top 5 ways to use artificial intelligence, as well as the benefits and challenges of each. 

1. Predictive maintenance and artificial intelligence

Predictive maintenance is a critical area in which Artificial Intelligence is transforming the logistics industry since downtime can be costly in delays and lost revenue. 

What is predictive maintenance?

Predictive maintenance is a proactive approach to maintenance that uses data analysis and machine learning algorithms to predict when maintenance is required, helping logistics companies to identify problems before they occur. 

It is a technique used to predict when maintenance tasks, such as equipment repairs or replacement, are required. This is done by analysing data and identifying patterns that indicate when maintenance is likely to be needed. 

By combining historical data and current data, businesses can make intelligent decisions, address problems before they become serious and cause downtime. It’s highly cost-effective, saving roughly 8% to 12% over preventive maintenance and up to 40% over reactive maintenance (according to the U.S. Department of Energy).

AI is used in predictive maintenance to analyse large volumes of data and identify patterns that are not easily recognisable by humans. 

For example, AI can be used to analyse the data from machines such as conveyor belts or forklifts to identify patterns that indicate when maintenance is required. This could include changes in the machine's temperature, vibration, or other factors that may indicate a problem is developing. 

By using AI to analyse this data, businesses can identify issues before they become serious and take preventative action that minimise business disruption.

Not only can predictive maintenance save physical machinery, but it can be a valuable approach for updates and maintenance of IT infrastructure, ensuring optimal performance and minimising downtime. By leveraging AI and predictive analytics, businesses can proactively monitor and assess the health of their IT systems and infrastructure components. 

This involves collecting and analysing data from various sources, such as system logs, performance metrics and historical patterns to identify potential issues or anomalies that may lead to system failures or performance degradation.

Benefits of AI in predictive maintenance

The use of AI in predictive maintenance is changing the logistics landscape and has numerous benefits for businesses, including:

• Reduces downtime: By identifying issues before they become serious, AI can help businesses avoid unplanned downtime of machinery and systems, which can be costly in terms of lost productivity and revenue.
• Identifies problems before they occur: AI can detect patterns and identify issues, helping to avoid equipment failure, unsafe working conditions and emergency repairs/replacements, which can be significantly more expensive than predictive maintenance itself. AI could also be used as a warning mechanism if addresses or other data points are not indicated correctly.
• Improves equipment lifespan: By identifying maintenance needs early, AI can help businesses extend the lifespan of their equipment, reducing the need for costly replacements and improving the overall efficiency and effectiveness of logistics operations.

The use of predictive analysis in the logistics sector is becoming increasingly important as businesses seek to reduce costs and improve efficiency. By using AI to analyse data and predict maintenance needs, businesses can address issues before they affect operations and productivity.

2. How AI streamlines fulfilment

AI can streamline fulfilment in several ways. One way is by automating repetitive and time-consuming tasks, such as sorting, labelling and packaging, which can save valuable time and reduce errors. 

AI can also optimise order picking and routing, reducing the time it takes to fulfil orders and improving delivery times, as well as analyse customer data to predict demand and optimise inventory levels, reducing overstocking and understocking. 

Benefits of AI in fulfilment

By streamlining processes, businesses can improve their efficiency across the following areas:

• Order processing: receiving orders, verifying them and updating inventory systems. AI comes into play early on in the process for error identification in order data, with the ability to identify, for example, if an order has an unusual combination of items or an incorrect address. By detecting patterns and anomalies that may indicate potential errors in future orders, this can help reduce errors in order fulfilment, which can result in better customer satisfaction and fewer returns or complaints. 
• Picking and packing: locating the items ordered, packing them and preparing them for shipping. Here, automated methods can reduce human error and optimise warehouse efficiency by collecting products that are shelved in the same location at the same time, even across multiple orders.
• Shipping: AI is used in shipping to optimise carrier selection, transportation routes, labelling parcels, arranging transportation and sending the products to the customer, as well as to monitor shipment status and identify potential delays or issues. 
• Delivery: delivering products to the customer. We’ll dig into this one in the next section…

3. AI and route optimisation

We’ve all used Google Maps to find the most efficient way of travelling from A to B. Is it the most fuel-efficient route? The most economic route? Does it avoid tolls or motorways? This sort of route optimisation has become second nature when we jump in our cars and head off to a new destination.

A similar approach can be applied to ecommerce logistics, but on a larger scale…

What is route optimisation?

Route optimisation is a critical aspect of logistics, as it determines the most efficient route for goods to travel from one point to another. In logistics, this is done by considering various factors such as distance, traffic conditions and delivery schedules.

By using AI to analyse large volumes of data, logistics companies can optimise their delivery routes to reduce fuel costs, improve delivery times and enhance driver safety. 

How AI is used in route optimisation?

AI algorithms can process data from various sources such as traffic sensors, GPS tracking, and weather forecasts to determine the best possible route.

For example, AI can be used to analyse real-time traffic data to identify the fastest route for delivery vehicles, considering both manually driven and autonomous vehicles. This includes considering factors such as road closures, accidents and congestion. AI can also be used to optimise routes based on weather conditions, taking into account factors such as rain or snow.

Benefits of AI in route optimisation

The use of AI in route optimisation has numerous benefits for logistics companies, including:

• Reduces fuel costs: By identifying the most efficient routes, AI can help businesses reduce fuel costs, which can be a significant expense in logistics.
• Improves delivery times: By selecting the fastest and most efficient routes, AI can help businesses improve delivery times, which can enhance the overall customer experience.
• Enhances driver safety: AI can be used to optimise routes based on factors such as road conditions and weather, which can help enhance driver safety.

Overall, the use of AI in route optimisation is becoming increasingly important in logistics. By using AI to analyse data and optimise routes, businesses can save time and money, while improving the overall delivery experience.

4. Inventory management with AI

Inventory management is a critical function for any ecommerce business. The success of an ecommerce business depends partly on how effectively it manages its inventory. However, inventory management can be a challenging task, especially for businesses that deal with a large number of SKUs (Stock Keeping Units). 

This is where artificial intelligence (AI) can be a game-changer! 

In this section, we will explore how AI is used in inventory management and the benefits it can provide for ecommerce businesses.

What is inventory management?

Inventory management is the process of managing and tracking the goods that a business has in stock. It involves managing inventory levels, ordering and restocking products, and ensuring that inventory is managed efficiently.

How AI is used in inventory management?

AI-powered inventory management systems can help ecommerce businesses make better inventory decisions by automating many of the manual processes involved. Traditionally, these manual processes include tasks such as physical counting of inventory, manual data entry for tracking inventory levels and manual reordering of items when stock levels run low. These processes can be time-consuming, prone to human error, and may not provide real-time insights into inventory levels and demand patterns.

On the other hand, AI can be used to analyse large volumes of data and provide insights into inventory levels, demand and supply in real-time. This includes analysing data from point of sale systems, historical sales data and supplier data.

It can also be used to predict demand for products (more on this later), helping businesses to adjust their inventory levels and avoid overstocking or understocking. This can help etailers to reduce the costs associated with holding inventory and improve order fulfilment times.

Benefits of AI in inventory management

The use of AI in inventory management has numerous benefits for logistics companies, including:

• Reduces overstocking and understocking: By analysing data and predicting demand for products, AI can help businesses to avoid overstocking or understocking warehouses, which can result in increased overheads for storage or lost revenue for items in demand but not in stock.
• Increases accuracy: AI can be used to improve the accuracy of inventory data as it removes human error. This can help businesses to make better-informed decisions about inventory levels and order fulfilment.
• Improves order fulfilment times: By accurately predicting demand for products and managing inventory levels, AI can help businesses to improve order fulfilment times through faster picking and packing, identifying bottlenecks and optimising delivery routes, enhancing the overall customer experience. 

As businesses seek to reduce costs, improve efficiency and provide better customer service, AI is an ideal solution for analysing data and managing inventory levels.

5. AI in demand forecasting

Demand forecasting is a crucial aspect of supply chain management, especially for ecommerce businesses. Accurate demand forecasting can help ecommerce businesses optimise their inventory levels, improve their supply chain planning and minimise the risk of stockouts and waste.

However, demand forecasting can be a complex and challenging task, especially for businesses dealing with a large number of products and market segments. This is where artificial intelligence (AI) can make a significant difference.

What is demand forecasting?

Demand forecasting is the process of predicting the future demand for a product or service. In ecommerce, demand forecasting is critical for businesses to optimise their supply chain operations and ensure they have enough inventory to meet customer demand, especially in peak seasons.

How AI is used in ecommerce demand forecasting

AI-powered demand forecasting systems can help ecommerce businesses make more accurate sales predictions and optimise their inventory levels, resulting in improved supply chain efficiency and better customer satisfaction. 

By analysing vast amounts of data from various sources, including historical sales data, social media activity and market trends, AI can provide accurate predictions of future demand.

AI can also be used to identify patterns and trends in customer behaviour, which can help businesses to adjust their marketing strategies and product offerings to better meet customer demand.

Benefits of AI in demand forecasting

The use of AI in demand forecasting has numerous benefits for ecommerce businesses, including:

• Improves accuracy of sales predictions: AI can provide more accurate predictions of future demand, helping businesses to better plan their inventory levels and supply chain operations.
• Enables better supply chain planning: By accurately predicting future demand, businesses can optimise their supply chain operations to ensure they have enough inventory to meet customer demand while minimising waste and stockouts.
• Helps reduce waste and minimise stockouts: AI can help businesses to avoid overstocking or understocking, eliminating the risk of lost revenue or increased costs.

Overall, the use of AI in demand forecasting is transforming the way ecommerce businesses operate. By leveraging AI to analyse data and predict future demand, businesses can optimise their supply chain operations, improve customer satisfaction and increase revenue.

Research shows that “successfully implementing AI-enabled supply-chain management has allowed early adopters to improve logistics costs by 15%, inventory levels by 35% and service levels by 65%, compared with slower-moving competitors.” According to DataRobot, “AI can reduce costs and minimise supply chain challenges by driving more informed choices across all aspects of supply chain management.”

How AI is being implemented: 

• Use case prioritisation and assessment: Identify high-value use cases that can be implemented with minimal effort, considering the timeline for AI to effectively enhance productivity and optimise decision-making.
• Detailed scoping and planning: Define the problem to be solved, determine the users and their requirements, and establish key metrics for evaluating success. Develop a project plan and document the proposed solution.
• Data preparation and model creation: Prepare the data, ensuring compatibility and functional interfaces, and create and test initial models.
• Deployment and user training: Implement the data product and initiate AI operations. However, it is crucial to prioritise training for users to effectively utilise the solution in their everyday work, even post-implementation.

Final thoughts on AI in logistics

Artificial Intelligence is truly a force to be reckoned with in the logistics industry, bringing incredible opportunities to automate routine tasks, improve efficiency and reduce costs. 

By implementing these uses of AI in logistics, businesses can reap the rewards and stay ahead of the competition. Whether we’re talking about more traditional logistics, or ecommerce logistics, all businesses can reap the rewards of AI, although ecommerce tends to lend itself more easily to AI implementation given its online nature and reliance on technology.

With the continued advancement of AI technology, the possibilities for the logistics industry are endless; data integration and compatibility, scalability, accurate forecasting, bridging gaps in skills and knowledge, improving efficience, the list goes on!

Codept and logistics automation

Without strictly incorporating AI, Codept offers a solution that brings algorithms and automations to businesses to help them reduce costs, streamline processes and ultimately improve operational efficiency.

Reducing costs

Codept’s particular solution helps businesses reduce costs not only through a fair and transparent pricing model, but through our solution itself. 

With a simple processing fee per order and one-time integration cost, businesses save money in the long-term since they are already set up to scale, with no hidden fees later down the line.

Not to mention, our solution itself saves businesses time and money by automating previously manual tasks. This reduces operational costs and staff overheads.

Streamlining processes

We have processes established with automated error checking in order data and our secure systems, regular system updates and new ISO standards ensure safe and efficient data transfers. This functionality saves time on manual checking and cross-referencing data, keeping logistics processes moving. 

Operational efficiency 

At Codept, we understand the pressures placed on drained IT resources and the limited capacity that businesses have in this field. Our one-time integration eases the setup and adoption process, meaning IT staff are freed up to work on higher level strategic roles and more urgent priorities, such as strategy or tools and technologies like AI. Not only do we make setup and integration simple, we also take over support activities, so internal IT-resources’ workloads are reduced significantly.

Furthermore, the state-of-the-art technology of Codept’s Integration Platform specifically helps businesses to reduce their downtime through early error detection, proactive monitoring of systems, regular software updates and infrastructure testing, guaranteeing a 99.97% uptime.

In addition, with carrier optimisation, customers can create standardised labels using the recipient's address, order weight and other product specifications, automatically sending this information to the carrier, who then sends back the label that can then be printed out and put onto the parcel accordingly. Not only this, but customers can choose their carrier dependent on location, product specifications or specialised delivery methods and/or ethical factors such as sustainability which not only improves operational efficiency but can achieve with wider business goals.

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FAQs about AI in Logistics

What is the role of AI in ecommerce logistics, specifically?

AI plays a crucial role in ecommerce logistics, helping businesses to streamline their operations and improve efficiency. It can be used for route optimisation, predictive maintenance, inventory management, demand forecasting, and customer service.

How is ecommerce logistics different from traditional logistics?

Traditional logistics and ecommerce logistics have some similarities, but there are some key differences as well. Traditional logistics typically refers to the process of managing the movement of physical goods from one location to another, typically within a supply chain that involves multiple parties such as manufacturers, distributors, and retailers. 

Ecommerce logistics, on the other hand, specifically refers to the logistics involved in managing and fulfilling online orders from customers. This often involves additional steps such as order processing, picking and packing, and last-mile delivery to the recipient's address. Ecommerce logistics also tends to place a greater emphasis on speed and accuracy, as customers expect faster delivery times and are more likely to leave negative reviews or choose a competitor if their order is delayed or incorrect. As a result, ecommerce logistics often requires more advanced technologies such as AI and automation to keep up with these demands.

How can AI help businesses reduce costs in ecommerce logistics?

AI can help businesses reduce costs in several ways, such as optimising delivery routes, predicting maintenance needs to reduce downtime and reducing overstocking/waste of inventory.

What are the benefits of using AI in ecommerce logistics?

The benefits of using AI in ecommerce logistics include reduced downtime, improved delivery times, enhanced driver safety, increased accuracy in inventory management, improved order fulfilment times, improved accuracy in sales predictions, better supply chain planning, and faster and more accurate responses to customer inquiries.

Is AI replacing human workers in ecommerce logistics?

While AI is automating some tasks, it is not necessarily replacing human workers in ecommerce logistics. Instead, it is augmenting their abilities, helping them to work more efficiently and make better decisions.

How can businesses implement AI in ecommerce logistics?

Businesses can implement AI in ecommerce logistics by first identifying areas where it can provide the most value. They can then explore different AI solutions available in the market or develop custom solutions in-house. It is also important to train employees on how to use and integrate AI solutions into existing workflows.

How can software solutions help logistics companies to reduce overheads?

Software solutions can drastically reduce overheads by reducing the amount of manual input required to process and fulfil orders.