AI in Reverse Supply Chain. Solving the Challenges of Reverse Logistics

The reverse supply chain is a complex and often overlooked area of logistics. It involves the movement of goods and materials in the opposite direction of their original flow. In recent years, there has been a growing trend of product returns in the supply chain. This can be attributed to several factors, including globalization, increased consumer choice, and shorter product life cycles. The problem is only going to get worse as we move further into the era of eCommerce. In this blog post, we will explore how AI can be used to solve the challenges of reverse logistics.

What Is the Reverse Supply Chain, and Why Is It Important?

In a forward supply chain, the manufacturer is the first link, and the customer is the last. After the product gets manufactured, it passes a long way to its final destination, passing through the hands of wholesalers, retailers, shipping and logistics companies. That path can extend or shrink depending on various factors. But it’s always one direction - the customer’s home.

Reverse logistics, as the name suggests, invert that order. In a reverse supply chain, the goods travel all the way from the customer to the manufacturer (or, less commonly, from the next elements of the chain, like the shops or wholesalers) and get reused in different ways. There might be various reasons for that, starting from damages to the end of the product’s lifecycle.

Regardless of the reason, the way back is usually more challenging. That’s because the further from the manufacturer, the more complex the network of dependencies becomes. The customer knowledge gap also contributes to the issue - to launch the return process smoothly, the clients need a bunch of information that is not always easily accessible. The manufacturers have it all organized, but when the product travels from the bottom up, there are naturally more obstacles on the way.

With the growing emphasis on sustainability, companies are starting to pay more attention to effective reverse logistics. Customers want to be able to return the products conveniently and be sure that they will return to the manufacturing cycle, being repaired or recycled. Now that e-commerce has dominated the retail landscape, the demand for refined reverse procedures is more urgent than ever.

Some technologies, like augmented reality, will likely decrease the frequency of returns from online purchases in the nearest future. However, it will still remain high as more and more people will convert to e-commerce. There is no escape from that, and AI can help us streamline this transformation.

Sources of Reverse Supply Chain

Before delving into the details of AI’s possibilities, let’s look into the most common channels that call for reverse logistics procedures. The sources of the closed loop supply chain include:

• Product Returns
• Warranty Returns
• End of Life Products (products that have reached the end of their lifecycle and make it impossible for the users to receive updates and services)

How Does the Reverse Logistics Process Work?

The reverse logistics process may adopt different forms depending on the complexity of the supply chain and product specifics. Let’s dive into the general path the product has to pass from the bottom up in order to return to the cycle. Without getting into the details of different scenarios, we can divide the reverse logistics processes into five key components:

• collection
• transport
• reception
• inspection
• sorting

After the last stage, the manufacturer can decide what to do with the item. In the case the product is damage-free, it may be suitable for reselling (reCommerce), which is the simplest path. The company may also repair it or recycle it for materials or parts. This way, the product circle is closed.

The Challenges of Reverse Logistics

Reverse logistics generates a few challenges that the supply chain participants can work out with the help of good management and technology. In the traditional supply chain, every stage the product reaches next is more complex than the previous. In reverse one, we start from the biggest complexity and have to return all the way back to that single string. Reverse logistics create additional problems along the route.

In the end, we have an end user who is often unfamiliar with the return rules and has to put extra effort into preparing the item for this path. That can discourage them from launching the reverse logistics flow, particularly in the case of products that do not cost much.

The retailers and manufacturers may be discouraged, too, as bringing the product back to the cycle may not be as profitable as its disposal. If the process is not planned and automatized properly, that may be the result indeed. In order to be able to launch an efficient reverse logistics process smoothly, all the participants of the chain need to employ an efficient tracking system and optimize their communication across channels. Then, they need to make optimized decisions regarding the item’s fate.

How Can AI Be Used to Solve the Challenges of Reverse Logistics?

Finding the most optimized way for the product to be transported and utilized back in the factory requires tons of data. In order to process it manually and make informed decisions on its basis, retailers would have to spend a lot on human resources. With the help of AI, they can partially automatize these elements that often are error-prone and require mundane, repetitive work. Even if there is a person at the end, the whole process shortens, generating savings for the supply chain members.

The artificial intelligence-backed systems automatically track and draw the product data essential in the particular stage of the reverse supply chain so that the item can move further on its way back to the factory. The products may return to the manufacturer with missing parts and labels, but if they are properly tracked, all the data will be within the system.

AI also helps manufacturers gain insights into their products in order to offload the reverse supply chain. Based on the returns-related data, they can find weak points and quality issues, reducing returns frequency in the future.

Use Cases for AI in Reverse Supply Chain

AI can benefit the reverse supply chain in various ways. Here’s our summary of its possibilities.

Reverse Logistics Management

The product has to pass a long way from a customer to the retailer or manufacturer. For it to be cost-effective, the supply chain members need to plan this route in the shortest and most efficient way. The AI system helps them connect the dots, starting from the first chain link.

When the customer reports a return, such a system finds a matching delivery service provider based on the provided data, including the location, destination, type, size, and weight of a shipment.

Having determined the carrier, the system can find the fastest and the most cost-efficient route, relying on real-time and historical data (traffic, carrier demand, the carrier’s average service time, etc.). Alternatively, it can first estimate the route and then find a matching service. Thanks to AI, the whole pickup scheduling process can happen automatically without manual processing. This way, the retailer does not have to act as an intermediary. You can read more about it in our recent publications on parcel delivery (and return) optimization and AI in last-mile delivery.

Related case study: Implementing AI model to optimize routes and timelines of deliveries

A 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 a detailed case study of this project.

Predictive analytics are applied to various stages of supply chain management. Retailers and logistics companies also use predictive analytics to estimate reverse logistics needs. Based on the historical return-related data, the predictive model is able to extract patterns that help them optimize their planning and estimate the staffing and equipment needs better. This way, they remain prepared for the work-intensive periods and manage their resources in a more cost-effective manner.

According to the National Retail Federation, returns cost retailers $428 in 2020 alone. Since returns are so costly, any strategy that can leverage expenses makes a difference for retailers. By consolidating shipments, they can reduce transportation costs significantly.

AI can support their decisions in this field with suggestions, combining the shipment data (product data, pickup points, and destinations) and carriers’ offers to find the most cost-efficient configuration. ML algorithms may also estimate the most profitable path for a particular item. What’s the probability of reselling it? Should it be sold with an off-price retailer? With the support of artificial intelligence, companies can find answers based on data.

Returns Management

Once the item gets to the retailer’s warehouse, artificial intelligence can contribute to its placing and tracking. Computer vision-fuelled scanners supported with character recognition help to extract relevant data from the labels and documents so that it is all easily trackable in the system. Once the item is ready to travel further, autonomous vehicles or drones can easily find and localize it in the inventory.

AI’s role becomes even more significant when the product arrives back at the factory. That’s where the item’s future is determined. Machine learning-based sorting systems can automatically categorize the product using the data already existing in the system and the one extracted with computer vision. The trained algorithm analyzes the captured images of the product to detect and localize defects or missing parts and directs it to the particular unit on this basis. The equal process can take place at the earlier stage when the product gets back to the retailer.

Let’s not forget that artificial intelligence can contribute to the return logistics even before the purchase is even made. Relevant recommendations can significantly reduce the probability of returns. In order to maximize their relevance, retailers can engage powerful AI-based recommendation engines that extract insight from user data and behavioral history, learning with every purchase choice they make.

This strategy can be even more powerful when paired with Augmented Reality. Many brands, including Gucci, Nike, and L’Oreal, have already introduced virtual trying rooms to their apps to make their online customer’s choices more conscious.

Reselling Products

Sorting systems help retailers and manufacturers automatize the preselection of items suitable for reselling. Once they fulfill a set of conditions, the company has to find the most efficient path to put them back on the shelves. The predictive algorithm may, for instance, estimate the probability of selling a particular item in the same season on a secondary market as very low.

With that knowledge, the retailer can decide how and where to sell the product once again and estimate the price that will increase the likelihood of purchase while generating maximum profit. Dynamic pricing can trigger sales growth while helping the companies avoid stock-ups.

Recycling and Remanufacturing Products

If the product has defects that disqualify it from reselling procedure or is missing parts, the retailers can take other paths to close the cycle. One of the options they have is to send back the items to the manufacturer so that they undergo a remanufacturing process. AI-based systems help the manufacturing units track and extract all the necessary information about the product, like the technical specification, serial number, return cause, damage description, etc. That helps them streamline the procedure and bring back the compact-new condition to the product in the most efficient way possible. Predictive analytics allows them also to find the most cost-effective recycling paths, combining data on material costs and demand, etc.

Benefits of Improving Reverse Logistics with AI

Value for Customers - Improved Customer Satisfaction

Valuable experience is one of the main things customers look for when they purchase a product. If they are dissatisfied with what they get, e.g., due to a defective item, it will take much more effort on the company’s side to make them want to come back. To avoid that, businesses need to focus on ensuring customer satisfaction at all stages of their journey, including personalized product offerings and well-organized returns management. Customers need to have streamlined processes for refund and exchange policies, repairs and replacements, and opportunities for reward/buyback programs.

Value for Manufacturers and Retailers - Lower Operating Costs & Increased Sales

The returns are a costly affair for retailers and manufacturers. By optimizing their return logistics with AI, companies can not only improve customer satisfaction but also save on costs related to processing returns and inventory management. In fact, research by Gartner estimates that AI-based solutions can help organizations cut their supply chain and logistics expenses by up to 30%.

The ability to track and monitor products as they move through the reverse logistics process provides businesses with a wealth of valuable data. This data can be used to improve the efficiency of future returns processes, identify areas where cost savings can be made, and track which products are being returned most frequently. By automating various tasks and introducing innovative solutions, companies can save time and money while providing their customers with a better overall experience. In other words, everyone wins.

Value for Society and Environment - Waste Reduction and Sustainable Development

As the eCommerce market grows, so does the need for a more sustainable and eco-friendly supply chain management. Reducing the ecological footprint of a company has become one of the main priorities for businesses all over the world. Reverse logistics contribute to reducing waste - by reusing, recycling, and repairing products instead of discarding them. Reducing the amount of waste produced by businesses has a positive impact on the environment and society as a whole. When products are returned to manufacturers or retailers, they often end up in landfill sites. This takes up valuable space and releases harmful toxins into the ground, water, and air.

In addition, the transportation of these goods back to their point of origin uses large amounts of fuel, which contributes to climate change. AI can help businesses to avoid these issues by finding ways to recycle or reuse returned items instead of sending them straight to landfills. By using recycled materials instead of virgin resources, companies can cut costs and save energy. Businesses that have a solid reverse logistics plan can play their part in developing a more sustainable economy and preserving our planet for future generations.

The Future of Reverse Logistics

Reverse logistics has been developing dynamically in recent years. Both the customers and the controlling units create increasing pressure on sustainability, and optimized reverse supply chains are an answer to that. As new regulations are emerging, retailers and manufacturers are obliged to reduce their environmental impact by closing the products’ cycles and adjusting to the principles of the circular economy.

AI can help them put the products back into the cycle, either for reselling or recycling purposes. However, returns are still inevitably linked to losses – for instance, because of the state sales tax. That’s why we assume that, in the future, the emphasis will shift to prevention. Artificial Intelligence can fuel that trend, too, helping retailers understand their users even better and prevent unfitting purchases, resulting in reduced returns and increased customer loyalty.

If you see potential in an AI-based reverse logistics system for your business, drop us a line! We would love to answer all your questions and show you the possibilities of such solutions on practical examples.