AI in Supply Chain: Optimizing Inventory and Reducing Emissions

Large enterprises are increasingly adopting AI into their supply chains. Before we look at how AI is used in the supply chain, it’s essential to comprehend the need for AI in such workflows. For example, in demand forecasting, traditional methods often struggle to keep pace with rapidly changing consumer preferences and volatile markets. Inaccurate predictions can lead to overstocking, wasting resources and tied-up capital, or stockouts, which harm customer satisfaction and operational efficiency. Overstocks cost the average retailer 3.2% in lost revenue, around $123.4 billion annually.

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Natural disasters and other global events, like the pandemic, can disrupt supply chains. To overcome such issues, supply chains must optimize logistic operations, such as transportation routes, delivery schedules, and modes of transport. Inefficiencies in logistics can lead to increased costs, delayed deliveries, and environmental harm. 

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‍Source: https://www.nber.org/digest/202404/supply-chain-disruptions-and-pandemic-era-inflation

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Another area for improvement is effective inventory management. In the United States, total retailer inventories rose by $78 billion to around $740 billion during the pandemic, an increase of about 12%. Effective inventory management involves forecasting demand, establishing optimal reorder points, and proactively addressing potential supply chain disruptions to ensure seamless operations. As Maggie Brommer, head of procurement for Unilever’s Prestige Products, noted, “When there is a supply-chain crisis, the key to being competitive is to be faster at finding alternative suppliers than everyone else because everyone’s looking to do the same thing.”

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New and innovative technologies, such as AI in supply chain management and AI-based automation methods, are essential for streamlining modern supply chains. Automation can handle repetitive tasks in supply chains, such as warehousing, procurement, and administrative work, with high accuracy and speed. This article explores how AI in supply chain management revamps operations through warehouse automation with robotics, improved inventory management, and enhanced sustainability. We’ll also explore how companies like DHL, Walmart, and Unilever use AI to address challenges in their supply chain operations.

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How AI in Supply Chain Solves Inventory Challenges

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When you walk into a store and find the product you’re looking for, it may feel simple, but a lot is happening behind the scenes, with technologies like AI hard at work. AI in supply chain management is quietly transforming how businesses forecast demand. By leveraging advanced algorithms and machine learning, companies can now analyze massive amounts of data - everything from historical sales and market trends to shifts in consumer behavior and even the weather. Businesses using AI can make smarter decisions about what to stock and when, preventing issues like overstocking or running out of products. Thanks to AI, supply chains are running smoother, with consumer packaged goods companies, for instance, reducing their inventory by up to 20%.

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In terms of disruption management, AI in supply chain management can enhance the resilience of supply chains by predicting potential disruptions and automating response strategies. By monitoring global events in real-time using AI, risks can be identified before they impact the supply chain. AI can also streamline communication across the supply chain, automating responses to ensure rapid actions are taken by all parties, minimizing downtime and losses. According to Forbes, 82% of consumers said it is essential that retailers proactively communicate through every fulfillment and delivery stage.

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AI also plays a pivotal role in optimizing logistics by calculating the most efficient routes and schedules. Traffic, vehicle capacity, fuel consumption, and delivery windows are considered to reduce costs, improve delivery times, and lessen the overall environmental impact. Additionally, AI-driven predictive maintenance can ensure that vehicles are serviced before issues arise, preventing breakdowns and delays. As Ken Chadwick, VP Analyst in Gartner’s Supply Chain Practice, explained, “Enhancing productivity is the key factor that will drive future success, and the key to unlocking that productivity lies in leveraging intangible assets. We see this divide, especially in the digital domain where the best organizations are far ahead in optimizing their supply chain data with AI/ML applications to unlock value.”

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Reducing Human Errors and Improving Asset Visibility in DHL’s Supply Chain with AI

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DHL, a leading logistics company, was facing various operational challenges in its supply chain. For example, they were relying heavily on manual procedures for tasks like detecting defects and maintaining company assets. This led to round-the-clock employee availability and human errors becoming a common concern for DHL. Additionally, assets such as pallets, cages, trolleys, and other equipment frequently went missing, leading to costly efforts to locate, return, or replace them. However, DHL was able to mitigate these issues using AI, computer vision, and robotics.

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Computer vision, a subfield of AI that enables computers to interpret visual information, was used for predictive maintenance by consistently monitoring assets, detecting defects, and automatically alerting maintenance teams so that they could intervene before any issues arose. This capability allows managers to schedule repairs and maintenance, extending the lifespan of assets and preventing failures.

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DHL also uses computer vision systems to count and locate assets, assessing their status in real time to provide visibility and improve efficiency. Deep learning algorithms can detect and classify objects in images or video streams, identifying specific items and repeating the process to count all instances of each object. Assets can be identified by type (such as roller cages, racks, or forklifts) or by unique identification codes linked to a single asset or multiple assets within the camera's field of view.

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DHL also uses AI-powered sorting robots for warehouse sorting tasks, which are labor-intensive and time-consuming. Using these robots, DHL increased its sorting capacity by over 40%. One of their sorting robots, made by Boston Dynamics, could transfer up to 700 pieces onto a conveyor belt every hour. Sally Miller, Chief Information Officer and Global Digital Transformation Officer at DHL, emphasized the importance of AI in supply chain management, stating, “By focusing on orchestration, robotics, and AI, we are not just keeping pace with technological advancements but actively shaping the future of logistics. These investments will continue providing our business and clients unparalleled efficiency, agility, and a sustainable competitive edge.”

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Eden: Walmart's AI-Driven Approach to Optimizing Freshness and Reducing Waste

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Walmart, a multinational retail corporation, needed help managing its vast inventory. The mismatch in supply and demand, especially during the COVID-19 pandemic, led to excess inventory and inefficient stock handling, resulting in higher markdowns. Traditional inventory management methods made it difficult to balance overstock and stockouts. 

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In particular, Walmart faced overstocking issues that resulted in product waste, leading to inefficiencies and reduced profits. Walmart’s store associates also spend a lot of time on manual, repetitive tasks like tracking and handling inventory items, reducing overall productivity. These factors limited employees' ability to focus on enhancing customer service. Walmart turned to AI in supply chain management to solve these challenges.

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Walmart’s AI inventory management system, known as Eden, is a suite of AI apps that employees can use at every stage of the supply chain to ensure customers receive the freshest products. The system uses technologies like machine learning and computer vision to check the shelf life of food products that are waiting to be shipped from distribution centers to stores. It can analyze photos of food products like apples and bananas every day to predict their shelf life. This allows employees to save time by quickly assessing the freshness of food products and their remaining shelf life. Walmart also plans to use Eden to recalculate the freshness factor and reroute shipments to closer outlets in order to optimize freshness more effectively.

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Using AI in supply chain tools like Eden, Walmart plans to eliminate $2 billion in food waste over the next few years. Eden is used in 43 distribution centers and has prevented $86 million in waste. According to Parvez Musani, Walmart’s vice president for supply chain technology, Walmart is planning on expanding Eden’s reach to their supplier farms so that they can have a hand in every aspect of product production and sales.

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Unilever's Path to Sustainability: Leveraging AI for Emission Reductions

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Recently, environmental sustainability has been a central part of how businesses compete and stay responsible. Yet, a surprising fact emerges when you dive deeper: more than 70% of a company’s emissions come from its supply chain, according to the UN Global Compact. However, studies show that only 38% of business leaders consider sustainability in their decision-making process. A major fraction of emissions are slipping through supply chains unnoticed during activities like the extraction of raw materials, their processing, and transportation. These activities, whether it’s mining or agriculture, are heavy contributors to carbon emissions, making the supply chain a key area for change. Companies like Unilever are using AI in supply chain strategies to enhance operational efficiency and sustainability.

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Unilever is a multinational consumer goods company based in the UK. It uses AI in supply chain management to identify alternative ingredients for its products, strengthening its supply chains and making them more cost-efficient and sustainable. It also uses machine learning to create the best product formula (for fabric conditioners) based on the local conditions in each market. If the formula needs adjustment, it can be quickly re-optimized to meet new conditions and updated within hours. 

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Unilever has also created an AI model that integrates real-time forecast and sales data between the company and customers. This synchronizes consumer purchases with material sourcing, allowing unprecedented data sharing. Such improved forecasting boosted Unilever’s supply chain efficiency, optimized inventory, reduced road trips, and ensured only the right products were delivered at the right time.

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According to Unilever, using AI in supply chain demand forecasting can reduce human efforts by 30%, freeing up employees to focus on other tasks. Juan Carlos Parada, Global Head of Customer Operations at Unilever, says, “We’ve been on a path to not just ‘do’ digital but rather ‘be’ digital. We’re moving into an environment where meaningful portions of the work are getting done by machines, guided by people. It’s a mindset shift that is leading to some real breakthrough thinking.” In terms of sustainability, they have lowered operational emissions by 64% and are on track to achieving their 70% reduction by 2025.

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Investing in AI: Key to Better Supply Chain Management

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AI in supply chains is tackling key challenges like demand forecasting, logistics, and sustainability. We discussed case studies from leading companies like DHL, Walmart, and Unilever highlighting how AI is reshaping supply chains for greater agility, efficiency, and environmental responsibility. To similarly adopt or further improve the use of AI in supply chain management, enterprises can explore where AI tools can add value, stay updated on all the best practices, involve cross-functional teams, and seek help from AI experts if needed. Subit Mathew, US SAP Supply Chain Offering Leader of Deloitte, elucidated, “Imagine if you can actually take your Integrated Business Platform, put AI on top of it, and be able to simulate any and all supply chain events that could happen. You have a set of answers before it actually even happens.” By embracing AI, companies can stay ahead of the curve and confidently navigate future supply chain complexities.

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