Artificial intelligence reshapes retail supply chains by transforming inventory management, demand forecasting, and last-mile delivery with unparalleled precision and agility. As Mark Venables discovers, with consumer expectations for personalisation, sustainability, and rapid delivery growing, and disruption increasingly impacting global operations, AI has become essential for navigating complexity and ensuring supply chain resilience.
Artificial intelligence has evolved from a useful but optional tool to a critical enabler of efficiency, precision, and agility in modern retail supply chains. For decades, AI was viewed as a means to solve specific problems, often in isolated contexts. Retailers might have used it to address narrow challenges like optimising a delivery route or managing a complex scheduling issue. However, as consumer demands have grown increasingly diverse and complex, and global supply chains have become more intricate, the application of AI has broadened and deepened significantly. Today, AI is an indispensable tool for managing the full lifecycle of products, from design and production to inventory and final delivery.
The retail sector exemplifies the pressures that have driven this transformation. “The demands placed on retailers are unique because consumer preferences are now more variable than ever,” Adrian Wood, Director, Strategic Business Development, Dassault Systèmes, says. “In the past, customers may have been content with standardised options or slower delivery times. That is no longer the case. Consumers now expect products tailored to their tastes, available in various options, and delivered almost instantaneously.”
Industries such as fashion highlight the urgency of this challenge. Fashion retailers must predict customer preferences for specific colours, sizes, and styles, often months before products reach the shelves. If these predictions are inaccurate, the consequences can be severe. Overproduced items result in waste and erode profit margins, while underproduced items mean missed opportunities that cannot be reclaimed once a season ends. “AI helps bridge these gaps by enabling more accurate forecasting, real-time adjustments to production and inventory, and optimised distribution strategies,” Wood adds. “Where traditional tools and methods falter, AI shines, particularly in managing the sheer volume of data and variability inherent in retail supply chains.”
Supply chain challenges in the retail sector
The modern retail supply chain faces extraordinary challenges, many driven by shifting consumer expectations. Today’s consumers demand more than just quality products; they also want them personalised, delivered quickly, and produced sustainably. This convergence of requirements places enormous strain on supply chains, requiring unprecedented precision, adaptability, and innovation. AI is crucial in meeting these demands by providing tools to manage complexity, streamline operations, and optimise outcomes.
“Internal challenges within retail organisations compound consumer-driven pressures,” Wood continues. “Many retailers continue to rely on outdated systems not designed to handle the complexity of modern supply chains. Homegrown solutions and legacy ERP systems dominate the landscape, and while they excel at managing transactional data, they fall short in areas like predictive analytics, dynamic planning, and optimisation. Remarkably, many companies still use spreadsheets for critical planning functions. This reliance on manual tools creates inefficiencies and limits the ability of organisations to respond quickly to changes in demand or supply.”
External disruptions add further layers of complexity. Global supply chains are inherently vulnerable to geopolitical tensions, natural disasters, and localised events that can have far-reaching consequences. For example, incidents such as the Evergreen blockage in the Suez Canal or major port closures disrupt the flow of goods and create cascading delays. Retailers must also navigate regulatory pressures, particularly as governments and consumers demand greater transparency and accountability. Sustainability has emerged as a top priority, with retailers expected to demonstrate that their products are ethically sourced and their operations environmentally responsible.
“The result is a highly constrained environment in which retailers must balance numerous competing priorities,” Wood says. “They need to deliver high-quality products at competitive prices, ensure rapid delivery, and maintain profitability—all while adhering to increasingly stringent sustainability standards. AI provides a way to manage these complexities, offering decision-making tools that integrate vast amounts of data to identify optimal solutions.”
Dassault Systèmes’ approach to supply chain transformation
Dassault Systèmes takes a unique approach to addressing these challenges, focusing on providing a decision-support layer that integrates seamlessly with existing systems. The company’s 3DEXPERIENCE platform bridges transactional systems, such as ERP platforms, and the broader strategic goals of supply chain optimisation. Unlike transactional systems that manage purchase orders and other operational data, the 3DEXPERIENCE platform enables companies to model their supply chains virtually, allowing for dynamic scenario planning and optimisation.
Central to this approach is the virtual twin, an advanced iteration of the digital twin that incorporates real-time data and provides a highly accurate, dynamic representation of supply chain operations. “This virtual twin is continually updated with data from various sources, including transactional systems, external data feeds, and predictive analytics,” Wood explains. “Companies can make informed decisions more quickly and effectively by simulating different scenarios, such as the impact of geopolitical events on supply chains or changes in consumer demand.”
One of the key strengths of Dassault Systèmes’ platform is its ability to handle ‘what if’ planning. For example, a company might explore the implications of adding a new supplier, expanding into a new market, or shifting production to a different region. The platform’s AI capabilities enable it to process vast amounts of data and generate actionable insights that help businesses confidently navigate complex decisions.
Expanding the scope of AI in planning and optimisation
AI’s role in supply chain management has evolved to encompass operational efficiency and strategic decision-making across the entire product lifecycle. Dassault Systèmes’ ability to combine virtual twins of products, supply chains, and manufacturing facilities provides a comprehensive framework for optimisation. This interconnected approach allows businesses to address challenges holistically rather than in isolated silos.
Predictive analytics has become a cornerstone of demand forecasting. “Traditional forecasting methods often relied on static models or historical trends, which were insufficient for industries with high variability or rapid product turnover,” Wood says. “AI and machine learning have transformed this process by enabling dynamic forecasting that accounts for real-time data and complex correlations. For example, machine learning algorithms can identify patterns in consumer behaviour, even for new products with no historical data. This capability is particularly valuable in retail, where demand signals change rapidly.
“Multi-echelon inventory optimisation is another area where AI excels. By analysing historical data and real-time inputs, AI helps companies determine the optimal stock distribution across their supply chains. This reduces the risk of stockouts and overstocking, which can have significant financial and operational impacts.”
Addressing data quality and integration
One of the most significant challenges in AI implementation is ensuring data quality. Poor data input leads to suboptimal outcomes, making it essential for companies to invest in data cleansing, standardisation, and integration. The virtual twin model helps address this issue by surfacing hidden data and providing a clear visual representation of supply chain operations. This allows companies to identify gaps, inconsistencies, and areas for improvement.
AI implementation also requires collaboration with experts in data science and optimisation. “Companies must have the right expertise to select appropriate algorithms, interpret results, and make informed decisions,” Wood adds. “Sometimes, this means partnering with trusted vendors or investing in in-house capabilities.
Last-mile delivery is a quintessential example of an optimisation challenge ideally suited to AI. The process involves balancing numerous variables, such as cost, delivery time, and customer satisfaction. AI algorithms analyse these factors in real-time, generating delivery plans for dynamic conditions, including traffic, vehicle availability, and customer preferences.
“Advances in computing power and algorithm development have enabled near-instantaneous planning,” Wood says. “In the past, generating an optimised delivery plan might have taken hours. Today, it can be done in seconds, allowing companies to adapt to new customer orders or disruptions almost immediately. AI also incorporates sustainability metrics, enabling companies to reduce emissions while maintaining efficiency.”
AI adoption for smaller businesses
AI is not exclusively for large enterprises. Smaller companies can implement AI solutions in targeted areas, such as shop floor scheduling or supplier selection, without requiring extensive infrastructure. “Virtual Twin as a Service offers an accessible entry point, allowing smaller organisations to benefit from AI-driven insights without significant upfront investments,” Wood explains. “This approach enables businesses to address specific challenges, validate the technology’s value, and scale implementations over time.
One major hurdle in AI adoption is overcoming the challenge of AI as a ‘black box’ and trusting in the results. Many decision-makers hesitate to rely on AI because they cannot easily understand its reasoning. “Explainable AI and generative models help bridge this gap by allowing users to interact with AI systems in natural language, asking questions and receiving clear, contextualised answers,” Wood continues. “This builds trust and ensures that decision-makers feel confident in the technology’s recommendations.”
Future directions for AI
According to Wood, the future of AI lies in its integration across the business ecosystem. “Dassault Systèmes is investing heavily in expanding AI’s capabilities, particularly in areas like generative AI and machine learning,” he concludes. “These technologies will enable companies to optimise not only their supply chains but also their product design, manufacturing processes, and overall business strategies. By addressing the root causes of inefficiencies and integrating insights across multiple domains, AI has the potential to transform industries and drive meaningful, long-term improvements.”
