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Embracing Gen AI: Rethinking supply chain dynamics for a digital-first future

Dinesh Tomar, Annabel Cussons, Adeel Butt, Tatiana Horsham
July 3, 2024

Today’s businesses are experiencing a dynamic shift as they eagerly strive to embrace generative AI to stay competitive in an ever-evolving market

Generative AI (Gen AI) is the subset of AI that focuses on the creation of new content, such as text, images, video, audio, and software code autonomously using sophisticated machine learning models called deep learning models. Though traditional AI works through machine learning to complete tasks and learn or make decisions independently, it cannot create new information.

The future of supply chains, fueled by generative AI, will transcend traditional optimization and enter a realm of unprecedented adaptability and resilience. Gen AI’s unique ability to create, simulate, and predict will revolutionize areas like demand forecasting, where it can generate nuanced scenarios based on vast datasets and real-time signals.

Gen AI will be instrumental in achieving self-healing supply chains. While traditional AI can analyze data and identify patterns to predict potential disruptions, Gen AI takes it a step further. It can create and simulate a wide range of scenarios, enabling the supply chain to proactively identify vulnerabilities and generate innovative solutions to mitigate risks before they materialize. Gen AI’s ability to create novel solutions and adapt to changing circumstances is crucial for achieving true self-healing capabilities in supply chains.

Where Gen AI departs from traditional AI

Imagine a supply chain where traditional AI acts like a vigilant guard, constantly scanning for potential threats. It might identify a potential delay in a shipment due to weather conditions, raising an alarm. Gen AI, on the other hand, is a resourceful problem solver. It takes that alarm and springs into action, not just identifying the problem but also brainstorming and generating a multitude of potential solutions. It might propose rerouting the shipment, reallocating resources to expedite other deliveries, or even proactively contacting alternative suppliers to ensure seamless fulfillment. In essence, Gen AI transforms the supply chain from a reactive system to a proactive one, where problems are not just identified but also actively and creatively solved before they disrupt operations.

Currently, 30% of supply chain leaders actively plan to deploy generative AI for supply chain in the next six months.[i] The projection for increased adoption is driven by Gen AI’s ability to significantly reduce costs, improve efficiency, harness complex data, and increase revenue within business units that deploy the technology. Moreover, a recent Gartner survey of 127 supply chain leaders found that ‘Chief Supply Chain Officers (CSCOs) are dedicating 5.8% of their budget to Gen AI in 2024.’ The mass adoption of this emerging technology is further validated in the same report, with the finding that only ‘2% of respondents say they have no plans to leverage Gen AI.’[ii]

Capgemini Invent recognizes the vast potential of Gen AI to revolutionize supply chains. While numerous opportunities exist, this perspective focuses on two key areas: scenario modeling and demand planning. In future communications, we will explore additional applications of this transformative technology. Additionally, we will highlight how with the right approach, businesses can unlock and scale the benefits of Gen AI.

“In the world of generative AI, supply chains will evolve from reactive systems to proactive networks, anticipating needs, optimizing resources, and seamlessly adapting to changes in real time.”

Phil Davies – Global Supply Chain leader, Capgemini Invent

Supply chain scenario modeling: a strategic perspective

In an era of unprecedented disruptions, supply chains are under immense pressure. Natural disasters, geopolitical events, pandemics, and even minor internal operational hiccups can trigger cascading effects that lead to production delays, shortages, and financial losses. In the urgent search for solutions, many business leaders are beginning to explore Gen AI’s capabilities as a means to tackle this issue.

The cost of not managing such disruptive supply chain risks effectively is immense, as evidenced by the billions of dollars lost in recent years. A recent Gartner study revealed that ‘75% of supply chain leaders expect an increase in high-impact disruptions compared to the rate of disruptions over the past 5 years.’[iii] This highlights the urgent need for innovative solutions. For many organizations, Gen AI has proven to be invaluable in this regard.

Gen AI doesn’t just predict the future; it creates it!

Gen AI: the game-changer in scenario modeling

Scenario modeling is a crucial capability of the supply chain, enabling companies to simulate various risk scenarios and prepare appropriate responses. There is a potential for Gen AI to start leveraging models to simulate potential disruptions, companies can gain valuable insights and develop effective contingency plans.

Generative AI in supply chains can revolutionize scenario modeling by creating diverse, novel scenarios beyond historical data, analyzing unstructured sources, such as news and social media. By synergistically combining Gen AI’s generative capabilities with existing AI/ML techniques, supply chain scenario modeling can achieve a new level of sophistication. This powerful combination enables organizations to anticipate disruptions, explore innovative strategies, and make more informed, data-driven decisions, ultimately leading to improved efficiency, resilience, and adaptability in the face of ever-changing market conditions.

Gen AI will exclusively build upon the existing AI and machine learning (ML) capabilities in supply chain scenario modeling to create a more powerful and comprehensive approach. While reinforcement learning (RL), simulation modeling, and agent-based learning provide the foundation for optimizing decision-making, Gen AI is capable of so much more. Unlike traditional AI, Gen AI’s ability to process vast complex data and generate insights facilitates more comprehensive and proactive risk management in today’s supply chains. By modeling the impact of everything from equipment failures and labor shortages to geopolitical events, natural disasters, and cyberattacks, businesses can gain a deeper understanding of their vulnerabilities and develop proactive mitigation strategies.

Gen AI will democratize scenario modeling, enabling leadership to run simulations directly through large language models (LLMs), reducing the need for specialized modelers and tech developers.

The future is what we make it

Despite being its most prominent capability, this transformative technology can unlock benefits far beyond scenario modeling. In fact, Gen AI does not just predict the future; it creates it. Gen AI enables companies to stress-test their supply chains in a virtual environment, experimenting with different scenarios and responses to find the most effective solutions. This ability to “play out” potential disruptions before they occur provides a level of preparedness and resilience that was previously unattainable.

Companies that embrace Gen AI-powered scenario modeling can gain a significant competitive edge. They can achieve the following outcomes:

  • Anticipate disruptions: By simulating potential risks, companies can identify weaknesses in their supply chains and take preemptive action to mitigate them.
  • Optimize operations: Scenario modeling can help companies optimize inventory levels, allocate resources more effectively, and design more resilient transportation networks.
  • Respond faster to crises: When disruptions do occur, companies with Gen AI models can quickly assess the situation, generate alternative scenarios, and choose the best course of action.
  • Innovate and adapt: By continuously learning and adapting to new data, Gen AI models can help companies stay ahead of the curve and respond to changing market conditions.

Clearly, there are great potential benefits to integrating Gen AI for scenario and risk modelling!

Demand planning: Bridging the Gaps

Demand planning is a critical component of supply chain management that involves forecasting to ensure a company can meet future customer demand. The future of demand planning is not just about better predictions – it’s about unlocking a new level of understanding. Gen AI is not replacing current AI and ML models; it’s supercharging them. Imagine a demand planning system that does not just crunch numbers, but grasps the nuances of market shifts, consumer behavior, and global events. This is the promise of Gen AI. By synthesizing vast amounts of structured and unstructured data, it creates a dynamic, real-time picture of demand. This is not just about accuracy; it is about adaptability. Supply chains become agile, responding to disruptions with foresight, not hindsight. It is about empowering planners with insights that go beyond numbers, enabling them to make strategic decisions that drive growth and resilience.

Gen AI: the next big bet in planning

The unique capabilities of Gen AI are transforming how companies forecast demand, optimize inventory, and ultimately, improve their bottom line. The sources of these benefits primarily stem from the unique capabilities of Gen AI:

Unstructured data processing

Data is a crucial input for accurate demand planning. Gen AI enables you to now harness large amounts of real-time unstructured data from diverse sources, such as social media, weather forecasts, and geopolitical events, to provide more accurate and advanced planning and forecasting insights

Collaboration across functions is essential for successful demand planning. Gen AI has the potential to eliminate communication silos by providing insights and predictive alerts through cognitive chat agents. This technology can help ensure that data and insights are seamlessly shared, and both the known and potentially unknown business drivers are uncovered and understood. This helps provide one single version of truth for a better business outcome

Generative AI solutions could involve co-creation of demand plans alongside human oversight. It can speed up plan creation, harnessing complex data through large language models (LLMs) and create customized plans through chatbot functions that align to requests, such as weather impacts, the demand planners insight, or strategic business decisions. This could help to free up time for collaboration with their key stakeholders and enables more strategic discussions based on advanced data and analytics. Managing this task requires unwavering effort, continuous creativity, and resourcefulness to adapt and refine plans

Gen AI models are not static; they continuously learn and adapt as new data becomes available. This allows them to stay up to date with changing market conditions, consumer behavior, and supply chain dynamics, ensuring that their predictions and recommendations remain relevant and accurate.

These qualities set Gen AI apart from traditional AI capabilities that cannot generate new data or content and are restricted to analyzing existing datasets only. This enables planners to experiment with different scenarios to stress-test their supply chains and formulate optimal strategies for a variety of market conditions.

In today’s market, we are seeing Gen AI-powered demand forecasting systems improve forecast accuracy by up to 10% already by using Gen AI to predict and optimize the forecast. It incorporates market data and signals in addition to historical order and shipment information, employing a library of probabilistic and deep learning models to identify accuracy and reduce bias across product, geography, and time hierarchies.[iv]

Gen AI presents a powerful opportunity for supply chains leaders to empower their demand planners, who can in turn guard against uncertainty and transform their supply chains in ways that will ensure they thrive despite uncertainty.

Scaling and readiness for Gen AI

As part of the Gen AI revolution, a multitude of use cases have been identified where the technology can potentially drive or unlock significant business value. However, recent research highlights that more than 85% of Proof-of-Concepts (PoCs) for Gen AI have failed to move to production. Therefore, it is crucial to thoroughly assess their feasibility, ascertain their true value, and evaluate their scalability within an organization to fully realize the associated benefits.[v]

To support this, businesses should look to establish processes and create a shift in mindsets whereby use cases can move beyond ideation to be fully assessed, tested, deployed, and adopted to deliver value. Leaders must move away from traditional, reactive risk management approaches and embrace a proactive, data-driven mindset. Based on our experience, the following guidelines are provided as the core pillars to enable successful Gen AI in supply chain deployment.

GenAI business readiness

  • Gen AI Strategizing: organizing dedicated workshops to clearly define your Gen AI strategy and identify use cases that will provide the most benefits.
  • Build the right toolkit: Leverage frameworks and toolkits to develop Gen AI solutions from proof of concept to enterprise ready.
  • Benefits at scale: Implement the right operating model, people, processes, technology, risk management, and controls to safely scale across the organization.

Finally, leaders in organizations will play a pivotal role in driving the adoption and implementation of Gen AI in supply chain scenario modelling. To succeed, leaders must prioritize the following actions: Championing change, defining a clear vision, and facilitating continuous improvement.

Final thoughts: The future of supply chain management

Whilst addressing two of the many supply chain opportunities in this report, it is clear that Gen AI will make an impact across the End-to End (E2E) chain. With that in mind, here are some parting considerations:

  • The future of supply chain management belongs to those who are willing to embrace change and harness the power of AI and Gen AI.
  • This is not just a technological challenge; it’s a leadership challenge. The companies that succeed will be those that have the vision, courage, and agility to transform their supply chains for the emerging age of AI.
  • This is not just another tech trend; it’s a paradigm shift. Generative AI is democratizing access to sophisticated risk modeling capabilities, enabling even smaller companies to compete on a global scale with unprecedented resilience.

The future of supply chain management is here, and it is powered by AI and Gen AI. Leaders must seize this opportunity to transform their supply chains, ensuring they are not only resilient but also capable of thriving in an increasingly unpredictable world.

Capgemini has multiple frameworks and project blueprints to support and accelerate the development, deployment, and operation of Gen AI within supply chain. Contact our experts to learn more:


Dinesh Tomar

Director, Intelligent Industry, Capgemini Invent
Seasoned Supply Chain leader with over 20 years of global consulting experience across top-tier firms. This includes transforming operations for efficiency and competitive edge, as well as shaping the future of the industry by driving strategy, value, and innovation. Dinesh drives end-to-end supply chain strategy, value creation, and next-gen tech adoption, including Supply Chain generative AI initiatives.

Annabel Cussons

Senior Consultant, Intelligent Industry, Capgemini Invent
Annabel joined the Capgemini group in 2021, within the Supply Chain practice. She is an expert in global tech implementations and End-to-End Supply Chain transformations, working closely on Gen AI within the sector. Annabel brings over six years of experience and insight from the retail industry and has a degree in buying and merchandising. She has a passion for helping clients grow their business through digital transformation.

Adeel Butt

Consultant, Intelligent Industry, Capgemini Invent
Adeel is a supply chain consultant with a focus on enabling successful digital transformation for clients via state-of-the-art technology solutions. With a keen interest in AI, Adeel works closely with clients on their journey to deploy sustainable technology and enable a digital-first future. Adeel holds a master’s degree in mechanical engineering from the University of Bristol, along with multiple industry certifications in technical and management streams.

Tatiana Horsham

Associate Consultant, Intelligent Industry, Capgemini Invent
Tatiana is a Supply Chain consultant, who focuses on End-to-End transformation. She has a strong interest in digitalisation and is excited about the future of AI and Gen AI, with the potential it holds to unlock ground-breaking innovations. Tatiana has several years of expertise gained from varied industry experience across the banking and wider sustainable development sector. She graduated from the University of Bath in International Development with Economics and continues to grow her wealth of knowledge and qualifications alongside her career.

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