GenAI Enhances Supply Chain Management Efficiency

Generative Artificial Intelligence (GenAI) is becoming a game-changer in supply chain management, especially in sourcing and procurement. The global market for AI in Supply Chain Management (SCM) is expected to reach $58.55 billion by 2031, growing at an impressive 40.4% annually from 2024 to 2031. This growth shows just how transformative GenAI can be. Companies using this technology are seeing significant shifts in efficiency and operations. But it's not all smooth sailing. A $10-50 billion company will likely have thousands of suppliers; as enterprises attempt to bring more order to their supply chains with GenAI, they must confront data quality deficits, complex integrations, and change management gaps. The potential of GenAI is vast, but making the most of it takes clear strategy and determination.

Critical Benefits of GenAI in SCM

In our experience, the value of GenAI in SCM is currently most apparent in five use case areas:

1. Supply Chain Planning 

GenAI enhances supply chain management by providing more accurate insights, optimizing operations, and enabling proactive decision-making. We see a broader applicability of GenAI across supply chain functions, particularly in demand planning and incorporating external factors affecting demand. Customers are actively integrating various external sources like competitor information, economic indicators, and other market trends to boost the accuracy of the forecast. GenAI allows for more dynamic and responsive supply chain management, helping businesses to better anticipate and react to changes in demand. We see active customer interest in using GenAI to make inferences from KPIs and dashboards, assist planners, and build strong collaboration across different stakeholders in the supply chain — all of which can lead to more synchronized operations.

2. Supply Chain Resiliency 

GenAI plays a critical role in bolstering the resiliency of existing supply chains by mitigating the effects of disruptions. Customers are building early warning systems by analyzing various risk factors — such as geopolitical events, weather patterns, and economic indicators — to predict potential disruptions in the supply chain. GenAI can provide real-time alerts and actionable insights, allowing businesses to proactively address emerging challenges before they balloon into larger problems. These capabilities help maintain smooth operations, minimize financial losses, and guarantee customer satisfaction.

3. Intelligent Sourcing 

Customers are leveraging GenAI to make the sourcing process more intelligent, leading to improved efficiencies, reduced costs, enhanced compliance, and more informed decisions. We have seen many pilot projects that automate supplier identification by analyzing large datasets to find potential suppliers matching specific criteria. This process involves evaluating historical performance data, compliance records, market reputation, and financial stability to rank suppliers based on risk profiles. GenAI also streamlines the RFP (Request for Proposal) and RFQ (Request for Quotation) processes by generating detailed and tailored documents while analyzing bids based on price, quality, delivery timelines, predictive insights, and compliance.

GenAI identifies effective strategies for suppliers and negotiation scenarios to help companies make advantageous deals. It can automatically review contracts for compliance, identify potential risks, and highlight key terms and conditions. By tracking contract milestones, managing renewals, and adhering to obligations, GenAI provides alerts for important dates and tasks. Furthermore, by analyzing a wide range of supplier data, GenAI generates insights that can lead to more strategic and informed decision-making, considering factors such as supplier performance, social sentiments, technical and operational capabilities, financial position, pricing, and risk profiles.

4. Digital Concierge 

Customers are using GenAI-powered conversational assistants to transform supply chain management processes by enabling interactive and intuitive conversations. Order managers can use conversational AI to track orders, manage inventory, and resolve real-time issues to improve operational efficiency. GenAI-powered chatbots, voice assistants, or other digital interfaces can make supply chain management more responsive and user-friendly.

5. Training and Knowledge Management 

Think of GenAI as a personal coach for supply chain teams. It can revolutionize employee learning experiences by providing personalized training modules and real-time guidance. In supply chain management, having a well-trained workforce is crucial. GenAI can create customized training programs tailored to employees' needs and skill levels, ensuring they receive relevant and practical instruction. GenAI-powered knowledge bots can also assist with Standard Operating Procedures (SOPs), providing quick access to essential information and guidance.

Challenges and Considerations in Implementing GenAI

While the benefits of GenAI in supply chain management are substantial, several unique challenges must be addressed to realize its full potential. High-quality and consistent data is crucial for accurate decision-making, but supply chains rely on data from various sources, and inconsistencies can hinder GenAI's effectiveness. These data quality issues can lead to inaccurate analyses and suboptimal outcomes. Additionally, supply chains are inherently complex and dynamic, requiring GenAI models to adapt continuously to changing conditions and disruptions. This adaptability is vital for maintaining the relevance and accuracy of AI-driven insights in real-time decision-making.

Moreover, safeguarding data privacy, avoiding bias, and complying with regulations are critical ethical considerations in implementing GenAI. It is also essential to establish necessary guardrails related to security, hallucinations, computational power costs, and the time required for fine-tuning large language models to guarantee responsible AI outputs. Companies must navigate these challenges while integrating GenAI with existing systems, which presents technical hurdles and requires careful planning and expertise. The seamless integration of new AI capabilities with current infrastructure is vital for maximizing the benefits of GenAI. Successful implementation demands effective change management strategies to address resistance and train employees and suppliers using GenAI tools. This process involves developing comprehensive training programs and fostering a culture that embraces AI-driven decision-making.

At Wipro, we are at the forefront of leveraging GenAI to drive innovation in supply chain management through our ai360 initiative and Wipro Enterprise Generative AI (WeGA) frameworks. These frameworks are designed with guardrails to mitigate challenges like hallucinations, which can lead to inaccurate or misleading outputs. We also account for the significant computational power required to run and fine-tune large language models (LLMs), ensuring that the benefits of GenAI are maximized while maintaining cost-effectiveness. We enable clients to transform their supply chains confidently by addressing these concerns. For example, using GenAI, we built a taxonomy of events from the last 100 years of historical data to identify high-vulnerability suppliers for a leading energy company, resulting in 5-10% savings on procurement costs.

Looking ahead, supply chain organizations can expect concrete business value from GenAI. GenAI will streamline inventory management, forecast demand more accurately, identify potential bottlenecks, and optimize logistics and transportation routes. It will drive significant improvements in operational efficiency, cost reduction, and customer satisfaction.

As GenAI technology advances, we can expect more sophisticated algorithms to handle increasingly complex supply chain scenarios and optimize decision-making in real-time. This transformation requires organizations to steadily and systematically evolve into truly Intelligent Enterprises (IEs) capable of adapting to shifting business conditions, stakeholder demands, and ecosystem potential. It envisions an AI platform of the future (with an intelligence fabric built across various systems and databases, including supply chain and non-supply chain functions). It incrementally builds capabilities to address enterprise use cases across the value chain. An IE equips its workforce to improve productivity and aids in developing and implementing ethical and reliable AI applications. Wipro has a unique methodology to derive the Enterprise Intelligence Quotient (E-IQ) that benchmarks the as-is process using an intelligence maturity framework and defines a program roadmap to roll out AI-infused business processes.

GenAI is poised to dramatically reduce the cost of bringing products to market. However, for companies to fully capitalize on this potential, it's crucial to integrate GenAI with their existing technology investments. Those who proceed too slowly in adopting data-driven SCM transformation risk falling behind competitors who can harness the power of GenAI quickly. By leveraging new and existing technologies, companies can ensure a seamless transition and avoid competitive disadvantages. GenAI will be a critical enabler for an autonomous supply chain, riding on an intelligence fabric for enterprises — making it a game changer in supply chain management.

About the Authors

Sudhanshu Raj
Managing Consultant, Wipro Consulting

Sudhanshu is responsible for delivering business value to clients and specializes in supply chain transformation. With more than 18 years of business and technology experience, he has worked with esteemed clients across the globe to drive business transformation programs and deliver high quality large-scale projects across industries.

Vaidyanathan Shankar
Practice Director, Data, Analytics & AI, AI for Supply Chain & Manufacturing

Vaidy is an industry and domain expert pioneering digitization and business transformation leveraging state of art technology. He has 27 years of experience in leading transformation initiatives in areas of supply chain planning and execution, supply chain optimization and visibility, factory of future, operational excellence, omni-channel commerce, and sustainability. He has diverse industry experience working across verticals including CPG, retail, logistics, agriculture, automotive, chemicals, electronics, oil & gas, industrial equipment, and metals. In his current role in Wipro’s Data Analytics and AI practice, he leads the industry offerings portfolio for AI in Supply Chain and Manufacturing, developing innovative solutions to drive AI/GenAI adoption.

Rahul Deshpande
General Manager and AI Industry Solutions Practice Head

Rahul has 30 years of IT experience, with the last 7 years specifically focused on AI and the past 20 years in data analytics consulting across BFSI and manufacturing industry clients. Rahul has established hyper-scaler capabilities and partnerships for the AI practice, including partnerships with Google, Microsoft, AWS, and IBM. Today he leads the AI Offerings Practice, which is developing industry solutions for functional (supply chain, finance, marketing, etc.) and vertical (banking, energy, retail, healthcare, etc.) use cases, leveraging AI/GenAI to solve for industry problems. Rahul is also the co-author of the Enterprise Intelligence Quotient (E-IQ), a methodology used to benchmark intelligence of a business processes and discover AI use cases. He aids global organizations in defining and refining their vision of technology transformation in a disruptive business environment using artificial intelligence (AI).