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The recent and roaring launch of ChatGPT to the public has left the business world scrambling to figure out what generative AI is all about, why it’s different from traditional AI, and how to deploy it if company leaders decide it’s a must-have for business success.

In that context, FOMO (Fear of Missing Out) is likely playing a role in generative AI dynamics, since many experts predict that organizations able to effectively implement it will outperform the competition.

In our first post in this series, we tapped into two McKinsey & Company resources to help lay a generative AI foundation.

In this second installment, we’ll take a look at what some experts say about how generative AI can provide value for supply chain management (SCM) — as well as which SCM roles may be impacted as a result of its use.

Potential use cases for generative AI in SCM

Describing the many challenges associated with an increasingly complex and evolving global supply chain, Cem Dilmegan, principal analyst at AIMultiple, says that generative AI offers “promising solutions” to tackle them.

“By leveraging the power of generative AI, supply chain stakeholders can analyze massive volumes of data, generate valuable insights, and facilitate better decision-making processes,” Dilmegan writes.

He also provides his list of the top 10 potential use cases for generative AI in SCM:

  1. Demand forecasting: “Generative AI can analyze large amounts of historical sales data, incorporating factors such as seasonality, promotions, and economic conditions. …”

  2. Supply chain optimization: “Generative AI models can make data analysis on various sources, such as traffic conditions, fuel prices, and weather forecasts, to identify the most efficient routes and schedules for transportation. …”

  3. Supplier risk assessment: “By processing large volumes of data, including historical supplier performance, financial reports, and news articles, generative AI models can identify patterns and trends related to supplier risks. …”

  4. Anomaly detection: “By analyzing data across various aspects of the supply chain, generative AI models can identify unusual patterns or deviations from the norm. …”

  5. Product development: “Generative AI can process market data, customer feedback, and competitor information to generate insights about potential gaps or opportunities in the market. …”

  6. Sales and operations planning: “Generative AI solutions can integrate data from sales, marketing, production, and distribution to generate more accurate and comprehensive plans. …”

  7. Price optimization: “Generative AI models can analyze factors such as customer demand, competitor pricing, and market conditions to generate optimal pricing strategies. …”

  8. Transportation and routing optimization: “…By analyzing vast amounts of data from various sources, AI can generate efficient transportation plans, save time, and improve the overall efficiency of supply chain logistics.”

  9. Inventory Management: “Generative AI models can analyze demand patterns, lead times, and other factors to determine the optimal inventory levels at various points in the supply chain. …”

  10. Financial optimization in supply chain: “…the use of generative AI in financial services and operations can significantly benefit supply chain management by improving efficiency, reducing risks, and enhancing decision-making processes.”

Microsoft Dynamics 365 Copilot

According to Dilmegan, Microsoft is the first company offering the ability to use generative AI in SCM.

“…in March 2023, Microsoft announced Microsoft Dynamics 365 Copilot, an AI-driven assistant integrated into CRM [customer relationship management] and ERP [enterprise resource planning] systems,” he explains. “Copilot in Microsoft Supply Chain Center has a news model that gathers all the supplier-related news that can potentially affect supply chains, such as natural disasters and geopolitical situations.”

Dilmegan says that when a supply chain manager receives a notification about an issue, they “can send AI-generated and targeted emails to suppliers with Azure OpenAI Service.”

In a March blog post describing the new offering, Ray Smith, VP, Resilient & Sustainable Supply for Microsoft says supply chains are a “prime area for the application of AI,” since there are such massive amounts of data and processes involved.

“Supply chains have evolved over the years, with emerging technologies and innovations that enable businesses to optimize their operations, reduce costs, and improve customer satisfaction,” Smith writes. “Yet, while statistical models have been used in processes such as inventory management, forecasting, production planning, and scheduling, there hasn’t been a significant shift in the industry beyond improving algorithms.”

But generative AI is changing that.

“The next generation of AI will transform the industry by making it more agile, efficient, and responsive to changes,” he says.

Like Dilmegan, Smith describes several potential use cases for generative AI in SCM, including the ability to:

  • Mitigate risk: “By unifying data sources and business applications and combining them with next-generation AI, companies can better predict and act on disruptions across channels, suppliers, and geographies. …”

  • Optimize order fulfillment: “Microsoft Dynamics 365 Intelligent Order Management (IOM) enables organizations to intelligently orchestrate fulfillment and automate it with a rule-based system using real-time omnichannel inventory data, AI, and machine learning. …”

  • Improve forecast accuracy: Noting that AI has been used extensively in demand forecasting, Smith says related models have not always provided the accuracy needed and that the “next-generation of foundation models” could potentially change that: “[The] ability to get answers through AI forecast explainability and natural language querying will help demand planners breeze through their demand plan analysis, reducing the time needed for fine-tuning and adjusting demand plan from days to minutes.”

  • Use data Q&A to mitigate order delivery risks: Generative AI can help with monthly reviews of vendor performance by analyzing order delivery dynamics, identifying the impact on the organization, and creating data-informed questions to help low-performance vendors identify root causes.

  • Create autonomous supply chains: “AI can address the complexities of mapping a multi-tier network model from several disconnected systems across the value chain, including external business partners. Further, with advances in AI such as reinforcement learning, the networks can be adaptive and self-regulated with different sub-network agents operating toward a common goal of increasing resilience, profitability, and customer service….”

  • Provide “intelligent process automation”: “As next-generation AI innovation emerges, it will increasingly deliver on the promise of automating many of the recurring decision touchpoints in supply chain management, freeing up valuable human resources to focus on higher-level productive tasks that require creativity, judgment, and complex problem-solving skills. …”

  • Offer intelligent inventory visibility and optimization: With inventory intelligence, “…AI can balance inventory more accurately to reduce stockouts and improve customer satisfaction and loyalty. … Intelligent inventory visibility is also revolutionizing the way businesses search and view their stocks and products, empowering users with unparalleled accessibility and efficiency. …”

  • Help “shorten warehouse inventory cycle times”: “In the future, AI could be applied to analyze incoming orders (or look further upstream in the supply chain) to forecast demand better. Based on this analysis, combined with data like physical product dimensions and the storage capacity of bin locations, recommendations for re-slotting can be offered to warehouse managers––allowing plans to be proactively set in motion to ensure that on-hand inventory is available at the time of picking.”

Smith says the list provides only a few examples and is just a starting point: “AI has the potential to revolutionize supply chains, offering new possibilities for improved efficiency, cost savings, and customer satisfaction.”

Potential impacts on supply chain roles

While the increased adoption of generative AI may be a good thing in terms of efficiency and productivity, supply chain workers whose roles may be impacted might view the potential through a slightly different lens.

In “A new era of generative AI for everyone. New tech, new jobs, new value: How generative AI will reinvent supply chains,” global consulting firm Accenture makes several predictions about the potential impact of generative AI on supply chain roles.

“As humans working with generative AI ‘virtual colleagues’ become the norm, every role in every supply chain has the potential to be transformed,” the firm says. “Companies that adopt a strong digital core in this way can drive innovation and help accelerate a new performance frontier as part of a broader strategy of Total Enterprise Reinvention.”

Predicting that “supply chain roles will change dramatically,” Accenture lists five specific areas in which generative AI will have a “profound impact, supporting people right across the supply chain,” and includes related roles that could potentially be impacted.

Accenture says that if generative AI is applied to:

  • Advising, roles that could potentially be impacted include category managers, supply chain planners, field engineers, and plant workers: “By putting new kinds of hyper-personalized intelligence into human hands, Generative AI will support many different supply chain tasks. …”

  • Creating, roles that could potentially be impacted include product engineers, retailers, and materials planners: “Generative AI will become an essential creative partner for people, revealing new ways to reach and appeal to target audiences and bringing unprecedented speed and innovation in supply chain areas…”

  • Coding, roles that could potentially be impacted include data scientists working in supply chain operations: “By enabling rapid conversion of one programming language to another, generative AI will dramatically boost the productivity and speed of professionals working to optimize supply chain processes and onboard new supply chain players; generative AI will also democratize access to AI and analytics for professionals without niche expertise.”

  • Automating, roles that could potentially be impacted include call-center staff, planners, and buyers: “Generative AI will transform business process automation. Hyper-efficiency and hyper-personalization will be the norm in back- and front-office operations. …”

  • Protecting, roles that could potentially be impacted include sustainability and compliance officers, and risk managers: “In time, Generative AI will support enterprise governance and information security, protecting against fraud, improving regulatory compliance for sustainability and responsibility, and proactively identifying risk – all mission-critical supply chain responsibilities.”

Accenture also couches its enthusiasm about the potential of generative AI to transform the business landscape with a word of caution: “However they plan to use generative AI, companies need to ensure that these new models and applications do not create unacceptable risks for the business. It’s critical that generative AI development is responsible and compliant by design.”

And that’s a topic we’ll cover in next week’s post, which will be the last in our series about generative AI in SCM, so please join us.

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