7 minute read 27 Jul 2023
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How supply chains benefit from using generative AI

Authors
Sumit Dutta

Principal, Supply Chain & Operations, Ernst & Young LLP

Supply chain aficionado. A true global citizen and explorer with experience working in over 25 countries. Father and personal tutor/homework helper to two daughters.

Glenn Steinberg

EY Global Supply Chain and Operations Leader

Helping companies transform, create value and optimize business performance. Thirsty for knowledge. Ski enthusiast. Husband and father of two Michigan Wolverines.

Contributors

Ayoub Abielmona

7 minute read 27 Jul 2023
Related topics Consulting Supply chain AI
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Across the end-to-end supply chain, the buzzworthy technology adds extra capabilities to AI tasks and promises a simplified user experience.

In brief:

  • These use cases exist today, and whether you win or lose in the market may soon depend on having the best AI models and the data quality to match them.
  • To begin, identify a business need and then embolden it with generative technology, whether in planning, sourcing, manufacturing or delivery. 

This article is co-authored by:

  • Asaf Adler, EY Americas Supply Chain Emerging Technology Leader

Corporations have been increasingly relying on artificial intelligence (AI) in supply chain for demand planning and procurement, while exploring its use in other areas, such as standardizing processes and optimizing last-mile delivery. Even amid the global pandemic, enterprises were focused on evolving their AI supply chain pilots into operationalization. But, suddenly, another evolution of AI seized the spotlight — generative AI, popularized by ChatGPT — and upended our notions of what’s possible.

What is generative AI in supply chain?

Generative AI creates new content, such as images, text, audio or video, based on data it has been trained on. While the technology isn’t new, recent advances make it simpler to use and realize value from. As investors pour cash into the technology, executives are racing to determine the implications on operations, business models and to exploit the upside. For those who diligently pursue innovation guided by strategy and an understanding of the limitations — not by an impulse to chase after the latest shiny object — generative AI can prove to be an agile co-advisor and multiplier in strengthening supply chains.

What once seemed like science fiction even a year ago is now being discussed as possibilities and already being leveraged in real-world use cases across the end-to-end supply chain. These projects are enabled through generative AI’s ability to:

  • Classify and categorize information based on visual or textual data
  • Quickly analyze and modify strategies, plans and resource allocations based on real-time data
  • Automatically generate content in various forms that enables faster response times
  • Summarize large volumes of data, extracting key insights and trends
  • Assist in retrieving relevant information quickly and providing instant responses by voice or text

How EY can help

Supply chain and operations

Based on your overarching strategy, we’ll help redefine your end-to-end supply chain and operations to support your enterprise objectives.

Read more

Plan

Generative AI adds simplicity to interactions throughout tech-enabled planning efforts. The “chat” function of one of these generative AI tools is helping a biotech company ask questions that help it with demand forecasting. For example, the company can run what-if scenarios on getting specific chemicals for its products and what might happen if certain global shocks or other events occur that change or disrupt daily operations. Today’s generative AI tools can even suggest several courses of action if things go awry. Risk management may be the most promising area, particularly in preparing for risks that supply chain planners haven’t considered.

  • Demand forecasting

    Many organizations are using AI to analyze large historical sales data sets, market trends and other variables to create real-time demand models. ​With generative AI, optimal inventory levels, production schedules and distribution plans can be created to meet the customer demand efficiently.​

  • Production planning

    AI helps with plan production and scheduling by considering factors such as customer changes, production capacities, resource availability and order priorities.​ Similar to its demand forecasting capabilities, generative AI can make production plans, schedule sequences and allocate resources effectively to minimize bottlenecks and optimize production efficiency.​

  • Risk management

    Today, AI can be harnessed to analyze historical data, market conditions, weather patterns and geopolitical events, among other data sources, to identify potential supply chain risks. But instead of prepopulated dashboards, for example, generative AI can be prompted to produce risk assessments, scenario simulations and mitigation strategies on demand to help planners manage and mitigate the risks proactively.

Source

One leading US retailer built bots using generative AI to negotiate cost and purchasing terms with vendors in a shorter time frame, noting that this early effort has already reduced costs by bringing structure to complex tender processes. The technology presents the opportunity to do more with less, and when vendors were asked how the bot performed, over 65% preferred negotiating with it instead of with a human at the company. We have also seen instances where companies are using generative AI tools to negotiate against each other!

Beyond negotiations, generative AI presents an opportunity to improve supplier relationships and management, with recommendations on what to do next. These tools are useful to quickly extract information from large contracts and help you better prepare for renewal discussions, for example.

  • Supplier management

    Leverage natural language processing to gain insights from supplier communications and data points. Support, monitor and analyze supplier interactions; identify potential issues; and improve supplier relationships.

  • Sourcing

    Support the supplier selection process by analyzing a wide range of supplier data and generating insights. By considering factors such as supplier performance, capabilities, pricing and risk profiles, generative AI algorithms can provide recommendations or rankings for making informed decisions.

  • Contracts

    Contract analysis is aided by automatically extracting key information from contracts and generating summaries or insights. Review and compare contract terms, identify risks and help ensure compliance. Contract negotiations and renewals are supported by providing data-driven recommendations.

Make

Generative AI in supply chain presents the opportunity to accelerate from design to commercialization much faster, even with new materials. Companies are training models on their own data sets, and then asking AI to find ways to improve productivity and efficiency. Predictive maintenance is another area where generative AI can help determine the specific machines or lines that are most likely to fail in the next few hours or days. This can help improve the overall equipment effectiveness (OEE) — one of the most important manufacturing metrics.

For instance, one leading industrial manufacturing company in Europe partnered with a tech leader to use generative AI for factory automation and product lifecycle management, shortening the product development lifecycle and boosting efficiency with automated inspection processes.

  • Product design

    Rapidly generate and evaluate hundreds of alternative designs based on predefined criteria, significantly speeding up the innovation process. This could be used for everything from designing new parts for machinery to creating consumer products that are more efficient, durable or aesthetically appealing.

  • Predictive maintenance

    By learning from data collected from machines on the factory floor, generative AI models can create new maintenance plans to correlate with the time that equipment is likely to fail. This allows manufacturers to adjust their maintenance schedules to only when it is necessary, reducing downtime and costs while also extending the life of their equipment.

  • Material science and engineering

    Generative AI can be used to discover new materials and optimize existing ones. By processing vast amounts of data on material properties and iterating on different combinations, it can propose new materials with desired properties or suggest optimizations for existing ones. This could lead to the creation of more efficient, sustainable or durable materials in manufacturing.

Move

How is generative AI used in logistics? Here is an example: One of the biggest logistics companies in the US is using a proprietary AI platform to optimize picking routes within its warehouses, boosting workforce productivity by about 30% while slashing operational costs through optimized space and materials handling. While this is not a new use for AI, the generative component offers added dimensions of customization — say, optimizing based on less fuel, or to prioritizing certain deliveries or considering many other factors in a user-friendly application. Chatting with its customized tool helped the company understand if its trade network was optimized, and it even offered suggestions for improvement.

  • Global trade optimization

    Analyze the myriad variables, including tariffs, customs regulations, trade agreements and shipping costs, to suggest the most efficient and cost-effective trade routes and strategies. This aids companies in navigating complex international trade networks, helping ensure compliance while minimizing costs.

  • Logistics network design

    Optimize the design of logistics networks considering factors such as warehouse locations, transport links and demand patterns to generate the most efficient configuration. This leads to reduced delivery times, lower costs and improved service levels.

  • Last-mile dynamic route optimization

    For logistics operations, one of the major challenges is routing in real time. Generative AI can continually update and optimize delivery or pickup routes based on changing factors like traffic conditions, weather and the priority of deliveries. This leads to increased efficiency, reduced fuel consumption and improved customer satisfaction.

Get started today

While generative AI is a powerful tool with certain limitations, it is not a strategy. Focus on the business value and define a roadmap to shape and impact the organization, guided by three steps:

  1. Focus on domain-wide transformation: Pinpoint high-impact use cases, envisioning a cohesive ecosystem that synergizes with traditional business models and unlocks possibilities.
  2. Coordinate organization collaboration: Discuss the implications and identify the required skills across functions, going beyond technical roles.
  3. Keep an open mind — and guard against the risks: Implement proof-of-concept pilot initiatives to learn more, drive quick wins and strive for scalable adoption.

Summary

AI in supply chain management will help enterprises become more resilient, sustainable and transform cost structures. While it does have limitations, generative AI presents a multiplier in what humans and technology can achieve together in building efficient and resilient supply chains — whether in planning, sourcing, making or moving. Thanks to recent updates that make it simpler to use and more effective in realizing value, organizations are now forced to determine how these advances will impact their sector or risk disruption.

About this article

Authors
Sumit Dutta

Principal, Supply Chain & Operations, Ernst & Young LLP

Supply chain aficionado. A true global citizen and explorer with experience working in over 25 countries. Father and personal tutor/homework helper to two daughters.

Glenn Steinberg

EY Global Supply Chain and Operations Leader

Helping companies transform, create value and optimize business performance. Thirsty for knowledge. Ski enthusiast. Husband and father of two Michigan Wolverines.

Contributors

Ayoub Abielmona

Related topics Consulting Supply chain AI
Upvote