01 Sep 2023

The Modern Supply Chain with Generative AI

As the new tech whiz kid on the block, Generative AI has injected impetus in supply chain functioning right from ground zero planning to execution strategies. Let’s dive in to know exactly the titular tech works its magic.

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In Paul S. Wang's book, 'From Computing to Computational Thinking,' the author mentions the world simply being a representation of 1s & 0s.

And, with Generative AI being the talk of the industry & conquering the imaginations of people alike, it's fair to say that the world is prioritizing its action in context to the tech that will make it more feasible, sustainable, efficient, and more importantly scalable in terms of revenue prospects.

The impact of Generative AI on industries and their leaders is both equally praiseworthy & one that requires a detailed interaction on its efficacy & relevance in the time ahead. The titular tech relevance in the supply chain industry, too, has been nothing short of a disruption, with stakeholders aggressively packing in heavy investment for what they term to be the tech for tomorrow. A notion that reflects well in a recent survey conducted by Gartner, which states that almost 30% of organizations that participated have implemented the titular tech in some capacity. But before we dive into algorithms & their positive spin on the value chain industry, let us first understand what defines a Generative AI.

What is Generative AI in Supply Chain?

By definition, Generative AI is a form of artificial intelligence that works on the framework of machine learning algorithms to generate brand-new content that never had any precedent before.

In context to supply chain industry, this model comes close to a sentient being by analyzing vast amounts of information from procurement to delivery process to come up with unique output that more or less contributes to the betterment of the overall functioning of an enterprise. The result can be in various forms based on the information fed to the principal processing of the automated platform. Major tech conglomerates have developed their Generative AI offerings in the form of search engines and content generation applications, among others. Needles to say, that the widespread benefits of adopting generative AI in the supply chain has made it a sweeter prospect for the SCM.

What makes GenAI so sought-after?

With conversations gaining traction every day of the week, it really is insightful to know how exactly this trending technology is becoming such a chartbuster. With features making a huge impact in minimum turnaround time, the next stages of supply chain management have seen advancements to the tune of max. Here are a few pointers on what makes it tick.

This image highlights the advantages of using generative AI in supply chain management
  • Focus on insights: With algorithms exhaustively checking your historical data, GenAI platforms churn out standout insights that were previously inaccessible owing to unstructured data formats.
  • User-friendly: With its conversational capabilities allowing seamless interaction, generative AI usage has simplified the approach to getting solutions to complex questions. The best example is the widespread acceptance generative chatbots have received – that allows users simply to type in their query and get instant responses.
  • Content Generation: GenAI's biggest virtue is its ability to generate responses by understanding the user's inputs. From driving information-based decisions in the right direction through verified outputs, generative artificial intelligence software has revamped the roadmap to the final conclusion.

How does Generative AI work for the Supply Chain?

The world cannot function without a supply chain. To disrupt a value chain process is to disrupt the usual way of living as we know it. While an SCM is always on course to find ways to mitigate unwarranted scenarios through the virtues of data analytics – a Generative AI platform, through its algo prowess on case studies, can prove to be a worthy mentor that can help the entire supply chain functions from procurement to delivery go from strength to strength. Here’s the roadmap to how the titular tech enables its ability for a supply chain industry.

  • It first classifies & categorizes information based on the information presented.
  • Does complete analyses to strengthen adaptable strategies & resource allocation based on real-time data.
  • Information/Content generation in required forms that benefits faster response time.
  • Summarizes large chunks of data to get crucial insights & market trends.
  • Extracts relevant information according to the query given.

Use Cases of Generative AI in Supply Chain

The practical application of GenAI in supply chain has essentially accelerated the many processes the framework involves. From planning to production to efficiently managing the performance of the applied resources to keeping tabs on their maintenance cycles, the marquee tech virtues has simplified the inherent complexities that come attached with a value chain process. With the context for Generative AI abilities for value chain set – let us look at how the application finds its use cases in SCM.

This image showcases real-world applications of generative AI in supply chain management
  1. Demand Forecasting and PlanningGenerative AI can help optimize inventory levels, minimize stockouts, and enhance customer satisfaction by analyzing historical data, market trends, and external factors. With algos finding meaningful patterns out of complex variables, Generative AI can help counter demand fluctuations and align optimized production levels & inventory keeping, resulting in efficient operations & capital savings.
  2. Inventory Strategies By reducing excess inventory, Generative AI models figure out efficient strategies for distribution & storage by accounting for lead times, transportation costs, and demand fluctuation, as mentioned above, thus maximizing market opportunities, and contributing to the overall revenue goals.
  3. Vendor Selection & RelationshipEvery supply chain management requires multiple vendors to manage the scale of their operations – and the selection of a vetted value chain partner is an equation for smooth end-to-end functioning. By leveraging Generative AI capabilities, leadership can recce supplier's performance, pricing matrix, overall qualities, and geographical presence, among other decisive factors, to finalize their supply chain network. Not only this, after onboarding the vendors, the automated platform tracks daily activities, suggests improvement, and charts out strategies that mutually benefit the growth of both parties (organization and vendor), resulting in better relationship management.
  4. Logistics OptimizationAs an integral part of the overall value chain functioning, logistics has to remain one of the most robust processes from a management perspective. To make things easier in transportation planning, Generative AI can optimize route planning, delivery scheduling, and resource allocation by considering traffic conditions, weather forecasts, vehicle capacities, and customer demands, which funnels down the cost incurred by the enterprise. The new-age platform further considers unforeseen circumstances and adapts to real-time scenario changes to improve the resiliency of the supply chain.
  5. Risk ManagementInsights driven by data always lend wisdom for the upcoming time. The biggest virtue defining Generative AI is helping leadership chart out ways to navigate risks and supply chain bottlenecks by making the end-to-end process proactive rather than reactive. And the titular technology does it with quite aplomb.
  6. Product BettermentA company has to constantly evolve to keep up with the market trends & customer's ever-changing requirements. To address the law of systematic obsoletion, Generative AI can help brainstorm new concepts & systematic approaches to the desired configuration, all in the realms of a defined budget & living up to the user's expectations. In simple terms, we can say that the GenAI can predict the best market-fit product right from the outset to help elevate a brand’s standing.
  7. Sustainability & Environmental ImpactESG laws highlight the first line of thought process that management ponders to build up its brand call & the relevance of its products in the market. To keep up with the environmental, social, and governance norms, Generative AI can help enormously by optimizing logistics operations, keeping tabs on overall emissions, monitoring guidelines, ensuring regulatory compliance, and deploying environmentally friendly practices throughout the supply chain.
  8. Enhances Safety ConditionsAn essential requirement for a setup is to ensure the safety of its workforce. Among the most noted capabilities of Generative AI is its competence in notifying decision-makers about preventive measures. Highlighting any potential chink in the armor by running regular checks to predict the machine's performance & maintenance, GenAi standardizes operating procedures to keep intact safety aspects.

Generative AI challenges in Supply Chain

The boon doesn't exist without the bane, and the same maxim holds for the breakthrough tech that has been Generative AI. With its implementation comes certain challenges that must be addressed to utilize the platform successfully.

Key challenges associated with implementing generative AI in supply chains
  1. Data Availability & QualityData is the fuel that makes organizations and their applications work. Generative AI is no exception to the rule either. For successful implementation of the platform, stakeholders have to ensure that there's the availability of a high volume of information that is vetted firsthand. Leadership has to ensure that the knowledge being processed matches their set parameters, or else the insights won't be constructive for the management.
  2. Model Integration & TrainingA Generative AI, at initiation, is bereft of any knowledge. When the information is fed to the system, it starts to make sense and subsequently churn out new content based on the industry it is being employed in. And for any application to achieve its objectives, it needs thorough training with relevant data, which can be time-consuming and intensive. In addition, the application of such AI tools needs finetuning regularly, and during such a learn & re-learn process, the fluctuating performance of the platform can pose a challenge.
  3. Understanding the computationSCM needs to be entirely on board with the logic by which the Generative AI model processes their output. Management needs to have complete transparency and should be aware of the general rationale by which the automated platform is arriving at the defined outcome. It is essential to remember that these outputs heavily influence the supply chain decision-making that can work for or against motion.
  4. AdaptabilityIt's a very competitive environment for businesses, and adaptability on the go is the mantra that keeps organizations up and running with innovations and the latest framework that allows more flexibility and subsequent business value for their product. This requires a Generative AI platform to be much more responsive to the changes undertaken by the management and also be adaptable to the various frameworks it'll work alongside.
  5. ScalabilityExpansion is always in plans for a leadership group, and to perfect the blueprint of scalability, the symbiosis with a generative platform must ensure that it responds to the ground plans the organization has for itself. With the prospects of extension, the said automated tech has its task cut out to run analysis on data, which will only increase multiple fold as an enterprise progresses.

If these challenges are addressed with the required expertise in AI handling, then supply chain management will see itself benefitting immensely in the form of operational efficiency of the highest order and more thoughtful decision-making from a leadership perspective. And, in its quest, it will certainly checklist the journey from computation to computational thinking for numerous industries.

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