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 Generative AI has had 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. But before we dive into algorithms & their positive spin on the value chain industry, let us first understand what defines a Generative AI.
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 the 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.
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.
With the context for Generative AI abilities for the value chain set – let us look at how the application finds its use cases in SCM.1. Demand Forecasting and Planning
Generative 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 & Relationship
Every 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 Optimization
As 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 Management
Insights 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 Betterment
A 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.7. Sustainability & Environmental Impact
ESG 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.
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.1. Data Availability & Quality
Data 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 & Training
A 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 computation
SCM 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. Adaptability
It'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. Scalability
Expansion 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.