Just as supply chain disruptions became the frequent subject of boardroom discussions in 2020, Generative AI quickly became the new topic of 2023. In spite of everything, OpenAI’s ChatGPT reached 100 million users in the primary two months, making it the fastest-growing consumer application adoption in history.
Supply chains are, to a certain extent, well suited to the applications of generative AI, given they function on and generate massive amounts of information. The variability and volume of information and the differing types of information add additional complexity to an especially complex real-world problem: tips on how to optimize supply chain performance. And while use cases for generative AI in supply chains are expansive – including increased automation, demand forecasting, order processing and tracking, predictive maintenance of machinery, risk management, supplier management, and more – many also apply to predictive AI and have already been adopted and deployed at scale.
This piece outlines just a few use cases which might be especially well suited to generative AI in supply chains and offers some cautions that provide chain leaders should consider before investing.
Assisted Decision Making
The primary purpose of AI and ML in supply chains is to ease the decision-making process, offering the promise of increased speed and quality. Predictive AI does this by providing predictions and forecasts which might be more accurate, discovering latest patterns not yet identified, and using very high volumes of relevant data. Generative AI can take this a step further by supporting various functional areas of supply chain management. For instance, supply chain managers can use generative AI models to ask clarifying questions, request additional data, higher understand influencing aspects, and see the historical performance of choices in similar scenarios. In brief, generative AI makes the due diligence process that precedes decision-making significantly faster and easier for the user.
Furthermore, based on underlying data and models, generative AI can analyze large amounts of structured and unstructured data, mechanically generate various scenarios, and supply recommendations based on the presented options. This significantly reduces the non-value-added work that provide chain managers currently do and empowers them to spend more time making data-driven decisions and responding to market shifts faster.
A (Possible) Solution to the Supply Chain Management Talent Shortage
Over the past few years, enterprises have suffered from a shortage of supply chain talent due to planner burnout, attrition, and a steep learning curve for brand new hires as a result of the complex nature of the job function. Generative AI models may be tuned to enterprises’ standard operating procedures, business processes, workflows, and software documentation after which can reply to user queries with contextualized and relevant information. The conversational user interface commonly related to generative AI makes it significantly easier to interact with a support system and affords the flexibility to refine the query, further accelerating the time it takes to search out the suitable information.
Combining a generative AI-based learning and development system with generative AI-powered assisted decision-making can assist speed up the resolution of varied change management issues. It might probably also speed up ramp-up of recent employees by reducing the training time and work experience requirements. More importantly, generative AI can empower individuals with disabilities by enhancing communication, improving cognition, reading and writing assistance, providing personal organization, and supporting ongoing learning and development.
While some fear that generative AI will result in job losses over the approaching years, others think it should level up work by removing repetitive tasks and making room for more strategic ones. Within the meantime, it’s predicted to resolve today’s chronic supply chain and digital talent shortage. That’s why learning tips on how to work with the technology is vital.
Constructing the Digital Supply Chain Model
Supply chains should be resilient and agile, which requires cross-enterprise visibility. The provision chain must “know” your entire network for visibility. Nevertheless, constructing out the digital model of your entire n-tier supply chain network is commonly cost-prohibitive. Large enterprises have data spread across dozens or a whole bunch of systems, with most large enterprises managing greater than 500 applications concurrently across ERPs, CRMs, PLMs, Procurement & Sourcing, Planning, WMS, TMS, and more. With all this complexity and fragmentation, it is incredibly difficult to logically bring this disparate data together. That is compounded when organizations look beyond the first- or second-tier suppliers to where collecting data in a structured format is unlikely.
Generative AI models can process massive amounts of information, including structured (master data, transaction data, EDIs) and unstructured data (contracts, invoices, images scans), to discover patterns and context with limited pre-processing of information. Because generative AI models learn from patterns and use probability calculations (with some human intervention) to predict the following logical output, they’ll create a truer digital model of the n-tier supply network – faster and at scale – and optimize inter- and intra-company collaboration and visibility. This n-tier model may be further enriched to support ESG initiatives including but not limited to identifying conflict minerals, use of environmentally sensitive resources or areas, calculating carbon emissions of products and processes, and more.
Despite the fact that generative AI provides a major opportunity for supply chain leaders to be modern and create a strategic advantage, there are particular concerns and risks to contemplate.
Your Supply Chain is Unique
General uses of generative AI, like ChatGPT or Dall-E, are currently successful in addressing tasks which might be broader in nature since the models are trained on massive amounts of publicly available data. To actually leverage the capabilities of generative AI for the enterprise supply chain, these models will should be fine-tuned on the respective enterprise data and the context specific to your organization. In other words, you can’t use a generally trained model. The information management challenges like data quality, integration, and performance that hamper current transformation projects also can impact generative AI investments, resulting in a time-intensive and dear exercise without the suitable data management solution already in place.
Generative AI depends on understanding throughout the training data and if supply chain professionals have learned anything within the last three years it’s that provide chains will proceed to face latest risks and unprecedented opportunities.
Security & Regulations
The essential requirement of generative AI models is access to vast amounts of coaching data to grasp patterns and context. That said, the human-like interface of generative AI applications can result in user impersonation, phishing, and other security concerns. While limited access to model training can result in underperformance by the AI, granting unfettered access to produce chain data can result in information security incidents where critical and sensitive information is made available to unauthorized users.
It’s also unclear how various governments will select to manage generative AI in the longer term as adoption continues to grow and latest applications of generative AI are discovered. Several AI experts have expressed concern in regards to the risk posed by AI, asking governments to pause giant AI experiments until technology leaders and policymakers can establish rules and regulations to make sure safety.
Generative AI offers an abundance of improvement opportunities for those organizations that may tap into this technology and create a force multiplier for human ingenuity, creativity, and decision-making. That said, until there are models trained and explicitly designed for supply chain use cases, one of the best technique to move forward is a balanced approach to generative AI investments.
Establishing proper guardrails shall be prudent to make sure the AI serves up a set of optimized plans for every user to review and choose from which might be aligned with business processes and objectives. Businesses that mix “business playbooks” with generative AI shall be best capable of increase teams’ capability to plan, resolve, and execute while still optimizing desired business outcomes. Organizations also needs to consider a powerful business case, security of information and users, and measurable business objectives before investing in latest generative AI technology.