In today’s data-driven supply chain landscape, access to timely and relevant insights is crucial for staying ahead of the competition. Despite substantial investments in data and analytics, many organizations still need help to achieve tangible business impact.
Many factors contribute to this gap between data investment and business impact. These may include issues such as data silos, where valuable information is scattered across various departments or systems, hindering a comprehensive view of the supply chain, challenges in integrating disparate data sources to create a holistic picture, and finally, the desperate need for a cultural shift to promote data-driven decision-making at all levels of the organization.
The third instalment of ‘Course 5 Compass GenAI Series’ on the “Democratization of Supply Chain Insights Using Large Language Models (LLM)” addressed this pressing need and highlighted how LLMs are revolutionizing the way organizations interact with their data to make informed decisions.
In this webinar, I had the pleasure of sharing the table with Charles Blevins, Ramesh Murthy, and Jasmeet Sraw, discussing the challenges enterprises face in leveraging data effectively and underscoring the need to democratize data and insights throughout the supply chain.
So, let’s jump in and uncover the key takeaways from the webinar.
The Democratization of insights is all about making it possible for everyone in an organization to easily collect, understand, and use valuable insights from data and take smart decisions.
Adoption of large language models in SCM has been a game-changer in this regard, reshaping how supply chain stakeholders access and leverage valuable, actionable information.
LLMs like GPT-3.5 and its successors introduce a user-friendly and natural language interface to interact with complex data and analytics. This interaction encompasses the following aspects:
#1 Natural Language Interaction
This way, stakeholders can seamlessly engage with data and analytics using everyday language, regardless of their technical background. For example, supply chain managers can use these capabilities by putting simple queries in the system and getting accurate answers to gain more visibility and control over their operations.
#2 Data Exploration and Visual Storytelling
LLM users can now explore and visualize supply chain data more intuitively through natural language queries. The language model can retrieve relevant data and present it in digestible formats, such as knowledge graphs, charts, or narratives, facilitating the visual storytelling.
#3 Decision Intelligence
Along with strong visualization capabilities, LLMs provide on-the-fly analysis and recommendations, essentially supporting the decision-making processes within supply chain. For instance, they can help with more agile decisions in inventory optimization and demand forecasting or identifying potential disruptions in the supply chain with real-time insights.
#4 Empowering Citizen Data Scientists
Lastly, democratizing insights using LLMs enables more people to be involved in the process compared to a situation where only the data experts are close to the data and the models, which used to create a bottleneck. It can significantly reduce the reliance on data analysts or scientists for basic data retrieval and analysis tasks, allowing them to focus on other significant strategic issues.
For a Large Language Model (LLM) like GPT-3.5 to be successfully deployed and used responsibly, numerous technical, ethical, and practical considerations must be addressed. Drawing upon the insights gained from the discussion, here are some factors for you to address to ensure successful LLM implementation:
Addressing data quality and availability challenges would be the first step for businesses leveraging generative AI and enjoying a connected supply chain. While many business leaders often learn the hard way and spend a lot of time trying to understand and interpret their data, only to find out that it is not reliable or incomplete.
Having a Robust Data Model
A robust data model is an important element to ensure a successful LLM implementation. It can bring the requisite internal and external data under one roof, say in a data lake or, in some cases, a data mesh, that can handle different data types and still produce reliable and accurate results. Such data architecture enables data consumption into several meaningful and connected KPIs.
The Usefulness of Taxonomy
A comprehensive taxonomy with a set of keywords everyone agrees on is crucial. At different levels, it can help translate between these keywords. So, when you ask a question, the system knows what you mean, even if the data uses a different word.
For instance, a business user queries about parts, items or SKUs, however the information is stored as item number in the database. In such scenario, the taxonomy will allow ‘part’ to be used in the question and equate that with ‘item number’. Further the NLQ will parse the question and convert it into an SQL query which will retrieve the item number and its required details accurately.
Smart Inquiry Engine
The inquiry engine ensures that all the rules and database requirements are followed consistently across the organization. Moreover, this engine can understand specific conditions of the developed insights and, using business rules, create a narrative that serves as an action to be sent to specific supply chain groups or stakeholders.
Enterprises put in a lot of effort to get closer to the data and understand its patterns. Understandably, they rely heavily on domain experts to develop the initial learning models because it’s not possible to develop accurate models without the understanding of domain.
However, the next challenge is how to move away from that expertise and provide business users with faster and accurate insights. This is where the citizen data scientist becomes an essential part of the process. As discussed earlier, the democratization of insights through LLMs enables them to perform all the basic tasks that traditionally a data analyst would have handled. This saves lot of time in processing and analyzing the data, allowing them to come up with answers more efficiently.
Large language models (LLMs) are taking the world by storm. The number of use cases we see daily doesn’t seem to slow down. Various industries – previously thought to be “safe” from automation – see their core business change at neck-breaking speed.
While these advancements offer exciting possibilities, addressing a few considerations and implications arising from their development and deployment is essential.
Bias Mitigation – These models may be biased as they learn from different data sources, which requires transparent bias detection mechanisms and corrective actions.
Security – Enterprises looking to embrace the LLM potential must also strengthen their digital defences. Keeping data safe from unauthorized access, hacks, and cyber risks is extremely important.
Privacy – Privacy of data is non-negotiable. Enterprises must know where data resides and flows while rigorously securing it against unauthorized access or sharing.
Responsible AI Uses – Enterprises must uphold ethical principles and define clear parameters for LLM engagement, particularly when LLM-driven insights impact stakeholders.
In conclusion, LLMs are going to play a crucial role, particularly in delivering insights at the speed of business for supply chains. This journey is not without its hurdles, encompassing aspects such as data quality, availability, privacy, security, bias, and ethical considerations. However, enterprises should consider deterministic ways to embark on this journey without awaiting the perfect ecosystem.
Sign up to get the latest perspectives on analytics, insights, and AI.