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Supply chain
Beyond the hype cycle: GenAI use cases in supply chain planning  Â
How can businesses employ large language models in supply chain management?
GenAI’s rapid recent advancement is captivating the imagination of supply chain professionals, but a noticeable gap remains: while consumers have quickly adopted these technologies, businesses are lagging behind. Why is this?
One explanation is that the value proposition for consumers is clear, and the service is mostly offered for free. For businesses, on the other hand, according to Gartner, value-creating use cases are decidedly less clear and, in many cases, have become over-hyped to the point of inflated expectations. Another explanation is a lack of understanding about what the technology is and what it can do, as is often the case with emerging technologies.
The fact is, if we look at our planning and decision processes, 80% of enterprise knowledge at global companies is still “tribal” or institutional – accumulated experiences, skills, and know-how – and it is extremely siloed. That is why, as a business executive, if you ask questions like, “Why did we miss the forecast for product X in Market Y last month? What commercial actions can help increase demand for product X in Q3 to be 10% higher, and can the supply chain support the incremental demand? And at what incremental cost?”, answers are not instantly forthcoming.
With this article, we want to shed some light on how to accelerate the digitization of expertise and tribal knowledge of key functions and processes in customer-facing, planning, supply chain, commercial, and product innovation domains. We believe that enterprises of the future will compete against each other based on the quality of the digital knowledge models driving their processes.
So, what are the technology’s fundamental capabilities and limitations? What are the current practical applications of GenAI already in beta testing with marquee clients of leading software planning companies such as o9 Solutions?
The strategic deployment of agents
One of the hallmarks of GenAI is its ability to perform tasks. It achieves this through “agents” – AI-driven models or systems capable of performing tasks, interacting, or generating content based on given inputs. They are ultra-efficient digital personal assistants who can juggle multiple tasks at high speed, from data analysis to report writing.
These AI agents learn your preferences and needs, adapting to serve you better. While they excel at handling the heavy lifting of data and tasks, they still rely on human direction to set goals and make key decisions.
There are two main types of agents: atomic and composite. Atomic agents handle specific, standalone tasks, while composite agents manage a series of these tasks, integrating their outputs to achieve a more complex, overarching goal. This teamwork between the focused expertise of atomic agents and the holistic oversight of composite agents is what makes sophisticated AI systems so powerful and versatile.
Atomic agents
Atomic agents are the foundational elements of more complex composite agents. They perform basic tasks, such as data retrieval or editing, but with contextual understanding and conversational user interaction.
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For example, when asked, “Give me my forecast,” an atomic agent would sequentially identify the specific forecast needed (sales, marketing, etc.), the relevant time, region, and product set, locate the report containing this information, and summarize the findings. By training large language models (LLMs) in business practices, these agents can autonomously navigate these steps.
If additional context is needed, they can ask clarifying questions or infer from previous conversations, translate requirements into actions or code to retrieve information, and interpret report data to provide a synthesized response. Examples include generating report summaries, editing data, and more.
Composite agents
Composite agents integrate individual tasks to perform more complex actions. Consider a “Forecast vs. actuals post-game” analysis. This process involves connecting various data retrieval and synthesis sequences to explain business outcomes. For instance, a composite agent could analyze and report on the forecast changes for a product across markets, identify top performance variations, and assess risks and opportunities.
“Supply chains of the future will be steered by human creativity but powered by AI and intelligent technologies.”— Subit J Mathew, US SAP Supply Chain Market Offering Leader at Deloitte
Example 1: Forecast vs actuals analysis
User: “How much has the January cycle forecast increased for Product X for 2024 across all markets?”
o9 GenAI: “The January cycle forecast for Product X for 2024 across all markets has gone up by 2% over the April cycle forecast. [Forecast Summary Report link]”
User: “What are the major changes from the last cycle?”
o9 GenAI: “The top upsides from the last cycle are in the US with an increase of 2,538 units, up by 18.54%, and in EMEA… The top downsides are in APAC… [Cycle over Cycle Change Report link]”
User: “What risks should we be aware of?”
o9 GenAI: “The top downside risks to the forecast include the US recession and inflation impact and a lesser impact than planned from Global Marketing program XYZ. The top upside opportunities are… [Risks and Opportunities Report link]
Another scenario might involve a composite agent running a supply chain impact simulation for a 20% demand increase. The agent would first clarify market specifics, then create a scenario, run the supply chain analysis, and provide a summary with potential constraints and opportunities. This example illustrates the combination of atomic agents (data retrieval, forecast editing, and constrained plan execution) and composite agents (synthesizing past actions to suggest future steps).
Example 2: Supply Chain Impact Scenario
User: “Run a supply chain impact simulation for a 20% demand increase.”
o9 GenAI: “Certainly. But first, can you please tell me if this demand increase is for all markets or for a specific market?”
User: “US market, all channels.”
o9 GenAI: “Ok, thanks. I’ve created a demand upside scenario, increasing Q3 demand for the US by 20%. Running the supply chain solver now, it will take a minute or two…”
o9 GenAI: “Here’s the summary: 90% of the incremental demand can be supported. [Supportability Report link] The remaining 10% is constrained by Capacity on Resource R at Factory X and Material M1 at Factory X. [Constraint Summary Report link] Typically, Factory X has been able to add extra capacity with one month’s lead time. Investing $Xm in extra capacity could potentially meet 98% of the requested increase. The incremental demand could generate $5m in gross margin versus $2m in extra costs. So, it’s a viable decision if the increase in demand is confident. [Scenario Comparison Report link]”
Preparing your organization
Adopting GenAI into a business requires a strategic approach, with a deep understanding of the technology and strategic investment in data and product strategies. Here are three fundamental steps to prepare your organization:
1. Digitizing tribal knowledge
The first step is to document and digitize your existing business practices and insights. This involves converting your organization’s tribal knowledge into a digital format. GenAI can assist in this process by indexing unstructured documents, using language models to identify key information, and automating the validation process to detect inconsistencies or gaps. This digital transformation lays the groundwork for creating a knowledge base that AI can effectively utilize.
2. Translating digital knowledge into AI-compatible formats
Once your tribal knowledge is digitized, the next step is translating this digital information into formats that can be understood and utilized by LLMs. This involves structuring the data into a format suitable for AI processing, commonly known as vector embeddings. These embeddings are then stored in specialized vector databases, such as Redis or Postgres, optimized for textual document retrieval. This step is critical in ensuring that the GenAI model can accurately retrieve and utilize your business-specific knowledge.
3. Investing in Suitable Platforms and Co-developing GenAI Algorithms
The final step is to invest in platforms capable of supporting LLMs and to participate in co-developing your own GenAI algorithms. Publicly available language models may not be tailored to your specific business needs, as they are not trained on your proprietary data. By investing in the right platforms and co-developing algorithms, you can tailor AI capabilities to your specific business context. This includes training AI on your unique data sets, refining algorithms to suit your operational needs, and ensuring that the AI solutions integrate seamlessly with your existing systems and workflows.
Expected value and risks GenAI offers significant opportunities for productivity gains (30-50%) and converting tribal knowledge into digital formats (80% to 100%). However, there are several risks and recommendations we should be aware of if we want to succeed in using them.
The probabilistic nature of LLMs necessitates careful planning and guardrails for effective deployment in enterprise settings. These systems often produce outputs that may not always be factually reliable, a phenomenon commonly referred to as “hallucinations.” While creative fields like writing or image generation might find these hallucinations stimulating or even desirable, they create significant challenges for enterprises where accuracy is paramount. Enterprises intending to deploy generative AI for planning must carefully address this tendency to ensure effective use. Implementing guardrails – essentially, secondary checks to validate the outputs generated by the AI – is a crucial approach for mitigation.
Bias and privacy are paramount concerns. Many AI/ML systems, including generative AI, can inherit biases present in the data they’re trained on. Organizations building their own generative AI solutions, or even those fine-tuning existing models, must be vigilant to ensure any biases in the training data don’t become ingrained in the final product.
An approach to this is to manage the hierarchies or levels of knowledge. This means that when faced with a question you will define the sequence from which to generate the answer. E.g. First o9 business practices and global documentation, then your own specific company knowledge, then the open LLM.
Finally, protecting sensitive data from potential leaks through generative AI is critical. Data privacy must be a key pillar of any generative AI implementation. Ensuring you tailor your organization’s LLMs and that these work within your private environment instead of in the open space is key.
Overall, generative AI promises groundbreaking capabilities in planning and decision-making. If we approach it with a focus on safety and reliability by addressing potential inaccuracies, mitigating biases, and safeguarding sensitive data, we will truly harness its power over the next years.
Authors
On campus program
Leading the Future Supply Chain
Build your digital, resilient, sustainable value chain
Develop a digital supply chain management strategy that embeds the right technologies for your business.
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