Leveraging AI in Supply Chain for the Maximum Effect
Artificial Intelligence is significantly changing supply chains and other industries, leading to a major shift that affects virtually everything. This will create disruption and it will create new winners and losers.
However, there are challenges and potential pitfalls in utilizing AI successfully in supply chain management. Before the emergence of AI, supply chain management relied on advanced processes within companies and between companies, as well as sophisticated planning algorithms and seamless integration of planning and execution. These approaches focused on precision and determinism. AI brings capabilities that greatly enhance human-level pattern recognition and analysis, making it a valuable complement to the strengths of previous deterministic approaches. To achieve optimal results in supply chain management, a combination of both approaches is necessary.
Where Supply Chains Can Benefit from AI
The obvious example is in the area of demand and other types of forecasting. These processes were already conceptualized as pattern recognition-based approaches, so it was a natural leap to apply AI to these processes. Indeed, the new cutting-edge AI forecasters are already outperforming (sometimes dramatically) the previous generation of forecasting. One reason for this superior performance is that Machine Learning (the branch of AI getting the most attention currently) learns from huge amounts of data and can detect causal patterns that a human or earlier algorithms could not detect. Improving forecast accuracy is one of the known process improvements that drive direct bottom line benefits to companies.
Other examples of processes that fall into this category (i.e., already sort of utilized pattern-based approaches) were processes like multi-echelon inventory optimization, ETA predictors, etc. These processes are seeing similar dramatic improvements with direct bottom line benefits.
Lurking below the surface of these “obvious” areas is a vast set of processes which were essentially pattern based but the patterns were too complex to process and mostly were the domain of human analysts, planners, etc.
Some examples of these areas are policy setting. i.e., what policies should one set to drive improved outcomes. Modern supply chain systems (even highly templatized with best practices) come with a huge number of policies that can be set, but figuring out how to set these takes time, even with experienced human analysts and planners.
Another area that falls into this category is in the realm of problem prioritization or “attention focusing.” Modern supply chain systems enable real-time visibility and alerts across a multi-enterprise supply chain. This can produce a blizzard of issues demanding attention. The question is what should the analyst/planner be focusing on.
The traditional approach would be some sort of high/medium/low classification. But AI can now do a much better job of focusing the analyst’s attention on the subset of issues/alerts that are truly the most critical. It can look at patterns of the underlying supply chain as well the user’s context to produce much better prioritization of issues that require attention. This allows much better utilization of the user’s most scarce resource (time).
Beyond visibility, the next step up is in the realm of decision making. Supply chain has a notoriously “high dimensional” decision space. In plain language, there are so many interacting factors and everything seems to impact everything else that it becomes hard to solve problems effectively.
In the past some of this was “solved” by doing planning with mathematically pristine objective functions and constraints. While, still very useful, these approaches had the major problem of not dealing well with the rapidly-shifting realities of real supply chains (especially the closer one got to execution). Here things would get increasing chaotic and users would resort to the process euphemistically sometimes called “expediting” and “firefighting.” However, with the advent of AI, it can now look at the complex planning and execution picture and suggest real-time, context-sensitive, Smart Prescriptions™, which guide the user towards optimal outcomes.
All of the aforementioned applications of AI have the advantage that they also continuously learn based on a tight feedback loop between action and outcome. This in turn means that even when there are “phase shifts” in supply chain characteristics, the AI picks it up.
The next level up in human-level pattern-based analysis and action comes from processing human languages. This is where the Large Language Models (LLMs) like ChatGPT are playing a significant role. On the one hand these models enable chat, but if one zooms out a bit, what they fundamentally do is open the world of textual data to be incorporated into supply chain decision making. It turns out that there is a gigantic volume of useful textual information out there, but these could never really be processed at scale. LLM’s change that.
For example, supply chain risk data is primarily found in textual sources and for the first time these can be processed, semantically analyzed, classified and applied. This is allowing supply chain risk management to finally be done at scale in a systematic manner. If the last few years have taught us anything, supply chain risk is a critically underemployed capability and is needed now more than ever. Along with the sudden availability of risk-information-at-scale, risk-enabling the “deterministic” side (multi-echelon constrained planning and execution) of this is also needed and being done.
Realizing the Full Benefits of AI in the Supply Chain
AI is significantly changing supply chains and benefiting companies that adopt it effectively. However, this requires a comprehensive change in how modern systems are architected.
This involves combining traditional combinatorial optimization with AI, rethinking the user experience to have a more natural chat-based interface, addressing issues related to AI’s lack of transparency, incorporating AI into the IT infrastructure of supply chain systems for faster results, and building supply chains that consider all the limitations of AI.
Ultimately, we are moving towards developing advanced AI assistants for supply chains that enhance human productivity. These assistants will possess knowledge about general supply chain processes as well as the specific needs of individual users.
