Managing supply chain risks: 4 ways to avoid trouble
In a volatile business environment, companies must identify supply chain risks and act proactively to reduce the impact of disruption. Here are four key strategies.
Artificial intelligence can’t predict, months in advance, if, when, and where a hurricane may strike—and chaos theory reminds us of the impossibility of doing so. But AI can help specialty chemical companies prepare their supply chains for greater or lesser surprises of all sorts, be they unpleasant or happy, meteorologic, geopolitical, regulatory, economic, or competitive.
What’s more, two of the hottest areas of AI interest in specialty chemicals—AI-powered product development and predictive quality—themselves have direct supply chain implications, as do demand planning, production planning, production control, quality control, natural-language chat capabilities, and other AI-augmented systems and processes.
While diverse, these systems all share the ability to digest enormous, diverse quantities of structured and unstructured operational and business data as well as external data (such as, for example, hurricane forecasts), to draw connections and distill actionable conclusions for human intelligence to vet. As AI speeds decision making, the supply chain must adapt apace.
When planners change input assumptions based on evolving truths or speculative hunches, AI helps understand the impact and find courses of action. Transportation management is a straightforward example of AI enhancing supply chain efficiency and resilience.
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Conversational planning in the face of potential disaster
For a hypothetical, let’s go with an Atlantic hurricane, of which 11 are expected this year. Enhancing transportation management systems with AI lets supply chain planners run scenarios based on possible effects of different storm paths, because the impacts of a U.S. Gulf Coast landfall will differ from one that strikes South Florida.
The system takes into account variables such as product type, transport capacity and costs, delivery reliability, lead time, projected revenue, and sustainability. It then helps to find alternatives that propose varying transportation routes, different combinations of transportation modes (ocean, rail, road, air), and alternate production locations and then distills them down to a handful of recommended options.
That’s already happening today.
What’s coming soon takes supply planning optimization to an entirely new place – and just in time – according to 2025 manufacturing trends, supply chain and ecosystem stability is top of mind for execs in the future.
Generative AI-based conversational planning will let planners quickly find and vet sources of supply, check available stock, consider transport costs and constraints, evaluate supply chain risk, and more. The planner brings their knowledge of the situation at hand and likely courses of action; based on the planner’s prompts, the system taps into diverse business and operational data to generate what-if scenarios the planner can then assess and refine (again, conversationally).
Importantly, GenAI can provide justification for the scenarios it deems to be most fruitful, providing the planner with a view into the infamous black box of AI decision-making. Said differently, a key element of conversational planning is providing the human planner context and logical justification for the decisions AI is taking.
In our approaching-hurricane scenario, a planner will be able to ask the system to estimate impacted deliveries, suggest alternative warehouse stock or sources of new supply outside the likely hurricane-affected area, and run through the transport options as noted above, quickly and through a single intuitive interface.
Supplier evaluation and selection
Another vital aspect of supply chain management is in supplier selection and relationship management. That’s about understanding who your suppliers are, what they’re doing for you, and how well they’re doing it.
AI can evaluate suppliers based on criteria such as cost, quality, and reliability. It can take risk factors (such as production facilities in hurricane zones) into account and help establish portfolios of supply alternatives. That’s crucial in an era where dependence on particular vendors, or even particular regions, can represent unacceptable business risk.
Let’s move way up the supply chain to the two aforementioned areas of AI that are, based on my discussions around the industry, of principal interest to specialty chemicals producers: AI product development and AI predictive quality.
AI-based product design and the supply chain
AI product development is fast becoming an industry standard for specialty chemicals, sparing untold hours of speculative lab work through:- Predictive modeling of new recipes
- Formulation optimization to improve effectiveness and stability
- Molecular-level simulations of reactions and processes
- Process optimization to boost yield, cut waste, and recognize safety and regulatory risks that may be associated with new compounds
Integration with business systems can add to the (conceptual) mix the commercial success of similar formulations, among other variables. From the perspective of the business, AI product design boils down to adapting products to meet changing market demands, which enhances supply chain stability by meeting market needs and thereby stoking demand for the supply chain to fulfil.
At the same time, turbocharging of the pace of product innovation with AI will put immediate pressure on the supply chain. There will be new inputs for existing suppliers to deliver; new suppliers to vet and bring into the fold quickly; new raw materials to source; new safety and regulatory conditions to manage; and new production, storage, and transport approaches to model and realize. It all will have to happen at an unprecedented pace that will all but demand supply chain AI capabilities.
AI product design is shaping up to be among the greatest business catalysts this industry so deeply familiar with actual catalysts has ever seen.
AI predictive quality and the “golden batch”
AI predictive quality—a close runner-up to AI product development in terms of the specialty chemicals industry’s AI priorities at the moment—also has serious supply chain implications. AI predictive quality takes aim at the outputs of AI product design, the goal being the consistent production of “golden batches” for those new products. (AI predictive quality systems also work with existing products and processes derived from biological ingenuity, of course.)
AI predictive quality simulations take into account technical data such as raw material properties, processing temperature, reaction times, as well as business data such as facts related to the supplier of the raw material to help monitor and adjust production processes to optimize every batch.
The importance of predictive quality for the supply chain lies in the fact that it avoids unplanned delivery problems due to poor quality or recognizes them at an early stage, so that countermeasures can be taken as quickly and effectively as possible.
Those considerations don’t end at the walls of your own production facilities. The quality of your product depends on the quality of its inputs. Rarely are the compositions of production inputs perfectly consistent with their nominal specifications, and even “pure” substances have their impurities.
AI predictive quality systems can analyze the performance of inputs from different suppliers and establish how certain inputs affect end-product quality—information you can feed back to suppliers (and they can feed back to their suppliers) to boost quality down the chain.
The promise of AI for specialty chemicals
Predicting AI systems’ ultimate influence on the specialty chemicals industry is no easier than predicting hurricanes months in advance. We know what realms are the likely targets, and we know they’ll have a major impact sooner than later.
Where the hurricane of AI differs is that, where an actual hurricane’s influence is spatially and temporally limited, AI capabilities will ultimately touch the entirety of a specialty chemicals business, and they will combine operational and business data to help make better decisions quickly.
AI is shaping up to be a hurricane unto itself. The good news is, this particular hurricane looks to be an overwhelmingly positive force for change.