SUPPLY CHANGE MANAGEMENT
The bottom line is that combinations of ML, genAI, and agentic AI will enable supply chain experts to quickly digest and act upon a far broader collection of information inputs than was possible before large language models came to the fore. planning that the combination of genAI and machine learning( ML) enables lets users better predict lead times for their own customers. That improves customer service and boosts the firm’ s value in customers’ eyes
Agentic AI
Agentic AI shows promise is helping on all those fronts, even if companies have rightly hesitated to grant AI permission to execute on decisions without human oversight. Given the stakes with respect to product quality and volume, safety and logistical considerations in speciality chemicals, this industry is wisely proceeding with caution.
However, AI agents that tee up proposed actions for human approval and action are already demonstrating impressive value in this and other industries. Some examples include:
• Sourcing agents that create tailored RFPs by analysing data and past sourcing events, shortlisting suppliers and suggesting questions for staff to make realtime adjustments
• Supplier onboarding agents orchestrate invitations, monitor supplier progress and handle escalations
• Bid analysis agents drive fast and thorough bid comparisons, highlighting trade-offs and
providing immediate access to key information
• International trade classification agents analyse product characteristics, classify goods and recommend customs tariff numbers and commodity codes to ensure compliance
• Accounts receivable agents analyse overdue receivables and follow up with customers to reduce uncollectable write-offs
• Maintenance planner agents analyse real-time data, recommending maintenance schedule updates and streamlining maintenance planning
• Field service dispatcher agents ensure the right resources for jobs and autonomously schedule and optimise service orders
• Production planning and operations agents keep orders moving by checking material, capacity and scheduling availability, recommending workarounds and automating the release of production orders
These agents and others in development represent huge opportunities to streamline supply chain planning and management, and they’ re increasingly linking together related tasks, iteratively reasoning through problems, and collaborating with other AI agents.
Digital transformation
GenAI and agentic AI do best when they can feast on a vast menu reliable data. That requires a harmonised data model and core systems capable of managing that data. We are talking digital transformation here, the foremost tenet of which is having core systems in the cloud.
Speciality chemicals companies have not generally been leaping into that transition but a hunger for what AI can do today and an instinctive grasp of its huge potential have ignited this industry’ s interest. The pace of AI innovation is brisk and the competitive advantages it increasingly confers in the supply chain and beyond are clear. Speciality chemicals companies should be laying the groundwork for AI throughout their organisations, because the surprises are going to keep coming. ●
Marko Lange
GLOBAL HEAD OF CHEMICAL INDUSTRY SOLUTIONS
SAP k + 49 6227 747474 J marko. lange @ sap. com j www. sap. com
MAY / JUN 2026 SPECCHEMONLINE. COM
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