During the 30th Annual ARC Advisory Forum on February 10th, the session “The New Frontier of Operations and Supply Chain” offered 1.5 hours of valuable learning. It provided a platform for professionals to share their experiences and insights about the future of supply chains. The session delved into real-world end-user case stories, high-level discussions on implementing innovative solutions, and integrating AI into operational processes.
How Avantor Got Started on Its Decision Intelligence Journey
Jared Guckenberger was the end-user presenter during the session, showcasing the results of implementing Aera Technology’s Decision Intelligence solution. Jared is the VP of Global Supply Chains at Avantor. Avantor provides mission-critical materials and tools to life science companies, biopharmaceutical producers, and medical R&D organizations. Avantor has a global reach of 175 countries, 40 distribution centers, plus various college closet storage sites.
Scale:
10,000 supplier/ source combinations
250,000 SKUs sold per year
1.5 SKU-location combinations
10M+ purchase + customer orders per year
Jared shared Avantor’s supply chain challenges, including very high transaction and data volumes. Inventory challenges: too much, too old, and too little at the same time (excess, write-offs, and stockouts). Jared expressed the need to sense, decide, and act faster: integrate better with suppliers (many are low-tech, non-EDI). Many solutions must be “change-ready”, scalable, and usable by many roles, not fully autonomous AI.
Jared played a pivotal role in establishing a working relationship with Aera Technology to address the company’s supply chain challenges by utilizing Aera’s Decision Intelligence solution. It’s not only “Agentic Ai but also classic machine learning, decision logic, which are all orchestrated into repeatable decision processes. The core idea of Decision Intelligence is that it lets the system make thousands of routine decisions humans don’t have time for, rather than “smarter than humans” decisions.
At the start, Avantor focused on three reasonable target skill areas, focusing on decisions and processes that were traditionally inefficient. Avantor focused on stock rebalancing, purchase order cancellation, and purchase order prioritization to address inventory issues and improve customer service.
Stock Rebalancing: In the past, it was largely a manual, monthly exercise with lots of churn, and distribution centers were reluctant to spend days loading trucks; only top items were prioritized. Now: System scans twice a week to find dead or slow-moving stock and target locations with demand. DI produces move recommendations: planners approve or reject. On approval, stock transport orders are created in SAP automatically. The overall impact includes moving from infrequent “chunks to continuous, every other day rebalancing. Captures many “small” opportunities humans previously ignored: reduces write-offs and dead stock.
Purchase Order Cancellation: Traditionally, dynamic demand (orders canceled or changed) had a slow response time of 2-3 weeks. Now, systems scan weekly and propose PO cancellations. They send recommendations to suppliers via email (no EDI required). Suppliers reply by email; the system parses the response and summarizes it. The buyer then decides whether to accept or deny the cancellation. This process has reduced the cycle time from weeks to about a week or less. In the early phase, this approach has already saved $300K in inbound POs within 1-2 weeks using a small group.
Purchase Order Prioritization: Avantor has transitioned from a reactive to a proactive approach to managing stockouts. Now: The new system predicts potential stock shortages based on current demand and supply data. It then automatically emails vendors with requests like, “Can you move this delivery up by C days?” The vendor’s response (yes, no, or partial) is processed by the system and presented to the buyer, who then confirms the changes after considering associated costs. This proactive service enhancement improves the customer experience without relying on Advanced Planning and Scheduling (APS) tools like SAP.
Key Takeaways:
“Don’t wait for perfection; go live, then iterate.” Avantor currently has 62 enhancements still in the backlog. Pick skills with clear, immediate business impact and strong business sponsorship. Simplify processes where possible before or alongside automation. Lastly, have a roadmap ready: successful pilots quickly create demand for more AI/decision-intelligence use cases, which must then be prioritized and funded.
Executive Leadership Q&A Discussion:
After the presentation portion of the session, we moved into a Q&A style format with various industry professionals, including:
Peter Quimby of Aera Technology
Jeremy Hudson of Open Sky Group
Bryan Batchelder of Datex
Jared Guckenberger of Avantor
With over an hour of discussion, here are some of the top-line questions and answers from our panelists.
Question 1: As a system integrator, what best practices should customers follow when integrating new solutions, especially around data and AI?
Jeremy Hudson responded that many prospects want to attack the “gnarliest” use cases or copy flash keynotes (digital twins, robots, etc). Jeremy suggests you start with the simple, high-impact problems (where not everyone sends ASNs/EDI) rather than trying to boil the ocean. Focus on supporting decision makers, accept that data won’t be perfect, and choose tools that can work with and around bad data.
Question 2: How do you handle a go-live when the system isn’t perfect yet? Did you run a parallel (head-to-head) system, and how did you manage the risks?
Jared responded that there was no parallel legacy system because they had zero systems to begin with, so they could not do a clean head-to-head comparison. He also added that there was a “steering team” which was set up to communicate extensively with their distribution network. Citing an example of having to explain that spending $15 on UPS to avoid losing $1,000 in inventory.
Peter Quimby added that this is about Decision Intelligence, explicitly designing, evaluating, and learning from decisions. You “bring some eggs to make the omelet”: accept early friction to start capturing learning signals.
Question 3: Bryan, your role at Datex is part back-end developer and front-end product manager. What capabilities have you been recently working on, and how have your customers responded?
Bryan spoke in great detail about how the Datex platform is built on a low-code app platform that enables professional services teams to implement customizations faster and more cost-effectively. Additionally, some capabilities that Bryan is working on include embedding AI and agentic coding tools to help users define data sources (essentially queries over the data), which enables those data sources to be wrapped into reports. Lastly, they are striving towards building their own multi-agent orchestration and execution environment. Which would essentially mean that their customers would have their own agent that is loaded with all available context from sales prospectability to post-implementation debriefs.
Final Thoughts:
The session highlighted the current thinking and actions of leading companies in the supply chain market. A paradoxical trend is emerging: a significant rise in disruptions is occurring alongside the achievement of new levels of operational efficiency. Both end-users and providers are navigating distinct challenges, yet they share the common goals of increasing resilience and efficiency, and maintaining a competitive edge through digital transformation.
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