INDUSTRY · LIFE SCIENCES & MEDICAL DEVICE
The A2go Decision Intelligence Platform (ADIP) helps life-sciences, medical-device, and pharma-adjacent manufacturers make lot-aware, expiry-aware supply decisions in minutes instead of days, with every decision proposed, approved, logged, and reversible.
THE PATTERN WE SEE
Inventory that expires
In this industry, inventory is not fungible. A lot with 90 days of shelf life left is not the same asset as a fresh one, but most ERP allocation logic treats them identically. Expiring stock gets discovered at write-off, FEFO exceptions get decided over email, and available-to-promise numbers quietly include lots that will not survive the lead time. See how ADIP handles the ATP short-ship decision in detail.
Qualification is slow. Demand windows are not.
Qualifying a new supplier, site, or alternate line takes months of validation work. The demand events that make you want that capacity, such as a tender award, a contract win, or a competitor's supply failure, arrive with weeks of notice. The planning question is not "can we make more" but "which qualification should we have started six months ago," and few teams have a systematic way to ask it.
One network, many legal entities
Contract manufacturers, sister entities, and distribution partners each run their own systems. A transfer between entities is a supply decision, a quality decision, and an accounting event at the same time, and no single system sees lot genealogy from raw material to the customer. The people who make these decisions reconcile it all by hand.
WHAT ADIP DOES ABOUT IT
A2go agents run against your live data across Plan, Source, Transform, and Fulfill decisions (see our SCOR DS grounding). They surface the decision, the data behind it, and a recommended action. A person approves. Here is exactly how agents act.
Expiry and allocation
Watches lot-level inventory against demand across the network. Flags stock that will expire before its assigned demand consumes it, and proposes reallocations or FEFO exceptions with the reasoning and the affected orders attached.
Qualified-supply coverage
Tracks demand signals against your qualified sources and lines. Proposes when to shift volume among already-qualified capacity, and flags where demand trends argue for starting a qualification now, with the lead-time consequences of waiting made explicit.
Network balance
Proposes inter-entity transfers that respect lot genealogy, entity boundaries, and remaining shelf life at the destination, and packages the decision record the transfer will need as part of the proposal itself.
GOVERNANCE
Your quality culture expects every consequential action to have a record: who proposed it, on what data, who approved it, and how to undo it. That is the native shape of every decision in ADIP.
Each agent proposal carries its data lineage. A named person approves or rejects it. The decision, its reasoning, and its outcome are logged, and approved actions are reversible. When an auditor or a customer asks why an allocation changed in March, the answer is a record, not a reconstruction. This is the Judgment Layer, and it is the same governance model on every page of this site because it is the same model in the product.
FIT
At this size you have real network complexity, several entities, contract partners, and thousands of lot-tracked SKUs, but not a standing data-engineering team to feed a decision-support tool. ADIP is built for that gap: it works from the systems you already run, and the people using it are planners and supply chain leaders, not engineers.
At a roughly $500M industrial manufacturer, master scheduling went from 18 hours to 15 minutes. In a published Databricks customer story, an A2go customer cut forecasts from 28 hours to under 1 hour, with 25+ market-intelligence users running scenarios daily without data-engineering support. More on both is on our results page.
NEXT STEP
Pick one decision, such as expiry-driven reallocation, a recurring short-ship call, or an inter-entity transfer that takes a week of email. The survey takes a few minutes and maps where decision intelligence would pay back first in your operation.
Map your highest-pain decision