Systems level research study of distributed medical inventory under uncertainty.

Medtronic’s Field Inventory Analysts (FIAs) operate as the last-mile orchestrators of distributed medical inventory across trunks, hospitals, warehouses, and peer networks. On paper, the role centers on scanning and reconciliation. In reality, it functions as a real-time triage engine layered on top of fragmented systems and inconsistent data reliability. I led a deep field investigation to understand what the role actually is, and where structural gaps were creating artificial work.

The challenge

Unpredictability is constant. The system depends on personal vigilance instead of structural resilience.

The FIA day is shaped by add-on cases, “patient on the table” calls, overnight messages, missing product mid-procedure, and reactive transfers. Systems are fragmented across IMI, Insights, MStar, Excel heat maps, Salesforce, MMX, Email, Teams, and GroupMe. Data reliability is inconsistent: MStar is described as “live or die by,” yet noted as only ~50% accurate in practice, with serialized accuracy targets at 90% and lot accuracy targets at 40%. Warehouse redistribution takes 3–4 weeks; field redistribution takes 1–2 days. The result is constant cross-checking, detective work, and physical driving to compensate for digital opacity.

My approach

I led the strategic research effort from immersion to executive alignment. My responsibility was to turn field reality into decision grade clarity.

I owned the end-to-end research strategy for understanding the Field Inventory Analyst role and its systemic constraints. That included defining the investigative lens, structuring the fieldwork, identifying where data reliability and fragmentation were generating artificial labor, and shaping the narrative that repositioned the FIA as a cognitive orchestrator rather than a scanner of inventory. I also designed and facilitated a cross-functional service design workshop and produced the resulting artifacts (journey maps, structural realities, opportunity frameworks, and north-star positioning) that became the foundation for executive discussions around FieldLink™ and RFID-enabled visibility architecture.

The work unfolded in three deliberate stages:

I conducted immersive ride-alongs and time-structured observation to document how the FIA role actually operates under uncertainty, from overnight escalations to multi-system reconciliation rituals. This surfaced the constant switching across IMI, Insights, MStar, Excel, Salesforce, MMX, and messaging platforms, as well as the cognitive modeling required to balance expiration risk, case urgency, and redistribution timing.
I synthesized those observations into system level visualizations: fragmentation maps, expiration pressure flows, incentive conflict loops, and accuracy gap models. These artifacts reframed the challenge from operational inefficiency to structural opacity.
I facilitated a service design workshop structured around those visuals. Rather than ideate features, the session focused on structural leverage, where predictive visibility, unified dashboards, and redistribution logic would reduce driving, stress, and write-down risk. The outputs became alignment tools for strategic investment decisions.

The work unfolded in three deliberate stages:

I conducted immersive ride-alongs and time-structured observation to document how the FIA role actually operates under uncertainty, from overnight escalations to multi-system reconciliation rituals. This surfaced the constant switching across IMI, Insights, MStar, Excel, Salesforce, MMX, and messaging platforms, as well as the cognitive modeling required to balance expiration risk, case urgency, and redistribution timing.
I synthesized those observations into system level visualizations: fragmentation maps, expiration pressure flows, incentive conflict loops, and accuracy gap models. These artifacts reframed the challenge from operational inefficiency to structural opacity.
I facilitated a service design workshop structured around those visuals. Rather than ideate features, the session focused on structural leverage, where predictive visibility, unified dashboards, and redistribution logic would reduce driving, stress, and write-down risk. The outputs became alignment tools for strategic investment decisions.

The impact

The FIA role is anticipatory intelligence compensating for system design limitations. The outcome wasn't a feature list, it was strategic realingnment.

The visible work, scanning and transfers, is only a fraction of the role’s value. The real contribution lies in cognitive modeling: balancing trunk vs hospital vs warehouse inventory, managing case risk and expiration risk simultaneously, running redistribution scenarios mentally, and mediating across competing incentives. Current tools are built to record transactions, not forecast risk. As a result, physical movement substitutes for predictive visibility. The strategic north star that emerged was clear: Drive Less. Orchestrate More.

The research reframed the FIA from cost center to revenue and risk lever. It clarified where technology should augment human orchestration rather than replace it and directly informed executive conversations around FieldLink™ and RFID-enabled end-to-end visibility.

Opportunity areas defined through the work included:

Unified inventory dashboard across IMI, Insights, MStar, and field systems
Location-level visibility (trunk, hospital, hub)
Predictive short-date matching and auto-suggested swaps
Pre-case validation alerts
Time-to-expiration routing logic
Formalized, system-supported field redistribution

By externalizing invisible cognitive load and aligning stakeholders through service design, the work positioned predictive visibility, not reactive driving, as the path to reduced write downs, earlier revenue recognition, and stronger field resilience.