Roche Workflow Automation & Operations Intelligence

Overview

The Roche engagement taught me what real execution looks like in an operational environment: ambiguity at the beginning, constraints throughout the project, and a final standard that is not “interesting” but usable, measurable, and adoptable.

Across three phases and three semesters, the work focused on understanding diagnostic lab workflows, identifying operational bottlenecks, designing automation-ready improvements, and packaging the solution so it could survive handoff.

The problem

The team needed better visibility into process flow, handoffs, intake friction, delay drivers, and daily assignment planning. In a high-throughput environment, even small workflow issues can compound into delays, rework, and inconsistent communication.

The challenge was not only to identify insights. The challenge was to translate insights into implementation-ready artifacts.

Users and stakeholders

The stakeholders included operational teams, process owners, pathologists, project mentors, faculty advisors, and client-side communication partners. Each group cared about a different part of the problem: throughput, accuracy, assignment efficiency, reporting, and adoption.

My role

I contributed as a data engineer focused on process workflow and automation. My work involved process mapping, workflow analysis, automation concept development, dashboard thinking, optimization logic, and handoff documentation.

Product and operational decisions

The strongest decision was to treat the project like a product system, not just an analysis exercise.

That meant every recommendation needed to answer:

• What workflow problem does this reduce?
• Who uses it?
• What exception cases exist?
• How would the team measure whether it works?
• What would the next team need to continue it?

Deliverables

The engagement produced five core deliverable areas:

1. Operational workflow insights — mapped end-to-end process, handoffs, bottlenecks, and drivers of delay/rework.
2. Sample intake automation concept — designed an automation-ready intake flow with clear exception handling.
3. Pathologist optimization tool — developed a decision-support approach for daily planning and assignment efficiency.
4. Performance dashboard — created KPI views for throughput and operational health.
5. Standard operating procedures — packaged the work so adoption would not depend on the original project team.

Impact

The project moved from ambiguity to a structured operational improvement package. The work gave the team clearer workflow visibility, automation-ready concepts, optimization logic, dashboard framing, and documentation for handoff.

The biggest takeaway was simple: insight is useless without implementation. If the work cannot be explained, measured, and transferred, it will not survive.

What I learned

This project changed how I think about product and operations. A dashboard is not valuable because it looks good. An automation flow is not valuable because it sounds efficient. The value comes from whether it changes the day-to-day behavior of the system.

PM / APM interview story

Situation: A diagnostic operations workflow had ambiguity, manual friction, and limited visibility into bottlenecks and handoffs.

Task: Create process, automation, dashboard, and optimization deliverables that could be understood and adopted beyond the original team.

Action: I helped map the workflow, identify delay/rework drivers, design an intake automation concept, develop pathologist optimization logic, create dashboard views, and package SOPs.

Result: The team delivered an implementation-oriented package across three phases that connected operational insight to adoption-ready artifacts.

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