Illustrative composite: How Acme Manufacturing Ran an Automation Discovery Sprint

The Starting Point: A Maturing Program Seeking New Opportunities

Illustrative composite — figures: Acme Corporation's automation program was three years old and had delivered significant results - 68 live automations, primarily in Finance and HR, with a well-functioning CoE team of 10 specialists. But the program had a problem that its leader, Sarah Chen (VP of Finance and Operations Automation), described plainly: 'We'd automated everything Finance and HR were willing to give us. We needed to expand into Operations and Supply Chain, but we had no relationships with those departments and no infrastructure for high-volume discovery in a manufacturing context.'

Manufacturing operations present specific intake challenges. The processes are often complex, involving physical and digital components, regulatory requirements (ISO, FDA, OSHA documentation), and equipment integration points that are unfamiliar to automation analysts from software-centric backgrounds. The business users - floor supervisors, quality engineers, supply chain planners - have limited time and limited familiarity with automation concepts. Standard intake forms designed for office processes don't translate well.

Sprint Design: Adapting to a Manufacturing Context

Illustrative composite — figures: Sarah's team spent two weeks designing the sprint before opening it to submissions. The design choices reflected the specific challenges of a manufacturing context:

• Stakeholder mapping included operations managers, quality leads, supply chain planners, and maintenance supervisors - not just the department heads who typically engage with CoE programs.
• Illustrative composite — the intake kickoff sessions were 30 minutes rather than 60, respecting the limited time availability of operations staff during production cycles.
• VARA's technology catalog was configured with manufacturing-specific tools: SAP S/4HANA, Aveva MES, OSIsoft PI (now AVEVA PI), and the company's quality management system.
• Illustrative composite — the sprint ran in two phases, with Quality and Operations first and Supply Chain and Finance second, to manage the review workload.

What the Sprint Found

To apply this composite manufacturing lesson, evaluate IntakeOS on one approved production-support process. Interview the performer about handoffs, system dependencies, and exceptions, then have the process owner check the map and workload assumptions. Keep safety, operational-technology access, and implementation approval with specialists; the scenario's invented outcomes do not establish your expected performance.

Illustrative composite — figures: The 47 submissions produced by the sprint surprised the CoE team in two ways. First, the volume was higher than expected - the combination of the structured intake format, the 30-day time boundary, and the promise of rapid feedback drove participation rates well above historical norms. Second, the pattern distribution was unusual compared to the program's Finance/HR baseline.

The Pattern Distribution in Manufacturing

Illustrative composite — figures: The pattern breakdown from the sprint revealed the distinctive character of manufacturing automation opportunities. System Integration / API represented the largest share in this scenario - manufacturing environments typically run multiple specialized systems (ERP, MES, QMS, historian databases) that need to exchange data in real time. RPA and IDP were also represented across Supply Chain, Finance, and quality documentation. These figures describe the composite scenario, not a manufacturing benchmark.

Illustrative composite — figures: Process Mining was recommended for a subset of candidates, reflecting the complexity of manufacturing processes and the value of understanding actual execution paths before automating them. VARA flagged several candidates specifically as 'process mining before automation' - processes where the described process and the likely actual process were sufficiently different that building an automation without data-led discovery was a significant risk.

"Illustrative composite — figures: The system-fix findings were the most valuable output of the sprint. VARA identified eight processes where the right answer was to fix the underlying system - SAP configurations, QMS updates, PI historian integrations - not to build a bot. Those eight redirections saved us from building automations that would have created maintenance burden and blocked future system improvements."

- Sarah Chen, VP Finance and Operations Automation, Acme Corporation

Converting the Pipeline: First 90-Day Results

Illustrative composite — figures: In the 90 days following the sprint, Acme's CoE launched development on the top 12 candidates by ROI priority. Eight have been deployed to production, producing an annualized run rate of approximately $890,000 in documented efficiency savings. The remaining four are in development with expected completion in Q3 2026.

Illustrative composite — figures: The sprint has become a recurring practice. Sarah's team ran a second sprint focusing on additional manufacturing sites. The process was faster to execute using the first sprint as a template and produced comparable output. The CoE now has a rolling pipeline that extends beyond Finance and HR for the first time in the program's history.