Workflow Demo
Executive Summary
This independently initiated improvement addressed POS migration and product initialization work across Odin, SAGE Menu Builder, and LunchTab. The project combined business analysis, data controls, reconciliation, guided review, and upload-ready file generation into two field-tested Python desktop proof-of-concept applications.
The business-analysis value is in the operating model around the tools: preserving source files, validating identity and schema, routing ambiguous records to review, enforcing approval gates, and blocking exports when acceptance criteria fail.
Context and Observed Problem
The scope covers account reconciliation and family-balance transfer, product/menu database initialization, inventory enrichment, and XLSX/CSV transformation into LunchTab-ready outputs. The work reduced manual handling while keeping review, auditability, and exception management visible.
Stakeholders and User Needs
Operators and reviewers need clear import steps, exception visibility, data-quality checks, and confidence that records are not silently transformed without review. Executive and operational audiences need concise status, control totals, and evidence that the workflow is ready for acceptance review.
Current State
The manual baseline was 15.4 hours. The source environment involved three source types with account, balance, product, menu, and inventory data moving toward LunchTab upload readiness.
Target State
The target workflow uses guided processing, configurable matching, family aggregation, category and barcode review, POS-name generation, reconciliation, and export blocking when acceptance criteria fail. The initialization workflow is described as a six-step guided process with nine run artifacts.
Requirements and Business Rules
Key requirements include source-file preservation, identity validation, schema validation, quarantine of ambiguous or unsafe records, duplicate handling, review gates, control totals, and privacy-conscious manifests. Business rules also include POS-name handling, including a 15-character POS constraint where applicable.
Options and Design Decisions
The selected design emphasized exception-driven review instead of silent automation. That choice preserved operator control while allowing repeatable transformation, reconciliation, and upload-file preparation.
Solution and Prototype
The solution set consisted of two tested Python desktop applications or proof-of-concept solutions. Together they supported migration, reconciliation, initialization, transformation, review, and export readiness.
Validation, Testing, and UAT
Validation included 134 passing tests, guided approval gates, control totals, data reconciliation, blocked exports when criteria fail, atomic timestamped run folders, and audit artifacts.
Outcomes and Metrics
- 1,478 accounts
- 15.4 hour manual baseline reduced to approximately 3 minutes of automated runtime
- Approximately 99.7% cycle-time reduction
- Approximately 308 times faster
- 134 passing tests
Role and Disclosure
This was a work-related, independently initiated improvement, solo-led across business analysis, solution design, development, functional testing, and UAT. Codex served as an AI assistant and development collaborator. The employer did not commission, fund, endorse, or deploy the solution enterprise-wide.
Lessons Learned and Next Steps
The core lesson is that automation is strongest when paired with explicit controls: preserved inputs, review queues, reconciliation, audit outputs, and blocked exports. The demo video follows the guided product initialization workflow from source selection through export readiness.