Allocation & replenishment platform for global retail
Built and launched a multi-tenant allocation platform from MVP to production in about two months, then rolled it out across North America, EMEA, Japan, and China. The business value was straightforward: replace manual, hard-to-audit allocation workflows with a standardized process that could support regional operations and create a clear decision trail.
Reported impact
~2 months
MVP to production
Reported delivery timeline from build start to production launch.
Reported impact
4 regions
Global rollout scope
Launched across NA, EMEA, JP, and CN.
Reported impact
90%+
Unit test coverage
Quality gates and release discipline were built into the delivery motion.
Business thesis
Turned manual allocation workflows into an auditable multi-region retail platform, launched from MVP to production in about two months.
Confidentiality
Details generalized due to confidentiality.
Context
Why the transformation mattered
The strongest programs start with business pressure, operating constraints, and a clear definition of what has to change.
Planning and supply-chain teams were working through manual allocation workflows that were difficult to scale across regions and difficult to audit consistently.
The need was not just automation, but a system that could preserve decision transparency, handle large SKU data, and fit into existing operational processes.
Transformation lens
This should be positioned as a business transformation story rather than a technology showcase. The strongest angle is that research-grade problem framing, supply-chain domain expertise, product clarity, and solid engineering turned a manual regional process into a scalable, auditable platform.
Solution
How Vitartha turned complexity into an operating system
The delivery combined research-grade rigor, domain understanding, product judgment, and strong engineering execution.
- Built AWS serverless services to run allocation workflows with the flexibility needed for regional operations.
- Delivered data-ingestion pipelines for large SKU datasets and resilient Excel handling to reduce operational friction.
- Integrated Oracle RMS and operational dashboards so the allocation engine fit downstream retail workflows.
- Structured the product as a multi-tenant platform to support rollout across multiple regions without fragmenting the operating model.
The Vitartha edge
Research
Research-grade rigor
The operating model starts with structured problem framing, quality bars, and repeatable evaluation.
Domain
Domain-aware decisions
Industry realities shape priorities, risk tradeoffs, and what the business actually needs to change.
Product
Product understanding
The solution is designed around operator workflows, adoption, and long-term maintainability.
Engineering
Senior engineering execution
The implementation is built to survive production pressure, handoff, and operational scale.
Retail operations flow
From fragmented allocation decisions to a global operating platform
The platform connected data ingestion, decision logic, enterprise integrations, and rollout governance into one operating model.
Step 1
Manual regional allocation workflows
Teams were making planning decisions through fragmented and hard-to-audit manual processes.
Step 2
Allocation engine and data ingestion
Serverless workflows and large-SKU ingestion created a consistent decision layer.
Step 3
Oracle RMS integration and dashboards
The platform fit downstream systems and gave teams visibility into allocation status and outcomes.
Step 4
Multi-region production rollout
The result was a scalable, auditable platform deployed across four regions.
Outcome
Business impact, not implementation theatre
The strongest case studies should read like operating leverage, throughput, risk reduction, revenue impact, and delivery confidence.
Outcome narrative
- Production rollout in ~2 months across NA, EMEA, JP, and CN.
- 90%+ unit test coverage with quality gates and release discipline.
- Standardized allocation decisions with clear audit trails.
Technology foundation
Some impact is directly reported from the engagement. Where modeled impact is shown, it is clearly labeled as an estimate rather than a reported client claim.
Related brief
Working through a similar transformation?
Start with the operating problem, the systems involved, and the business outcome you need to unlock.