Logistics Data Platform

01 Context

Logistics SaaS platform work at Foray Software, in a Wayfair client environment — not direct Wayfair employment.

02 Problem

Operational decisions depended on delayed data and weak forecasts. Teams could not reliably plan delivery and logistics when availability lagged by hours and forecast quality was insufficient.

03 Users / Stakeholders

• Operations and logistics teams
• Product and engineering counterparts on the SaaS platform
• Stakeholders accountable for forecast quality, release cadence and platform cost

04 My Scope

Roadmap and architecture for a logistics SaaS platform: forecasting, data availability, operational dashboards, release cadence and cloud-cost trade-offs. Formal title: Product Manager — Logistics Data & Analytics at Foray Software, in a Wayfair client environment.

05 Product Decisions

• Treat forecast quality and data availability as product capabilities, not backend chores
• Translate roadmap into platform work that operations could actually consume
• Include platform economics in product trade-offs rather than leaving cost solely to infrastructure

06 Technical Constraints

Forecasting and data pipelines had to operate inside an existing SaaS architecture and cloud cost envelope. Internal client architecture is not disclosed.

07 Trade-offs

• Forecast richness versus operational timeliness
• Release velocity versus platform stability
• Capability growth versus cloud cost

08 Measurement

Forecast accuracy, data availability (6 hours to 30 minutes), release velocity and annual cloud savings.

09 Outcome

+25% forecast accuracy, data availability 6h → 30m, +30% release velocity, and ~₹40L annual cloud savings.

10 Reflection

Data platforms become products when availability, forecast quality and cost are managed as explicit trade-offs for operational users, not as engineering backlog items.