from pathlib import Path import zipfile, shutil out_dir = Path("/mnt/data/rainsy_analytics_updated_site") out_dir.mkdir(exist_ok=True) html = """ Rainsy Analytics | Operational Analytics Systems
Rainsy Analytics logo Rainsy Analytics
Operational analytics systems

Analytics systems for teams managing capacity, workload and performance.

Rainsy Analytics helps operations teams replace fragmented reporting with decision-ready analytics systems for resource planning, manufacturing performance and portfolio delivery.

Request a diagnostic Explore systems
Rainsy Analytics dashboard showcase

The problem is not a lack of dashboards. It is a lack of operating visibility.

Most operational teams already have data. The challenge is that the numbers are scattered across planning tools, ERP exports, timecards, trackers and spreadsheets.

Capacity decisions made from incomplete workload views
Manufacturing performance hidden behind manual reporting
Portfolio priorities disconnected from resource reality
Meetings used to explain data that should already be clear

Specialist analytics systems for operational decision-making.

Built around recurring operational problems, not one-off report requests.

Capacity Intelligence System

For teams that need to understand demand, availability, allocation, utilisation and future resource risk.

Capacity vs demand Timecards Resource forecasting Scenario modelling

Manufacturing Performance System

For operations teams that need clearer visibility of output, workload, bottlenecks, downtime and financial impact.

OEE concepts Downtime Production workload Engineering stores

Portfolio Delivery System

For project and portfolio teams that need to connect intake, prioritisation, staffing and delivery performance.

Planisware-style data Project intake Prioritisation Delivery risk

Consultancy-grade portfolio pieces.

Current showcase areas focus on the operational problems where analytics creates measurable business value.

Resource Planning & Capacity Management

Demand vs capacity, resource allocation, forecasting, utilisation, workload gaps and executive-level delivery risk.

Resource planning and capacity management dashboard

Manufacturing Operations Analytics

Production workload, OEE-style performance, downtime, bottleneck identification, engineering stores and output trends.

Manufacturing performance dashboard

Portfolio Delivery Analytics

Project intake, prioritisation, portfolio demand, resource forecasting, delivery risk and stakeholder visibility.

Portfolio delivery dashboard

Timecard vs Workload Reporting

Planned workload compared against actual time recorded, capacity assumptions, utilisation and operational pressure.

Dashboard image coming soon

A system-first method.

Rainsy Analytics is built for operational teams that need a repeatable analytics layer, not another disconnected dashboard.

Diagnose

Clarify the operating problem, decision points, stakeholders, data sources and current reporting pain.

Model

Design the data model, metric definitions, business logic and reporting structure around the real process.

Build

Create practical Power BI reporting with clear navigation, executive views and operational drill-through.

Operationalise

Turn the report into a reliable management system with refresh, governance, ownership and improvement cycles.

Request an operational analytics diagnostic.

Recommended starting point: a free 30-minute fit call, followed by a paid diagnostic sprint where the reporting process, data sources and business case are mapped properly.

  • Identify the highest-value operational analytics opportunity
  • Map current data sources and reporting pain points
  • Define the first system or prototype worth building
  • Create a practical roadmap for dashboard, data model and adoption
""" html_path = out_dir / "index.html" html_path.write_text(html, encoding="utf-8") # Include logo if available for convenience logo_src = Path("/mnt/data/rainsy-high-resolution-logo.png") if logo_src.exists(): shutil.copy(logo_src, out_dir / "rainsy-analytics-logo.png") zip_path = Path("/mnt/data/rainsy_analytics_updated_html.zip") with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as z: z.write(html_path, arcname="index.html") logo_file = out_dir / "rainsy-analytics-logo.png" if logo_file.exists(): z.write(logo_file, arcname="rainsy-analytics-logo.png") print(f"Created {html_path}") print(f"Created {zip_path}")