Customer Case Study · Finance & Asset Operations · 8 automations
For an Indian renewable energy business, HMC automated eight workflows across generation, treasury, regulatory and lease accounting. Each chore was matched to the right tool: Excel formulas, VBA, Power Query or Python.
Figures indicative; validated metrics available on request.
Running wind and solar assets produces a steady flow of data: SCADA logs, bank statements, government energy accounts and lease agreements. Most of it reached Excel by hand, and each team had built its own workarounds.
Bank statements and regional energy accounts arrived as PDFs with shifting layouts. People read them and typed the numbers in.
SCADA exports, DGR sheets and lookups moved between workbooks by hand every day, and each pass was a chance for an error.
Lease models held long nested formulas over 14,000 rows. They were slow to recalculate and hard for anyone but their author to change.
Some users wrote macros and others avoided formulas. Any fix had to be a button or a form that everyone could use.
It started with an on-site week in Pune in March 2024, sitting with each team, and grew into a portfolio of eight automations shipped through the year.
Select a step to see what happened.
We sat beside analysts in generation, treasury, commercial and lease accounting.
Deliverable: Chore inventory and priority list.
No platform rollout, just the lightest tool that fits each job.
Deliverable: Design note per chore, signed off by its owner.
Each automation was built against real files and checked against last month's manual output.
Deliverable: Eight working tools, each with test evidence.
Every tool shipped with a walkthrough and a one-page guide.
Deliverable: Training sessions, guides and handover.
Patterns built for one site or bank were reused for the next.
Deliverable: Reusable templates and a backlog of next chores.
Eight cases, four toolkits, four teams. The map shows where each one sits. Every case below includes the original screens and a working demo you can try.
Bubble size shows manual hours saved per month (indicative).
No single tool changed the business. Together, eight of them gave the teams about four working weeks a month back.
Stacked by case, as each automation went live
Hours saved per month, sized by case
Figures indicative; validated metrics available on request.
A formula beat a macro, and a macro beat a new system. Python came in only where websites and messy PDFs left no other option.
None of the eight saved more than 35 hours a month on its own. Together they returned about four working weeks a month.
Every tool shipped with a walkthrough and a one-page guide, so the people who run the chore own it, not only the person who built it.