Case studies
Four projects, each with the same structure: starting point, decision, implementation, result with measurement, and what I would do differently.
- Case study A
Forecasting platform: sole developer from the first line to daily operation
An internal platform for forecasting and planning, built and run by one person. 13 legacy systems and manual processes replaced, usage grown from 9 to 28 people per month between April and July 2026.
35 of about 40possible people active in a single week, September 2026, at two of three sites13legacy systems and manual processes replaced - Case study B
Forecast accuracy, shown honestly
Walk-forward over 159 weeks against the previous-week carry-forward. At the decision level, 20.6 instead of 28.2 percent error. At the top aggregate level the simple carry-forward is marginally better, and that is stated here, not in a footnote.
159weeks of walk-forward, all out of sample20.6 %error at the decision level, instead of 28.2 % for carry-forward - Case study C
Data platform in operation
57 assets, 16 jobs, around 164 orchestrated steps per working day over around 155 million rows from four source systems. Hardened after real incidents. Third site connected, with peak memory brought down from 39.3 to 9.7 gigabytes.
57assets in 16 jobs164orchestrated steps per working day, rounded - Case study Dongoing
Replacing an integration platform
Ongoing. Inventory captured automatically: 99 scheduled processes, 1,278 jobs, around 8,200 processing steps with dependency graph and risk classes. First process migrated and verified against the original: 501,546 rows identical.
99scheduled processes captured automatically1,278jobs in the inventory