A Comparative Analysis of Gregorian, Fiscal, Retail 4-4-5, and Manufacturing Calendar Models in Power BI Analytics Environments

Abstract:

Every measure in a Power BI analytics environment inherits the period structure of its date dimension, yet organisations routinely default to the Gregorian calendar without examining whether that structure serves the decisions at hand. This study presents a controlled comparative analysis of four calendar models — Gregorian, fiscal (April to March), retail 4-4-5, and manufacturing (13 × 4-week periods) — implemented in parallel over an identical fact model in Microsoft Power BI. A synthetic-but-calibrated dataset of a multi-channel retail-manufacturing enterprise covering financial years 2020 to 2024 (260 weeks) was modelled four times over, with one DAX date dimension per calendar. The models were evaluated on six dimensions: KPI correctness and period integrity, year-over-year comparability, forecast accuracy (MAPE at one-, two- and four-quarter horizons), statutory fit, restatement workload, and perceived strategic fit rated by 24 finance and planning professionals.

The retail 4-4-5 calendar produced the largest gains: quarters of exactly 91 days, peak-season year-over-year distortion of 2.1 per cent against 8.9 per cent under the Gregorian calendar, and forecast MAPE lower by 27 to 32 per cent at every horizon. The fiscal calendar scored highest on statutory fit (4.8 of 5) but retained the month-length distortions of the Gregorian structure. The manufacturing calendar best supported capacity planning (4.6 of 5). Gregorian and fiscal models required restatement of 22 and 18 per cent of periods respectively, against 4 to 5 per cent for the week-based models.