Most glazing companies already know their numbers. Square footage installed, how many lites came back cracked, how long the tempering furnace queue is running, what a job is worth on average. What they usually do not have is one page that puts all of it against a target, in the same units, with a colour that says whether it is good.
The Glass Installation KPI Dashboard in Power BI is that page. Twelve glazing KPIs, seven groups, a month picker, and a traffic light for the month and for the year on every single row. This post walks every page of the report and explains the December 2025 sample numbers that ship inside it, so you can decide before you buy whether the build matches how you actually run the shop.
Which product this is, and which it is not
NextGenTemplates ships three families with deliberately similar glass names, so it is worth being blunt about which one this is.
- This is the KPI Dashboard line. A month picker, a target-versus-actual scorecard, three-state traffic lights, a KPI Trend page and a KPI Definition page. Four visible report pages, two hidden tooltip pages.
- It is not the analytical “Dashboard” line, which slices revenue, jobs and volume by crew, branch or product type. Different question entirely.
- It is not the KPI Scorecard family – the lighter single-file build that ships as Glass Installation KPI Scorecard in Google Sheets and in Excel, with its own shorter KPI list.
They complement each other rather than repeat. If you already own the scorecard, this adds prior-year comparison, direction-aware scoring and a definition page; it does not replace it.
The twelve KPIs, and why these twelve
The KPI list was written for a company that both fabricates and installs – storefront, curtain wall, insulated glass units, shower enclosures and mirrors – rather than for generic field service. Every one carries a group, a unit, a formula, a written definition, an owner, a priority and an up-to-better or lower-to-better direction.
| KPI | Group | Unit | Direction | Owner |
|---|---|---|---|---|
| Total Glass Area Installed | Field Operations | Sq Ft | Higher is better | Field Operations Director |
| On-Time Job Completion Rate % | Field Operations | % | Higher is better | Operations Manager |
| Glazing Rework Rate % | Field Operations | % | Lower is better | Field Operations Director |
| Lift & Scaffold Utilisation % | Field Operations | % | Higher is better | Equipment Manager |
| Glass Breakage Rate % | Quality | % | Lower is better | Fabrication Manager |
| IGU Seal-Failure Warranty Claims | Quality | Ratio | Lower is better | Warranty & Service Manager |
| Fabrication & Tempering Lead Time | Production | Days | Lower is better | Plant Manager |
| Custom Lite Supplier Lead Time | Supply Chain | Days | Lower is better | Procurement Manager |
| Quote Turnaround Time | Sales | Hours | Lower is better | Estimating Manager |
| Average Job Value | Sales | USD | Higher is better | Sales Director |
| Gross Margin per Square Foot | Finance | USD/Sq Ft | Higher is better | Finance Manager |
| Safety Recordable Incident Rate | Safety | Ratio | Lower is better | Safety Manager |
Five are higher-is-better, seven are lower-is-better. That split matters more than it looks, and it is the reason the report scores the way it does.
How the scoring works
Achievement % is direction-aware. A higher-is-better KPI scores Actual / Target. A lower-is-better KPI scores Target / Actual. So when the December sample shows Glass Breakage Rate % at an actual 1.73% against a 1.96% target, it scores 113.3% – a green light, because breaking fewer lites than planned is a win, not a shortfall. Get that backwards and half your scorecard reads as failure every month.
Three states, applied identically to MTD and YTD:
- On Target – 100% or more
- At Risk – 95% up to 100%
- Missed – below 95%
There is a second rule buried in the data pack’s Read Me that is worth stealing even if you never buy the file: for a rate, ratio or index, YTD is the average of the months so far, never a sum. A completion percentage that adds up to 1,900% by December is the single most common mistake in a KPI pack, and it makes every YTD chart meaningless.
Page 1 – KPI Scorecard
Five slicers across the top: Month, KPI Group, Owner, Priority and Direction. Under them, five summary cards, each with its own 12-month trend bars and a month-on-month delta. The shipped December 2025 sample reads:
- Total KPIs 12, flat on November
- MTD Target Met 6, up 20.0%
- MTD At Risk 3, up 50.0%
- MTD Target Missed 3, down 40.0%
- Achievement MTD 99.6%, up 1.6%
Then the scorecard table itself, one row per KPI: name, group, unit, a 12-month sparkline, Actual (MTD), Target (MTD), Achievement % (MTD), Status Light (MTD), YoY Arrow (MTD), Actual (YTD), Achievement % (YTD) and Status Light (YTD). Rows sort worst first, so the December sample opens on Gross Margin per Square Foot at 76.2% and ends on Safety Recordable Incident Rate at 117.3%. The meeting starts where the problem is.
The two-horizon design earns its keep on rows that disagree with themselves. Glazing Rework Rate % is At Risk for December at 96.1% but Missed for the year at 90.9% – a month that looks survivable sitting on top of a year that is not. Quote Turnaround Time does the reverse: At Risk for the month at 96.2%, On Target for the year at 101.6%. Across the sample, MTD splits 6 met / 3 at risk / 3 missed while YTD splits 7 on target / 1 at risk / 4 missed.
Page 2 – KPI Trend
A “Select KPI Name” list runs down the left with all twelve KPIs in it. Pick one and the whole page becomes that KPI. Six context cards name the selected KPI, its group, unit, direction, owner and priority – so Average Job Value shows Sales, USD, Higher is Better, Sales Director, High. Five value cards follow: KPI Actual (MTD) 19,880, KPI Target (MTD) 22,312, KPI Achievement % (MTD) 89.1%, KPI Status (MTD) Missed, KPI YoY % (MTD) +3.3%.
Under those sit two line charts, each with three lines – this year, last year, target:
- CY MTD vs PY MTD vs Target MTD by Month – the month-on-month picture, twelve points, Jan through Dec.
- CY YTD vs PY YTD vs Target YTD by Month – the cumulative picture, which smooths out a single bad month.
The prior-year line is not a modelled estimate. The data pack carries 288 rows of actuals – twelve KPIs by twenty-four months, 2024 and 2025 – so every PY line and every YoY arrow comes from typed history.
Page 3 – KPI Definition
This is the page most KPI packs leave out, and the one that stops the monthly argument before it starts. The same KPI selector is synced across from KPI Trend, so whichever KPI you picked is already applied when you switch tabs. For it you get the formula and the plain-English definition, side by side.
Average Job Value, for example, reads “Total Contracted Revenue / Jobs Signed”, defined as “Average contract value across signed storefront, curtain wall, shower enclosure, and mirror jobs”. Glass Breakage Rate % reads “Panes Broken / Panes Handled” – lites cracked or damaged during shop handling, tempering or transit, before installation. IGU Seal-Failure Warranty Claims is claims per 1,000 units shipped. Nobody has to remember which of those includes transit damage; it is printed on the page.
Below the definition cards sits a Monthly Detail for the Selected KPI table covering all twelve months plus a total row, with Actual (MTD), Target (MTD), Achievement % (MTD), Status (MTD), Actual (YTD) and Achievement % (YTD) per month. For Average Job Value the sample runs from 17,059 against a 21,196 target in January (80.5%, Missed) to 19,880 against 22,312 in December (89.1%, still Missed) – a KPI that improved all year and still never cleared the bar. Two charts sit beside it: MTD Actual vs Target by Month, and Achievement % by Month.
Page 4 – Get More Templates
A short catalogue and customisation page. The useful half is the “Using and Customising This Template” block: change the month and the scorecard follows while the sparkline stays on the full year; pick a KPI on KPI Trend and every visual on the page becomes that KPI; swap in your own data through the three sheets in Data.xlsx and press Home > Refresh; set UTB or LTB per KPI so lower-is-better metrics score above 100% when they beat target; recolour in one place, because a single custom theme file drives every visual.
There are also two hidden tooltip pages – KPI Detail and Trend Detail, both 280×360 – that surface on hover and never appear in the page tabs.
What is in the download
One ZIP holding three files:
- The .pbix report – fully editable, native Power BI visuals only, nothing locked and nothing to install from AppSource.
- Data.xlsx – four sheets. Read Me explains the model. KPI Definition is one row per KPI with group, unit, formula, definition, UTB/LTB type, owner and priority – 12 rows. Input_ Target holds 144 rows, twelve KPIs by twelve months. Input_ Actual holds 288 rows, because it carries 2024 as well as 2025.
- A Power BI Dashboard user manual PDF.
Keep all three in one folder – the report reads the workbook from beside it.
Setting it up with your own numbers
- Unzip all three files into one folder.
- Open the .pbix in Power BI Desktop, free from Microsoft and Windows-only. Look around first; the December 2025 sample is already loaded.
- Repoint the query: Home > Transform data > Data source settings > Change Source, aimed at your copy of Data.xlsx. The path saved in the file is the folder it was built in.
- Edit the three sheets. KPI name is the join key across all of them, so a rename has to happen in all three.
- Press Home > Refresh. The counts, the scorecard, the selector lists and every chart rebuild around whatever KPIs now exist – nothing in the report hard-codes a KPI name, so you can add, delete or rename freely.
What it does not do
Worth saying plainly, because a KPI dashboard is easy to oversell.
It does not connect to anything live. The model imports one Excel workbook and builds a Date table in Power Query – no API, no gateway, no connector to QuickBooks, Xero, Sage, ServiceTitan, Jobber, Housecall Pro or Procore. You type actuals in and refresh. There is no per-job, per-crew or per-elevation drill-down either: this is a company-level monthly scorecard by design, and individual jobs are not a dimension in the model. There is no right-click drill-through page.
It is not a takeoff, estimating, optimisation or cut-list tool, it does not schedule crews or route trucks, and it is not accounting software. It does not file OSHA 300 logs, administer warranty claims, or verify glazier certification or safety-glazing conformance – Safety Recordable Incident Rate and IGU Seal-Failure Warranty Claims are figures you already produce elsewhere and type in.
And every number shipped in the file is sample data, targets included. There is no benchmark set and no trade-association standard behind any of the twelve targets. Replace them with your own before anyone treats a traffic light as meaningful.
Related Power BI KPI dashboards
The same scorecard build ships for a long list of trades. If you want the plant side of glass rather than the install side, look at the Glass Manufacturing KPI Dashboard in Power BI or the Glassware Production KPI Dashboard in Power BI, both of which score furnace, yield and defect metrics instead of install metrics.
For neighbouring trades on the identical page structure, the Fencing Contractor KPI Dashboard in Power BI and the Insulation Contractor KPI Dashboard in Power BI are the closest reads – same slicers, same traffic lights, different KPI lists.
Where to get the Glass Installation KPI Dashboard in Power BI
The Glass Installation KPI Dashboard in Power BI is $12.99 (regular $19.99) on NextGenTemplates – a one-time payment with lifetime access to the file, no subscription and no per-seat licence.
Get the Glass Installation KPI Dashboard in Power BI
Prefer to work in a spreadsheet? The same trade ships as Glass Installation KPI Dashboard in Excel and in Google Sheets – separate builds with their own KPI lists and page sets, not one file exported three times. The lighter Glass Installation KPI Scorecard in Google Sheets is the option if twelve KPIs and a definition page is more machinery than you want.
Built by PK – Microsoft Certified Professional with 15+ years of Excel, Google Sheets and Power BI experience, and founder of NextGenTemplates. Every template is hand-built and tested before release.
