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An operations dashboard puts operating cost, accepted output, and staffed hours on a comparable basis by site. This worked example explains a cost increase, separates volume from unit-cost changes, and proves the totals against the exports.
A Power BI operations dashboard should show operating cost, accepted output, and throughput by site, then explain what changed between comparable periods. The central question is whether higher spending bought more output or reflects a higher cost for the output already produced.
Microsoft describes a Power BI dashboard as a single page of visualizations, supported by detailed reports. The layout below is a business review example, built around measures a site manager and controller can reconcile. Covirage is not affiliated with Microsoft, the maker of Power BI.
Suppose a US packaging company wants to explain September 2026 spending at Dayton, Ohio, and Tulsa, Oklahoma, against August. Both sites make the same standard shipping case. The example therefore uses accepted cases as a comparable output unit.
The operating cost export contains the site, posting month, account, and amount in USD. The production export contains the site, completion month, and accepted cases. A time summary supplies staffed operating hours, defined here as the hours the production line was staffed, including stops during those shifts.
The cost boundary includes production labor, packaging materials, and the agreed site overhead accounts. Corporate selling expenses and equipment purchases are outside it. Keep the same boundary in both months. The number is cost per completed case, not an inventory valuation or a customer profitability measure.
Start the review with total spending and output, follow with site exceptions, and keep the source tie-outs beside the analysis. The executive dashboard examples cover the smaller set of measures needed for the leadership meeting; the broader dashboard examples place operations alongside other departments.
The following table is the complete simplified example. Each row is one site for one month, after the exports have been totaled separately to that level.
| Month | Site | Operating cost (USD) | Accepted cases | Staffed operating hours |
|---|---|---|---|---|
| August | Dayton | 120,000 | 10,000 | 500 |
| August | Tulsa | 80,000 | 8,000 | 400 |
| August total | 200,000 | 18,000 | 900 | |
| September | Dayton | 132,000 | 11,000 | 550 |
| September | Tulsa | 88,000 | 8,000 | 400 |
| September total | 220,000 | 19,000 | 950 |
Cost per accepted case = Included operating cost / Accepted cases
Throughput = Accepted cases / Staffed operating hours
Period change = Current period / Prior period - 1
| Month | Site | Cost per accepted case (USD) | Throughput (cases per hour) |
|---|---|---|---|
| August | Dayton | 12.00 | 20.00 |
| August | Tulsa | 10.00 | 20.00 |
| August total | 11.11 | 20.00 | |
| September | Dayton | 12.00 | 20.00 |
| September | Tulsa | 11.00 | 20.00 |
| September total | 11.58 | 20.00 |
Spending increases by (220,000 - 200,000) / 200,000 = 10.0%. Accepted output increases by (19,000 - 18,000) / 18,000 = 5.6%, rounded to one decimal place. Company cost per accepted case increases by 4.2%, using the unrounded ratios. Throughput remains 20.00 cases per hour because the extra output comes with more staffed hours.
Unit costs and throughput are displayed to two decimal places. Keep full precision when computing changes and reconciliation checks.
The total cost increase is $20,000. Separate it with a consistent sequence: value each site's output change at its August unit cost, then value its unit-cost change at September output. This arithmetic explains the movement without pretending to identify its underlying cause.
| Site | Output effect (USD) | Unit-cost effect (USD) | Total cost change (USD) |
|---|---|---|---|
| Dayton | (11,000 - 10,000) × 12 = 12,000 | 11,000 × (12 - 12) = 0 | 12,000 |
| Tulsa | (8,000 - 8,000) × 10 = 0 | 8,000 × (11 - 10) = 8,000 | 8,000 |
| Total | 12,000 | 8,000 | 20,000 |
The proof is 200,000 + 12,000 + 8,000 = 220,000. Dayton's increase is consistent with more output at the same unit cost. Tulsa's output is unchanged, so its higher unit cost accounts for its spending increase. The controller should examine Tulsa's expense categories before attributing that increase to wages, material prices, or overtime.
For a maintenance-specific explanation, use the maintenance dashboard example. Downtime belongs in this review only when its asset scope and operating calendar match the throughput denominator.
For each month, site cost must sum to the included ledger accounts, accepted cases must sum to the production summary, and staffed hours must sum to the time summary. September throughput has an additional arithmetic check: 20 cases per hour × 950 hours = 19,000 cases.
In Excel, SUMIFS adds values meeting multiple criteria. With site in A, month in B, and operating cost in C, the September Tulsa subtotal is:
=SUMIFS(C2:C1000,A2:A1000,"Tulsa",B2:B1000,"September 2026")
Use standardized month labels for this formula. Repeat the check on the separate output and hours summaries rather than joining their individual records to expense lines.
Now change only Tulsa's September operating cost from $88,000 to $80,000. Its accepted cases and staffed hours stay unchanged.
| September measure | Original | Changed case |
|---|---|---|
| Tulsa operating cost (USD) | 88,000 | 80,000 |
| Tulsa cost per case (USD) | 11.00 | 10.00 |
| Company operating cost (USD) | 220,000 | 212,000 |
| Company cost per case (USD) | 11.58 | 11.16 |
| Company throughput (cases per hour) | 20.00 | 20.00 |
The changed case reconciles as 132,000 + 80,000 = 212,000, and 212,000 / 19,000 = 11.16 per case. The remaining increase from August is entirely Dayton's $12,000 output effect. Throughput cannot change when neither output nor staffed hours changes.
The site manager owns the operational explanation; finance owns the cost boundary and ledger tie-out. If the team is deciding how to conduct the wider review, see Power BI alternatives.
Bring a sample of your own site expense, production, and staffed-hours export or Google Sheet. Covirage maps the columns, and its tools compute and check the cost and throughput comparisons; the external AI model explains the result with every figure traced to its rows. Create a free Covirage account, which takes about a minute, then set up your business and bring your file.
See cost variance analysis for the related business questions.
Start with operating cost, accepted output, staffed operating hours, cost per accepted unit, and accepted units per hour. Show the reporting period and site scope. Add measures such as downtime only when the underlying records support a consistent definition and a specific operating decision.
Output is the number of accepted units completed during a period. Throughput adds a time denominator, such as accepted units per staffed operating hour. Output can rise while throughput stays unchanged when a site simply operates for more hours.
Use total included operating cost divided by total accepted units. A simple average gives small sites the same influence as large sites. If sites make different products or use different output units, separate the comparisons or agree a defensible common unit first.