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Stockpile reconciliation explains the gap between surveyed volume, production records, and system inventory, then documents how that gap was resolved. This guide walks through an eight-step process for checking inputs, investigating variance, aligning teams, and building an audit trail that holds up to review.
Across aggregates and mining operations, measuring the pile is only part of the job. The harder part is explaining why the number changed, why it differs from the books, and tracing that answer back months later. This guide gives you a repeatable way to do that.
Here is what you’ll have when you’re done:
A clear definition of what “defensible reconciliation” means, and why it is different from measuring a pile. A checklist of inputs you need before you start. A repeatable eight-step workflow for investigating and explaining variance. And a month-end checklist plus a reporting template you can copy into your own process.
Reconciliation is different from measurement. Measuring a stockpile gives you a volume at a specific point in time. Reconciliation connects that measurement to the number recorded somewhere else, such as an ERP, production report, royalty calculation, or finance system. When the two do not match, the goal is to understand why.
That difference, or delta, can come from several places. It might relate to a missed dispatch, product classification, an unrecorded rehandle, density assumptions, pile boundaries, base surfaces, or the timing between surveys and reporting periods. The cause matters because different problems require different fixes. A dispatch issue is handled differently from a measurement methodology issue. A useful reconciliation process traces the variance back to its source and documents what happened, so the same reasoning can be followed later.
The field number depends on pile boundary, base surface, survey date, processing method, and density conversion. The book number depends on opening balance, production records, sales, dispatch, waste adjustments, and rehandles. The plan system may use a different density, a different reporting cutoff, or a different pile grouping than either.
Each number can make sense within its own system while still differing from the others. Reconciliation helps identify and explain those differences.
A stockpile number is easier to defend when someone can trace where it came from, understand how it was calculated, and reproduce the result later. That means keeping the key details connected from capture to report.
Capture: survey date, method (PPK, RTK, or a ground control network), and equipment used. Processing: when the data was processed, by whom, and with what settings. Method: which volume calculation method (smart base, reference level, or custom base), which boundary rules, and which base surface definition. Density and conversion: density, swell or shrinkage factors, their source, and when they were last reviewed. Reporting: the reporting period and the systems or records used for comparison.
If those details are incomplete or split across multiple places, it becomes harder to trace and explain the final number.
Consistency also matters from one survey to the next. The same pile should use the same boundary rules, base surface method, and density assumptions regardless of who processes it, the same discipline a survey or engineering team applies to any repeatable measurement. The volume will change as material moves. The methodology should stay consistent unless there is a clear reason to update it.
That makes variance easier to investigate. If the methodology changes at the same time as the pile, it becomes harder to tell whether the difference came from real material movement or from the way the measurement was calculated.
Before running any reconciliation, confirm you have each of these. If any one is missing, the comparison will produce a variance you cannot explain. Most of them live in, or connect back to, the measurement platform.
Measurement data: current survey or model with a known capture date, method, and processing record. Boundary rules: a documented polygon boundary per pile, confirmed as current. Base surface definition: method (smart base, reference level, or custom base) per pile, confirmed as consistent with prior surveys. Density and conversion factors: current bulk density per material type, swell or shrinkage factor, and their source. Reporting period and cutoffs: start and end date of the period, and production and sales cutoff timestamps. Book inventory figures: opening balance, production additions, sales and dispatches, rehandles and transfers, and waste adjustments. Systems of record: which system holds each number and how they were last reconciled.
Get the inputs right before you run the comparison.
Eight steps take you from a raw variance to a documented, defensible number. Here they are at a glance, then in detail.
Before anything else, agree on the exact period being reconciled. What date does the opening balance reflect? What is the closing date for production and sales figures? Are the survey dates consistent with those cutoffs, or is there a timing gap that will create a phantom variance? A survey captured on the 28th and compared against sales data through the 31st includes a three-day timing gap. That difference may reflect material movement between the survey date and the reporting cutoff rather than a measurement issue. Documenting it upfront makes the variance easier to explain. Document period start, period end, survey dates, production cutoff, and sales cutoff.
Confirm that each pile is using the same volume calculation method as the previous period. Smart base, reference level, and custom base can produce different results from the same pile geometry. Check for the same method, same polygon boundary, and same base surface definition as the last survey. If anything changed, note it and quantify the expected impact before running the comparison.
Check that the polygon boundary still matches the current extent of the pile, including any movement at the toe since the last survey. If the boundary has changed, part of the volume difference may come from the boundary itself rather than material movement. For custom base surfaces, confirm the base still reflects the ground beneath the pile. Sloped pads and piles against walls may need a custom base rather than a flat reference surface. Document any boundary changes and why, and any base surface updates and their effect on volume.
If the book number is in tons and the survey is in cubic meters, the conversion is where most invisible errors live. Confirm which bulk density figure was used and for which material, whether swell or shrinkage is applied before or after conversion, and whether the conversion factor is stored in the platform or applied manually downstream. Conversions that live in spreadsheets are the most common way a defensible measurement becomes an indefensible number. The moment a volume is copied into a cell and a density is applied separately, the chain of custody breaks.
With the period confirmed, the method validated, and the conversion applied, run the actual comparison.
Record both figures and the variance in the same units. If the sign is counterintuitive, for example you have more than expected, check the sign conventions in your ERP before assuming a problem.
Once you have the variance, work through the most likely causes one by one. Density assumptions: check whether the same density was used in the book calculation and the volume conversion, because even a small difference can create a meaningful tonnage gap. Shrink and swell: confirm that shrinkage or bulking factors were applied consistently when material was moved, processed, or rehandled. Rehandles and pile transfers: check whether movements between piles were recorded, using loader and truck records or machine telematics from DirtMate to cross-reference cycle counts at the machine level. Survey cadence: look at the gap between the survey date and the reporting period, and tighten survey cadence where material moves fast. Timing and cutoffs: compare dispatch dates with the dates transactions appear in the system. Boundary and toe movement: compare pile boundaries between surveys, since a changed polygon can move volume on its own.
For each identified cause, write a short explanation of what changed, what effect it had on the variance, and what should happen next. A simple format keeps the record easy to review later: cause identified (for example, a density assumption mismatch), effect on variance (for example, a large share of the total delta), action (update the density figure to reflect the latest lab test and align with finance), and owner and due date (technical services, before the next survey). Keeping this context with the reconciliation makes it easier to understand the same issue if it appears again.
Bring the supporting records together so someone who was not involved can still follow how the final number was reached. The package should include the survey record (date, method, platform export or PDF report), the boundary and base surface settings, the density and conversion factors with source, the period and cutoff confirmation, the book figures with source system and export date, the reconciliation comparison, the delta explanation from Step 7, and the actions and owners. Tie it to your reporting rhythm and keep it somewhere the team can access together, so the trail is shared rather than stranded on one person’s desktop.
When a delta shows up, the pattern usually points to the cause. Use this to move from symptom to test to fix.
| What you see | Likely cause | How to check | Fix or prevention |
|---|---|---|---|
| Delta grows at period end, shrinks mid-period | Timing or cutoff mismatch | Compare dispatch timestamps against book entry dates | Align cutoffs between survey schedule and sales reporting |
| Delta is consistent but unexplained | Density assumption mismatch | Compare density used in volume conversion vs. density used in book calculation | Store density in the measurement platform; align with finance on an approved figure |
| Delta varies pile-to-pile with no pattern | Boundary noise | Overlay polygon boundaries from consecutive surveys | Lock boundaries per pile; confirm the toe line matches physical extent each survey |
| Opening balance never reconciles cleanly | Accumulated base surface drift | Re-run prior periods with the current base surface definition | Lock base method per pile; document changes to the base surface definition |
| Loss from one pile, unexplained gain in another | Unrecorded rehandle or transfer | Check loader and truck records for inter-pile movements | Add inter-pile movement tracking to dispatch records |
| Survey shows more than expected | Swell applied inconsistently | Check where in the workflow swell is applied (survey side vs. book side) | Apply swell at one point in the workflow; document which side and why |
| Survey shows less than expected despite no dispatches | Material degradation or contamination | Check material type and grade records; compare survey date against last grade test | Flag degradation events; track grade separately from volume |
| Variance is large in winter, small in summer | Moisture and density variability | Compare density assumptions against seasonal lab results | Use seasonally adjusted density figures; validate at least quarterly |
Three short checklists carry a clean reconciliation from before the survey to a packaged result. Run them as a repeatable month-end site check on your reporting process.
Watch the webinar: From measured to defensible: how to reconcile stockpiles with confidence, the on-demand recording.
Download the playbook: The Defensible Stockpile: a reconciliation playbook for aggregates operators, a gated asset with the full template set.
Explore the platform: See volume calculations and stockpile measurement, and how machine telematics cross-references cycle counts against your survey record.
See it for your site: Request a demo to walk through reconciliation on your own piles.
We’re happy to show you how Propeller can power your worksite, and boost productivity.
