50,000 operations a day, without growing the headcount
How a major mining operation moved manual operational data flows — procurement, inventory, maintenance, compliance — to fully automated, 24/7 execution across 30+ processes.
Mining · Mining group · Orchestrator · Studio · Robot · 9-min readAbout the customer
A major mining group — multi-site operations, 50,000+ employees, multi-billion-dollar annual revenue, complex multi-jurisdictional regulatory environment, and an operational model that runs continuously across procurement, supply chain, maintenance, and compliance functions.
The group's automation programme is one of the larger industrial RPA deployments in Eastern Europe — 30+ production processes covering high-volume operational data flows across the back-office and the link between back-office systems and field operations.
Tens of thousands of transactions a day — every one done by hand
The group's operational model produced enormous volumes of repetitive transactions: purchase orders generated against framework contracts, inventory updates flowing in from sites, maintenance schedules pulling from CMMS, compliance documents flowing out to regulators on cyclical timelines. Most of these transactions were rule-based and high-volume, with occasional non-standard cases requiring judgment.
Three operational realities compounded.
First, scale: tens of thousands of transactions per day across the procurement, inventory, maintenance, and reporting functions. Manual handling required several dozen FTEs across functions, and the team was permanently behind on backlog — particularly during commodity price cycles when procurement volume spiked.
Second, error rates: manual handling at this scale produced an error rate in the low single digits, which sounded acceptable in percentage terms but translated to several hundred operational errors per day in absolute terms. Many of these errors required downstream remediation — financial reconciliations, supplier relationship management, regulatory corrections.
Third, the work itself: the people doing it were operations specialists with deep domain knowledge being asked to spend most of their time on data entry. The retention story was getting worse year over year.
The non-negotiables: industrial-grade reliability (24/7 operations do not pause for IT issues), full audit trail for regulatory environments, and integration with the group's existing ERP, CMMS, and field-data systems without rebuilding any of them.
How we built it
Wave 1 — Procurement and inventory (months 1–6)
The first wave targeted the highest-volume, most rule-based transactions: purchase order generation against framework contracts, three-way matching, inventory updates from site systems to ERP, and routine vendor master data hygiene.
Each process was rebuilt in Primo Studio with explicit handling of the non-standard cases — inputs that fell outside framework contracts, mismatches that triggered exception routing, exceptions escalated to a human reviewer within a defined SLA. The exception logic was deliberately conservative: when in doubt, route to a human.
Wave 2 — Maintenance and supply chain (months 7–12)
The second wave layered in CMMS integration — pulling maintenance schedules, generating work orders, syncing parts and consumables against inventory, and feeding completion data back to ERP. Supply chain workflows extended to logistics handoffs, vendor handoffs, and inter-site material movements.
This wave benefited from a pattern established in Wave 1: every workflow had a parallel "manual fallback" mode, used during initial validation and available as an emergency path. Within Wave 2, the manual fallback was activated three times — twice during scheduled system maintenance, once during an unplanned ERP outage. The fallback worked as designed; the automation team had operated under the assumption it would.
Wave 3 — Compliance and reporting (months 13–18)
The final wave moved cyclical compliance reporting and operational rollups onto the platform. These workflows are lower-volume than procurement but higher-stakes: the regulatory submissions are time-bound and the cost of a missed or incorrect submission is significant.
Across all three waves, the workflows were unified under a single Primo Orchestrator instance with full audit trail, role-based access, and integration into the existing monitoring stack.
At 50,000 operations a day, a 5% error rate means thousands of failures, every day. We got it to 0.3%. That is the only number that matters.
The numbers
What changed for the team
The quarterly compliance fire drill disappeared. What had been two weeks of all-hands work — everything else backlogged, operations specialists pulled off their core roles — became scheduled workflows that run without intervention. That capacity came back permanently.
The programme also changed what the team could see. Exception patterns that previously surfaced weeks late are now flagged within hours. The team stopped working around recurring failures and started eliminating them.
Mining operations at scale — deployment architecture
What's next for this customer
The mining group is moving into AI-assisted exception handling on the procurement and supply chain workflows that currently route non-standard cases to humans. Primo AI Server will absorb a subset of the exception volume — specifically, exceptions that are non-standard but pattern-matched to historical resolutions — while genuinely novel cases will continue to route to human reviewers.
The CoE is also extending the automation footprint into the group's HR and finance back-office functions, applying the same wave-based deployment model that worked for operations.