From 61% to 79% OEE.
Measured,
not estimated.

The Problem

The manufacturer operates [VERIFY: five production lines] producing packaged food for retailers across the Western Balkans. Like many sector peers, the organisation had invested heavily in production equipment but much less in production instrumentation — lines ran, operators recorded downtime in paper logbooks, and monthly production reports were rebuilt manually by the plant manager for the board.

The gap between "running" and "running efficiently" was invisible to executive management. When the board asked for OEE data, the answer was an estimate based on operator memory and invoice patterns. The CFO suspected there was significant hidden loss — scrap not properly recorded, downtime attributed to changeovers when it was actually breakdowns, minor stoppages simply not counted — but the organisation had no way to verify.

The trigger for change came from a retailer customer. During a supply-chain review, the retailer flagged inconsistent lead times and asked for visibility into production capacity. The manufacturer could not answer the question with real data — and realised that competitors who could answer it would win the category review.

Why Virtual Era

The manufacturer approached three technology partners. Two proposed Siemens or Rockwell MES installations at enterprise scale — with deployment timelines of 24-30 months and capital costs that required board approval above the CEO's signing authority. Virtual Era proposed a phased deployment on a mid-tier MES platform — production instrumentation in the first three months, executive reporting by month six, and full OEE/SPC/genealogy in twelve.

The proposal was accepted on three grounds: the phased-value delivery (first benefits visible within a quarter, not a year), the commercial model (fixed-scope delivery with benefits tracking) and the sector reference — we had delivered a similar programme for an FMCG manufacturer two years earlier.

The Approach

Phase one (months 1-3): Production-line instrumentation. Sensors and PLCs on all five lines, data collection, and a basic daily OEE dashboard. No process change for operators — we simply started measuring what was actually happening.

Phase two (months 4-7): MES core deployment. Work orders, scheduling, downtime coding, quality recording, ERP integration. At this point operators started using the system for their daily work — the early win was that downtime coding became easier than the paper-based process, so adoption came naturally.

Phase three (months 8-14): Full SPC, genealogy, and executive reporting. Plant-manager dashboards for operational decisions, executive dashboards for commercial decisions, retailer-facing reporting for the customer relationship that had triggered the programme.

The Outcome

Measured OEE moved from a starting-point estimate of 61% to a verified 79% at month 12 post-launch. The gap between estimate and reality in the baseline was itself diagnostic — many of the "unexplained" losses were minor stoppages and changeover inefficiencies that simply weren't being recorded.

Scrap rate dropped 22% once the quality module exposed which lines and which SKUs were driving scrap — a pattern invisible in the paper-based baseline. Three SKUs were redesigned, one was discontinued, and one was moved to a different line.

The retailer that had triggered the programme expanded its category share with the manufacturer by [VERIFY: 34%] — partly on capacity visibility, partly on the operational-maturity signal the MES deployment sent.