Back to case studiesIndustry 4.0 across three food plants in Morocco
A digital manufacturing wave across three food plants: thirteen use cases from real-time OEE to smart energy, an edge-to-cloud data architecture, end-of-line robotics — and the people track that makes it stick.
13
mandatory digital use cases per plant, from OEE to energy
3
food plants on one digital manufacturing blueprint
Context
Three food plants in Morocco — a drinks factory, a cheese and dairy site, and a fresh-milk site, each running eight or more production lines — entered the company's digital manufacturing wave together. The ambition was archetype-level: take plants run on experience and paperwork and turn them into intelligent, connected operations. As the IT/OT manager for the national footprint, my track was the enabling horizon everything else stands on: foundations, connectivity, the edge layer, and the data platform.
The problem it fixes
Each plant lived on islands: legacy SCADA and historians on site, line performance tracked in standalone tools or spreadsheets, data moved by manual imports, maintenance managed in a ticketing system disconnected from the machines, energy consumption effectively invisible, and end-of-line work done by hand. Decisions ran on stale numbers, improvement projects argued from anecdotes, and every new digital idea died on the same question — where does the data come from?
The idea: one blueprint, thirteen use cases
Instead of letting each plant digitize randomly, the program defined a single blueprint: thirteen mandatory use cases grouped into four levers — asset utilization, labor, quality, and energy — plus shared enablers. Each plant worked the same pipeline: surface pain points, map them to levers, attach the KPIs that matter (equipment effectiveness, losses, energy per ton, staffing), and build a business case per use case. Maturity runs in two steps everywhere: monitoring and alerting first, analytics second; basic, then advanced. No plant skips to AI before its data flows.
The solutions it provides
On connectivity: streaming controller data through industrial connectivity software into a local edge integration layer, stored in a time-series database and forwarded to a central data platform feeding dashboards — with new energy, process and drain sensors closing the blind spots. On performance: real-time line monitoring with OEE, so losses stop being discovered at month end. On energy: metering plus a management platform driving cleaning-process optimization and utility tracking. On quality: statistical process control on connected lines and vision cameras for maintenance. On labor: digital workforce management and scheduling. On the floor itself: end-of-line robotics — autonomous carts and automatic case packers — where the ergonomic and throughput case closed.
From as-is to to-be
The architecture tells the story. As-is: on-premise factory networks with SCADA, a historian database, barcode-based warehouse tracking, and central tools fed by hand. To-be: controllers on the OT network streaming through the edge layer, machine logs and sensor data modeled centrally, performance and energy visible in real time, maintenance and scheduling tools working from the same source. Each plant got its own as-is mapped and its own target drawn — same pattern, local reality.
People and change
The deck's longest track is the human one, and rightly so: workforce planning across digital-transformation scenarios, competency gaps in digital literacy, data-driven management and IT-OT knowledge closed by training, communication in the languages the floor actually speaks, and genuine social dialogue before any role changes. New tools arrive with new procedures and new KPIs — without the training, the RACI and the follow-up, the sensors stream data nobody uses. Technology was at most half of this program.
Results
The three plants left the study phase with funded use-case portfolios, drawn target architectures, and change plans owned by local transformation teams — connectivity implementing, sensors installing, robotics arriving at end of line, performance and energy systems going live. The honest status of a wave at this stage: the blueprint is proven, the data is starting to flow, and the trajectory from manual imports to real-time operations is set. That is what a pre-read is for — decisions taken on evidence, not enthusiasm.
Next steps
This is the exact work INOPSIO runs today: take a plant from SCADA islands and spreadsheets to streaming data, real-time performance and energy visibility — with the connectivity, edge and change discipline that makes use cases stick. If your lines still report through manual imports, a free assessment call is a short conversation away.
Similar case studies
IT/OT foundations across 100+ food plants worldwide
