Digital Factory

See every line live. Fix problems before they happen.

For: Samsung Electronics Egypt, production leadership
Today: Vision, live demo, and a 12-week pilot plan
Duration: 20 minutes
Decision requested: Approve one pilot line

One line, twelve weeks, a measured result.

The opportunity

Downtime, quality escapes and energy are the largest controllable costs on an electronics line. Most of the data needed to reduce them already exists on the floor.

The solution

A live digital twin of the plant with AI that predicts failures, inspects every board, and recommends the next best action, built on top of existing systems.

The decision

Approve a 12-week pilot on one SMT line. Success criteria are agreed up front and measured by your own team.

Every machine already produces data. Most decisions still use yesterday's numbers.

Unplanned stops

One failure on an SMT line stops assembly, test and shipping behind it.

Quality escapes

A defect found in the market costs far more than one caught on the line.

Energy costs

Tariffs are rising, and export customers ask for CO₂ per unit.

Know-how at risk

Expert judgement lives in people's heads and leaves at shift end.

The world's leading factories, including the WEF Global Lighthouse Network, solved this the same way: connect everything, see it live, let AI predict, and let people decide faster.

An operator's gloved hand on a machine on a production line

Samsung's goal: every factory AI-driven by 2030. Beni Suef can be ready early.

March 2026Samsung announces AI-driven factories worldwide
This quarterPilot on one Beni Suef line (proposed)
2030Every Samsung factory AI-driven

Digital twin

Simulation across the whole chain, from materials warehousing to production and shipping.

In the demo: live twin, time machine, what-if

AI agents

Agents for quality, production and logistics that plan and act toward goals.

In the demo: six agents with an autonomy dial

Safety AI

Early detection of hazards and automatic prevention across environment, health and safety.

In the demo: safety agent, 0.4 s response

Robotics

Logistics, assembly and safety robots, with humanoids phased in over time.

In the demo: AGVs dispatched by AI

Digital Factory is a local, fast way to put these pillars to work on one Beni Suef line, starting this quarter.

Source: Samsung Electronics announcement of 1 March 2026 on AI-driven factories by 2030, as reported by The Korea Times and Korea JoongAng Daily.

The results are public. The gap is in execution.

−78%

Lead time

Siemens electronics plant, Nanjing. Named a WEF Lighthouse in 2026 with more than 50 AI applications and +14% productivity.

−40%

Defect rate

Haier water-purifier factory, Qingdao. Quality costs down 72% after 32 digital solutions (company-reported).

≈ 50%

Faster factory setup

Foxconn plans plants and trains robots in a digital twin before building them (company estimate).

238

Lighthouse sites

Generative AI already makes up 23% of the top solutions in the WEF network.

Sources: WEF Global Lighthouse Network (2025 report, 2026 releases); Siemens press release on Nanjing; Haier press release; NVIDIA and Foxconn case study. Results depend on the starting point.

From reacting to problems to preventing them.

TodayWith Digital Factory

Machines are repaired after they failRepairs are planned days before a failure

Inspection by sampling and human eyesEvery unit inspected by AI at line speed

Yesterday's report in this morning's meetingA live view of every line, every second

Big decisions based on experience aloneEvery decision tested on the twin first

Answers depend on finding the right engineerAnswers from the copilot in seconds

Four layers. One clear picture of the plant.

Nothing is replaced. Digital Factory sits on top of the machines, MES and ERP you already run.

Every signal

PLCs and SMT equipment
AOI, SPI and testers
Energy meters and AGVs
MES, ERP and CMMS

Live digital twin

Every line, station and unit
OEE, yield and takt, live
Floor to boardroom
Screens and mobile

AI foresight

Remaining machine life
Vision defect detection
Root-cause analysis
Demand and energy forecasts

Clear decisions

Actions ranked by value
Automatic work orders
What-if simulation
Production copilot

A row of industrial air connectors on a machine

The command room, running right now.

Open full screen

Ten capabilities, working today. Most plants have none of them.

Repair on our schedule, not when the machine decides.

Without AI · reactive

Running4.5 h unplanned stop

The bearing fails mid-shift. The line stops, parts are rushed in, and overtime is needed to recover.

With Digital Factory · predictive

RunningAI alertRunning · parts reserved25 min

The AI warns about 40 hours ahead. The repair fits into the shift change. Output is protected.

Vibration, temperature and motor current on every critical machine
AI learns each machine's normal behaviour and forecasts its remaining useful life
The work order, spare part and technician are arranged automatically
Published results: 30–50% less unplanned downtime and 20–40% longer machine life

Source: McKinsey & Company research on predictive maintenance. Results vary with the starting point.

Every board. Every component. Every time.

100%

Inspection coverage

Deep-learning models inspect every unit at line speed. No sampling, no fatigue, no night-shift dip.

≈ 12 ms

Per decision

Solder bridges, missing parts, tombstones, misalignment and scratches, each with a confidence score.

Root cause

Not just detection

Defects are linked to process data, for example "bridges up 4×, printer pressure drifted", and fixed at the source.

Works alongside existing AOI to cut false calls, so operators review only what matters
Published studies report AI visual inspection improving defect detection by up to 90% compared with manual inspection

A copilot for every manager. A twin for every decision.

Production copilot

Ask in plain language. Answers come from live plant data, with their sources.

“Why did Line 1 slow down?”
“What should we fix first this week?”
“Summarize this shift.”

What-if simulation

Test a plan on the twin before committing people, overtime or capital.

Seasonal peak +35%: which line breaks first?
A third shift for the whole plant, or only the bottleneck?
A faster takt: what happens to yield?

The result: morning meetings that make decisions, instead of meetings that collect numbers.

Open standards. Industrial grade. Your data stays in the plant.

ApplicationsCommand roomLeadership dashboardSupervisor mobile appProduction copilot

AI and twinDigital twin engineAnomaly and remaining-life modelsVision models on edge GPUsDiscrete-event simulation

Data platformEvent streamingTime-series storeUnified namespaceISA-95 asset model

EdgeOPC UAMQTTIPC-CFX and SECS/GEMRetrofit sensorsEdge compute

Shop floorPLCsMounters, printers, reflowAOI, SPI, ICT, FCTEnergy metersAGVsMES, SAP, CMMS

Security by design

On-premise or hybrid, your choice
OT and IT networks kept separate
Read-only access first
IEC 62443 practices
Role-based access and full audit trail
No data leaves the plant without approval

Integrates with

Existing MES and SAP
Global corporate systems
Current AOI and test vendors

What is it worth to your plant?

Production lines in scope Unplanned downtime, hours per line per month Cost of one hour of line downtime (EGP) Scrap and rework cost per month (EGP) Energy bill per month (EGP) Programme investment (EGP)

Less downtime (30%)

—

Less scrap and rework (20%)

—

Energy savings (8%)

—

Payback

—

Estimated annual value

—

One line. Twelve weeks. Proof on your own floor.

ConnectWeeks 1–2Site walk, choose the pilot SMT line, read-only connection to machines and MES, agree the baseline.
SeeWeeks 3–6Live twin of the pilot line. OEE, yield and energy on screens and mobile.
PredictWeeks 7–10Predictive maintenance on critical machines, vision AI on AOI, copilot for the line team.
ProveWeeks 11–12Results measured against the baseline and signed off by your team, with a plan to scale.
ScaleMonths 4–12All lines, energy, warehouse and AGVs, toward a lighthouse-class plant.

−30%

Pilot target: unplanned downtime

+5 pts

Pilot target: OEE on the pilot line

−20%

Pilot target: scrap and rework

0 h

Production impact: no stop needed to install

You did not see slides. You saw working software.

Speed

We build fast and improve on your floor. The demo you just used shows how we work.

One complete team

Industrial IoT, data engineering, AI and computer vision, simulation, web and mobile.

Local and on site

Egyptian engineers next to your team, in your time zone, from day one.

Open, no lock-in

Open standards. Your data, your models and your IP stay yours.

Paid for proof

Pilot success criteria agreed up front and measured by your team.

Built for electronics

SMT, final assembly, test and pack. The twin speaks your line's language from day one.

Approve one pilot line. We will prove the rest on your floor.

This weekChoose the pilot line together
Week 2Site walk, NDA and read-only data access
Week 3Kick-off. The twin is live within 30 days

Open the live demo Project website