Smart factory services

A smart factory starts with one measurable number

HiTechCloud connects shop-floor equipment, captures operating data in real time and feeds it to an analytics platform — so decisions on output, quality and maintenance rest on figures rather than instinct.

Book a site survey Manufacturing software

Architecture layer: Devices · Edge · Cloud · Analytics
Key metrics: OEE, downtime and first-pass yield
Deployment model: Edge at the plant, analytics in the cloud
Starting at: One line first, expanded on measured results

Overview

A smart factory is not an equipment purchase

Many factory digitization projects start by buying sensors and screens and end with a handsome dashboard nobody uses to decide anything. The cause is usually the same: data was collected before anyone had a question to answer.

HiTechCloud works the other way round. We start from the problem costing you most — unexplained machine stops, high scrap at one stage, late delivery from material shortages — and measure only what answers that question.

The infrastructure behind it is our existing strength: edge servers at the plant for local processing, linked to cloud for long-term storage and analytics, with connectivity, security and backup run by HiTechCloud.

Common problems

Signs the plant is deciding on gut feel

Output figures copied by hand each shift

Data is written into a logbook, then re-entered into a spreadsheet, and by the time it is consolidated the shift is long over.

No one knows why a machine stopped

Downtime is recorded but never categorized, so there is nothing concrete to improve.

Maintenance is either on a fixed date or after a breakdown

Equipment is serviced before it needs to be, or only repaired once it has already stopped the line.

Every machine has its own software

Equipment from different vendors speaks different protocols, so the data never comes together into a plant-wide view.

Service

The capability layers HiTechCloud delivers

Equipment connectivity (IIoT)

Collecting data from PLCs, CNC machines, sensors and meters over common industrial standards, and retrofitting sensors to older machines with no data port.

Edge infrastructure on the factory floor

On-site servers process data locally, keeping the system running without internet and cutting how much data has to be sent.

Real-time OEE monitoring

Overall equipment effectiveness across availability, performance and quality, down to each machine and shift.

Energy monitoring

Metering power by line and by order, spotting idling machines and unusual consumption periods.

Predictive maintenance

Analyzing vibration, temperature, current draw and failure history to warn before a machine stops unexpectedly.

Management dashboards and alerts

Shop-floor screens for supervisors, reports for management and alerts pushed to whoever owns the threshold.

An industrial robot arm on an automated line

Delivery principles

Measure first, automate second

Automating an unmeasured process only makes the mistakes happen faster. The right order is connect, measure, understand, then optimize.

The first stage is only about getting accurate, trustworthy data off the equipment.
Every figure on screen must reconcile with how the plant records it by hand today.
Alerts routed to the person who can act on them, with context — not a list nobody reads.
Only automate steps where the data already proves it pays off.

Roadmap

Four stages to a smart factory

01
Current-state assessment

Surveying equipment, plant networking and available data, and choosing the first problem to solve.
02
Connectivity and data collection

Installing gateways and extra sensors, building the edge layer and starting to capture data from the pilot line.
03
Visualization and analytics

Building dashboards, setting alert thresholds and reconciling the figures against real operations.
04
Optimize and roll out

Introducing predictive maintenance, tuning operating parameters and extending the model to the other lines.

Technician monitoring an industrial robot from a tablet

How it is delivered

Start on one line and leave the rest alone

We fit collection hardware to one running line, reconcile the figures against the current hand-written records, and only then discuss expanding.

No change to the control programs already running on the machines.
Run in parallel with the paper logbook for the first few weeks.
Expansion to other lines reuses the configuration already proven.

Technology

The technology stack we use

Connectivity

OPC UA
Modbus
MQTT
IIoT Gateway

Processing

Edge Computing
Time-series DB
Kafka
Kubernetes

Analysis

Real-time dashboards
Threshold-based alerting
Predictive models

Infrastructure

Cloud Server
Object Storage
VPN Site-to-Site
24/7 monitoring

Customer

Who this solution is for

Plants still recording figures by hand

Output and downtime are written into a logbook each shift.
Consolidated reports only appear after the shift has ended.
You want to start on one line to prove the value.

Plants with equipment from many vendors

Machines speak different protocols and the data cannot be pooled.
Some older machines have no data port at all.
You need a collection layer rather than replacing every machine.

Businesses with several plants

Each plant reports in its own format.
Management needs one shared set of figures for comparison.
Metrics have to be defined consistently before sites can be compared.

FAQ

How do you connect old machines with no data port?

The usual approach is external sensors — counting output, measuring current, vibration or temperature — to infer machine state without touching the equipment. Machines with a PLC can usually be read over an industrial protocol without affecting the running program.

Does production data have to go onto the internet?

Not necessarily. The default architecture processes on an edge server inside the plant; only aggregates sync to the cloud for long-term storage and remote reporting. You can also keep everything on the internal network.

Roughly what is the upfront investment?

It depends on how many machines need connecting and how available the data is. The single-line pilot is priced and scoped separately so you can judge the results before expanding.

How soon will we see results?

For a pilot line, operating data and dashboards typically arrive within 6–10 weeks. Downtime reduction and predictive maintenance need several more months of history before the models are reliable.