Manufacturing operations generate more data than most businesses can act on. Machine sensor readings, production line throughput, inventory levels, quality inspection results, and maintenance schedules are tracked — often across disconnected systems. Automely builds the software that connects these data sources, automates the manual workflows between them, and makes operational data visible and actionable. Dedicated senior developers, onboarded in 7 days.
Dedicated developers • 7-day onboarding • ERP, MES, IoT & AI • NDA on day one50+
Clients Served
120+
Projects Delivered
7 Days
Average Onboarding
4.9/5★
Clutch / GoodFirms Rating
ERP systems like SAP, Oracle, and Microsoft Dynamics handle the standard manufacturing operations record — bills of materials, work orders, inventory positions, and financial reporting. They do not handle the custom logic that individual manufacturers need: the specific quality inspection workflow that their production line requires, the supplier scoring model built around their category's lead time variability, or the shopfloor data collection system that reads directly from CNC machines and PLCs.
Industry 4.0 — the integration of digital technology, IoT connectivity, and data analytics into manufacturing operations — is not a single product or platform. It is a capability that manufacturers build incrementally: connecting machine data via OPC-UA or MQTT protocols, building real-time production dashboards, creating digital twin models of production lines to simulate operational changes before implementing them, and using AI to move from reactive maintenance to predictive maintenance.
Automely's AI and IoT engineering capability applies directly to this layer. We build the custom software that sits between your machines and your management team — making operational data visible, automating manual workflows, and giving production managers the information they need to make better decisions.
WHAT WE BUILD
Every manufacturing software engagement is scoped to your specific production model, existing systems, and operational data requirements — not a generic Industry 4.0 template.
Custom ERP modules for manufacturing operations where standard ERP functionality does not match the specific production model. Custom bill of materials management, production scheduling with real-world constraint logic, multi-site inventory visibility, quality management integration, and cost accounting tied to actual production data. Integration with existing SAP, Oracle, or Dynamics deployments.
Build Your ERP Solution →MES software connecting shopfloor operations to production planning: work order release and dispatch, real-time production tracking against schedule, quality inspection data capture at each production stage, operator instruction delivery on shopfloor terminals, machine status monitoring, and OEE (Overall Equipment Effectiveness) calculation.
Build Your MES →Industrial IoT platforms connecting machine data to operational dashboards: OPC-UA and MQTT protocol integration for CNC machines, PLCs, and SCADA systems, real-time sensor data ingestion pipelines, production KPI dashboards (cycle time, downtime, reject rate, throughput), edge computing for low-latency machine data processing, and predictive maintenance models using vibration, temperature, and current draw data.
Connect Your Machines →AI inventory management: demand forecasting using sales velocity, seasonality, and external data signals; automated reorder point calculation adjusted by supplier lead time variability; safety stock optimisation across multi-site inventory; and AI-powered supplier risk scoring. Supply chain visibility platforms tracking purchase order status, inbound shipment ETAs, and supplier on-time delivery performance.
Optimise Your Supply Chain →Digital twin applications for manufacturing: 3D process simulation models that mirror production line behaviour using real machine data, what-if simulation for production scheduling changes and capacity planning, digital models of individual assets for predictive maintenance, and plant layout optimisation tools. Built using Three.js or Unity for visualisation with real-time data feeds from the IoT connectivity layer.
Build Your Digital Twin →Custom quality management systems: incoming goods inspection workflows with supplier scorecard integration, in-process quality check capture on shopfloor terminals, non-conformance tracking and root cause analysis workflows, statistical process control (SPC) charting, customer complaint management, and quality reporting for ISO 9001 or IATF 16949 compliance audits.
Build Your QMS →HOW WE WORK
Six stages built around the specific challenges of manufacturing software — from operational discovery through machine integration and AI model deployment.
01
We map your manufacturing operations, existing systems (ERP, SCADA, MES), data sources, and the specific workflows that off-the-shelf software cannot handle. The output is a technical architecture document that identifies integration points and data flows. Deliverable: Operational process map, system integration diagram, and phased development roadmap.
02
For IoT projects: protocol selection (MQTT or OPC-UA), edge agent design, data ingestion pipeline architecture, and time-series database schema for sensor data. For ERP projects: data model design, integration API specification, and migration planning. Deliverable: Architecture specification approved before any development begins.
03
Backend API and database development in parallel with dashboard and interface development. Two-week sprints with working software at the end of each cycle. For IoT projects: edge agent development and initial machine connectivity established in the first sprint. Deliverable: Testable software increments delivered every sprint.
04
Integration with physical machines, PLCs, and existing systems — the most unpredictable phase of any manufacturing software project. We build integration with realistic tolerance for the variability of industrial communication protocols. Deliverable: Verified machine connectivity with documented data flow and error handling.
05
Machine learning model training using historical production data, model validation against held-out data, and deployment of prediction endpoints into the operational platform. For demand forecasting: backtesting across multiple historical periods. Deliverable: Deployed and validated ML models with accuracy metrics documented.
06
Controlled rollout to pilot production line or pilot facility with monitoring in place. Operator training and documentation. Your dedicated developer remains available for model retraining, feature additions, and scaling to additional sites. Deliverable: Live system with monitoring, operator documentation, and a rollout plan for additional sites.
Manufacturing software projects have specific failure modes — integrations that break when the protocol specification does not match reality, IoT platforms that collect data nobody acts on, and ERP customisations that work in demo but not on the shopfloor. Automely's manufacturing developers know these failure modes before the first sprint.
The Problem You Face
What Automely Does Differently
Standard ERP systems handle the standard production model — not the custom scheduling logic, quality workflows, or shopfloor data capture that individual manufacturers actually need
We build custom ERP modules that extend your existing system with the specific workflows your production model requires — without replacing what already works
Machine data lives in the machine — inaccessible to production managers making scheduling decisions from incomplete information
We connect your machines via OPC-UA and MQTT, making real-time production data visible in operational dashboards that production managers actually use
Predictive maintenance is a reactive process — machines fail, production stops, and maintenance is called
We build ML models on vibration, temperature, and current draw data to predict failures before they occur, shifting maintenance from reactive to scheduled
Generic software consultancies quote manufacturing projects without understanding OPC-UA, PLCs, or the integration complexity of industrial communication protocols
Our manufacturing developers have hands-on experience with industrial IoT protocols, ERP integration, and the specific data quality challenges of shopfloor environments
Inventory reorder decisions are made manually using spreadsheets and intuition — leading to both stockouts and excess inventory
AI demand forecasting models predict future demand at the SKU level with seasonality and external signal adjustments, automating replenishment recommendations
Digital twin implementations become expensive 3D visualisation projects with no connection to real production data
We build digital twins on live IoT data feeds — the visualisation is the output, but the real value is the what-if simulation and predictive maintenance capability
TECH STACK
Every technology below is used in live manufacturing software deployments — industrial IoT connectivity, ERP integration, and AI-powered production analytics.
Node.js / NestJS
Python
Go
PostgreSQL
Below are examples of manufacturing software projects delivered by Automely. All client details are kept confidential.
SECTORS WE SERVE
Our manufacturing software developers understand the specific compliance requirements, production models, and integration challenges for each manufacturing sector below.
Automotive & Tier 1 Suppliers
Production scheduling, quality management (IATF 16949), IoT machine connectivity, and ERP integration for automotive manufacturers and component suppliers.
Automotive Manufacturing Software
Consumer Goods
Demand forecasting, multi-site inventory management, configurable product BOM management, and supply chain visibility for consumer goods manufacturers.
Consumer Goods Software
Food & Beverage Manufacturing
Recipe management, batch production tracking, allergen and compliance management, cold chain monitoring, and supplier traceability for food and beverage producers.
Food Manufacturing Software
Pharmaceutical & Life Sciences
21 CFR Part 11-compliant manufacturing execution, batch record management, environmental monitoring, and quality management for pharmaceutical manufacturers.
Pharma Manufacturing Software
Electronics & High-Tech
Component traceability, PCB assembly tracking, test data management, IoT yield monitoring, and supply chain visibility for electronics manufacturers.
Electronics Manufacturing Software
Industrial Equipment & Machinery
Digital twin development, predictive maintenance platforms, service lifecycle management, and IoT connectivity for industrial equipment manufacturers.
Industrial Equipment Software
FREQUENTLY ASKED QUESTIONS
What is Industry 4.0?
Industry 4.0 (also called the Fourth Industrial Revolution) refers to the current phase of industrial transformation driven by the integration of digital technology, data, and automation into manufacturing and production operations. The term was coined at the 2011 Hannover Messe. The core enabling technologies of Industry 4.0:
What is a digital twin in manufacturing?
A digital twin in manufacturing is a real-time virtual model of a physical asset, production line, or entire factory that mirrors its real-world counterpart using live sensor and operational data. The digital twin receives continuous data feeds from IoT sensors — temperature, vibration, pressure, cycle time, energy consumption — and updates the virtual model accordingly.
| Digital Twin Type | What It Models | Primary Use Case |
|---|---|---|
| Asset twin | Individual machine or equipment | Predictive maintenance, performance monitoring |
| Process twin | Production line or workflow | Throughput optimisation, bottleneck analysis |
| System twin | Entire factory or supply chain | Capacity planning, scenario simulation |
What is a Warehouse Management System (WMS)?
A Warehouse Management System (WMS) is software that controls and tracks the movement and storage of inventory within a warehouse. It covers: inbound receiving (purchase order matching, quality inspection, put-away logic), storage management (bin locations, zone allocation, batch and serial number tracking), outbound picking and packing (pick list generation, wave picking, packing verification, carrier label printing), inventory accuracy (cycle counting, stock adjustment workflows), and integration with the ERP system for inventory valuation and order fulfilment status. Modern WMS implementations add real-time location tracking using RFID or barcode scanning, AI-powered slotting optimisation (placing high-velocity items closest to dispatch), and integration with automation equipment including conveyor systems and goods-to-person robotics.
How does AI inventory management software work?
AI inventory management software uses machine learning models to automate and optimise decisions that traditional inventory systems handle with static rules. Traditional systems calculate reorder points using fixed formulas (average demand × lead time + safety stock). AI systems replace fixed formulas with models that learn from historical demand patterns, account for seasonality and trend, incorporate external signals (promotions, weather, economic indicators), and adjust dynamically when patterns change. In practice: demand forecasting models predict future demand at the SKU level more accurately than time-series averages; safety stock calculation accounts for actual lead time variability rather than assumed variability; and replenishment decisions are generated automatically and reviewed by a buyer rather than calculated manually. For multi-site inventory, AI models optimise stock allocation across locations to minimise total fulfilment cost.
Building custom manufacturing software, connecting machine data with IoT, integrating with your ERP, or deploying AI for predictive maintenance and inventory optimisation? Tell us what you are trying to solve and we will match you with a developer who has built for manufacturing operations before.
No lock-in contracts • NDA on day one • ERP, IoT & AI expertise
