Present-day manufacturing presents complex challenges like:
- big data management,
- complex supply chains,
- global stakeholder alignment,
- and advanced manufacturing technology.
But with Industry 4.0, manufacturing data collection systems also keep advancing.
In this article, we’ll explore those systems & their evolution, and delve deeper into how they enable manufacturers to uncover ever more powerful insights, streamline their production lines, and ultimately achieve higher efficiency by harnessing the power of data.
Let’s get into it.
Looking for a strategic manufacturing partner, tailored to your unique needs? Talk to Alpha Software today and discover how it can transform your manufacturing operations!
- Manufacturing data collection systems play a crucial role in optimizing production processes and enhancing efficiency.
- The evolution of technology has increased the demand for accurate and real-time data in the manufacturing industry.
- A variety of data types and collection methods can be utilized for a tailored approach to data collection in manufacturing.
What is Data Collection in Manufacturing?
Data collection in manufacturing is all about gathering, measuring, and analyzing data from various sources. The goal is to use it in the decision-making to find areas where companies can improve production, reduce waste, and increase overall productivity.
Data can be obtained by tracking:
- part production cycle times,
- machine performance,
- equipment runtime,
- maintenance time,
- and downtimes.
In manufacturing, you would usually collect data with manual (visual) inspection or with automated inspection (like cameras, sensors, computer counting, etc.). Advanced technologies like Internet of Things (IoT) devices also allow for real-time monitoring and data collection across the globe.
When setting up these data collection systems, it's all about focusing on data points that matter for your unique environment. It's not just about collecting data — it's about smartly channeling resources for the best results.
Some examples of important manufacturing data points include:
- Cycle time and throughput,
- Energy consumption and costs,
- Equipment runtime and downtime,
- Machine availability and efficiency,
- Overall Equipment Effectiveness (OEE),
- Quality and percentage of rejected products.
The Past & Evolution of Data Collection in Manufacturing
The First Industrial Revolution
The First Industrial Revolution started at the end of the 18th century and marked a shift from manual work toward mechanization with the steam engine. At that point, collecting manufacturing data meant:
- maintaining written logs and ledgers,
- simple counting mechanisms,
- manual part inspections,
- worker’s observations,
- and timekeeping.
The Second Industrial Revolution
Almost a century later, the Second Industrial Revolution emerged, characterized by the advent of electricity, gas, and oil. Key innovations in manufacturing data collection included:
- telegraph communication,
- scientific instruments,
- mechanical counters,
- quality control,
- and photography.
The Third Industrial Revolution
Fast forward to the second half of the 20th century when the Third Industrial Revolution unfolded. Nuclear energy became available, and advancements in electronics, telecommunications, and computers again revolutionized manufacturing data collection with:
- digital sensors,
- barcodes and scanners,
- networks and databases,
- Programmable Logic Controllers (PLCs),
- Enterprise Resource Planning (ERP) systems,
- Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM).
The Fourth Industrial Revolution - “Industry 4.0”
In the early 2010s, the Fourth Industrial Revolution (or "Industry 4.0") marked the beginning of a new era. An era, in which data collection is central to manufacturing, and where advances and adoption of new data collection technology happen daily.
Here’s how data collection in manufacturing works today.
How To Collect Manufacturing Data in Industry 4.0
Advanced Specialized Sensors
The often-forgotten backbone of modern data collection, specialized sensors are the first in line for collecting data on manufacturing lines.
These sensors are built to measure every physical metric possible, the most common of which include:
- Pressure — Resistive strain gauges allow for electronic pressure measurements
- Temperature — Electronic temperature sensors like thermistors
- Physical presence — Includes capacitive & inductive industrial sensors and end-switches
- Vibration — These can measure vibration frequencies and amplitudes
- Chemical — A wide variety of sensors that can measure chemical properties
Connected to integrated systems, these sensors offer real-time feedback, ensuring immediate response to variations outside set parameters. For example, vibration sensors can detect machinery wear, prompting early maintenance.
The main use of cameras in modern manufacturing is in tracking high-speed production lines, where processes are too fast for the naked eye.
They are also often coupled with machine vision software to track barcodes or QR codes — enabling advanced tracking methods like product lifecycle management. Another use of machine vision is in automated visual product inspections (also where AI comes into play).
Big Data Analytics
Includes all tools meant for parsing large datasets, identifying patterns and trends, and facilitating informed decision-making. Furthermore, predictive analytics, a subset of big data, anticipates machinery failures or maintenance needs, reducing downtime.
AI and Machine Learning
AI can process and analyze vast datasets, converting raw data into usable insights. These technologies are often used to identify patterns, forecast trends, and automate decision-making.
For example, through machine learning, systems can predict equipment failures by analyzing historical data, enabling proactive maintenance. Additionally, if interconnected with sensors and IoT devices, AI and machine learning enable real-time feedback and adaptive control. This ensures that manufacturing processes are consistently optimized.
Digital Twin Technology
A new method of analysis in Industry 4.0, digital twinning involves creating virtual replicas of physical assets or processes and monitoring and simulating them in real-time.
The mirrored representations collect and reflect data from real-world counterparts that are monitored with sensors and compared to simulated conditions. This lets manufacturers predict failures, optimize operations with immediate adjustments, and test changes without disrupting actual production.
IoT devices are connected to the internet and integrated with cloud systems, allowing for remote monitoring and decision-making. This means operators, maintenance crews, and decision-makers don’t need to be present in factories at all times anymore.
Coupled with big data analysis and AI-aided decision-making, they enable predictive maintenance, spot inefficiencies, trigger automated responses, and alert for human intervention when necessary. All while allowing monitoring all around the world.
AR and VR
These new technologies bridge the virtual and physical realms, enabling data visualization and interaction.
AR overlays of digital information onto real-world views. For example, technicians can receive real-time data during machine maintenance, guiding their actions.
On the other hand, VR immerses users in a fully digital environment, allowing for simulations or training without physical constraints.
The cloud provides a platform for storing, accessing, and processing data remotely. Offsite storage ensures data redundancy and security while enabling teams across different locations to access and analyze the same dataset, ensuring consistent decision-making and stakeholder alignment.
Cloud computing is also easily scalable — without large infrastructure investments. And it supports the ever-growing data demands of manufacturing.
The most common cloud solutions are software as a service (SaaS) platforms like Alpha Anywhere, that let you capture, store, display, and use data seamlessly in your manufacturing processes.
Types of Manufacturing Data Collection Systems
Standalone systems vs. integrated systems
Standalone systems are data collection solutions that operate independently, focusing on specific machines or processes. These typically require manual configuration and may only collect limited types of data.
In contrast, integrated systems automatically collect data from multiple machines and processes across the floor, creating a more comprehensive view of your manufacturing operations. Integrated systems connect with other business systems like ERP, MES, or WMS, enabling more seamless communication and data flow between them.
On-premises vs. cloud-based solutions
On-premises data collection systems are installed and managed within your organization's local network, giving you greater control over data storage and security. However, this approach can be more resource-intensive, requiring dedicated IT staff and regular system maintenance.
Cloud-based solutions, on the other hand, store your data remotely on secure servers managed by a third-party provider. They often offer easier scalability, reduced IT burden, and real-time data access from anywhere. Cloud-based systems usually come with a subscription-based pricing model, while on-premises systems often require an upfront capital expenditure.
Custom-built vs. off-the-shelf systems
Custom-built systems are designed and developed specifically for your organization to address unique needs or niche requirements. They are flexible and customizable but more expensive and time-consuming to build and maintain. An example of this is hiring a contractor to build software for you.
Off-the-shelf systems are ready-made data collection solutions with standardized features that cater to most manufacturing processes.
These systems are generally more affordable and faster to deploy, and they often come with regular updates and improvements from the vendor. However, they might not entirely align with your organization's specific needs or provide the same level of customization as a custom-built system.
An example of this kind of system would be a SaaS platform.
But there’s an option that gives you the best of both worlds for manufacturing data collection — Alpha Software.
The Alpha Software Advantage
Alpha Software gives you the flexibility and customizability of a custom-built solution, coupled with the affordability and speed of deployment of an off-the-shelf system.
Also, you can use Alpha Software as either a standalone solution, or integrate it directly into your existing processes, like in the case of Igloo Coolers.
Here’s a breakdown of both its products for manufacturing data collection.
1. Alpha TransForm (No-Code App Builder)
Alpha TransForm is a secure and scalable cloud-based system for rapid, no-code application development.
It allows you to create offline-capable data capture apps for all kinds of on-site data collection — inspection work, maintenance, inventory tracking, product lifecycle tracking, and more.
The major benefit of Alpha TransForm is the ease and speed of app creation. Anyone on your team can create one in under 30 minutes, no matter how skilled they are in coding. That allows for rapid iteration, adoption, improvement, and data collection.
All apps created with Alpha TransForm are hosted on Alpha Cloud. It’s a scalable, secure, and reliable solution that ensures constant uptime of your apps, with no work required on your end.
Want to create data collection apps without writing a single line of code? Get a free Alpha TransForm license!
2. Alpha Anywhere (Low-Code App Development)
On the other hand, Alpha Software also offers Alpha Anywhere, a low-code environment for developing complex and flexible applications.
It’s made for developers who need fast development cycles for any kind of business or SaaS app — iOS, Android, or Web.
The major benefit of Alpha Anywhere is that it allows you to build apps from the ground up all in a single environment, front-end, back-end, and everything in between.
Plus, the low-code functionality means you don’t have to know a bookshelf of coding languages. Just set everything up inside the environment, then jump into the code editor and if you need any specialized modifications.
It’s a great option for any manufacturer that needs a custom data collection platform but doesn’t have the time and resources to build one from scratch
Want to create a custom data collection solution? Build apps for free with Alpha Anywhere!
Benefits of Alpha Software’s Solutions
- Customizable and User-Friendly Platforms: Alpha Software includes specialist-designed apps and forms as templates, coupled with step-by-step instructions. This means easy customization, even if you have no coding experience. This is especially valuable for manufacturing companies looking to digitize manual data collection processes on the factory floor.
- Comprehensive Reporting Tools: One of Alpha Software’s stand-out features is its range of reporting options. The capability to generate everything from intricate charts to detailed business reports ensures robust, mobile-friendly tools at your disposal to interpret collected data.
- Seamless Integration Capabilities: The open architecture of Alpha Software ensures seamless integration with web services, data sources, and existing workflows. It is especially beneficial for manufacturing sectors that rely on various data sources and need real-time data synthesis.
- Advanced Functionalities for Developers: For those with coding know-how, Alpha Software offers Python integration— granting access to a vast library of prewritten, open-source modules. This ensures that the apps are not only customizable but can also be enhanced with third-party integrations and data connections.
- Digital Transformation with Efficiency: Alpha Software’s solutions are designed to streamline data collection processes by facilitating easy data entry on the shop floor and offering real-time data-sharing options. The platforms are also easily scalable, allowing businesses to adjust as their data collection requirements evolve.
See how Igloo Coolers adopted Alpha Software’s Quality Manufacturing Solution and improved its quality control, enhanced data accuracy, and saved an estimated $145,000. You may also like our other case studies, where we go into detail on how Alpha Software helped build data collection systems and further support manufacturing processes for companies like Goodman, Bruker, and MSS Lasers.
Interested in how Alpha Software can help you? Talk to us today to see how you can take your manufacturing data collection to the next level!
Frequently Asked Questions:
What is the role of data in optimizing production?
By analyzing data, you can identify trends, patterns, and anomalies in manufacturing processes that can help improve efficiency. For example, data patterns can reveal bottlenecks, areas of waste, and opportunities for automation, enabling you to streamline your processes and maximize output.
How can production dashboards improve manufacturing?
Production dashboards are visual tools that display real-time manufacturing data, such as production rates, downtime, and key performance indicators (KPIs). These dashboards allow you to:
- Monitor production in real-time and quickly identify issues.
- Gain insights into the performance of your equipment and processes.
- Benchmark and track KPIs.
- Make data-driven decisions to optimize production and reduce costs.
What types of data are essential in manufacturing processes?
Essential data in manufacturing processes typically includes:
- Production data: Quantity, rate, and efficiency metrics
- Machine data: Equipment status, downtime, and operating conditions
- Quality data: Product measurements, defect rates, and deviations
- Inventory data: Raw material levels, work-in-progress, and finished goods
- Process data: Cycle times, changeover times, and labor input
Which methods are effective for gathering production data?
Effective methods for gathering production data include:
- Direct data input: Workers enter data manually into a system
- Sensors and connected devices: Real-time monitoring through the use of Internet of Things (IoT) devices
- Barcode and RFID solutions: Tracking materials, parts, and products throughout the production process
- Machine vision systems: Automating inspection and quality control tasks
- Manufacturing Execution Systems (MES): Software that manages and monitors production processes
The appropriate method depends on your specific manufacturing environment and needs, but a combination of these methods provides more comprehensive data.
In what ways is data processing used to enhance manufacturing efficiency?
Data processing is essential for turning raw data into actionable insights. In manufacturing, data processing is used to:
- Identify patterns and trends in production data to drive continuous improvements.
- Detect anomalies and deviations, triggering alarms or corrective actions.
- Generate reports and dashboards that visualize key production metrics.
- Perform root-cause analysis on issues such as defects or equipment failure.
- Optimize resource allocation and scheduling based on predictive algorithms.
Wrapping it up
Manufacturing, as an industry, is ever-evolving. With technologies like Alpha Software, manufacturers can stay ahead of the curve, ensuring they are not just producing but excelling in their domain.
Alpha Software works with manufacturers to replace their paper forms with trustworthy and powerful data collection apps (including dashboards and workflow) that improve product quality, customer satisfaction, and enhanced product reputation.
Talk to us today to learn how we can make your production data collection faster, more accurate, and actionable.