Data is the basis of our decision-making process. Put data in context and it becomes information that we can use for more efficient problem-solving.
In Industry 3.0, we could see some movements toward automated data collection. It quickly became clear that this approach is vastly superior to manual data collection.
Automation brought faster, more accurate, and wholesome results in the system controlling, production reporting, and records compliance.
Fast forward to Industry 4.0, and we now rely on data extensively to maximize efficiency in production systems and tackle daily manufacturing challenges.
We now have augmented reality (AR) applications capable of combining collected data from cameras with real-time machine performance data.
Additionally, Industry 4.0 allows us to have a visual illustration of a physical product or process. It can also provide us with automated analytics and ProjectPro Machine Learning Projects that can predict major equipment failures by locating trends in machine data.
Sounds like the future is now, and we are here to help you turn it to your advantage.
We gathered expert insights from this article source, to answer the questions below in detail and help you better understand the latest trends in manufacturing data collection so that you leverage them to boost your efficiency:
- What stages can we identify in collecting manufacturing data?
- What type of data collection should you focus on?
- How can manufacturing data increase your efficiency?
1. What stages can we identify in collecting manufacturing data?
Collecting manufacturing data moves through the following four stages:
This is the phase where you collect data from your equipment. It requires having a data acquisition (DAQ) system installed. This system normally consists of DAQ measurement hardware, sensors, and a computer with programmable software.
Data recording is usually performed by automated software. This software has a data recording ability to combine machine-generated data and manually input data. It stores data in a grouped and structured manner for easier usage.
This is the phase where you contextualize all of your collected data to draw relevant information. This phase requires data management software (DMS) installation. DMS serves to store all your manufacturing data in on centralized location.
Data analytics and reporting
In this phase, you use your collected and organized data to gain insights into your manufacturing efficiency and find where there is room for improvement. We can differentiate four types of analytics – diagnostic, predictive, descriptive, and prescriptive.
2. What data collection should you focus on?
Some of the most important manufacturing data you should keep an eye on include:
Data is the core of your business. It can be divided into the following categories:
- Sensor data – where machines’ sensors measure pressure, temperature, power levels, humidity, accelerations, vibrations, etc.
- Log data – where machines use databases for logging all sorts of data via various applications, web servers, and file systems.
- Network data – where you monitor network integrity for interaction between machines via edge devices and wired connections.
This data includes the number of orders and the time needed for order processing. It helps with knowing orders’ production status and provides order-related feedback.
Material data helps with inventory tracking. When put in context, it provides information that manufacturers use to monitor materials’ supply, consumption, and stocking.
Tool data serves for assessing the equipment’s health. It provides information about equipment downtime, time of use, number of cycles, and maintenance time.
This data covers personnel’s working hours and the number of produced items. It serves to calculate wages and other personnel-related metrics.
2. How can manufacturing data help your efficiency?
With the information extracted from manufacturing data, you can improve efficiency in the following areas:
Manufacturing data, when put in context, can help you optimize the use of your equipment. For instance, it could reveal that a certain machine operates more efficiently in a particular program setting.
You can also gain insights into factors that influence your manufacturing lines, such as system configuration and machine installation.
With the enhanced insights that your data provides, you can pinpoint the causes of products’ defects. You can also assess the quality continuance within chain production and detect variations in final products.
Quality assurance is especially important for sensitive industries like the pharmaceutical industry, where a product defect can result in serious consequences.
Manufacturing data can help you maintain your equipment more efficiently. Relying solely on a set schedule can lead to replacing components before it’s necessary.
With the information extracted from manufacturing data, you can predict arising problems and act accordingly. This will help prevent breakdowns and downtime and result in a significant maintenance cost reduction.
When you use manufacturing data to track information about energy usage, you can employ it in energy consumption reduction and optimization.
When you examine information derived from your manufacturing data, you may notice that a certain piece of equipment is consuming more power than other identical pieces of equipment.
You can then use gathered data to find the cause of this increased power usage and act accordingly.
Energy optimization is beneficial for both cost reduction and the environment.
Optimizing your inventory and managing it through a product recognition system can help with cost reduction, efficiency, and customer satisfaction.
A product recognition system that uses manufacturing data can enable you to efficiently control your inventory by monitoring the shelves and alerting staff of low stock conditions.
This is a critical task for businesses because too much inventory can drain your finances, and not enough can leave your customers unsatisfied.
Additionally, this system can help you meet your customers’ needs without resorting to excessive stocking.
Industry 4.0 terms to know
Here are the ten most important Industry 4.0 terms you should know:
- Enterprise Resource Planning (ERP);
- Internet of Things (IoT) – connections between sensors and machines and the Internet;
- Industrial Internet of Things (IIoT) – manufacturing-related connections between people, data, and machines;
- Big data – a large number of structured and unstructured data;
- Artificial Intelligence (AI) – computers’ ability to mimic human learning and thinking processes;
- Machine to Machine (M2M) – wireless or wired connection between various machines;
- Digitization – converting information into digital format;
- Machine learning – computers’ ability to learn and improve on their own with AI;
- Cloud computing – interconnected remote hosting servers that store, manage, and process information on the Internet;
- Cyber-physical systems – Industry 4.0 manufacturing environment for real-time data collection, analysis, and transparency of every manufacturing operation aspect;
- Smart factory – a factory that uses Industry 4.0 trends in technology, solutions, and approaches.
Collecting, coordinating, and contextualizing a great amount of manufacturing data requires significant time and effort, but the end results are well worth the struggle. You can expect to see increased efficiency through the optimization of your manufacturing processes.
Additionally, you will have the benefits of better energy consumption, optimized inventory management, and equipment maintenance. These will improve the quality of your end products.
It is only a matter of time before keeping up with Industry 4.0 trends becomes the norm for business success. Joining in early in the game could help you gain an edge over your competition.
We hope that our article helped you better understand how manufacturing data works and how to use it to increase your business’s efficiency.
Sophie Douglas is a digital marketing specialist and a journalist based in Columbus, state of Ohio.
Her characters are passionate, innovative, and ambitious.
Before becoming a writer for DigitalStrategyOne, she was writing short stories, screenplays, and directed short films
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