Data analytics for the manufacturing industry
What is Manufacturing Analytics?
Manufacturing data analytics are all the technologies, operations and data generated specific to the sector. The use of these new digital resources aims to increase product quality and industrial performance. By extension, they raise a new domain of applications : data manufacturing and more precisely big data manufacturing. These applications empower production processes and supply chains with tight data integration management to enable supply chain optimization and reduce production costs.
A new automated ecosystem
Manufacturing analytics is an integral part of the fourth industrial revolution, which relies heavily on the use of digital hardware and components that can self-manage and
self-diagnose by monitoring anomaly detection and predictive analytics. The cloud and the Internet of Things are its main resources. Unlike yesterday's manufacturing industry,
Industry 4.0 tends to limit human intervention in its business processes.
Manufacturing companies have struggeled with managing multiple overly complex activities necessary to the functioning of their manufacturing processes. Only large companies were able to use their data to establish a reliable supply chain that fed the production line, ensured delivery to the customer, and maintained a good level of after-sales service. The demands of global commerce have made this way of working completely obsolete without these tools. Today’s manufacturer must be able to optimize production in real time with a 360-degree view of all activities with the help of big data analytics provided by data analytics manufacturers. The big data work is becoming an important part of analytics big data dedicated to process supply chains and learning what is best for optimizing production.
An artificially intelligent production chain
Data science, Big data, the internet of things and artificial intelligence are the new tools that are now essential for production chains. Added the deployment of smart sensors that are capable of edge computing can make manufacturing processes as well as their supervision even faster. Analytics manufacturing, thanks to machine learning, provides agility to the business processes and enables the implementation of more intelligent and responsive production units.
The entire Industry 4.0 benefits from real-time action capabilities through continuous collection, storage, and analysis of production data in real time. This is precisely what Loamics offers with Loamics-Collect, Loamics-DataLake and the Loamics-AlgoEngine. The data is collected in real time and stored in a Data Lake, which accepts any type of file without limit of number or volume. They are then analyzed and exploited by reports and dashboards to create data analytics manufacturing. This new way of working has incredibly positive repercussions on the customers who see their products more personalized, delivered faster and with less defects.
How is data analytics used in manufacturing?
To fully understand how data analysis and Big Data can be used in the manufacturing processes, it is necessary to define data analytics use cases in manufacturing.
In fact, for this sector they are very numerous. It is about transforming the data collected into insights that in turn lead to decisions that are transformed into actions. They will positively affect your business processes. For all professionals in the sector, the objectives are generally the same : to improve the quality of their products, to benefit from a more reliable and efficient production tool and to increase their revenues.
Here are 4 of the most common use cases of data analytics and manufacturing depending on your needs, you may:
You will need to set up real time supervision of your business processes and the equipment they operate. Determine your production capacities; increase your productivity, and optimize your maintenance actions.
You will need to set up real-time measurement and supervision of product quality. This allows you to determine the defect causes. You may then improve the reliability of your business processes and increase the level of guarantee of your products.
You will need production analysis so you can forecast demand and better manage your orders. You can also optimize inventory and improve relationships with suppliers. Production analysis often integrates transportation optimization making it possible to deploy alert systems to manage any routing problems.
You will need the capacity of analyzing the efficiency of your transportation and delivery capabilities, to better support after-sales service and field support requests. Because your inventory can better managed, you can improve your supply performance.
How can manufacturers benefit from data analytics?
The benefits of data analysis for manufacturers are difficult to quantify because they are so numerous. In fact, it is a true revolution in production practices allowing them to envision rapid growth through high levels of decision making.
Production analytics provide continuous, real-time awareness of all production activities, from procurement to delivery. This gives industry decision makers an unparalleled competitive advantage over those who do not use production analytics. Manufacturers who have already made their digital transformation, and many have, can gain new production superpowers by adding a layer of intelligence to their existing digital assets. They optimize their costs, significantly increase the quality of their products, and can accelerate their decision making. It has a strong impact on the innovations made to their manufacturing methods and on the quality of their products.
If data is the new oil for most of 4.0 industries, it undoubtedly the new lever which allows the manufacturing sector to produce and deliver better goods, faster. This seems to be the only way to face competition, especially from Southeast Asia, which has cheap labor and ever cheaper supply routes. For production specialists, there is no doubt that the savings that some are making on labor costs can only be countered by gains made through more rational business processes achieved by intelligent tools.
