STORY INLINE POST
Defining or quantifying how much information people generate is quite tricky. There are many different approaches to this but some calculations point out that 2.5 quintillion bytes of data are created every day and 90 percent of all the data in the world has been created in the last two years alone. It is expected that the volume of data will double every two years.
Industrial processes are not entirely connected or digitalized but a significant part of the world’s exponential data growth will be generated in that arena. As they become more involved in digital transformation or Industry 4.0 strategies, manufacturing companies face the dilemma of making sense of the copious amounts of generated data. From control devices to CNC machines, CAD/CAM systems, metrology, and production planning software, the flow of information from industrial processes is nonstop.
In an article related to this digital transformation challenge, the World Economic Forum (WEF) warned those manufacturing companies about the disconnection "between data collection and usage, hindering or eliminating valuable use cases. With so many connected assets, manufacturers see the value in collecting operational data. However, the raw data collected does not provide the expected solution: a decision-making engine."
Determining what data is valuable and what data is not useful will be a crucial task for manufacturers. This will be the purview of analytics: providing the correct algorithms to discern among these vast fields of information the indicators that will help companies achieve continuous improvement.
In terms of data, manufacturing still works as silos, where connections sometimes are impossible due to the different languages and protocols within technologies. As mentioned in the WEF document, that condition locks information in their boxes or production area. Let's think about maintenance versus metrology or logistics versus flow process control. Production environments have too many different technologies, making it difficult to communicate among them.
But digital transformation brings with it open technologies, allowing us to communicate with legacy systems and connect machines and equipment, providing visibility at every stage of an industrial production chain, both within the company and among all the supply chain players, from raw material to end-user delivery.
Panel control technology deployment represents an essential component of information acquisition systems and the main entrance to analytics and intelligence. An Executive Dashboard allows decision-makers to permanently track Key Performance Indicators (KPIs), obtaining real-time operating information from devices and equipment on the plant floor and crossing data with managing systems.
Speaking of real-time operations, this data flow is essential for traceability. Still, once the data is stored, it feeds systems with the necessary parametrics to analyze production behavior, such as statistical deviations, accurate measurement of cycle times, or unpredicted shutdowns. This information then can be paired with demand planning or profit performance indicators.
Analytics allows the engineering team to make better decisions and to develop predictive models. Specialists can detect correlated events, which are difficult to identify when we don't have the information and the correct lecture. Thanks to analytics, companies get significant savings and throughput improvement.
Different data collection technologies feed analytics platforms, which are used to determine future conditions and predict probable failures or determine the future performance of a machine or its peripheral equipment before starting a production process.
Adding scheduling systems makes it possible to execute intelligence to deploy production improvements and demand planning adjustments by monitoring the whole process; when part of a manufacturing supply-chain suite, production monitoring goes beyond a site to encompass both ends of the supply chain, both suppliers and customers.
Some other technologies can be added, such as imaging, which provides companies with artificial vision capabilities to eliminate waste and ensure quality. Image recognition technologies have constantly evolved and have changed from the origins of the two-dimensional reading of bar codes to biometric and automatic systems.
Digital technology provides manufacturing companies with better processing information, data storage, and classification to develop analytical processes. This is one step behind deep learning technologies oriented toward artificial intelligence models.
Analytics offers the best of two worlds: On the one hand, cost reduction and, on the other, customer satisfaction.