Manufacturing Process, Data Analytics, and Industry 4.0
Itanta Analytics
Self Serve, NO CODE Industrial Reporting and Manufacturing Intelligence platform
Industry 4.0 is rooted in the belief that a multitude of connected, smart technologies could marry- and thus revolutionize- production. (Sniderman, B., Mahto, M., Cotteleer, M. 2016)
Yet, for all the talk about manufacturing intelligence and smart factories over the last decade, the reality on the ground is that many stakeholders are unclear as to what all this connectivity means for their companies- and manufacturing in general. The revolution seems stuck in its infancy.?
For many in the manufacturing and supply chain organizations, the Covid-19 pandemic foregrounded the multiple shortcomings of their data management strategies in the face of a dynamic business environment. Despite having access to huge amounts of data, they fail to gain an exhaustive and accurate view of their business to plan their growth, make data-driven decisions, and respond proactively to disruptions.
The problem is rooted in various production processes and technologies that aren’t designed to work in unison. A lot of enterprises in manufacturing do not yet have their machines connected to any network. With the different manufacturing systems working in silos, and no centralized systems in place, manufacturers face a lot of uncertainties across their manufacturing value chain. Each system generates data and reports independently, undermining the efficiency of the entire decision-making process.
So, before the manufacturers can explore the possibilities of digitization and predictive maintenance, they need to first get connected.
Data Analytics and Connectivity in Industry 4.0
As Joe Lichtenberg, Head of Product and Industry Marketing at InterSystems, once said, “... It isn’t just about connecting “things,” you have to connect the data they generate.”
A connected ecosystem makes it possible for you to build smarter supply chains and manufacturing processes. Manufacturers can analyze data aggregated from the integrated systems to uncover patterns, track movements, and ultimately, understand what customers want, and where- so they can better plan to provide it at the right time and place.
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The IoT ecosystem is the most essential element of Industry 4.0. It includes a host of connected technologies, such as high-quality sensors, powerful networks, robotics, AI, and cognitive technologies that network physical objects - ‘things’ - together with the internet. The data and information gathered through intelligent products and services enable manufacturers to gain actionable insight from massive amounts of data and make smart, data-driven decisions.
Scope of Data Analytics in Industry 4.0
The process of information creation, communication, and action are foundational to the modern manufacturing process. By augmenting the manufacturing value chain with Industry 4.0 technologies, manufacturers can use the data generated in various stages of production to:
Conclusion
A company trying to implement Industry 4.0 practices can face significant challenges during the management and integration of IT and OT systems. The lack of interoperability between new and legacy systems underpinning industry 4.0 applications can pose integration and security challenges.
Despite the challenges, there is little doubt that integration of Industry 4.0 concepts in companies' manufacturing processes is inevitable. It is impossible to overstate the importance of connectivity and data analytics in transforming the manufacturing industry. It’s important to note that digital transformation in Industry 4.0 is not a one-time process but rather a continuous improvement program.
The good news is these transformative efforts can be made easy by leveraging cognitive technologies. These technologies enable companies to creatively overcome the challenges presented by Industry 4.0. At the same time, the data aggregated by these advanced platforms provide manufacturers with telling insights that drive up the quality of their critical decision-making, improve future resilience, and greater overall efficiency.