In an era of increasing digitalization and connectivity, manufacturers are harnessing the power of their data to drive smart manufacturing. Today, data is not just an informational entity but a crucial asset that can unlock tremendous value and potential.
Manufacturing has always been a data-intensive industry, with every piece of the production process, from raw material sourcing to shipping the final product., generating massive volumes of data. However, with the advent of digital transformation and the digital worker, manufacturers now have unprecedented access to vast amounts of real-time data that offer significant potential for optimization and improvements.
Over the last decade, owing to increased data mining capabilities, data has become the new oil for manufacturers. But just like crude oil, it's not very useful in its raw form. It needs to be refined – that is, processed or transformed and analyzed – to extract valuable insights. The true potential of data in smart manufacturing lies not just in its collection, but in the outcomes that can be attained based on actionable insights.
Transforming Data into Actionable Insights
While data is unquestionably an invaluable element in smart manufacturing, gaining actionable insights isn't without its challenges. Each type of AI (from edge to cloud based) needs Data to learn and execute. Manufacturers often grapple with numerous obstacles in the journey from collection to utilization.
Here are some of the most common ones:
- Data Volume and Complexity
The sheer volume of data generated by smart manufacturing systems can be overwhelming. The vast array of sources, from IoT devices to enterprise resource planning (ERP) systems, leads to an extremely diverse and complex dataset. Managing, storing, and processing this massive amount of data is a significant challenge.
- Data Strategy and Integration
Data often exists in silos within different systems or departments. Historically grown and without a holistic strategy. Integrating this data to provide a unified, comprehensive view is a crucial yet challenging task. Disparate data formats, incompatible systems, and lack of standardization are common hurdles in data integration.
- Worker trust
Trust among plant-floor workers is fundamental as they significantly contribute to the lifecycle of data. However, the absence of clear communication and comprehension of the reasons behind data collection, and its relevance to their objectives, can lead to trust erosion. What's more alarming is the possible creation of a fear-driven atmosphere, resistant to change, and stirred by apprehensions of potential displacement.
- Skills Gap
Data analytics requires specific skills and expertise, such as statistical analysis, machine learning, and data visualization. There is often a skills gap in manufacturing organizations, with a lack of personnel proficient in these areas.
Preparing the Workforce for Autonomous Smart Manufacturing
As we navigate the era of AI in manufacturing, it's evident that the workforce is experiencing a substantial shift in the requisite skills. This evolution underscores the necessity of equipping employees with the competencies to thrive in the landscape of smart manufacturing.
To address this challenge, a dual-pronged approach is required. First, organizations need to prioritize training and reskilling initiatives. As the industry pivots towards data-driven, automated processes, workers must be adept in areas such as data analysis, AI, IoT, and robotics. Consequently, enterprises must commit to investing in ongoing education initiatives, providing employees with opportunities to master these new skills. It's essential to offer comprehensive training in critical areas, from machine learning principles to cybersecurity, to enable workers to flourish in their evolving roles.
Secondly, fostering a culture of learning is of paramount importance. In order to adapt to the rapidly changing manufacturing landscape, businesses need to cultivate an environment that encourages continual learning and curiosity. This culture should inspire employees to broaden their skillsets, reward those who proactively acquire new skills, and advocate open-mindedness towards novel technologies.
While the transition towards a more technologically advanced manufacturing environment may appear formidable, it also unveils opportunities for the workforce to develop and transform. By welcoming these changes and proactively preparing their workforce, leadership can ensure they are not merely responding to the challenges of smart manufacturing, but also shaping a more innovative, efficient, and sustainable industry.
The Future of Data and Technology in Manufacturing
As we look towards the future, the role of data and technology in manufacturing will continue to expand. The successful exploitation of data, powered by advanced technologies such as AI and IIoT, is central to manufacturing's future. While these trends offer exciting opportunities for the future, they also underscore the importance of preparing both workforce and infrastructure to harness these technologies effectively. As large quantities of data already exist, the goal should be to identify the quality data. Start with a foundational data strategy to determine what the right data is to store, then how and where it is shared, forwarded, and transformed into useful insights that deliver impact.
With the integration of more technology into the typical manufacturing workplace, the potential for growth and development is immense. By leveraging the power of data, manufacturers can continue to innovate, enhance productivity, and sustain growth for the long term.
Discover more about the power of your data here.
