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Barriers to Manufacturing Data Analytics Implementation: A Comprehensive Analysis
Manufacturing Data Analytics (MDA) holds great promise for transforming the industry, but its implementation faces significant challenges, according to a study from Pusan National University. The study, published in the Journal of Manufacturing Systems, identifies technological and organizational barriers that manufacturers must overcome to fully reap the benefits of MDA.
Technological Challenges
The study highlights that one of the main barriers to MDA implementation is the lack of interoperable, scalable, and budget-friendly analytics tools. Manufacturers often grapple with a mix of legacy systems, partial upgrades, and the need to adopt advanced technologies that may not seamlessly integrate with existing workflows. This makes achieving an interoperable and scalable analytics environment difficult, particularly for mid-sized manufacturers.
Moreover, investing in and integrating a suitable ecosystem of tools that are both future-proof and user-friendly while fitting within manufacturers' often limited IT budgets presents a significant challenge. The complexity arises because manufacturers must navigate a maze of technology choices, ensuring that their investments are not obsolete in a few years.
Organizational and Process Challenges
The implementation process itself is complex, involving multiple stages such as preparing data, defining analytic questions, gathering and cleaning diverse data sources, analyzing data, visualizing results, and embedding insights into manufacturing processes. At each step, risks of failure exist due to organizational and human factors alongside technological ones, requiring shifts in mindset and adaptation of existing processes to the data-driven approach.
Addressing the Challenges
The study emphasizes that MDA isn't a plug-and-play solution. It requires cross-functional fluency, trust between teams, and a willingness to learn new ways of working. Teams well-versed in data may not understand the complexity of manufacturing, while teams in manufacturing may overlook the subtleties of data extraction methods or miss hidden insights from the data they do collect.
To overcome these challenges, the study suggests that manufacturers need to invest in an ecosystem that's both interoperable and scalable, even if they have limited IT budgets. They should also foster a culture of data-driven decision-making, encouraging collaboration between data experts and domain experts.
The study identifies 29 potential parameters with potential for problems, reshaping the conversation surrounding MDA by using a full field view. This comprehensive set of challenges mapped to implementation stages provides a better understanding of the MDA implementation process as a whole, setting the stage for a more successful implementation.
In conclusion, the study from Pusan National University offers valuable insights for manufacturers embarking on their MDA journey. By understanding the friction points identified in this study, manufacturers can evolve towards an adaptive, efficient, data-driven system, making the MDA promise a reality.
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