Correcting problems in manufacturing operations can be exceedingly complex. Consider product quality, for example. Many variables, even seemingly minor ones, can directly impact product quality. Experienced operators and engineers understand that symptoms of a problem, such as temperature fluctuations, are often linked to multiple underlying root causes.
Yet, many organizations still rely on manual processes or narrow operational systems to identify the root causes of faults. In these instances, symptoms or single faults are often mistakenly identified as the problem source, instead of the true, often-complex and multivariate underlying causes.
As manufacturing becomes more digitized, organizations that can’t improve their use and analysis of data to transform operations will increasingly experience business performance issues due to lost production, lower quality, and increased risk. Ultimately, this inability to digitally transform will limit their ability to compete.
Overcoming the Barriers to Digital Transformation
Overcoming these barriers to digital transformation begins with better data management and analytics capabilities. To gain these capabilities, organizations must:
- Recognize data management weaknesses in current methods that limit the scope of data sources
- Use modern software, architecture, and services to accelerate device identification, data mapping, and machine learning
- Create a data-driven knowledge framework and use automated workflows to ensure analytics insight can be translated into corrective action
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Keywords: Reliability, Production, Root-cause Analysis (RCA), Analytics, Machine Learning, Industrial Internet of Things, Digital Transformation, Industrie 4.0, ARC Advisory Group.