White paper: Improving Manufacturing Production and Reliability
- Correcting problems in manufacturing operations can be exceedingly complex. Take product quality issues, for example. So many variables, even seemingly minor ones, can have a direct effect on quality. Experienced operators understand that symptoms signifying 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 for identifying these root cause faults. In these instances, symptoms or single faults are often mistakenly identified as the problem source instead of underlying, complex multivariate causes.
- As manufacturing becomes more digitized, organizations that can’t improve their use of data and analysis of it to transform operations will increasingly experience negative business performance, such as lost production, lower quality, and increased risk. Ultimately, this inability to digitally transform will limit their ability to compete.
- 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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