(140c) Moving Beyond Manufacturing Analytics to Knowledge

Manufacturing analytics providers have long been focused on helping customers improve operations and processes through the application of univariate and multivariate SPC as well as other advanced analytics methodologies.

Industry 4.0 and Digital Transformation initiatives were important drivers of the uptake and robust usage of manufacturing analytics to deliver performance improvements. Manufacturers were able to transition from the usual react & respond production paradigm to predict & prevent – predictively identifying and pro-actively preventing issues through the use of real-time manufacturing analytics before problems negatively impact the environment and bottom line.

Over the years, many vendors have improved their customers’ abilities to unlock more value from their existing data through sometimes novel (and often not very scalable) solutions. During that time, analytics have become (essential) table stakes in an ever-increasingly competitive manufacturing market.

From inbound raw materials to outbound finished goods and all stops in between, manufacturers are applying analytics to influence positive process outcomes, end-product quality, and customer satisfaction. They are driving immediate course corrections necessary to keep processes in control and product within specification. They are making analytics-based decisions to improve production efficiencies, increase supplier performance, and decrease waste.

What manufacturers are failing to do, however, is deliver lasting (and repeatable) value to their organizations with manufacturing analytics.

The next step-change evolution of manufacturing analytics is capturing the intellectual property that results from their use. Analytics-based knowledge spans the identification of early-warning signals, the codification of institutional knowledge and the amplification of process knowledge for quick and decisive action.

Attracting, retaining, and empowering workers should also be a key focus of any analytics technology investment. By capturing the knowledge that results from the application of manufacturing analytics (from data through result), a company builds a foundation to not only solve process issues faster but as importantly addresses three of manufacturing’s biggest human capital challenges¹ – the loss of institutional process knowledge through retirement; ever-increasing skills gaps between long-term employees and others; and time-to-productivity for new hires.

The session will feature real-life use cases and a live demonstration of how manufacturers are delivering on the promise of Industry 4.0 and Digital Transformation as they:

• Leverage existing process-related data and applying a wide variety of manufacturing analytics methodologies (e.g., univariate and multivariate SPC; machine learning, etc.), rules, formulas, and models to proactively identify and alert on potential process issues and problems.
• Connect specific actions and quantified best practices to each analytics alert and delivers those directly to users.
• Preserve process knowledge by retaining all relevant event information – data, analytics, actions, and results – for use anywhere across the enterprise as well as delivering better work processes for continuous process improvement and employee onboarding.

¹ Deloitte 2022 Manufacturing Industry Outlook