The terms sustainability and scalability could have different implications depending on context in manufacturing.
Moving from simply using manufacturing analytics to improve processes and product to leveraging the knowledge generated by their application addresses some of the industry’s top challenges including alleviating the loss of institutional knowledge, improving time-to-productivity for new hires and shrinking skills gaps. Additionally, the knowledge is also accessed and utilized across the enterprise to accelerate time-to-problem discovery and reduce time-to-issue resolution.
Getting to knowledge with manufacturing analytics requires identifying issues in real time; connecting analytics signals directly to internal subject matter expertise for immediate and consistent issue resolution; capturing all the information related to resolutions of analytics-generated issues; and proliferating the analytics-centric knowledge for use across the entire enterprise.
While seemingly most of the buzz in the industry centers on machine learning and AI, the vast majority of value derived from the application of manufacturing analytics is delivered by SPC analytics. Dow estimates over a three-year period, the use of real-time SPC generated an ROI in excess of $500,000,000.
Operationalizing analytics refers to the process by which the analytics-based process signals are connected to internal subject matter expertise in order to enable the recipient of any signal to take immediate, consistent action to correct process issues and upsets.
No on both accounts. Leading manufacturing analytics solutions leverage existing data sources (e.g., Process Historians, LIMS, MES< ERP, etc) without the requirement of having it copied to a data lake or any type of ETL.