Session - 16 - Guest Session
Session - 16 - Guest Session
Session - 16 - Guest Session
Sukanta K Padhy
Flow of my presentation
• Context setting : My understanding on Big
Data Analytics
• AI /ML algorithms
Use cases in Manufacturing
Analytics Maintenance Operations Quality
• .
Descriptive 1. Equipment Monitoring 1. Operations Monitoring 1. Quality Monitoring
2. Performance Analytics 2. Operator behaviour 2. Detect Quality Loss
3. Failure Root Cause Analysis 3. Defect Root Cause
(Insights on Analysis 3. Failure Root Cause Analysis
the present) Analysis
• GT is cured in a Press
Preliminary Hypothesis :
Quality & workmanship of the GT is critical
Scheduling on the right press types gives a better control on heating &
cooling cycle for a set of products
Recommendations
A cluster of presses found to be found to have optimum
level of Yield for a group of SKUs
Some TBMs are found to be not suitable at all – taken out
of production
Some moulds are found to have high level of scrap
irrespective of Presses & TBM combinations
Expected Impact
Improve Yield to 98.5%
Case study 2: Predictive Maintenance of Tools (Moulds)
Problem definition
Moulds are cleaned (insitu & Ice blasting) based on
certain frequency and inspection
Unexpected downtimes impacting OTD
Un-necessary downtimes impacting maintenance
costs and productivity.
Case study 2: Predictive Maintenance of Tools (Moulds)
Solution used
Predictive Analytics
Historical production, quality & Maintenance data used at
individual mould level
Predict types of cleaning required ( insitu & ice blasting)
well inadvance
Expected Impact
Reduction in unplanned downtimes by 50%
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