?Focus Group: Big Data Analytics for Smart Manufacturing Systems
Report by Sudarsan Rachuri
[email protected]
Improving Manufacturing Efficiency through Predective Analytics
?• 5% decrease in batch cycle time
• 10% improvement in machine reliability • 10% reduction in water consumption
• 5% reduction in energy costs
Source: www.ge-ip.com
?The new Program
Smart Manufacturing Systems Design and Analysis
• Objective: The objective is to deliver measurement science, standards and protocols, and tools needed to predict, assess, optimize, and control the performance of smart manufacturing systems.
Major Projects:
1. Reference architecture and open solution stack to enable and assess the composable SMS
2. Modeling methodology and associated tools to predict, assess, and optimize the operational performance
3. Data analytics and associated methods and tools to enable adaptive system
4. Methods and tools for system performance assurance.
??We need to understand the Predictive Analytics Workflow
???Standards and protocols for this information flow
Data visualization
Decision Storage/Decisi on Processing
??????????Data Raw extraction/Data input
Input validation
Data Pre- processing
Normalize, Discretize, Filter etc.
Predictive Data Post-
Model processing Prediction
????stream s
Outliers, missing values, invalid values
Scaling, Decision, Scores etc.
Standardize the predictive models
• Model definition
• Model Composition
• Model chaining
?????Interface Standard
Protocol Standard
define both the transmitter and receiver function at the same time.
ensures compatibility
?Promise of Big Data Analytics Solution!

?Application Layer CAPP, MES, FDC, YMS, …
??Integration Layer
???Model Life Cycle
Creation ? Deployment ? In-Use ? Disposal
Life Cycle Control
Duration Control, Uncertainty Resolution
?Analytics Modeling Layer
??Statistics Approach
R, …
Machine Learning Approach
Neural Network, SVM, Decision Tree, …
?Big Data Infrastructure Layer
R Hive, Hadoop, HDFS, MapReduce, …
?Data Layer
??Static Data
Process Plan (STEP-NC), Production Plan, Master, …
Dynamic Data
Monitoring (MTConnect), Metrology, Defect, …
?Manufacturing Process
Shop Floor Layer
???????Feedback Control
?Focus Group Discussion Points
• Data acquisition issues
– In Health care, Manufacturing (discrete,
– Cost of collecting data
– Availability of data (real world data and data simulator)
– What are the real implications of volume, velocity, variety, and veracity?
– Methods of collecting data (manual, automated)
– Open Data Repository
?Focus Group Discussion Points
• Standards for data acquisition
– Data attributes (meta data), unstructured (extracted from text,…), structured (standards), data sampling
– Data access and query
– Data modeling and data science
– Safety data, privacy of data (data masking??) – Open Data Initiative ??
– Measurement and metrics for V&V
?Focus Group Discussion Points
Analytics Modeling
– Problem classification: No need for DA, Good to have DA, must have DA
– Data driven Models, Architecture for Data Analytics (common issues for Manufacturing and Health Care
– Model Composition, chaining, reuse
– Correlation to Cause-Effect Analysis
– Analytics Workflow – Standards and Protocols
– Moving analytics to the data
– More research needed in understanding feature vector (minimal or optimal)
– Computing and IT infrastructure for DA
?Data Analytics – Past, Present and Future
Data Volume
??????Current DA
Reduce the information overload Can we get same level
of insights with less data?
???Past Present

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