Advanced Version of Digital Twin
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The document discusses the functionality and applications of RFID technology, which includes readers, tags, and antennae for data capture and business process enhancement. It emphasizes the importance of context, real-time analytics, and data control in improving business operations and decision-making. The document outlines a structured approach to effectively collect, organize, and react to data within supply chain management and operational processes.
Advanced Version of Digital Twin
- 1. Know…Now!
- 2. UNISION
- 3. RFID • Comprises Readers, Tags and Antennae • Working – Tags are like SIM Cards – but can only reply ‘ I am so and so..’ – Readers/Antennae are like cell towers – but can talk to a couple of meters only – Tagged objects (moving/moveable) communicate what Tags are programmed to, with the Readers through Antennae • Not a substitute for Barcode- sometimes more and sometimes less than a Barcode • Just another tool for Data Capture – part of AIDC Spectrum • Caveat - A tool is as good as the person using it
- 4. Perspectives • RFID can be implemented for point solutions • Can also be implemented for long-term Business Process improvements • Design an implementation for the long-term, also accommodating point solutions • A good implementation will use RFID data for Data Modeling and Synthesis
- 5. • Your ROI depends on your perspective RFID as business enhancer – Data collector and router Data Analytics, Modeling, BI et c RFID as solution facilitatorBAM, BPR et c RFID as Compliance tool RFID as technology-Point solutionsAccess Control, Automation etc. Benefit / Return Perspective Perspectives
- 6. Data Model • Objects (moving/moveable) – with temporal and historical attributes, relationships • Locations – absolute and relative to movement of objects • Transactions – interaction among objects-locations, change in object-object, object-location relationships • All linked through processes, time and space coordinates • All captured through an observatory (set of sensors like Readers)
- 7. Data Model An object tagged with required details – identification etc 8 10 10 30 20 50 25 8 15 relat’nship 1 2 3 timeid productid 111213
- 8. Data Model An object tagged with required details – identification etc Capture association (Containment) details. Also association with employees, locations (BIN) etc through sensors Capture Transaction Details – PO, ASN etc., Capture Change Parameters – Location, Transit, Transaction et c Capture Location (Shelf), Transaction, Process et c Capture Path, Stops, Basket (Tagged Trolley) etc.,
- 9. Data Model • Reactive • Silos of data – many disjoints – esp’ly physical context • Limited Collaboration - as no control over process execution • Aggregate Data only Business Processes Transactions happen real-time Business Analysis Non-granular data, Non- contextual, Off-line, Latency, • Proactive • Informed real-time Business Analyses • Collaborative • Contextual, Continual data – no disjoints • Granular Data • Control over Process Execution and Monitoring • Ability to take decisions real-time and feedback to the system • Broader distribution with many opportunities for collaboration • Merging Business Analysis with Business Process Real-Time, Contextual, Process-Centric Data
- 10. Business Case Approach CONQUER C – Collect & Control Data – Multi- protocol, vendor hardware, 24x7Health Monitoring of Grid O – Organize Data – Filter,parse N – Nurture Data - Convert data to valuable info, Selective Filtering Q – Qualify Information - Apply Business Context, define Impact U – Understand Information – Associate impacted business process component,Sharing and Collaboration E - Extrapolate Information – EAI – To the relevant business process component R - React – Execute responses on behalf of the enterprise
- 11. Business Analytics Custom Reporting Enterprise Apps Statistical Analysis Parameters Parameters Analytics Preparatory Relevance Filtering Aggregation Data Capture (AIDC) Black Box Data Parsing Selective Filtering Event Generation Rule Base Process Enabling Actions Space Ti m e Material Movement People Movement En’ronment Changes C O N Q U E R A Unique Approach
- 12. Business Analytics Custom Reporting Enterprise Apps Statistical Analysis Parameters Parameters Analytics Preparatory Relevance Filtering Aggregation Data Capture (AIDC) Black Box Data Parsing Selective Filtering Event Generation Rule Base Process Enabling Actions Space Ti m e Material Movement People Movement En’ronment Changes C O N Q U E R Tracking Material and people movement, sensing environmental changes – Pressure, Gas, Chemical sensors etc.,
- 13. Business Analytics Custom Reporting Enterprise Apps Statistical Analysis Parameters Parameters Analytics Preparatory Relevance Filtering Aggregation Data Capture (AIDC) Black Box Data Parsing Selective Filtering Event Generation Rule Base Process Enabling Actions Space Ti m e Material Movement People Movement En’ronment Changes C O N Q U E R
- 14. Business Analytics Custom Reporting Enterprise Apps Statistical Analysis Parameters Parameters Analytics Preparatory Relevance Filtering Aggregation Data Capture (AIDC) Black Box Data Parsing Selective Filtering Event Generation Rule Base Process Enabling Actions Space Ti m e Material Movement People Movement En’ronment Changes C O N Q U E R
- 15. Business Analytics Custom Reporting Enterprise Apps Statistical Analysis Parameters Parameters Analytics Preparatory Relevance Filtering Aggregation Data Capture (AIDC) Black Box Data Parsing Selective Filtering Event Generation Rule Base Process Enabling Actions Space Ti m e Material Movement People Movement En’ronment Changes C O N Q U E R
- 16. Business Analytics Custom Reporting Enterprise Apps Statistical Analysis Parameters Parameters Analytics Preparatory Relevance Filtering Aggregation Data Capture (AIDC) Black Box Data Parsing Selective Filtering Event Generation Rule Base Process Enabling Actions Space Ti m e Material Movement People Movement En’ronment Changes C O N Q U E R
- 17. Business Analytics Custom Reporting Enterprise Apps Statistical Analysis Parameters Parameters Analytics Preparatory Relevance Filtering Aggregation Data Capture (AIDC) Black Box Data Parsing Selective Filtering Event Generation Rule Base Process Enabling Actions Space Ti m e Material Movement People Movement En’ronment Changes C O N Q U E R
- 18. Business Analytics Custom Reporting Enterprise Apps Statistical Analysis Parameters Parameters Analytics Preparatory Relevance Filtering Aggregation Data Capture (AIDC) Black Box Data Parsing Selective Filtering Event Generation Rule Base Process Enabling Actions Space Ti m e Material Movement People Movement En’ronment Changes C O N Q U E R
- 19. Q&A
- 20. THANK YOU!