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The Value-driven Approach to Digitalizing Assets and their Supply Chains
- 1. Simon Rogers Vice President, Digital Solutions Yokogawa November 10, 2020 The Value-driven Approach to Digitalizing Assets and their Supply Chains
- 2. Agenda 1. Digital value chain transformation 2. Digital accelerators 3. Think big, start small, scale fast, drive adoption
- 3. From Supply Chain to Value Chain Optimization Outcome oriented Holistic Adaptive and smart Collaborative
- 5. 4 From Functional to Knowledge Organization
- 7. 66 IIoT enables Value Chain Optimization
- 8. Integration Model update Enterprise data Current Time 1 to 15 days 1 to 3 months 1 year 2 to 10 years1 day1 month IIoT data Corporate business plans Supply chain planning Variance analysis Production accounting Strategic Plan Fiscal Targets Feed and Inventory Plan Operating Plan Actual Production Investment planning Production planning Annual planning Supply chain scheduling Supply chain management Value chain optimizaton Production scheduling Supply Chain Status LP Least Squares Event-based simulation Custom Asset Optimization Process and off-sites automation Operating Targets Actual Operation Schedule Procedural automation APC Model Update Debottlenecking Process and utility optimization Profit improvement process unit management Feasibility study Process design Process Simulation Predictive control 7 Integrated Systems and Practices
- 9. ■ Data driven, automated identification of optimization using AI ■ Intelligent, automated work processes ■ Automated data management and integration ■ Cloud enabled to facilitate: ■ Scalability ■ Collaboration ■ Support ■ Rapid enhancements ■ Knowledge management ■ Integration ■ Visualization Digital Future
- 11. Data-driven Optimization 1 Knowledge graph for managing and visualizing supply chain data 2 Demand, price and performance forecasting 3 Reconciliation, variance analysis and opportunity identification 4 Automation of model updates 5 Automation of production scheduling 6 Asset-wide optimization 4 3 6 1 5 2
- 12. Value Chain Knowledge Graph ■ Global optimization ■ Visualize and manage entire supply chain ■ Connect data silos–linked data ■ Add structure and context ■ ML, text mining and NLP ■ Semantic search and AI ■ Automation, classification ■ Reporting, personalization Partner 2Partner 3 Plant 2 Plant 1 Place 2 Place 3 Place 1 Product 3 Product 1 Distanc e Cost Time Mode Feed Producti on Cost Product 3 Production database Simulation database Scheduling database Planning database ERP database Unstructured data Business Taxonomy and Ontology—Meta Data
- 14. First principles models Actual plant behavior Data clean-up and selection Planning/scheduling models Train Calibrate Update signal Historical lab/plant data On-line monitoring FCC Feed Composition Linearization Point Product Yield Linear Model Reality Mismatch Synthetic data 1) More variables 2) Less linear 3) Wider scope/more interactions 4) Invert model 5) Combine planning and scheduling Automation of Model Updates
- 15. Crude Scheduling Refinery Scheduling Blending Scheduling Shipping Scheduling Actions: Tasks State: Inventory (volume and quality) Streams (rate and quality) Constraint values Baseline Update Supervised ML classifier Opening inventory Inventory and task updates Supervised/reinforcement learning Constraint-based search Reward: Gross margin Δ from constraints Δ from plan Δ inventory Automation of Scheduling
- 16. Integration with Petro-SIM provides most accurate inferential calculations and gain updates Non-linear multi-unit or plant wide optimization ensures operation closer to global optimum Dynamic optimization reduces time to optimum operation Tight integration with APC ensures better disturbance rejection and easier maintenance Gain update assured by ML application On-line Petro-SIM rigorous model Plant Plant –wide APC and optimization DCS Sub Controller 1 Sub Controller 2 ３ 1/sec 1/5 mins 1/0.5~1hr OPC OPC Assay or feed properties Economics schedule SS Gain Update ( If changed ) MV / CV Limit ON / OFF Status Historian Plant-wide Optimization
- 17. Think Big, Start Small, Scale Fast, Drive Adoption
- 18. Think Big
- 20. Manage Your Data and Knowledge
- 21. Start Small - Digital Twin
- 23. Scale Fast—Co-Pilot Shared Digital Twin in the cloud facilitates integrated optimization
- 24. 23 Agility to Drive Adoption
- 25. Build an Integrated Asset Model
- 26. What Will the Future be Like?
- 27. The names of corporations, organizations, products and logos herein are either registered trademarks or trademarks of Yokogawa Electric Corporation and their respective holders. Thank You Excellence is never an accident. It is always the result of high intention, sincere effort and intelligent execution; it represents the wise choice of many alternatives— choice, not chance, determines your destiny.