Artificial intelligence (AI), machine learning (ML) and digital twin are the decision support tools of the century. They have now become synonymous with digital transformation. On paper and at face value they deliver phenomenal value. But too many people ignore the hard data plumbing work that needs to precede any of these kinds of initiatives. The AI, ML and digital twin engines can be working fine, but still output poor answers, or not perform at the speed which they’re meant to. This is usually because the fundamental readiness step has been underestimated and not undertaken fully. With reference to various industry case studies, this presentation will explore the fundamental readiness steps of any DX initiative needed to be taken as well as several incremental “quick wins” to score on your way to a more advanced data landscape. The presentation will also opine on some consequences when the readiness step is insufficiently addressed.

1. Lisa Williams
Director of Digital Management
KBC (A Yokogawa Company)
November 12, 2020
Avoiding The Common
Pitfall in DX Execution
Readiness and Situational Awareness

2. Start at the beginning – don’t cut corners !
• Readiness
• People
• Business
• Data
• Infrastructure
• Situational Awareness
• Heads-up Dashboards
• Layered Analytics
• Aligned to Goals and Targets
Readiness
Situational
Awareness
Decision
Making
Operational
Execution
Value
Sustainment

3. Reap
Reward
Mitigate
Risks
Identify
Gaps
Digital readiness is important
▪ Creates a business ready to accommodate changes
▪ Identifies gaps and mitigates risk in the plan versus actual
▪ Enables roadblocks to be removed or remediated avoiding project delays
▪ Builds an engaged organization aiming for success
Quantify
Value

4. Digital readiness: Leadership
▪ Relatable enterprise strategy
for the organization
▪ Strong informed leaders
▪ Engaged line organizations
▪ Clear understanding of the
value changes create

5. Digital readiness: People
▪ Assess the organization’s Digital skills
• What is the level of confidence using Digital
Tools at the office?
• How about at home?
▪ Understand the level of trust in your data
• Do you feel you can make business or plant
decisions with your system?
▪ Determine the organization’s use
• Are users working around technology?

6. Digital readiness: Business
▪ Policies and procedures aligned with strategy
▪ Ownership established
▪ Governance in place for development and
evergreen processes
▪ Communication path approved

7. Digital readiness: Data
▪ Assess data quality
▪ Ensure data is relevant
▪ Review propagation paths
▪ Evaluate the processes around manually
entered data
▪ Establish calibration a method for
“Digital Twin” models in use

8. Digital readiness: Infrastructure
▪ Do policies exist for connecting
systems internally?
▪ Do policies exist for connecting
systems externally?
▪ Is your infrastructure up to the task?
▪ Software inventory

9. Situational Awareness
▪ Supports Layered Approach to Analytics
• Data Cleansing
• Key Performance Indicators
• Line of Sight on Performance
▪ Agile in Development
• Iterate and Improve
▪ Dashboards with Roll-Up Analytics
• Simplify Daily/Monthly System Reviews
• Narrow Troubleshooting Efforts

10. ▪ The Ask
• Automation of the daily production report
▪ The Challenge
• Excel based report with hundreds of calculations
▪ Technology Applied
• PI Vision
• PI AF Analysis
▪ The Transformation
• Real-time visibility into production
• Visibility to at all levels the organization
• Always updated with trusted information
Case studies

11. ▪ The Ask
• Make data accessible in the field to maintenance teams
▪ The Challenge
• Outdated tools not accessible on modern devices
• Migrate culture from paper to electronic work tickets
▪ Technology Applied
• PI Asset Framework
• PI Integrator for Esri
• Esri GIS Maps
▪ The Transformation
• Reduced turnaround time and improved process efficiency
• Updated work process by combining multiple data sources
• Increased level of satisfaction and engagement for workers
Case studies

12. ▪ The Ask
• Create Work Ticket during investigation from screen
with supporting details
▪ The Challenge
• Complexity to enter a ticket was leading to no ticket
being entered
▪ Technology Applied
• PI Vision Extensibility
• Integration to Azure based ticket system
▪ The Transformation
• Reduce human performance errors transcribing details
• Reduced ticket entry time
• Updated work process ensures tickets are created and
properly prioritized
• Corrected latent organizational weakness
Case studies

13. The names of corporations, organizations, products and logos herein are either registered trademarks or
trademarks of Yokogawa Electric Corporation and their respective holders.
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.