Digital Transformation of Process and Functional Safety
David Hansen, CFSE – ISA Houston– SIS SILverstone
281-701-6476
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Presentation Sponsors
DefinitionsDigitization
• Convert analog information into digital. E.g. scanning paper documents
Digitalization
• Process of moving to a digital business.
• Projects.
• Unclear and used in different ways.
Digital Transformation
• Includes Digitalization projects
• Reorganization of the business around data platforms.
Digital Twins
• Computer models.
• Used to predict based on real or theoretical data.
• You probably have a digital twin.
Psychodigital Tranformulization
• I made this up
Object Class:Defense Attack Robot
Mark I (DAR)
Object:DAR 18709
Object:DAR 18710
Object Class:shopper
Object:Bill
Object:Jill
Data
Model number
Price
Number sold
Purchasers
Reviews
Profile of likely customers
Internal Data
Other purchases
Reviews
Returns
Views
Purchase
Review
Purchase
Review
Purchased Data
Politics
Friends and family
Occupation
Pets
Likes and dislikes
DAR is great! Took over a small country with its help!
DAR is terrible! It killed my cat!
Machine Learning Data Analytics Recommendation: Market to dictators, avoid cat owners.
Object Oriented - Shopping
Object Class:HAZOP/LOPA risk
scenarios
Object:Tower Overflow to
compressor Scenario
Object:Tower flood and
overpressure scenario
Object Class:Independent
Protection Layer
Object:Safety
Instrumented Function SIF-101Compressor S/D
Object:SIF-129
Tower feed S/D
Event Data
Diagnostics
Bypasses
Failures
Incidences
Activations
Object Oriented – Functional Safety
Object:PSV-134Tower
Object:LT-101
Object Class:Device
Object:XS-145
Object:XV-137
Object:PSV-134
Object:Process Equipment
Object:Process Units
Object:Facilities
Object:Enterprise
Key Performance Indicators (KPIs)
Other Data Connections
Financial Systems
Projects
Asset Management Systems
Process Simulators
Maintenance Systems
Analytics Systems
Data Analytics Recommendation: ACME Model 146 level transmitters are causing significant elevated enterprise risks.
Data Analytics/Machine Learning
Data Lake
Process Safety
Personnel
Devices
Maintenance
Asset Management
Location and Weather
• Digitalization= data overload.
• Need automated functionality to fully benefit.
• Machine Learning looks for patterns.
Theoretical Cases
1. Analytics discovers that elevated risks from seemingly random failures from different types of transmitters are not random. Analytics from users and vendors determine that a specific capacitor type is failing in hot weather conditions.
2. Analytics automatically discover outlier risk rankings for similar scenarios across the organization. Either too many resources are being allocated or risks are too elevated.
Digital Transformation is here!
• How we make money• Video game industry• Publishing (Youtube)• Investing• Onlyfans
• How we manage our money • How we manage money• Money itself (crypto currency)
• How we spend our money• Shopping from home• AI telling us what we want
• How we interact with each other• Social Media• Dating
• How we govern• Climate Change• COVID response• AI Gods
One portal to access all your information from any device anywhere.
Digital Transformation is not Here!
Process Industries Functional and Process Safety
• Online documents (PDF, Word, Excel)
• Disconnected tools
• PHA/LOPA database tool
• Word
• Excel
• SIL Calculation tool
• SRS Database
• Action Tracking software
• MOC software
• Document management software
• CMMS
• Paper test results
• Control system event logs
• All isolated from each other.
• Difficult to access.
Why the slow transformation?
• Engineering companies typically deliver PDF format.
• Hangover from financial system digital transformations.
• Consultants make lots of money the traditional way.
• Change makes experts into amateurs.
• Very cautious operating company IT departments.
• Companies are focused on product, not software.
• Limited software budgets competing for various initiatives.
• Perception that they are already safe.
• Ignorance is bliss. Hidden risks can be revealed by software platforms.
• Have already invested in existing isolated tools.
Digital Transformation is Coming
Data Lake
• Most major operating companies doing something:• From voicing interest to
• Tiny budgets to
• Significant budgets and software evaluations to
• Ambitious Data Lakes.
• Many pilot projects ongoing.
• Some facilities are transformed.
• Major driver is for high level process safety KPIs.
• Growing desire for efficiency given current economic challenges.
• More centralized government focused approached internationally.
• Germany’s Industry 4.0
• China 2030 agenda
Cloud Software
• Microsoft Azure and Amazon Web Services (AWS) big players.
• Software on cloud called Software as a Service (SaaS).
• Major shift in the last 4 years from user managed software to SaaS.
• IT department reductions.
• Data backed up on servers around the globe. No local event can lose data.
• Software accessible by software provider and contractors.• Updates.
• Projects.
• Trouble shooting.
• Makes global enterprise software more viable.
• Data lake.
• Cost declining.
Digital Transformation Risks
• Transformation effort fails and resources are wasted.
• Failed transformation damages careers. (Successful transformations boost careers).
• Transformation can reveal overwhelming risk issues. Legal risk of gross negligence.
• Risk data reveals weaknesses that can be exploited.
• Cloud company political activism (Parler). Shut down servers.
Why do digital transformations fail?• Software does not actually work. Vaporware. (Easiest to
overcome).
• Software does work but users think it doesn’t.
• Inadequate funding.
• Those executing the transformation projects do not understand the data.
• Software too difficult to use and users are not trained or supported adequately.
• Give up before benefits are realized. Benefits are not realized right away. Might be less efficient at first. Software systems require use to get benefit. Software licensing contracts limit access to too few. Benefits not realized. Use stops.
• Stop at digitalization. Fail to transform systems. E.g. still require paper reports printed from databases.
• Users not incentivized to use the new platform. Work processes not transformed.
• End users demand customization. End up with an orphaned system.
• Enterprise software decisions can be very political (internal rivalries). Which means hidden agendas and bad decisions.
Features
• Facilitate risk studies, SIL calculations, SRS, Test Procedures, Operations and maintenance in one integrated platform.
• Single source of truth.
• Entire enterprise accessible (cloud). KPIs rolling up through organization.
• Cloning/copying/auto generation and templates across organization.
• Data importers.
• Mass data editors.
• Automated SIS configurations.
• Automated data validation.
• Efficiently find device/function/risk/event correlations.
• Efficiently evaluate MOCs, bypasses and failures.
• Integrate with control system data, CMMS, other for automated analytics.
• Data analytics.
Benefits/Business Case• Reduced risk! Truly move from I Think to I Know.
• Reduced costs!
• Improve business performance!
• Aligns responsibility with knowledge for effective decision making and peace of mind for executives.
• Good for careers.
• Enterprise software ensures best practice consistency across the organization improves performance.
• Efficient data manipulation allows for better predictions and decision making.
• Align financial and risk information access to make better spending decisions.
• Reduced cost and improved software performance. Less software licenses to manage with greater accessibility and performance.
• Highly compensated personnel improve risk, plant performance and financial performance instead of entering or looking for data.
• Accrue knowledge. The more it is used the better it becomes.
• More effective allocation of resources. Balance available information between financial and risk.
• Retain and attract key personnel. Work brings greater value and recognition and satisfaction.
• Increase asset value of facilities.
Where will we be in 20 years?
a) Like most other businesses that use integrated data platforms?
a) Still using online documents and isolated mini-tools.
It is good, it is necessary, and it is coming.