Presentation - Aspen - The Untold Benefitsof a Digital Maintenance Strategy for Pharma.pdf

The Untold Benefits of a Digital Maintenance
Strategy for Pharma
John Campbell, PhD
Sr. Director, Product Management, Aspen Technology

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Aspen Born-On Date:
June 1997
Education:
PhD in Chemical Engineering from The University
of Wisconsin at Madison, M.S. from University of
Texas, B.S. from University of Illinois
Positions Held at Aspen:
APC Services engineer, Support Engineer, APC
Developer, APC Development Director, APC
Product Manager, Strategy and Planning Product
Manager, Mtell Product Manager
Your Speaker Today
John Campbell
Senior Director
Mtell Product Management

AspenTech® At-a-Glance
WORLD LEADE R IN ASSE T OPTIMIZAT ION FOR CAPITAL - IN TE N S I VE INDUST RIE S
2400
CUSTOMERS
WORLDWIDE
YEARS OF INNOVATION
40
$59B ANNUAL VALUE
CREATED
Industrial AI
Insights | Guidance | Automation
Data Insights
Data Management
AI/ML, Advanced Analytics
Domain Expertise
Engineering Fundamentals
Industry Experience
ADVISORY PARTNERS
VALUE-ADDED RESELLERS
IMPLEMENTATION SERVICE PROVIDERS
A RECORD OF FIRSTS
ESTABLISHED GLOBAL
PARTNERSHIPS
ENERGY – CHEMICALS – EPC –
PHARMA – METALS & MINING – FOOD
& BEV – POWER – PULP & PAPER
SAFETY – SUSTAINABILITY
PROFITABILITY – INNOVATION  Flowsheet Simulator
 Desktop Planning Tool for Refineries
 Model Predictive Controller
 Adaptive Control Technology
 Concurrent Conceptual Engineering
Workflows
 AIoT Hub
 Unified Environment for Planning,
Scheduling and Operations
 Hybrid Models

Market Forces Demanding New Levels of Operational Excellence
 Volume of Data
 Connectivity
 Artificial Intelligence
 Environment Footprint
 Waste Reduction
 Social Responsibility
 Supply & Demand, Prices
 Personalized Medicine
 Macro Economics
 Changing Expectations
 Skills Gap
 Remote Workforce
Volatility
Sustainability
Workforce
Technology
1. Five-year Investmentin Alliance to End PlasticWaste to reduce plasticin the environment;
2. Typicalvolume of productiondata for chemicalplantat 50-500TB acc to Accenture: Insightand Actions for the ChemicalIndustry,June 2019
3. CDMO Market Size, Share, Growth,Trends & Report,2026 (fortunebusinessinsights.com)
75%
Digital
Natives
500TB
Production Data2
$1.5B
Circular
Economy
Investment1
$279B
by 2026 (10% CAGR)3

Challenges
Unplanned downtime is causing disruptions in supply of life-
saving medicines
Lack of adequate warning is limiting decision making to adapt
quickly
Maintaining assets to
ensure consistent
product delivery
Provide better response times to help keep assets running
safely and cost-effectively

Digitalization – Enabler for Pharma 4.0TM
Pharma 4.0: ISPE Initiative to cross pollinate
Industry 4.0 to Pharma
Source: Pharma4.0 OperatingModel, Industry4.0, ISPE,InternationalSociety for PharmaceuticalEngineering
Enabling technologies include:
 Electronic Batch Records
 Process Analytical Technology (PAT)
 Predictive Monitoring & Control
 Predictive & Prescriptive Maintenance
 Scalable Digital Infrastructure, incl.
Cloud
 Industrial Artificial Intelligence
 Digital Twins
 Integrated Planning & Scheduling

Pharma 4.0 – Current Reality
Journey to digitally transform manufacturing operations
Source: Axendia
100%
of the Life-Science
companies are
interested in AI
76%
say digital
transformation
will take at least
2 to 3 years
19%
have digitally
transformed
manufacturing
operations
18%
are successfully
running
AI applications
41%
Life-Science
companies are
implementing
solutions

Digitalization – Shining Light on Operations and More

Accelerating Digitalization for Pharmaceutical Manufacturing
Scalable Digital
IoT
Architecture
Workforce
Optimization
Predictive &
Prescriptive
Maintenance
Overall
Equipment
Effectiveness
Continuous
Manufacturing
Hybrid
Models
Simulation
& Digital
Twins
Domain Expertise
Data Insights Industrial AI
Machine
Learning
Data
Management
Advanced
Analytics
Digital
Accelerators
Quick
Wins
 Supply Chain Resiliency
 Continuous Improvement
 Operational Excellence
 Sustainability
 Smart sourcing agility
 Security of supply
 Faster time to market
 Reduced environmental impact
 Self-Optimizing Enterprise
Lines of
Business
Industry
Initiatives
Outcomes
On-premise Cloud Hybrid Capability as a Service
Deployment
Options

Enabling Pharma Manufacturing End-to-End
Distribute
Optimize product distribution
Plant Digital Twin
Optimize OPEX by mirroring asset
Design & Debottleneck
Optimize CAPEX and time to market
Prescriptive Maintenance
Stay ahead of unplanned downtime
Assess Reliability
Minimize risk
Inform the plan
VALUE CHAIN OPTIMIZATION
Produce to demand
ASSET PERFORMANCE MANAGEMENT
What can the
Plant produce?
What’s the condition
of the Plant
Forecast
and Plan
Balance supply& demand
OPERATE MAINTAIN
DESIGN
PERFORMANCE ENGINEERING
Industrial AI Infrastructure
Connectivity: Edge to Cloud, IIoT, Data Analytics, Insights & Collaboration
Plan & Schedule
Align supply chain & operations
Monitor & Execute
Collect, monitor and automate execution
Control & Optimize
Maximizequalityand performance
PRODUCTION OPTIMIZATION

Enabling Pharma Manufacturing End-to-End
Distribute
Optimize product distribution
Plant Digital Twin
Optimize OPEX by mirroring asset
Design & Debottleneck
Optimize CAPEX and time to market
Prescriptive Maintenance
Stay ahead of unplanned downtime
Assess Reliability
Minimize risk
Inform the plan
Produce to demand
ASSET PERFORMANCE MANAGEMENT
What can the
Plant produce?
What’s the condition
of the Plant
Forecast
and Plan
Balance supply& demand
OPERATE MAINTAIN
DESIGN
PERFORMANCE ENGINEERING
Industrial AI Infrastructure
Connectivity: Edge to Cloud, IIoT, Data Analytics, Insights & Collaboration
Plan & Schedule
Align supply chain & operations
Monitor & Execute
Collect, monitor and automate execution
Control & Optimize
Maximizequalityand performance
PRODUCTION OPTIMIZATION

Move to Digitalized Pharma Value Chain Network for Increased
Resilience & Efficiency
The supply chain of the future is no longer a chain.
Redefined in its name and how it functions, the modernized value chain network enables optimization across every decision point, from
research and development through manufacturing and distribution to consumers.
aspentech.com/pharma

Solving the Right Problem
85% equipment fails in spite
of calendar maintenance – Boeing
63% scheduled maintenance
is unnecessary – automation vendor
process-induced
The problem is here
wear-and-tear
The spend is here

Traditional Problem-Solving Approach
Seal in a bead mill being replaced
after every 8 batches as a preventive
measure to protect against costly
production losses
Maintenance Costs
Maintenance Planning
Capacity Constraints

Optimizing Asset Performance & Protection
Existing Base Layer: Preventive Maintenance
Asset Performance
Failure
Motor Break Wear
Low Refrigerant
Damaged Wiring
Leaking Pipe
Bearing Failure
Measured
Degradation
82% of failures
not time or
wear based
- ARC
Insufficient Grease
Base Layer
Preventive Maintenance

Optimizing Asset Performance & Protection
Mid Layer: IIoT-Based Predictive Maintenance
Initial Damage
Detection
Measured
Degradation
Asset Performance
Failure
Shaft Misalignment
Insufficient Grease
APM 1.0
Damaged Wiring
Bearing Failure
Motor Break Wear
Low Refrigerant
Loose Mounting Bolts
Water in Oil 63% of scheduled
maintenance is
unnecessary
- Automation
Vendor
Dirty Oil
Lack of Cooling
Base Layer
Mid Layer
Condition Based (Vibration)

Layers of Protection Evolution
Early Operational Issues
Initial Damage
Detection
Measured
Degradation
Asset Performance
Failure
3rd Line of Defense
2nd Line of Defense
Condition Based (Vibration)
1st Line of Defense
Operational Mitigation
Cavitation
Fouling
Damaged Wiring
Liquid Carryover
Off Spec Product
Leaking Pipe
APM 1.0
APM 4.0
Dirty Filters
Low Refrigerant
Shaft Misalignment
Loose Mounting Bolts
Insufficient Grease
Dirty Oil
Lack of Cooling
Dirty Raw Materials
Operational Upsets
Operational Error
Water in Oil

P-F Curve Layers of Protection Asset Strategy
Condition-
Based
Preventive
1st Layer of Protection
Operational-Based
2nd Layer
of
Protection
3rd Layer
of
Protection
P
P
F
Time
Asset
Condition
Initial process
upsets/anomalies
Vibration
Oil
Analysis
Audible
Noise
Hot to
Touch
Ancillary
Damage
Catastrophic
Failure
 1st Layer of Protection – Operational-Based – Identify and address process behaviors that will lead to asset damage and eventual asset failure
 2nd Layer of Protection –Condition-Based – Identify and address early damage that will lead to asset failure
 3rd Layer of Protection – Preventive – Identify and address time and usage-based wear that will lead to asset failure
Start of
Damage
Correlated process
upsets/anomalies
Critical process
upsets/anomalies

P-F Curve Layers of Protection Asset Strategy Perspective
Maximizing Mitigation Options and Impact
P
F
Mitigation
Options
Enterprise & Supply Chain Mitigation Options
Production Mitigation Options
Maintenance Mitigation Options
Initial
Degradation/
Damage
Operational-Based Condition-Based
Preventive

P-F Curve Layers of Protection Asset Strategy Perspective
Financial Impact of Early Asset Risk Mitigation
P
Mitigation
Cost
Initial
Degradation/
Damage
Maintenance
Cost
Production
Loss
F
Operational-Based Condition-Based
Preventive

P-F Curve Layers of Protection Asset Strategy
P
P
F
Time
Asset
Condition
Initial process
upsets/anomalies
Vibration
Oil
Analysis
Audible
Noise
Hot to
Touch
Ancillary
Damage
Catastrophic
Failure
Start of
Damage
Correlated process
upsets/anomalies
Critical process
upsets/anomalies
Operational-Based View of P-F Curve
• Considers both the behavior of the asset and the process
• Earliest issues determined from process upsets and quality
• Identifies process issues that could potentially lead to failure
before failure initiates
• Provides data for collaboration between Maintenance, Process
Engineering and Operations to address process issues before they
become failures – maximizing financial optimization

Has this happened before?

~3 Months

What if it happened differently?
~3 Months
Failure
Prediction
Alert

~3 Months

~3 Months
 Predictive alerts of
failures and
‘anomaly’
situations
 Contribution
insights to guide
investigation

Product(s):
CHALLENGE SOLUTION
CAPEX
avoidance of
$10M Aspen Mtell
Aspen Mtell® Detects Seal Failure in Bead Mill
Reduction in lifecycle maintenance costs by 60%
The seal was typically
replaced as a preventive
measure every 8 batches to
protect against costly
production losses
 Changed periodic seal
replacements from every 8
batches to only when needed
(~ every 25 batches) following
Mtell alert.
 With all bead mills now
operating with higher
availability, the customer is no
longer required to purchase
additional bead mills to
increase capacity.
Multinational
Pharma
Company

Capabilities
 Enables Reliability Engineers to
rapidly build autonomous agents
that protect assets throughout the
plant
 Monitor's assets in the context of
their usage to provide the earliest
possible warning of asset damage
and failure
 Provides prescriptive guidance for
mitigating alerts
 Self-learning, adapting over time
Autonomous Asset
Agents
Aspen Mtell®
Avoids unplanned downtime by identifying the earliest possible indicators of
potential asset damage and failures using machine learning
Sensors “on and around machines”
ensure agents alert root cause of issues

Learns precise specific failure signature and
performs live monitoring, providing early
warnings of recurrences
And provides a time to failure
FAILURE AGENT
Learns baseline normal and performs live
monitoring to expose abnormal operations –
updates as conditions change
ANOMALY AGENT
How Aspen Mtell Provides Early and Precise Warning
Best-in-Class Technology so Operations and Reliability can Focus on their Jobs

Agents Do Things That Others Cannot and Do Not
See things earlier
Months vs. days (more time for investigation and planning)
Analytics to predict when maintenance
is unavoidable
Agents Know How to Stop Machines from Breaking
See things they cannot
To prevent process-induced damage
Analytics to prevent operations activities from
causing equipment damage – “do no harm”
Attach failure to root cause
In EAM system for both O&M guidance
Prescriptive: brings total digital work-scope from
SAP, Maximo, etc. You know what’s wrong and
how to fix it

Aspen Maestro™ for Aspen Mtell
Assist users in
selecting and
preparing data for
Agent-building
Assist users
with feature
engineering tasks
Incorporate domain
knowledge with
machine learning
Users report that identifying, selecting and preparing data
can consume a significant fraction – up to 50% of the time
spent analyzing a problem
Maestro

Product(s):
CHALLENGE SOLUTION
29 days in
advance Aspen Mtell
How do early alerts help?
Freeze Dryer Protection of Lost Batch due to Anomaly
Aspen Mtell® provided additional production capacity
 Historic experience of failure
leading to batch loss
 Batch loss impacts supply chain and
customer satisfaction
 Impact is in lost batches and
resulting capacity constraint
 Mtell was selected due to its ability to
work with:
– Multiple recipes giving variable normal
conditions
– Batch process conditions
 Protection of batch by pausing sequence
which allows stabilization of vacuum
chamber
 Prevents multiple Lost Batches in a
single year
 Increases capacity of batch production
 Adherence to production plan
Multinational
Pharma
Company

Aspen Mtell
Prescriptive Maintenance: Self Contained End-to-End Solution
ONE-CLICK RETRAIN
(Continuous improvement
of Agents)
SCALABILITY
(Transfer
learning)
PRESCRIPTIVE RESPONSES
(Retain and share knowledge incl. FMEA)
MACHINE
LEARNING
PREDEFINED SYSTEM
ADAPTERS
EAM / CMMS
Maintenance Data
Operational Data
PLANT HISTORIAN
Condition Monitoring Data – Process Data – Maintenance Data – Enterprise Data
CONDITION
BASED

Safeguarding Product Supply
1 Sample size: >27,000 pieces of equipment.
Source: McKinsey POBOS benchmarking database
Performance, 1 %
AI has the potential to help pharmaceutical companies significantly increase their equipment efficiency protecting the supply of product to
customers
35
60
20
5
15
12
15
10
10 8
5 5
CURRENT POTENTIAL
100
VALIDATION
EQUIPMENT BREAKDOWN
SPEED LOSSES TO MITIGATE
MECHANICAL ISSUES
CHANGEOVER
SHORT STOPS CAUSED
BY POOR EQUIPMENT
MANAGEMENT
OVERALL
EQUIPMENT
EFFICIENCY
POTENTIAL
IMPROVEMENT
THROUGH USE
OF ARTIFICIAL
INTELLIGENCE
(AI)

The Aspen Mtell Value
Reduced
Maintenance Costs
Time to plan for
interventions
Improved
Workflow
Profitable
Production
Data Readiness– Mtell works with available
data and performs cleaning & analysis
Data Integration– Mtell integrates with
existing Historian, EAM, MES, LIMS systems
Skill Requirements– Mtell works with
existing maintenance & process experts
Minimal deployment time– days/weeks
vs. months/years
Market-leading rapid scalability– ROI
within months – enterprise within year
In the cloud or on premise– solution fits
deployment needs

Product(s):
CHALLENGE SOLUTION ”
“
Millions
in benefits Aspen Mtell
Aspen Mtell® drives value from data and improves security of supply
Improving the robustness of the
supply chain supporting life-
saving medicines
Ensuring security of supply with a
predictive maintenance solution
rolling out to 30 sites- including
Aspen Mtell and AspenTech’s
Manufacturing Execution Systems
platform
Early & accurate warnings, speed & scalability of deployment, and ease of use were key drivers
in choosing Aspen Mtell for Predictive Maintenance. Aspen Mtell effectively predicts factors
causing pharma process disruptions, improving production uptime and avoiding plant
deviations.

Accelerating Digitalization for Pharmaceutical Manufacturing
Scalable Digital
IoT
Architecture
Workforce
Optimization
Predictive &
Prescriptive
Maintenance
Overall
Equipment
Effectiveness
Continuous
Manufacturing
Hybrid
Models
Simulation
& Digital
Twins
Domain Expertise
Data Insights Industrial AI
Machine
Learning
Data
Management
Advanced
Analytics
Digital
Accelerators
Quick
Wins
 Supply Chain Resiliency
 Continuous Improvement
 Operational Excellence
 Sustainability
 Smart sourcing agility
 Security of supply
 Faster time to market
 Reduced environmental impact
 Self-Optimizing Enterprise
Lines of
Business
Industry
Initiatives
Outcomes
On-premise Cloud Hybrid Capability as a Service
Deployment
Options

Value Delivered by Digitalization Initiatives
Digital Twin → API particle size
tuned for optimal
bioavailability
EBRs secure data integrity. 95%
reduction in order preparation
& 50% reduction in review time
Plant Scheduler → 30% shorter
batch length, 20% boost in
throughput
Batch End-point measured with
in-line Raman spectrometer,
$300k lost batches avoided
Predictive Maintenance rapidly
scaled across many sites giving
early accurate warning of failure
Batch-to-continuous process to
improve product purity reduced
levels from 100’s to 20 PMM
Japanese
Pharma Major

AspenTech – Recent Awards and Recognition
Processing Magazine
names Aspen Mtell® as
“Breakthrough Product”
for 2021
Pharma Manufacturing
names AspenTech PAT
Solution winner of 2021
Pharma Innovation
Award
Singapore Business Review
names Aspen Mtell®
winner of 2020 Technology
Excelllence Award for AI
Manufacturing

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