scott sink, senior advisor, the poirier group ben amaba ... · ‘torturing’ the data until it...
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The New Industrial and Systems Engineering:Operational Analytics & Data and Implementation
Sciences
IISE Annual Conference
Orlando 2019
Industry Practitioner Track
12 Nov 2019
Scott Sink, Senior Advisor, The Poirier Group
Ben Amaba, Global CTO, Data Science and AI, IBM
Industrial Mftg.
ISE and IISE for Life—how IISE supports
you for your entire Career…..
CISE
Young
Professionals
IAB
Career Path and TimelineYou can get involved in Societies, Divisions and also ‘Affinity Groups’ like Young Professionals, Industry
Advisory Board and the Council on Industrial and Systems Engineering
Young
Professionals
(early career)
IISE Student
Chapter
CISE
(seasoned
executives,
ISE
thought
leaders)
IAB
(Highly
successful
mid-career
ISE’s)
Professional Chapters are: Alumni Affinity Groups, Local/State/Regional Affinity
Groups, Industry and Practitioner Focused
Performance Excellence
Track—New Orleans 2020
Accelerate Career
Progress and Success
How to Improve Culture
Expand and Extend my Network of
Peers
Altitude on my life and
job and career and have some
Fun
Agile
Operational Analytics
Successfully Navigating
Politics
The Performance Excellence Track is
focused on: Technology (e.g. AI),
Strategy (e.g. shaping Cultures to
Support Lean), Process (e.g. Agile),
People (e.g. how to successfully
navigate politics).
Voice of Member and Customer
led us to this example of our
Programming for the Annual
Conference in New Orleans.
Webinar Line-up
13 June—Chapter #1 Annual Virtual Meeting
9 July—Operational Analytics: ideas on how to sustain visible measurement systems and the process improvement benefits you’ve worked to achieve (Scott Sink)
13 Aug—Virtual Mentoring: Career Choicepoint learnings, lessons, tips from Senior ISE Leaders (David Poirier, President, The Poirier Group; Ron Romano, Sr. Mgr. Business Process Reengineering, Walmart, Canada; Yves Belanger, VP Supply Chain, Wolseley Canada)
27 Aug—The next 7 Habits of Highly Effective Young (ISE) Professionals (Allen Drown, United Airlines; Michael Beardsley, Law Student, Case Western Reserve; Jagjit Singh, Discover)
10 Sept—Winners Presentations from the IISE Outstanding Capstone Sr. Design Projects from 2018-19 (Georgia Tech/Cisco; Ohio State/Abbott Nutrition; Virginia Tech/Eastman Chemical)
1 Oct—Being Successful as a “Covert” ISE (Sean Gionvese, IE Manager, Lockeed Martin)
12 Nov—ISE and Data and Implementation Sciences (Scott Sink and Ben Amaba, CTO, IBM Manufacturing)
3 Dec—The Art and Science of Selling your Ideas to various stakeholder groups in different situations (e.g. Private Equity supported firms) (Brent Miller, West Monroe Partners & David Poirier, CEO, The Poirier Group and President-Elect IISE)
AGENDA
12:00 Scott Kick-off—Framing Up The New ISE and
Operational Analytics and Data & Implementation
Sciences
12:20 Ben Data Sciences and AI
12:40 Q&A with Participants, Dialogue between Scott and Ben
12:55 Close-out Review upcoming Line-up--Scott
6
7
This was a “Seminal” piece of work back in 1990. Clear vision of
what has transpired and evolved the past 30 years, and also still
relevant for what is ahead.
Story Line Key Points for our Webinar today…..
1. Defining Operational Analytics, Data Sciences, Implementation Sciences
2. Frameworks for thinking about it and doing it;
3. Example of this—Digital Transformation in Healthcare
4. Accelerating Benefits Realization—Reducing the Latencies
5. Dialogue/Q&A
Operational Analytics
▪ This is a useful
visual that conveys
much of what we
mean by
Operational
Analytics.
How to build Visible
Measurement/Management Systems in
a way that Decreases Latencies
▪ “Above the line” analyst role
• Extract features based on questions you have to answer by
‘torturing’ the data until it speaks to you and others. Pick right
metrics of interest!!
• Apply curiosity & business acumen to data & analyses – create new
knowledge, insights, ‘aha’s’
• Apply data visualization techniques to aid in telling the right story –
as in life, so in business: the best story wins …Develop the Art
of Great Story Lines and Powerful Visualizations and stay
focused on driving the ‘end game’
Goal!!!
Story Line Key Points for our Webinar today…..
1. Good analytics come from good problem statements, access to the right data, and applying the right techniques
2. Some people have every skill – business acumen, data, technique – to perform a good analysis – but it tends to result in a slow ‘craft’ process
3. Investment in the right data foundation has a positive ROI, as analysts move faster when they trust the data – results in faster results
4. Good data visualizations can tell the right story quickly, because people are predisposed to believe what they see in a chart …
5. There is very positive ROI in getting these decisions right – small analytics teams can wield disproportionate influence on the bottom line
6. Good analytics drive positive action – indeed, in Intel’s supply chain environment, simple/influential beats complex/impotent every time
Adapted from S. Cunningham; Intel Corporation;
2013
Management Systems Model—
depicting latencies
The Business
Processes/Value
Streams
Upstream
Systems
and Inputs:
Suppliers &
customer
orders
Downstream
Systems and
Outputs:
Orders
Fulfilled
Data management
and Operational
Analytics
Data
entry
Data Organization
Leadership &
management team
(wisdom application,
data/facts to information
conversion process)
Data
capture
Information
portrayal
Information
perception/
understanding
/ insights
DecisionsActions
Data Capture
Latencies
Data
Analytics
Latencies
Decision-Making
and Action-
Taking Latencies
The ISE role in Service Systems Engineering:
Digital Transformation in Healthcare
November 2018
Michael Caesar, MBA
Executive Director,
Data & Implementation Science
University Health Network
Data & Implementation ScienceKnowing, Understanding, Changing in a Digital WorldUniversity Health Network - Not for Distribution (Used with Permission-DSS)
DATA-DRIVEN HEALTH ORGANIZATION
Data Supply Drivers Data Demand Drivers
Non-traditional care
environments
• Reduced hospitalization •
Video/tele health • Virtual
and augmented reality •
Continuous Data Streams• Wearable body sensors •
Implantable systems • Point of care
testing • smart sensors/bandages •
nanotechnology •
Rich New Data Sources• Electronic Health Records • Health
Information Exchange (HIE) •
Genomic Information Systems •
BioRepositories • Data lakes •
Natural language processing •
Benchmarking •
Patient Generated• Personal health applications
(apps) • Patient portals • CRM •
Patient engagement portfolios •
Smart Machines• Internet of things •
Intelligent processors •
Machine to machine •
Robotics •
Population health
• Patient stratification •
Disease prevention and health
promotion • Chronic diseases
• Value-based care delivery
models •
Personalized healthcare
• Shared decision making •
Personal ownership of health
record • Engagement &
persuasion hub •
Web and Social media• Online communities •
Public forums •
Predictive medicine
AI enabled care
• Shift from restrictive
gatekeeper to coordinator •
Rapid diagnosis and treatment •
Predictive modelling • Learning
health system •
PEOPLE, PROCESS, &
TECHNOLOGY
Integration of all clinical,
research, education data and
workflows (quality, standards &
contribution)
A Healthcare
organization that is
data-driven
Leading in evidence-
based practice
Enables evidence
generating medicine
Performance Operations• Workflow • Time stamps
• Effort • Investment •
Efficiency & Optimization
• Patient flow • Delivery
• Efficiency • Cost •
Caesar, M (2017)
Resulting in value-
based healthcare
Data & Implementation ScienceKnowing, Understanding, Changing in a Digital WorldUniversity Health Network - Not for Distribution
It is not about adopting digital technologies, it is about changing the way we work in response to
the nature of a digital world
University Health Network - Not for Distribution
DATA & IMPLEMENTATION SCIENCE
Our Data & Implementation Science approach will…
• Create a deliberate, bi-directional connection between data and value
• Link statistical analysis, computer science and business process understanding to drive insight and change
• Enable deep learning across the organization
• Develop data-driven capacity and capabilities
• Reflect our corporate purpose by providing insight into care, discovery and learning
GOVERNANCEDATA STRUCTURE INSIGHT CHANGE VALUE
DATA SCIENCE
ANALYTICS
Caesar, M (2017)
Data & Implementation ScienceKnowing, Understanding, Changing in a Digital WorldUniversity Health Network - Not for Distribution
TEAM: CAPABILITIES
Data Mining Experimentation
Insight-enabled
DATA ENGINEERING
DATA SCIENCE
IMPLEMENTATION SCIENCE
Design, build and manage data infrastructure
Develop insights & builds models through machine
learning & statistical methods
Inform decision-making and drive
process and behavioral change
Explore and
experiment with new
ways of using
algorithms, machine
learning and AI to
support, enhance and
automate decisions
Caesar, M (2017)
Collect and curate
value data while
improving data
accessibility within a
community and
ecosystem of creators
and users of data
Change the way we
provide care, discover
and learn
Identifying and building the skillsets to unlock the value of data benefiting the delivery of
care
Data & Implementation ScienceKnowing, Understanding, Changing in a Digital WorldUniversity Health Network - Not for Distribution
DATA SCIENCE: CAPABILITIES
Applications
Data Engineers
Business Analysts
Machine Learning Experts
Data Modellers
Data Architects
Source: Healthcare Analytic Adoption Model 2016 Health Catalyst
Models
Algorithms
ToolsData Scientists*
PARTNERS
Automation/AI
Operations Research
Analytics, Mining & BI
IT Infrastructure
Capability and capacity in Data Science enabling personalized medicine, predictive
modelling and operational efficiency
* Programatic alignment - including clinical, research and education
Data & Implementation ScienceKnowing, Understanding, Changing in a Digital WorldUniversity Health Network - Not for Distribution
IMPLEMENTATION SCIENCE: CAPABILITIES
Caesar, M (2017)
Identifying and building the skillsets to unlock the value of data benefiting the delivery of
care
Impactful delivery of solutions benefiting the health system
• Portfolio, Program & Project Management
• Lean SixSigma & Quality Improvement
• Agile & Scrum Approaches
• Business Analysis
• Process Engineering
• Change Management
• Benefits Realization & Value Measurement
Data & Implementation ScienceKnowing, Understanding, Changing in a Digital WorldUniversity Health Network - Not for Distribution
BEYOND THE TECHNOLOGICAL ADVANCEMENT
Decision Science
How are we going to
introduce impactful real
time information and
insight to support and
automate decision making
and change behaviour?
How do we balance
investment in governance,
processes & policies to
ensure data is of highest
fidelity and quality - without
creating barriers?
New paradigms of how
work gets done will be
introduced - how do we
surface, assess and
challenge our risk tolerance?
Our collective ability to
become more data-driven
and advance our trust in
automation will be one of
the largest barriers to
overcome
Data Fidelity Risk Tolerance Data Literacy
PATIENT & PROVIDER
Data & Implementation ScienceKnowing, Understanding, Changing in a Digital World
Enabling Digital Transformation in Healthcare
University Health Network - Not for Distribution
AGENDA
12:00 Scott Kick-off—Framing Up The New ISE and
Operational Analytics and Data & Implementation
Sciences
12:20 Ben Data Sciences and AI
12:40 Q&A with Participants, Dialogue between Scott and Ben
12:55 Close-out Review upcoming Line-up--Scott
21
VirtualizationFree services from
reliance on specific
software and
hardware and ensure
flexibility, adaptability
and robustness
Ubiquitous
Technology and
IOTCreate and ongoing
connection in areas as
varied as on-the-spot
service provision and
remote monitoring
Smart Devices
Develop an ecosystem
of apps and cloud
services that utilize
high-performance
devices
Big Data &
Analytics
Deeper insights into
customer behavior,
preferences, pathways
Cloud Computing
Manage huge data
volume in open systems
and provide services on
demand
Robotic Process
AutomationReplace humans in
work processes that
are entirely rule based
Bionic Computing
Interact naturally with
virtual agents, digital
devices and services
Cognitive
ComputingSimulate human
thought processes and
provide intelligent,
virtual assistance
9 Game-Changing Technologies Enabling Service 4.0
22
Adapted from BCG 2018 analysis on service 4.0: https://www.bcg.com/en-us/capabilities/operations/service-4-0-transforming-customer-interactions.aspx
Augmented Reality
Provide the necessary
information when
needed in areas as
varied as manuals,
pricing, alerts
23
3—ISE interventions that change the
system/process and in doing so evoke more
ideal Employee Behaviors
• At the highest level, it’s BPR but there are Solution Elements that are created that reflect the reality of the changes:
• 2-second Lean, Walk and Talks
• Visible Measurement Systems
• Agile/Scrums/Sprints• Value Stream Mapping and
Analytics• Hoshin Kanri• Boot Camps (personal and
professional mastery training)
• Stakeholder value exchange optimization (CRM expanded)
• Standard Work• etc.
Data accelerated the foundation to model, predict and
prescribe actions to expensive challenges. Terms like
software factories, data warehouses, “Lean”, Six Sigma and
agile techniques for data and software lead Industry 4.0 and
the Digital Transformation.
• Furnaces for heat treatment when the power went out, cost $60,000 for
a 20-minute power outage.
• Chemical companies can incur $2,000 per hour in wasted material
inputs. Up to $15,000 an hour.
• Drilling platforms can produce 200,000 barrels of oil each day, which
breaks down to roughly 8,300 barrels per hour. With oil prices hovering
around $60 per barrel, just one hour offline translates to $500,000.
• A mining machine down for 24 hours justifies a brand-new replacement
between $1 million and $1.5 million, which outweighs the cost of not
producing.
Industry 4.0 became the next era of
digitization and speed where Industrial
Engineers began defining the future.
▪ Industry 4.0 optimizes the computerization of Industry 3.0 (Forbes)
▪ The smart factory, also sometimes called “the factory of the future” is the keystone of the
fourth industrial revolution. Indeed, it’s often represented as the aggregate of all the
Industry 4.0 technologies: cyber-physical systems—physical assets connected to digital
twins—the Industrial Internet of Things (IIoT), data analytics, additive manufacturing and
artificial intelligence. (engineering.com)
▪ For business leaders accustomed to traditional linear data and communications, the shift
to real-time access to data and intelligence enabled by Industry 4.0 would
fundamentally transform the way they conduct business. The integration of digital
information from many different sources and locations can drive the physical act of doing
business, in an ongoing cycle. (Deloitte)
▪ Smart Manufacturing (SM)—the business, technology, infrastructure, and workforce
practice of optimizing manufacturing through the use of engineered systems that integrate
operational technologies and information technologies (OT/IT). (CESMII Roadmap)
26
Data as a source of truth, and Data as a strategic asset
provide higher margin points than the industry average.
27
Industrial engineers can transition data to new
business models and transform the impact.
Operational BI and DataWarehousing
Self-ServiceAnalytics
New Business Models
TRANSFORMATION
Val
ue
MODERNIZATIONCOST REDUCTION INSIGHT-DRIVEN
Mostare here
27
90% plan greater investments in data
85% view AI as a strategic priority
AI
Describe or Model -Assume Insight
Predict - Preconceived Prescribe – Action Taken
Cost Efficiency New Products and Services Customer IntimacyThe Discipline of Market Leaders‘ M. Treacy and F. Wiersema
Data as a source of Truth
Data as a strategic asset
28
McKinsey
Global
Institute on
Artificial
Intelligence
29
29
The Challenge to address is Data, Talent and Trust:
DATA TALENT TRUSTThe lifeblood of AI, but
complexity slows progress
60%Are challenge in managing data quality
AI skills are rareand in high demand
62%Are challenge to acquire talent [and build skills]
Skepticism of AI systems & processes
62%Need an approach to AI production readiness
find operationalizing, sustainingand scaling AI challenging
Stuck in Experimentation 51%
Based on 2019 Forrester “Challenges That Hold Firms Back From Achieving AI Aspirations”
30
Scripting, SQL, Python, R, Scala,Data PipelinesBig Data/ Apache Spark
Mathematical BackgroundComputational Science
Industrial and Process ExpertiseDomain Knowledge
Industry 4.0Digital TwinSupply ChainOEE, NPT, RAMS, HSSE
Industrial Engineers can work as Data Scientists combining skills across areas of Expertise
A Digital Professional vary in a combinations of these skills
Mathematics Statistician
ComputerScience
IndustryDomainExpert
Industrial, Systems, and Software Engineers combine their talent.
Critical to AI is the domain, process and human factors
including trust and factors of bias.
Key Skills – Systems Analysis and Design, Operations Research, Human Factors, Work Design, Logistics, Quality, HSSEThe Industrial Professional Engineering License considers the Minimum Standard of Care to protect the public’s interest.
31
Certain domains can be automated with AutoAIAutoAI automates data wrangling and model creation, lessening required skillsIBM can transform two of the three domains, but domain knowledge is still required.
AutoAI
Data Science
Automated ML andData Wrangling
Domain Knowledge
61% of a [ML] build time is spent on ”dreaded” data wrangling *
Less Required Skills with AutoAIRequired Skills
* Data Scientist Report, 2018 * O’Reilly Study, 2018
AppDev
Machine Learning Skills
Data Science
Math & Statistics
Domain Knowledge
Computer Science
31
AutoAITypical AutoML
Transfer learning ✓Neural network search ✓Data preparation ✓ ✓Advanced data refinery ✓Feature engineering ✓ ✓Hyper parameter optimization ✓ ✓One click deployment ✓Explainability and de-biasing ✓AI lifecycle management ✓
The Domain and Social Sciences are difficult to automate, and context becomes critical. People are foundational.
32
NaturalLanguageQuery
Enables advanced text analysis, and allows computers and humans talk seamlessly.
CriticalInsights
Generates systematic, predictive or prescriptive insights through big data processing, to enable critical business decisions to be made in real time.
Artificial Intelligence APIsSpeech to text | Text to speech | Conversation | Discovery | Knowledge Studio | Natural Language Understanding | Natural Language Query
Machine Learning
Enables a system to learn
from data rather than
through explicit
programming. This
progressively improves
performance on a
specific task and it
provides opportunity to
predict the future.
Pattern Tracking
Unlocks hidden value in data to find answers, monitor trends and surface patterns with the world’s most advanced cloud-native insight engine.
ChatbotCollaboration
Quickly builds and deploy chatbots and virtual agents across a variety of channels, including mobile devices, messaging platforms, and even robots.
Empowering an organization and professional to make faster and more accuratedecisions to mitigate disruptions and optimize supply chain operations
More than just data : Intelligence (AI) for the organization
33
Resources are being spent on key personnel that have
an understanding on the application of technology
and process.
Data Monetization and Analytics
Blockchain Other emerging tech (e.g., RPA)
Internet of Things
Artificial Intelligence
Embeds sophisticated
sensors and chips in
physical objects,
enabling real-time
monitoring and
understanding
Establishes an
immutable shared
ledger, allowing any
participant in the
network to see all
records of transactions
Robotics Process
Automation leverages
algorithms to automate
routine tasks,
accelerating time to
value and reducing
human error
Develops the ability of a
computer or robot to
perform tasks or actions
commonly associated
with intelligent beings
Collects, aggregates,
and derives actionable
insights from data to
create and capture new
value
2 14 5 3
Average salary of
$167,325 a year. Average salary of
$120,280 a year Average salary of
$123,936 a year.
Average salary of
$165,000 a year. Average salary of
$122,000 a year.
34
Industrial Engineering skills are sought out in China, US , UK, Germany and France where Industry 4.0 and Digital Twin projects could use data to optimize service levels, variability,
workflow, inventory, machinery, personnel and economic opportunities.
12/11/2019
What Questions do
you have?
What are your takeaways?
Any Aha Moments for you?
Anything you’d add?
What topics would you like to see us zero in on in upcoming Webinars
in 2020?See you at the Annual
IISE Annual Conference in New Orleans?
Dialogue We’d Like to Spark: Please use the Go2Webinar “ask question” Function and we’ll get to as many as we can
Webinar Line-up
13 June—Chapter #1 Annual Virtual Meeting
9 July—Operational Analytics: ideas on how to sustain visible measurement systems and the process improvement benefits you’ve worked to achieve (Scott Sink)
13 Aug—Virtual Mentoring: Career Choicepoint learnings, lessons, tips from Senior ISE Leaders (David Poirier, President, The Poirier Group; Ron Romano, Sr. Mgr. Business Process Reengineering, Walmart, Canada; Yves Belanger, VP Supply Chain, Wolseley Canada)
27 Aug—The next 7 Habits of Highly Effective Young (ISE) Professionals (Allen Drown, United Airlines; Michael Beardsley, Law Student, Case Western Reserve; Jagjit Singh, Discover)
10 Sept—Winners Presentations from the IISE Outstanding Capstone Sr. Design Projects from 2018-19 (Georgia Tech/Cisco; Ohio State/Abbott Nutrition; Virginia Tech/Eastman Chemical)
1 Oct—Being Successful as a “Covert” ISE (Sean Gionvese, IE Manager, Lockeed Martin)
12 Nov—ISE and Data and Implementation Sciences (Scott Sink and Ben Amaba, CTO, IBM Manufacturing)
3 Dec—The Art and Science of Selling your Ideas to various stakeholder groups in different situations (e.g. Private Equity supported firms) (Brent Miller, West Monroe Partners & David Poirier, CEO, The Poirier Group and President-Elect IISE)
2020 Chapter #1 Webinar/Cutting Edge ISE Innovations Emerging Line-up
The 2020 emerging line-up of Topics for Chapter #1’s Webinars & “Cutting Edge” Information Calls:
Still in Concept Design Stage right now but here are the topics that are on the drawing board, let us know what you think. [email protected]
Empowering and Engaging Employees to accelerate Performance Improvement
Lean Simulations on Steroids, Tom Duval, ISE at Auburn University
Deploying ISE and Lean at Chick-fil-A, David Reid (invited)
Navigating and Managing Organizational/Corporate Politics (will be an instant classic from our New Orleans Conference, the Performance Excellence Track) Sue Davis, GM; Chris Kelling, John Deere;
Creating Cultures to Support Operational Excellence (will be an instant classic from New Orleans Annual Conference) SreekanthRamakrishnan, IBM; David Poirier, The Poirier Group; Scott Sink, OSU
How to become more Skillful at Operational Analytics (instant classic from New Orleans) Scott Sink, The Poirier Group; Matheus Scuta, Ford;
How to Create Work and Life Balance in a 24/7, Hyper-Connected World (instant classic from New Orleans) Jessica Grela, E&Y; Jared Frederici, The Poirier Group;
Personal and Professional Mastery: Becoming a Change Master