ieom 2016 keynote dr. anwar ali · ieom 2016 keynote - anwar ali 14 descriptive prescriptive...
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My Background Studied engineering and worked as an engineer
Bachelor in Mechanical, major in Industrial Engineering
Held various engineering positions including process, machine vision, equipment development, factory IE, systems IE
27 years in American multinational companies (1988-2015) 2 years at Texas Instruments KL
25 years at Intel Penang & Kulim, including 2 years in Arizona
Created in-house Operations Research group in 2002 Have done simulation, math optimization, and the relevant
data integration to enable simulation and optimization
Completed 2 post graduate degrees while working full time M.Sc. in Decision Science in 2005
Doctor in Engineering (Eng Biz Mgt) in 2014
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Presentation Overview Share my “soul-searching” after exiting comfortable
working life in American multinational companies
What is unique that I can offer given the current business and technological landscapes
How Industrial Engineers (including myself) should adapt to the present environment
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The Forces Driving Our Future Digital future
Entrepreneurship rising
Global marketplace
Urban world
Resourceful planet
Health reimagined
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Ernest & Young Megatrends 2015
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The Forces Driving Our Future Digital future
Convergence of social, mobile, cloud, big data
Growing demand for anytime anywhere access to information
Entrepreneurship rising
Technology enabling machines and software to substitute for humans
High-impact entrepreneurs are building innovative and scalable enterprises
Many new enterprises are digital from birth with young faces
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Ernest & Young Megatrends 2015
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The Forces Driving Our Future Global marketplace
Innovation will increasingly take place in rapid-growth markets
War for talent; greater workforce diversity providing competitive advantage
Urban world
More cities across the globe
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Ernest & Young Megatrends 2015
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The Forces Driving Our Future Resourceful planet
Increasing global demand for natural resources
Growing concern over environmental degradation
Health reimagined
Increasing cost pressure require more sustainable approach
Explosion in big data and mobile health technologies
From delivery of health care to management of health
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Ernest & Young Megatrends 2015
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Digital Future Technology is also changing the ways people work, and
is increasingly enabling machines and software to substitute for humans. Enterprises and individuals who can seize the opportunities offered by digital advances stand to gain significantly, while those who cannot may lose everything
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Ernest & Young Megatrends 2015
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Anytime anywhere access to information. Machines and software substitute humans.
How IEs should adapt?
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Legacy IE Jobs Eliminate waste and improve efficiency
Time, money, materials, man-hours, machine time, etc.
Continuous productivity and quality improvement
Do more safely and faster with less resources
Capacity planning, line balancing and layout
What-if analysis
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Contemporary IE Jobs Eliminate waste and improve efficiency
Time, money, materials, man-hours, machine time, etc.
Continuous productivity and quality improvement
Do more safely and faster with less resources
Capacity planning, line balancing and layout
What-if analysis
Simulation modelling
Mathematical optimization
Analytics?
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Today’s Technology Buzzwords
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Big Data Data Visualization
Data Scientist
Business Intelligence
Analytics
Internet of Things
Cloud
Apps
Wearable
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Big Data and Traditional Analytics
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big data @ work, Thomas H. Davenport, 2014
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Analytics Landscape
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Descriptive
Prescriptive
Predictive
Degree of Complexity
Com
petitive A
dvanta
ge
Standard Reporting
Ad hoc reporting
Query/drill down
Alerts
Simulation
Forecasting
Predictive modeling
Optimization
What exactly is the problem?
What will happen next if ?
What if these trends continue?
What could happen…. ?
What actions are needed?
How many, how often, where?
What happened?
Stochastic Optimization
How can we achieve the best outcome?
How can we achieve the best outcome
including the effects of variability?
Source: IBM, Based on: Competing on Analytics,
Davenport and Harris, 2007
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Analytics Descriptive analytics (what has occurred)
The simplest class of analytics, condense big data into smaller, more useful nuggets of information
e.g. counts, likes, posts, views, sales, finance
Predictive analytics (what will occur)
Use available data to predict data we don’t have using variety of statistical, modeling, data mining, and machine learning techniques
Prescriptive analytics (what should occur)
Recommend one or more courses of action and showing the likely outcome of each decision so that the business decision-maker can take this information and act
Adapted from Information Week, definitions by Dr Michael Wu http://www.informationweek.com/big-data/big-data-analytics/big-data-analytics-descriptive-vs-predictive-vs-prescriptive/d/d-id/1113279
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Analytics Landscape
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Descriptive
Prescriptive
Predictive
Degree of Complexity
Com
petitive A
dvanta
ge
Standard Reporting
Ad hoc reporting
Query/drill down
Alerts
Simulation
Forecasting
Predictive modeling
Optimization
What exactly is the problem?
What will happen next if ?
What if these trends continue?
What could happen…. ?
What actions are needed?
How many, how often, where?
What happened?
Stochastic Optimization
How can we achieve the best outcome?
How can we achieve the best outcome
including the effects of variability?
Adapted from: IBM, Based on: Competing on Analytics,
Davenport and Harris, 2007
Operations Research
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What is Operations Research? O.R. is the discipline of applying advanced analytical
methods to help make better decisions
Also called Management Science or Decision Science, O.R. is the science of Decision-Making
Employing techniques from mathematical sciences, O.R. arrives at optimal or near-optimal solutions to complex decision-making problems
Determine the maximum (e.g. profit, performance, or yield) or minimum (e.g. loss, risk, or cost)
O.R. is not new as it started during World War II. But now it is in the top half of analytics!
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Business Intelligence Framework
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Back in Business, by Ronald K. Klimberg and Virginia Miori, OR/MS Today, Vol 37, No 5, October 2010, [http://www.informs.org/ORMS-Today/Public-Articles/October-Volume-37-Number-5/Back-in-Business]
OR/MS = Operations Research/ Management Science
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O.R. Leading Edge Techniques Simulation
Giving you the ability to try out approaches and test ideas for improvement
Optimization
Narrowing your choices to the very best where there are virtually innumerable feasible options and comparing them is difficult
Probability and statistics
Helping you measure risk, mine data to find valuable connections and insights, test conclusions, and make reliable forecasts
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O.R. Leading Edge Techniques Simulation (predictive)
Giving you the ability to try out approaches and test ideas for improvement
Optimization (prescriptive)
Narrowing your choices to the very best where there are virtually innumerable feasible options and comparing them is difficult
Probability and statistics (predictive)
Helping you measure risk, mine data to find valuable connections and insights, test conclusions, and make reliable forecasts
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Three Eras of Analytics
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big data @ work, Thomas H. Davenport, 2014
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Analytics Maturity (Gartner)
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Analytics Maturity (SAP)
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Key Messages to IEs Seize the opportunities offered by digital advances
Anytime anywhere access to information
Machines and software substitute humans
Be part of analytics initiatives
Optimization is at the top of Analytics
IEs are supposed to be the experts in Optimization
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Background: A company has embarked on analytics journey
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Scenario A A tech company headquartered in USA has invested in
data collection capability for its manufacturing equipment worldwide. In initial stage, analysis requires database queries and data transformation
The management of an offshore manufacturing facility feels the need to add equipment due to unable to fulfill all orders, hence CPA review process. They need to present to the HQ
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Scenario A The senior VP of manufacturing at HQ asked his
technical assistant to analyze equipment performance data prior to the CPA review
They discovered too much downtime with the equipment in the CPA request. The CPA request was rejected and the offshore factory was asked to reduce its equipment downtime
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Scenario B A tech company headquartered in USA has invested in
data collection capability for its manufacturing equipment worldwide. In initial stage, analysis requires database queries and data transformation
The engineers at HQ analyzed the data and informed the offshore factories on their products and equipment performance and the required actions to be taken. The offshore factory then realized that HQ knows more about their factory
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Scenarios Summary If useful data is available, analyze it to set directions on
which areas to work on. Otherwise, someone else will do it and provide leadership and technical direction
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Upskill Knowledge Send existing engineers to part-time graduate studies
in O.R., Computer Science, Statistics or Mathematics
If the above is not feasible, send existing engineers to relevant short courses and apply what have been learnt
When hiring new IEs, hire diverse candidates with bachelor degree in engineering and master degree in O.R., Computer Science, Statistics or Mathematics
Mixed-mode master degrees preferred
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Bad Excuses I can’t do computer programming
I can’t do data query, transformation and analysis
I have learnt but forgotten how to do linear programming
Doing discrete event simulation is difficult
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3 Classes of Business Value Cost reductions
Decision improvements
Improvements in products and services
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3 Classes of Business Value by IE Cost reductions
Capital dollars
Decision improvements
What-if analyses done by IEs
Production planning decisions
Capacity planning
Supply-chain decisions
Improvements in products and services
Analyze cycle time, utilization, inventory, yield, etc.
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Data-driven Capital Decisions IEs are involved in capital equipment decisions
In justifying new capital equipment purchase, management should see actual utilization data and compare it to the goal used in capacity planning
We deal with ‘noisy’ demand forecast data. If we reduce the time needed to procure capital equipment, we can make better decision later
There is no point planning too far out, except for strategic decisions such as new facility at greenfield site
IE analysis time must be ‘instantaneous’ and results not dependent on who is doing it
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Production Planning For complex factories, real-time live full-factory
simulation should be used as the engine for short-term product planning
Lot release decision
Equipment dedication and conversion strategy
Non-production activities (PM, training, etc.)
Used successfully in semiconductor wafer fabs
Deep expertise required in manufacturing execution, data integration and simulation customization
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Supply Chain Optimization We have computing power and capable solvers which
can do what is not possible previously
16-bit has 216 (65536) 64KB address space
32-bit has 232 (4,294,967,296) 4GB address space
Windows 32-bit memory limit for each process is 2GB
64-bit has 264 (10246) 16 exabyte address space
Windows 64-bit has user-mode address space of 8TB
Software which capitalizes on multi-core, multi-thread can solve large problems faster
Can either buy the capability or develop in-house
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Summary Seize the opportunities offered by digital advances to
gain significantly
Upskill knowledge given the current business and technological landscapes
Exploit opportunities in optimization, be part of analytics initiatives, and add business values
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https://www.linkedin.com/in/anwar-ali-mohamed-57b2bb54
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