council on industrial chapter # 1 the ohio state university€¦ · webinar #3: best in class ilss...

Session LeadersD. Scott Sink, Ph.D., P.E., Director, Integrated LeanSigma Certification Program, ISE at OSU

Team Lead Chapter #1

Jared Frederici, MBB and Senior Consultant, The Poirier Group and

Great Lakes Region Vice President for IISE

Vignesh Gundesha, Data Analyst, OSU Comprehensive Cancer Center

Matheus Scuta, Manufacturing Analytics, Ford Global Data, Insight and Analytics.

Team Leader The Michigan Chapter of IISE

Council on Industrial

and Systems

Engineering

The New Industrial (and Systems) Engineering:

Operational Analytics to Support Continuous Improvement

Smart Analytics in the Context of

Industry and Service 4.0

Chapter # 1

http://www.osu.edu/

Agenda

12:00 pm Scott Tee-up

12:10 Jared

12:25 Vignesh

12:40 Matheus

12:55 Scott

1:00 pm Adjourn

This Webinar is provided in partnership

with the following IISE ‘affinity groups’:

• The Michigan and Louisville Chapters

• The Industrial Advisory Board

• The Young Professionals Group

• The Data Analytics and Information

Systems Division

• The Council on Industrial and

Systems Engineering

• And, the Industry Practitioner Track

Program Committee for the Annual

IISE Conference in Orlando in May

2019.

This Series can be accessed at:

https://www.iise.org/details.aspx?id=46729

Webinar #1: Foundations 7 Dec 2017 (and GLR Conference)

Share the Framework, the Models, the Abstractions, the Principles

Management Systems Model

Intel “Triangle” Model

Webinar #2: Foundational Data Role--Measurement and Analysis

Planning March 2018Measurement Planning using Value Stream Maps, Data Models derive from refining the

Management System Model, The Data Management Role of ISE’s in Process Improvement

Projects

Webinar #3: Best in Class ILSS Project Final TG’s April 2018Showcase best in class projects, shine spotlight on Op Analytics

Webinar #4: Decision Support Role—M&A Execution June 2018Feature and Knowledge Extraction, Creating Chartbooks and VSM’s, supporting the

evaluation phase of DMAIC projects and then also the Control Stage.

Webinar #5: Putting it all together 26 July 2018

Revisiting the Management Systems Model with Case Examples

https://www.iise.org/details.aspx?id=46729

Upcoming Webinars from Chapter #1

and CISE and IAB and Young

Professionals and many IISE Divisions

Upcoming Webinars

“The most valuable personal and professional development investment I’ve made in myself to this point in my career. I had no idea these conferences were such a great opportunity. I had so much fun, learned so much, met so many new peers and won’t miss another IISE conference. I highly recommend ISE Students, ISE Young Professionals, as well as Leaders and Managers in charge of the “ISE” Function take full advantage of these annual opportunities.”

FIRST TIME ATTENDEE, ORLANDO 2018

“Provide an efficient, cost effective way for me to combine unwinding, getting altitude, networking and developing myself personally and professionally.”

VOICE OF MEMBER/CUSTOMER

“We listened and we have

created the program below

and have wrapped around the

development sessions some

networking and fun things to

round out your experience. You

voiced, we did something

about it.”

VOICE OF MEMBER/CUSTOMER

’

Get Answersto Your

Questions

Meet NewPeople

LearnMore

Connect with Community

Get Inspiredby Speakers

Have

Fun

Visit with Old Friends

Specifically Design for Young Professionals, Students, and Career Practitioners and ISE Managers

and Leaders

The Next Seven Habits of Highly Success Young

Professionals

Highly Successful ISE Young Professionals come together to summarize what comes after you master the Seven Habits of Highly Effective People. They share and coach you on their condensed version of the habits that help accelerate career progress and success (make more money faster, create more value) for Young Professionals.

Personal and Professional Mastery – Bootcamp 101

Two Senior Career ISE’s from the Council on Industrial and Systems Engineering, will expose you to Bootcamp 101. Working with concepts like intentionality, altitude is a choice, at-cause/at-effect, Trust/Speed of Trust, Feedback, you will experience and understand the basics of becoming a better change leader and manager.

Introduction to Industry 4.0

There is a Tsunami of Technology Innovation headed towards Manufacturing firms. Understanding, at a high level, what this is all about, from a systems perspective and how ISE’s will be impacted and can contribute to firm success with the transformation is essential.

Supply Chain Optimization – The Physical Internet

Jim Tompkins, David Poirier and Benoit Montreuil from the

Georgia Tech Physical Internet Lab will discuss the 4th Industrial Revolution from the End2End Supply Chain perspective.

Healthcare 4.0

Examples of Thought and Doer Leaders in Healthcare discuss how the fourth industrial revolution is impacting data and implementation sciences, Operational Excellence in Healthcare.

Award Finalists for the IISE/PSU Outstanding Innovation in

Service Systems Engineering

Listen to best in class organizations discuss innovations in Service Systems Engineering and Reengineering. Unique

opportunity to benchmark to best in class.

, ,

Operational Analytics

How to systematically develop your ability to do better

measurement, analysis and evaluation work to support more rapid

process improvement.

Solving Business Problems using Relational Data Bases

Practical and Pragmatic ways to develop and use improved data

bases to support operational analytics.

What Managers Look for When Promoting ISE’sIndustry Advisory Board members share tips and learnings.

Making Magic: How Disney ISE’s Bring New Experiences

to LifeDisney ISE’s share tools, principles, methods they apply to continue to

rapidly improve experiences of their quests.

Questions?How We’ll

HandlePlease write your question in the webinar

question web form. We will address as

many as we can at the end of the webinar

and send and email with follow up’s to

attendees for those not able to be

responded to.

Disclaimer, this is not an example of a good ‘meeting deck’, it’s a training deck. We’ve got too many slides

and the slides are ‘abstractions’ that are intended to be viewed as a ‘gestalt’ point in a series of points, and

this doesn’t stand alone without the trainer. We’ve decided to do this intentionally so that we have examples

for you ready, in the right spots if we feel we need them to make a point. If we don’t, then we’ll skip slides—

goal is to make our points not show all the slides nor discuss every slide in detail.

Agenda


12:10 Jared

12:25 Vignesh

12:40 Matheus

12:55 Scott

1:00 pm Adjourn

Provoke timely and effective decisions

and actions (shorten ‘latency’)

▪ “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!!!

http://www.osu.edu/

• the current state process in many large organizations splits data and analytics

• Data are stored in a common place, and are trusted and available

• “Above the line” analyst role

1. What are the fundamental Questions that have to be answered?

2. What data elements do those questions require?

3. Organize the data and facts and then export to your analytics app.

4. Extract features from data through integration and manipulation of data that move us closer to answers. (torture the data)

5. Apply business acumen to data & analyses – create new knowledge

6. Apply data visualization techniques to aid in telling the right story – as in life, so in business: the best story wins …

• Foundational data role

1. What do we need to know in order to achieve the performance objectives—what are the questions we have to answer?

2. Architect/Create the Measurement and Analytics Plan (Data Model included)

3. Select and gather data from many sources, preferably through automated extract, transfer, & load (ET&L) process

4. Create (observation, interviews, etc.) any data elements that don’t exist (ISE Measurement)

5. Assure data are cleaned & ready for analysts or you to use – data quality monitors

6. Assure data are integrated & can be joined with other data – think LEGOs

7. Assure data storage is high reliability & user-friendly – SSAS cubes, databases

8. Integration and organization of foundational data elements as well as derivative data and other key metrics of interest

• Most ISE/ILSS Process Improvement Projects require that the ISE/Belt do both roles, certification requires that

• Data is almost never stored in a common place and are not trusted nor available

The Framework we have been presenting for the Webinar Series

(follow the Yellow Brick Road)

Case Study: Why starting w/ the top

of the pyramid can be deceiving

and lead to rework…

Cleansed over

350 metrics

and created an

operational

metric

database

In many cases,

different

departments

were

calculating

metrics

differently,

wrong

decisions were

being made

1. Data Gathering – Challenges,

Advice, Examples

▪ My Data Doesn’t Exist

• Go get it!

• Determine data type needed

• Determine how much of it is needed

and work backwards:

• http://www.raosoft.com/samplesize.html

• Also think about what future tools

you may need it for (Minitab min and

max for normality example)

• Remember Power value targets (.8)

• Remember practical vs. statistical

significance and ROI of data

▪ My Data Does Exist

• May require different skills (ODBC,

SQL, VBA)

• IT tickets can take 6 months!

http://www.raosoft.com/samplesize.html

1. Data Gathering – Challenges,

Advice, Examples

▪ Measurement Plan is Everything!

• Ensure operational definitions are clear not only

with you, but your stakeholders

• Think with the end in mind, holistically, not just

about measurements you need right now

• What other measurements ‘could’ you get while

undergoing manual sampling or installing

measurement system? Could you reduce rework

later by adding elements?

What Tests Will Be Ran?

2. Data Selection – Challenges,

Advice, Examples

• So now you’ve got data, either from

your own sampling or from IT, etc.

• Look for systems in your data!

• Are you observing variation within

processes, or separate systems

(with perhaps different

measurement systems….?)

• Leverage rational subgroups!

3. Data Storage – Challenges,

Advice, Examples

▪ Data Storage

• How much did you obtain?

• Of what type is the data?

• Relational database or object

oriented? (SQL, noSQL)

• How secure does it need to be? Is

there any confidential information?

• How fast do you need to recall it?

• Will you have multiple users in the

system to pull data?

• Will you be doing analysis

alongside of the data?

• What if you get so much that you

exhaust excel and Access's

limitations?

• Think with the end in mind…!

Basic Storage and Analysis

▪ Microsoft Excel

▪ Microsoft Access

▪ Microsoft PowerPoint

▪ Visual Basic / VB.net

▪ Arena

Advanced Storage and Analysis

Existing Skills +

Data Analytics and Visualization

• SQL

• R

• SAS

Programming

• Java

• Python

Big Data Technologies

• Hadoop

• MapReduce

• Pig

• Tableau

• D3

• Ruby

• CPLEX

• Hive

• Hbase

• Aster

3. Data Storage – Challenges,

Advice, Examples

You may need to move from “Basic” to “Advanced”

4. Data Cleansing – Challenges,

Advice, Examples

▪ Data Cleansing

• Remember VersaCold example

• Sometime it’s easier when you collect

your own data…

• Tie source data interactions and

calculations to your final BI interface

• Often times issues are in calculations or

“between” interface work (example on

rounding issues)

4. Data Cleansing – Real Example

– KPI Tree

5. Data Integration – Challenges,

Advice, Examples

▪ Data Integration

• To begin to tell the entire story, you’ll

likely need to bring in data from different

sources

• Some could be from your own

measurement system, some from IT,

some extracts or “pulls”, some

automated via FTP, etc.

Note that you

typically need to

leverage some

data warehouse

“type” of interface

to handle this,

either yourself, or

within your

infrastruture

5. Data Integration –

Challenges, Advice,

Examples

5. Data Integration – Challenges,

Advice, Examples

Data CubeData Model

Organized Data in Pivot View

• Once you’ve isolated the sources, and

have brought them into a “data

warehouse” type of application, create a

data model

• Leverage “cubes” and “hypercubes” in

your data model for efficient processing

5. Data Integration – Case

Study (Part 1)

5. Data Integration – Case

Study (Part 2)

Agenda


12:10 Jared

12:25 Vignesh

12:40 Matheus

12:55 Scott

1:00 pm Adjourn

Analytics in Healthcare

James Cancer Hospital and Solove Research Institute

Data Analytics Specialist

Vignesh Gundesha

About OSUWMC & The James

• OSUWMC network 7 hospitals

• University Hospital, University Hospital East,

James Cancer Hospital, Ross Heart

Hospital, Brain and Spine Hospital,

Harding Hospital, Dodd Hall Rehab

• Beds: 1506

• Revenue [FY 18]: $3.7 Billion

• ED visits [FY 18]: 130,916

• Surgeries [FY 18]: 44,888

• The James Cancer Hospital

• 1 of 49 NCI - designated Comprehensive

Cancer Centers

• Surgeries [FY 18]: 10,759

• Robotics Surgery Program

• 4 DaVinci Xi’s

• 2 DaVinci Si’s

• 1 Training Robot

Slides, Information, Data are property of The Ohio State University James Cancer Hospital and are not for Distribution

Slides, Information, Data - are property of The Ohio State University James Cancer Hospital and are not for Distribution

• Multiple Hospitals catering to different specialties and services

• Government organization

• Teaching Hospital

• Robotics Program

• Capturing/Warehousing data electronically

• Utilizing data and transforming it into actionable information

About OSUWMC & The James


Analytics at the James Cancer Hospital

Quantified Decision Making [Data Driven]

Analytics

Warehousing Data / QA

Strategy• Analyst

• Strategy

• Why are we doing this?

• What is it going to help us accomplish?

• At the end of it all, will it matter? Or, are

we expending more resources and time

than the benefit gained ?

• Warehousing/QA

• Are we capturing what we need?

• Is it being captured correctly [GIGO]?

• How are we capturing it?

• Analytics

• Why is this happening?

• What variables are important?

• How am I/we wrong?

• What am I/we missing?

• What can we do about the analysis?

• How quickly can we react?

• Can we be proactive?


Case: Automated Transport Service Drop

Essentially a Pareto Chart• Where should we focus our time and

efforts to get the most value ?

• Do we even have the labor/manpower

to get it done ?

Q: Can we be clean and sterile at

6 a.m. everyday ?



OR [4th floor]

Sterile Supply [Basement]

Dedicated

Elevator

for ATS



1p

.m.

2p

.m.



Q: What is happening upstream?

Q: What is causing a bottle neck?

Q: Are we staffed according to

workflow/load ?

Context of Industrial Engineering/ ISE and Analytics

• Cause and Effect relationships

• If we have the resources to do it

and we still can’t get it done, what

is posing a challenge ?

• 5 Why’s?

• Pareto Principle

• Are we spending time and

effort on the right things?

• Do we have the resources to

do it ?



Case: Robotics ProgramQ: Is the Data even right ? GIGO/QA


Case: Robotics ProgramQ: Do we get rid of 1/2 robot(s) ? Is this temporary or is this a shift ?

Q: Are counts a valid metric ?


Case: Robotics ProgramQ: What happens when the program grows ? How do we deal with it proactively ?

Q: EOQ adjustments, in the context of the institutions time to respond?


Context of Industrial Engineering/ ISE and Analytics

• Measurement System Analysis

[Lean – Six Sigma]

• Does it need to be fixed ?

Process Thinking

• Are we looking at our measures

the right way ?

• What key aspects/variables are

we overlooking ?

• Can we setup a sustainable

process ?

• EOQ – Toyota Production System

[Lean – Six Sigma]


Case: Surgical Times and Case Volume

Q: Can we get better at surgical case time estimation to optimize

time, without causing patient safety issues ?

Q: Can that help us add more case(s) ?

Q: If we cannot squeeze/add more cases. Can we estimate, with fair

accuracy and precision, where we will be at the end of the month

with respect to the budget ? To help proactively manage and attain

budget numbers.

Q: How quickly or at the earliest can we get that information so that

valuable actions can be taken ? [Latency]



Do weather conditions/ temperatures have an effect on case volumes ?

Scraping and cataloging airport temperature readings.



How does surgeon inflow and outflow effect volume ?

Do we need to keep a track of this ? Is it sustainable ?

Does it affect latency ?



• Building blocks for the model

• Understanding and testing all

possible variables

• Am I accounting for everything

reasonably or am I leaving out

key variables

• Trying to keep the model:

• Simple/Less complex

• Easy for a lay person to

enter variable quantities

without complex definitions

• Keeping latency as low as

possible.

Variable input →Computation

→ Output → Decision Making



1000900800700

240

220

200

180

160

Case Volume

5D

C

Marginal Plot of 5DC vs Case Volume

1000900800700

320

300

280

260

240

Case Volume

7D

C


1000900800700

480

440

400

360

320

Case Volume

10

DC


Thinking one step ahead is important as an analyst.

We need an estimate at t = 5 because next week is already

booked out and there are usually only 20 business days. Which

gives us weeks 3 & 4 to take action and be proactive.



Model performance

The yellow dots should be as

close or within the red ring for

the model to be accurate and

precise.

As we can see in very rare

instances do the dots & rings

deviate significantly from each

other.

An alternative way to look at

the graph above.

The red bar compares how far

the estimate was from reality.

The blue bar compares how

far the budget was from reality.

The smaller the bars [closer to

the ‘0’ line] the better the

model or the budget predicts

case volume.



Warehousing Data / QA

Strategy Analytics

Quantified Decision Making [Data

Driven]

1000900800700

240

220

200

180

160

Case Volume

5D

C


Agenda


12:10 Jared

12:25 Vignesh

12:40 Matheus

12:55 Scott

1:00 pm Adjourn

Ford Motor Company & Analytics

Matheus Scuta02/26/2018

54

Agenda

• About me

• How is analytics changing manufacturing?– Past

– Current

– Future

• What is Ford’s Analytics Vision?– Ford’s structure

– GDI&A Overview

– Analytic Impact

– Types of Projects

• How to Prepare/Adapt? – What can you do?

55

About Me

Global Manufacturing Analytics ScientistFord Motor Company+1 (614) [email protected]

Matheus Scuta

About Me:

• The Ohio State University Class of 2017

• Hometown – Rio de Janeiro, Brazil

• Current Location – Detroit, MI

• Fun Fact: I have lived in 4 different countries (Brazil, USA, Nigeria and Colombia)

“Success is no accident. It is hard work, perseverance, learning, studying, sacrifice and most of all, love of what you are doing or learning to do.” - Pele

Most Fun

As internal analytics consultants, we get to be a part of projects that range across Ford’s entire Value Stream. This makes the entire experience very fun as you learn many new

things every day and you get to help the entire business across the globe with your solutions.

Career

• Global Manufacturing Analytics – Ford Motor Company• Jan 17 – Current

• Lean Six Sigma Consultant – Abbott Nutrition• Jan 16 – Jan17

56

Agenda

• About me


– Current

– Future


– GDI&A Overview

– Analytic Impact



57

How is Analytics changing Manufacturing?

Industry 4.0 – HighlightsAbility to collect, analyze and act on Big Data yielding a higher quality product at a lower operating expense & interconnection across all databases

VS

Getting the right data for better decision, not necessary ALL the data

• Shift from 90% Human & 10% Machine -> 30% Human & 70% Machine• Some technologies include: IOT, Blockchain, AI and ML.

Advantages• Increase Productivity, Revenue and Profitability• Manufacturing Process Optimization• Traceability

58

Agenda

• About me


– Current

– Future


– GDI&A Overview

– Analytic Impact



59

What is Ford’s Analytics Vision?Ford’s Structure

PD & Purchasing

Mobility

Manufacturing

Finance

IT

GDI&A

Marketing Sales & Service

60

What is Ford’s Analytics Vision?GDI&A Overview

900+ DATA SCIENTISTS

3.2K+ CITIZEN DATA SCIENTISTS

Fun Stats:Hadoop• RAM -> 90+TB• Usable Storage -> 7+ PB• CPU Cores -> 7500+

61

What is Ford’s Analytics Vision?Analytic Impact

Vehicle Life

Cost • $$$$ savings• Cost avoidance

Quality

Maintenance

Delivery

Safety

Environment

• Increase quality• Lower defects

• Preventative/Predict

• Scheduled

• Automated• Just in Sequence

• Worker Safety• Customer Safety

• Energy Optimization

• Emissions

Business Impact (Metrics)

62

What is Ford’s Analytics Vision?Analytic Impact on Manufacturing

Material Logistics

Plant Production

Sequencing and Scheduling

Freight and Customs

• Complexity / Batching

• Route Optimization• Material Flow• Customs, Duties and

TariffsPlant Floor

• Bottleneck Analysis• Preventive

Maintenance• Plant Floor Data

Visualization• Quality Tie Back To

Stations

Scheduling

• Vehicle Sequencing• Labor Optimization• Batch Scheduling• Economic Order

Quantities (EOQs)

Industry 4.0 in Plant

63


Predictive and Prescriptive

Descriptive – Look backwards (historical)

Predictive – What will likely happen next?

Prescriptive – What should you do?

Warranty Data

Customer Vehicle Data

Scheduling

Labor Data

Maintenance

Supplier Data

Repair Data

Quality

Production

Data

64


Behavioral & Advanced

Optimal Business Processes = Human interaction (Customers, Employees, et

al.)

How will the prescriptive/predictive insights drive human behavior?

Warranty Data

Customer Vehicle Data

Scheduling

Labor Data

Maintenance

Supplier Data

Repair Data

Quality

Production Data

Human Factor

Let’s see some projects..

65

What is Ford’s Analytics Vision?Types of Projects - EOL

Key Questions to Answer

• Can we advise repairmen on what is the best solution to repair vehicle?

• Can we tie back repairs to individual assembly stations responsible for errors?

• Can we generate real-time feedback to stations, allowing process owners to take corrective measures before overall production quality decreases? And compare performance between shifts?

Vehicle Testing

Repair

Dealer

Quality

Data

Testing

Data

Production Data

66

What is Ford’s Analytics Vision?Types of Projects - EOL

To Find Out

• Can we advise repairmen on what is the best solution to repair vehicle?

• Can we tie back repairs to individual assembly stations responsible for errors?

• Can we generate real-time feedback to stations, allowing process owners to take corrective measures before overall production quality decreases?

• And compare performance between shifts?

1.Data collection & cleaning

2. Database Merger

3. Visualization

& Tool Creation

4. Real-time Feedback Loop (to stations)

Quality

Data

Testing

Data

Production Data

67

What is Ford’s Analytics Vision?Types of Projects - Customer

Key Questions to Answer

• Can we approach rebates with a customer targeting approach?

• Will this affect buying behavior?

?Cust.

Profile

Target Rebates to Specific Customers

68

Agenda

• About me


– Current

– Future


– GDI&A Overview

– Analytic Impact



69

How to Prepare/Adapt?

• Benchmark companies, both competitor and non-competitors, that adopted analytics and evaluate

the overall impact

• Be a change agent, encourage employees to explore analytics

• Don’t think analytics is only for tech companies

• Educate yourself on analytics (be able to talk about it)

• Don’t resist, assist!

• Integrate analytics to your major and/or career (LSS, Mft, SC, et al.)

• Understand how Analytics can be applied in ANY field

• Think How Analytics can make your job more efficient

• Courses available online (Coursera, Udemy, et al.)

• Learn the basics of a programing language

• Understand that real-world problems are not cookie cut (especially with data)

• Take a basic analytics class before you graduate

Tim

e o

f A

nal

ytic

Jo

urn

ey

0 - 15

15-25+

70

How to Prepare/Adapt?

QLS GSPAS OptimizationManual Data

Input Dashboards

GTSV

FIS MFM

eCATSCMMS

NGAVS Visualization

Web DSS

App Integration

Statistical

Modeling

B

A

T

C

H

S

T

R

E

A

M

Alerts

ThingWorx

71

Thank You!!!

Global Manufacturing Analytics ScientistFord Motor Company+1 (614) [email protected]

www.linkedin.com/in/matheus-scuta-212253114/

Matheus Scuta

Upcoming Webinars from Chapter #1:

Becoming a Change Master

March 5, 2018

Soft Skills 4.0—Becoming a

Change Master

❑ Bob Gold, Founder, The

Gold Group, Behavioral

Technologist–

The Art and Science

of Persuasion

❑ Scott Sink, Director ILSS

and Operational Analytics

Certification Program, ISE at

OSU–

How to Become a

Change Master

Upcoming Webinars from Chapter #1:

IISE Annual Conference—

Industry Practitioner Track

March 19, 2018

The IISE Industry Practitioner

Track—Orlando

❑ Scott Sink, Director ILSS and

Operational Analytics Certification

Program, ISE at OSU– Overview of

our Track for Young Professionals,

Seasoned ISE’s, ISE Students

❑ Kaz Takeda, Disneyland Resort

Manager, Industrial Engineering

and Co-Chair Track-- Highlights for

Seasoned Practitioners

❑ Jared Frederici, Sr. Consultant and

Co-chair for Track– Highlights for

Young Professionals and

Students

Accelerate my Career

Progress and Success

Learn about Industry 4.0

Expand and Extend my Network of

Peers

Get some Altitude on my life and job and career and have

some Fun

Learn about Service 4.0

Operational Analytics

Strengthen my Soft Skills

So, First things First, take some

time out and invest in yourself

It Pays Off—I’ve attended 30+ IISE Conferences and the

Return on Investment has been 25+:1 !!!

council on industrial chapter # 1 the ohio state university€¦ · webinar #3: best in class ilss...

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