© 2005 KPIT Cummins Infosystems Limited
We value our relationship
Effective Training Program Deployment
We value our relationshipApril 10, 2023
© 2005 KPIT Cummins Infosystems Limited
Version 1.0
Presented By
Deepak Manjarekar
Version 1.0 Presenter: Deepak ManjarekarApril 10, 2023 2
Introduction
“If a man does not keep pace with his companions,
perhaps it is because he hears a different drummer.
Let him step to the music which he hears, however measured or far away.”
- Henry David Thoreau
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The white paper. (or should it be black on white?)
Using Linear & Logistic Regression along with Collaborative Filtering Technique for Effective Training Program Deployment
Part I
“Say what?”
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Questions, a lot of questions.
What has deployment of training programs got to do with technology and why it’s in Tech Expo?
How many of you have a math/stats background? How many of you have data mining background? How many of you send your subordinates for
various training programs on a regular basis? How many of you have recently finished a
training program? How many of you have read my technical paper?
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Some new trends in corporate training
Emergence of learning organizations (LO) within corporations
Reverse trend in in-sourcing the corporate training programs to LOs
LOs viewed as “cost centers”
Low or no ROI or hard to measure ROI for LO
Organizations are grappling with poorly performing LOs
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So why this matters to KPIT?
KPIT has a Learning Organization
The annual budget for KPIT’s LO has just been quadrupled from it’s last year’s annual budget
KPIT is planning to hire more than 1000 fresh recruits from college campuses across the country
There is a huge gap in what the engineers learn in schools and what they need to perform on a daily basis @KPIT
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So why this matters to KPIT?
We will need to train these new recruits in appropriate technologies
We must make sure that our training dollars give us the biggest bang for the buck (acceptable ROI)
So effective training deployment becomes an imperative at KPIT’s LO
This paper is an attempt to apply statistical methods and predictive modeling in effective training program deployment to gain maximum ROI on training
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Main reasons why LOs fail?
Poorly organized training programs
Ineffective training delivery
Improper selection of trainees for the training program (Scope of the paper)
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Reasons for improper selection of trainees
Favoritism Crying baby gets the milk Supervisors’ reluctance Many deserving candidates are busy in their day-
to-day work Supervisors want to maintain business continuity Peer pressure on employees Sending substitutes for the training when the
actual enrollee can’t attend Supply and demand for the training on a specific
technology Due to unforeseen circumstances
A lot of subjectivity goes in!
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The three stage OSCS Model The design of OSCS (Objective Subjective Candidate
Selection) model constitutes three major steps, First step will take out the bad subjectivity factor from trainee
selection process Two hypothesis testing
First hypothesis test using a survey mechanism and finding T-values
Second hypothesis testing using multi dimensional linear regression technique
Second step will make an objective candidate selection by using the results of the first step By building a predictive model using the logistic regression
technique
Third step will add the good subjectivity factor back into the selection process Self selection by the predicted candidate Using collaborative filtering technique to assess the
probability of the candidate liking the training based on the ratings given by other folks who are similar in profile and have undergone the training in past and rated it
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The three stage OSCS Model The design of OSCS (Objective Subjective Candidate
Selection) model constitutes three major steps
1 2 3
Stage 1: Remove bad subjectivity factorTwo experiments (Survey &
Regression)Stage 2: Build objective predictive modelLogistic Regression
Stage 3: Add good subjectivity factor backSelf Selection & Collaborative filtering
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The OSCS Model The design of OSCS model, the first step,
First hypothesis test using a survey mechanism and finding T-values
where µ is the population mean, x bar is the sample mean, and s is the estimator for population standard deviation and N is the sample size
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The OSCS Model The design of OSCS model, the first step,
Second hypothesis testing using multi dimensional linear regression technique
1010....22110 xxxy
At the end of the multi dimensional regression we will end up with the equation of the following nature
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The OSCS Model The design of OSCS model, the second step,
Building a predictive model using the logistic regression technique
Yes or No Decision
Candidate SelectionParametersFrom step 1
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The OSCS Model The design of OSCS model, the third step,
Self selection by the predicted (selected) candidate
OR
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The OSCS Model The design of OSCS model, the third step,
Using collaborative filtering technique The pictorial view for people like me!
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The OSCS Model The design of OSCS model, the third step,
Using collaborative filtering technique The Statistical View for the adventurous folks among us
(yuk!)
)(*),(ˆ **u
au
uc
aac vvuawvv
au
u
au
uc
aac uac
vvuacvv
|),(|
)(*),(ˆ
*
*
After simplification the equation looks as follows
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Coming to a full circle
“If a man does not keep pace with his companions, perhaps it is because he hears a different
drummer. Let him step to the music which he hears,
however measured or far away.”
- Henry David Thoreau
“if a trainees feels like a fly on the wall in a classroom, perhaps it is because they were
incorrectly selected for that class. Let us put them thru the OSCS model and let them step into the right classroom however small or far away.”
- Deepak Manjarekar
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Part II of this black & white paper
Next Steps Data Gathering Deal with data quality issues Conducting survey Data Sampling Conducting experiments in regression
analysis Predictive / Collaborative Model
building Model validation Trend analysis (to prove or disprove
theory) My God, that’s enough!
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Part II of this black & white paper
So stay tuned……
Thank You
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Backup Slides
*The subsequent backup slides are taken from the class presentations of Prof. Anand Bodapati of UCLA Anderson School of Management. (MGMT 267 One-To-One MKTG was one of the great classes I have taken at UCLA! “Thanks Professor!”)
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Collaborative Filtering: Vote illustration
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