effective training program deployment

© 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


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?”


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?


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


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


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


Main reasons why LOs fail?

Poorly organized training programs

Ineffective training delivery

Improper selection of trainees for the training program (Scope of the paper)


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!


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


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


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


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


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


The OSCS Model The design of OSCS model, the third step,

Self selection by the predicted (selected) candidate

OR



Using collaborative filtering technique The pictorial view for people like me!



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


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


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!


Part II of this black & white paper

So stay tuned……

Thank You

April 10, 2023 Version 1.0 Presenter: Deepak Manjarekar 21


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!”)


Collaborative Filtering: Vote illustration

effective training program deployment

Documents