1. 1
2018 LEBOW BRIDGE & VANGUARD
DIVERSITY & INCLUSION
CASE COMPETITION
B Y T E A M E F F I C I E N T F R O N T I E R
2. 2
CURRENT ISSUES IN THE ORGANIZATION
Current diversity statistics indicates significant misrepresentation
of executives in ethnic and gender diversity.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Overall Workforce Diversity Executive Ethnic Diversity
Ethnic Misrepresentation
Hispanic African American Asian Caucasian
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Overall Workforce Gender
Diversity
Executive Gender Diversity
Gender Misrepresentation
Female Male
3. 3
CURRENT ISSUES IN THE ORGANIZATION
Low retention rate is hurting the organization financially.
34
36
38
40
42
44
46
Sep-17
O
ct-17
N
ov-17
D
ec-17
Jan-18
Feb-18
M
ar-18
Apr-18
M
ay-18
Jun-18
Jul-18
Aug-18
Sep-18
Retention: Quits Levels And Rates (in %)
4 in 10 employees quit their
job on average in the last year
5. 5
Create a new diverse and inclusive environment at Home away from Home.
WHAT WE ARE ASKED TO DO
DIVERSITY
INCLUSION
&
6. 6
Create a new diverse and inclusive environment at Home away from Home.
WHAT WE ARE ASKED TO DO
DIVERSITY INCLUSION
The range of human
differences, including
but not limited to race,
ethnicity, gender, or
any other construct.
Inclusion is involvement
and empowerment,
where the inherent
worth and dignity of all
people are recognized.
When put together, they can create a new
environment with the free flow and
exchange of ideas.
7. 8
Goal Setting Theory
Set specific,
challenging goals
Give feedback
Promote inclusion
Diversity Problem
Solving Approach
INSPIRE A SHARED VISION
11. 12
Es p ous ed Values
Ob s erv ab le A rt ifac t s
B as ic Und erly ing A s s u m p t ions
THREE LAYERS OF ORGANIZATION’S
DIVERSITY EFFORT
12. 13
PROPOSITION:
INCLUSION COUNCIL
Ø A council comprised of ~10 diverse
members of management
Ø Volunteer-based – members who are
committed to D&I
Ø Merck, American Express, and Deloitte
already have councils
13
15. 16
T e a m D y n a m i c s F e e d b a c k
T a l e n t M a n a g e m e n t H i g h - l e v e l O v e r v i e w
ALGORITHMIC DECISION MAKING
16. 17
Team members rate each other
on a list of attributes
Computer generated matrix to
show different perspectives
17. 18
Team members
make a proposal
01 Teams discuss the
proposal
02
IMPLEMENTATION SCENARIO
Team members
give feedback
03 Algorithm evaluates
weighted decision
04
DECISION MAKING PROCESS
DECISION WEIGHTING CRITERIA
Team
Members’
Feedback
Gender &
Ethnic Diversity
Previous
Decision
Record
18. 19
37%
65%
IN PERSON TRAINING ATTENDANCE
• Low attendance rate
• High feedback response
• High satisfaction
WEBINAR TRAINING ATTENDANCE
• High attendance rate
• Low feedback response
• Neutral satisfaction
The past training has been considered ineffective with either low attendance rate or
lackluster satisfaction.
PAST TRAINING RESULTS
19. 20
TRAINING PLAN &
STRUCTURING
ü Utilize a hybrid training structure
ü Offer flexibility to employees
ü Utilize Webinar as an option to
make up one missed training
20. 21
TRAINING PLAN &
STRUCTURING
ü Training necessity will be shown by
actual changes happening to
company structure
ü Market to employees that changes
are a celebration of new ideas
ü End training with regional
celebrations of new path forward for
company
21. 22
DEPLOYMENT STRATEGYPilotStrategy
Phased Approach
Post-deployment
Refinement
Compare with past
training feedback
Phased Approach
• Slowly deploy the training strategy by leaders’ area of expertise
across 10 offices to get feedback for geographical differences
Initial Deployment Support and Refinement
• Segmenting the in-house designed feedback by offices and
tailoring the expectation for the testing by region
• Referencing the feedback from the previous phase to adjust the
expectation for each deployment phase
PHASE 1
PHASE 2
PHASE 3
22. 23
Rebranding to match diversity and inclusion outlook
Implement complete phased training approach
Successfully implement algorithmic decision-making and
inclusion council
CONCLUSION