Maersk Drilling - The Use and Usefulness of Employee Engagement Surveys: Myths and Realities
-
Upload
cbs-competitiveness-platform -
Category
Education
-
view
638 -
download
0
Transcript of Maersk Drilling - The Use and Usefulness of Employee Engagement Surveys: Myths and Realities
The Use and Usefulness of Employee Engagement Surveys – An HR Analytics Approach This session will take the participants through how The Maersk Group is using HR Analytics to work with Employee Engagement Survey data.
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Name Peter V.W. Hartmann, M.Sc. & Ph.D. Business Intelligence Expert Responsibilities in Maersk Currently: Responsible for HR Analytics/HR Business Intelligence in Maersk Drilling Previously: Responsible for Assessment Tool & HR Analytics in Maersk Group HR Background Research in Personality & Cognitive Ability Psychometrics & development of Assessment Tools Contact information [email protected] Phone: +45 63 36 18 22
Personal data
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
The Maersk Group What do we do?
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
The Maersk Group The A.P. Moller - Maersk Group is a diversified conglomerate with 89,000 employees in over 130 countries We serve customers worldwide mainly in the shipping & energy area- operating in five business segments: Container shipping Terminal operation Tankers, supply, towage & logistics Oil and gas activities Offshore Drilling
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Who is Maersk Drilling?
2.102.000.000 Financial turnover 2014 (USD)
903.000.000 EBITDA 2013 (USD)
478.000.000
Net Operating Profit After Tax 2014 (USD)
1972 Founded
3.899 FTE (2015)
Core business: Fleet in operation 2015
22 (+1 coming in 2016)
Marketing & Distribution
Refining Storage Transportation Exploration & Development
Production
Upstream Midstream Downstream
Maersk Drilling provides offshore drilling services to the upstream segment. To large international customers – e.g. ExxonMobil, Statoil, Shell, BP, ConocoPhillips.
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
HR Analytics at Maersk How do we see it?
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
HR Analytics at Maersk The practical application of metrics, statistics and research methodology to provide valid information for better decision making
Descriptive
To use simple statistics to display
standardized key metrics in a user-
friendly format for the purpose of
tracking progress
Linkage To use statistics and research methodology to generate new insights and translate these into recommendations
Predictive To use statistics and research methodology to predict future events and translate these into recommendations
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
HR Analytics at Maersk General Framework & Working Model
Communicate results
Display information in intuitive fashion
Construct easy understandable story illustrated with own data
Provide recommendations on actions
Analyze internal data
What data do we have? What does our data say? What does our ”existing knowledge”
say about this?
Consult existing knowledge to build model
What do the theories say? What does the research suggest? What Maersk experts do we have?
Identifying the key areas of interest
What is our strategy focusing on? What is critical to the business? Who internally can we partner with?
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
HR Analytics at Maersk What have we worked on & how?
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Descriptive HR Analytics To use simple statistics to display standardized key metrics in a user-friendly format for the purpose of tracking progress
Descriptive
To use simple statistics to display
standardized key metrics in a user-
friendly format for the purpose of
tracking progress
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Survey facts
Number of people invited
2014: Approx. 84.500 2015: Approx. 87.000
Survey Types 2014: Paper = 23,500 (27%)/
Web = 62,500 (73%) 2015: Transition to fully digital...
Languages 2015
22 Languages
5 point rating scale strongly disagree
disagree neutral agree strongly agree
N/A
Responses rates
2013: 87% 2014: 90%
Vendor & start date
Vendor = IBM Started 2006
2015 EES key dates
Survey opens
26 Aug
Survey closes
02 Oct.
Results available to managers
04 November
Employee Engagement Trend Four core elements of engagement at The Maersk Group: Satisfaction with the company
Pride in working for the company Willingness to recommend company to others Intention to stay at the company
64
66 66 67
69
75 76
72 73
58
60
62
64
66
68
70
72
74
76
78
2006 2007 2008 2009 2010 2011 2012 2013 2014
Engagement External Top 25% Benchmark
External Top 25% Benchmark is derived from 400+ organisations surveying in 200+ countries. On average, the distribution of blue-collar and white-collar respondents is 60/40 compared to Maersk Group’s 41/59.
Manager and Business Unit reports
•Each manager receives a detailed report on his/her direct reports and indirect reports (for leader of leaders) IF he/she has more than 5 respondents (to preserve anonymity)
•Reports provide information on item and scale level with comparison to
a) External benchmark (if any);
b) Maersk Group results;
c) Last year´s result
•Group HR provides “tool boxes” for
a) HR partners, on how best to facilitate the managers´ use of survey data
b) Managers, how to discuss results with team and decide on actions
c) Managers and HR Partners on how to address low engagement scores through focused follow up
•Engagement survey results can be on individual managers scorecard
Linkage HR Analytics To use statistics and research methodology to generate new insights and translate these into recommendations
Linkage To use statistics and research methodology to generate new insights and translate these into recommendations
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Linkage HR Analytics Example: Looking at external research for Engagement to guide analytics
Employee Engagement
Safety
(-0.22)
Performance
(+0.14/+0.30)
Turnover
(-0.23)
Absenteeism
(-0.26)
Customer Satisfaction
(+0.30)
Dispositional drivers
(e.g. Personality)
+0.24/+0.46
Situational & organisational drivers
+0.3/+0.5
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Linkage HR Analytics Example: Linking Engagement and Performance
Performance -> Engagement
Engagement –> Performance
Performance yr1 –> Performance yr2
Engagement yr1 –> Engagement yr2 There is a strong link between
a team’s Engagement from one
year to the next
There is a moderate link
between Performance from one
year to the next
There is a small but relevant
link between Engagement and
Performance
There is a small but relevant
link between Performance and
Engagement.
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Linkage HR Analytics Example: Linking Engagement and Business Unit Performance
Lo
ng
Term
P
erfo
rm
an
ce
Long Term Engagement
BU´s Trend line
External research (e.g. Judge et al, 2001; Harter, Schmidt & Killham, 2003) suggests a moderate relationship between Engagement and performance. Internal Maersk data shows the same
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Linkage HR Analytics Example: Addressing the issue of cause and effect in engagement
Willingness to put in a little extra effort
Individual and team
performance
Observing that discretional
effort matters
Feelings of success and
accomplishment
Increased optimism, joy &
team-spirit
Jury is still out on the true causality. Review of available External research as well as internal Maersk data suggests that it is likely to be subtle long term virtuous or vicious cycle where engagement feeds into performance and vice versa.
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
“The Morale of the troops is vital for winning the battle...”
Engagement is simply a practical way to
utilize that knowledge in a commercial
setting
The Engagement link to Customer
Satisfaction depends on how close the
business and customers are:
BU Distal BU Average BU Close
Engagement-
Customer
Satisfaction
Link
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Linkage HR Analytics Example: Linking engagement and customer satisfaction
Linkage HR Analytics Example: Linking Engagement and Absenteeism
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Linkage HR Analytics Example: Looking at the “spill-over effect” of the ability to engage employees
Ability to engage across levels
Team Leader of Others
Leader of Leaders
r=xy
Ability to engage across teams
Therefore, the
manager plays a crucial role
in engaging his / her team, over and above the teams individual
perception/circumstances
r=xy
Therefore, the
Manager´s manager plays a role
in engaging his / her organization; but
Good managers engage their employees irrespective of their
own conditions
Team A Team B
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Drives Engagement
Drives Performance
Energetic
Sense of urgency; Flexible;
Results driven
Sociable
Self confident; Persuasive;
Team worker
Optimistic
Takes calculated risks; See
challenges instead of problems
Respect
Fair; Respectful; Inclusive
Conscientious
Predictable; Clarifies roles &
responsibilities
Trust
Honesty; Trustworthy; Truthful
We find high
performing leaders
with
ALL kind of
personality profiles,
as well as leaders that
can drive engagement.
However, some
trends
emerged when
analysing leaders
personality profiles
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Linkage HR Analytics Example: Linking personality profiles to ability to drive engagement and performance
Linkage HR Analytics Example: Leadership training impact through the Kirkpatrick model
Reaction
•What is the participant´s initial reaction to the training course?
•Can be divided into affective (”I like this”) and utility (”I can use this”)
•Important for “face value” and to avoid scaring people off. The felt utility seems to a better prediction for later effects
Learning
•What knowledge & skills have been acquired?
•Can be divided into what has been learned, what is initially retained and what can the be reproduced – all in the training situation (can be further separated into content e.g. cognitive, interpersonal, psychomotor, etc.)
•Important for determining the “input” the participants are leaving with and potentially bringing back to the office
Behavior
•What “on-the-job” effect is there on behavior, performance, etc.?
•What “learnings” have been transferred e.g. actually brought back to the office and given the opportunity to use/display (e.g. manager support, opportunity to change and implement, etc.)
•Important for the actual impact on how the job is done and what can be directly observed
Results
•What impact does the training have on the overall “hard-core business metrics”?
•Ultimately, overall profitability, but can be broken into customer satisfaction, overall productivity, etc.
•Important for the final evaluation of the return on investment. Training cost vs. increased profitability
External research suggest a moderate effect size of d = 0,6 (but varies) for training for each of the four levels. However, the relationships between levels are weak (small correlation) and the spill-over is diminishing the more distal they become; e.g. Learning (0,66d)=>Behavior (0,44d). So one is not a ”guarantee” for the other, due to several factor influencing the ”flow between levels”
???
!!!
$
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Linkage HR Analytics Example: Testing Leadership training impact through longitudinal approach with control for sampling
2011
Q3
Collection of EES2011
2012
Q1
Appraisal scores for 2011
consolidated
2012
Q1-Q2
Possible attendance
2012
Q3
Collection of EES2012
2013
Q1
Appraisal scores for 2012
consolidated
2013
Q2
Data consolidated and analysed
Gain for not-trained
Gain for trained
Training effect
Potential intervention
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Predictive HR Analytics To use statistics and research methodology to predict future events and translate these into recommendations
Predictive To use statistics and research methodology to predict future events and translate these into recommendations
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Insert department name via
‘View/Header and Footer…’
Unknown Never been in the bottom in the past 2 years
Been in the bottom last year
Been in the bottom the last 2
years
1 in 4 1 in 8 2 in 5 1 in 2
21-23% 12% 39% 50%
Linkage & Predictive HR Analytics Example: Assessing risk of managers ending in the external bottom 30%
We need to intervene, otherwise the risk is very high for managers to end up being
in the bottom 30% again...
As a manager, what are your chances of falling in the bottom 30% on
Engagement next time?
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Insert department name via
‘View/Header and Footer…’
Linkage & Predictive HR Analytics Example: Looking a stability of managers ability to drive engagement
2011
2012
2011
2012
2011
2012
2011
2012
Engagement Manager
Effectiveness Values Index
Performance
Enablement
• Engagement, MEI, VI & Performance
Enablement remains fairly stable from
one year to the next.
• This year’s EES team results are
strong predictors for next year’s EES.
• That means that a team will remain
as Engaged or Unengaged as they
have always been,
unless something is done
about it...
What is the Engagement like for teams where
action has been taken since last year
EI MEI VI
2012
2011
We are unlikely to see a change in EES results, unless something is done about it...
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
r=xy r=xy r=xy r=xy
Linkage & Predictive HR Analytics Example: Looking at drivers of engagement
= EI
+1
Focus area A
+2
Focus area B +1,5
Focus area C
+ 2,5 Focus area D
+1,75
Focus area E
+ 3
How much do we need to improve on the 5 focus areas to increase Engagement by one point?
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Incidents
With injury:
LTI & fatalities
Without injury:
Observations, ”near-misses”,
etc.
Predictors
Training
Engagement
Linkage & Predictive HR Analytics Example: Linking Engagement and Safety in a BU => Lead to recommendation on how many LTI could be prevented by raising lower engaged entities to the BU average
Injuries:
LTI & fatalities
Safety Climate:
Organizational commitment to
safety
Personal Safety Attributes:
Knowledge & Motivation
Safety Performance:
Compliance & Participation
Safety violations:
“Near-misses” & Observations
The scientific model The actual model
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Drivers
HSE
Performance Customer Satisfaction
HSE
– Material & Personnel
Training Safety culture
Maintenance of
equipment
Engagement & Managerial effectiveness
Operational performance
Retention
Linkage & Predictive HR Analytics Example: Looking at drivers of operational performance and customer satisfaction
Safety is *one of the causes of operation performance and customer satisfaction *a consequence of performance excellence drivers
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Lessons learned
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
HR Analytics
Data quality
Data knowledge
Data availability
HR Governance
HR IT Systems
HR Processes
First lesson learned: Know your data and where it comes from Knowing your
data will increase the
validity of your analytics
All data comes from
somewhere
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Second lesson learned: Analytics requires several iterations
Descriptive
To use simple statistics to display
standardized key metrics in a user-
friendly format for the purpose of
tracking progress
Linkage To use statistics and research methodology to generate new insights and translate these into recommendations
Predictive To use statistics and research methodology to predict future events and translate these into recommendations
An iterative process
*Metrics & models need
to be constantly refined
*Insights need to fuel
adjustments or new
metrics
A long term investment
* A marathon, not a sprint
* Managing expectations
and communication
* No quick fixes
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Knowing the data
*Systems, processes
& governance
* Availability & quality
Third lesson learned: Success through relevancy, mind set & collaboration
The right
question
The right
stake-holders
The right SMEs
Success
It is easier when HR Analytics is truly an organizational mind set characterized by
open to discussion and willingness to investigate and change rather than just an
analytics function
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
All are these factors are necessary but not sufficient
to ensure success
Managing communication
Realistic expectations
Probabilities
A part of the answer
Takes time
Fixed expectations
Quick fix
One clear answer
Fuzzy expectations
Some insights
Cannot say
Managing expectations
Fourth lesson learned: Managing expectations & communication is essential
Effective communication
Likelihood scenarios
e-(β*X + α) ≈ 42%
Recommendations
Technical communication
p = e-(β*X + α)
Complex communication
All details, assumptions & disclaimers
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent
Fifth lesson learned: Analytics needs to be actionable
Actionable
• Ensuring that our insight are easy to understand and communicate as well as being actionable in order to support the business
Accuracy
• With inspiration from external knowledge, we strive to apply research methodology to generate actual insights and to reduce the margin of error of the recommendations
(c) Copyright 2013, A.P. Moller - Maersk. All rights reserved. Not to be distributed without written consent