Portfolio.

• BA math.• Took programming and simulation classes.• First job – programming at Royal Institute of Technology in

Stockholm.• Taught math, computer science in Swedish public school.• MBA University of Michigan.• CSHRP for 20+ years.• Consulting work – Jackson Hole Group and Woolcock

Consulting.• Discovered analytical work is what I love.

• Healthcare organization

• High-tech company

• Lessons learned

• Problem definition:

– “We have an aging workforce problem. Could you please do a 5-year staffing forecast?”

• What else is going on?

• What about turnover?

--“Not a problem. Lower than the national average. We don’t need to look at that.”

• What about that new facility I see you are building?

--“We have no idea what the staffing requirements might be for that.”

My Response

My Approach

• Made assumptions about retirement

• Included turnover data

• Did interviews of various function heads to get their estimates of staffing requirements for new facility

--Some new hiring required

--Some current employees moving over

• My client, the Director of Staffing, had trouble getting the data

• Needed to work with HRIS specialist

• He was not familiar with the database

• Got data as an excel file

Data Analysis

• Hired a programmer

• Data set big enough and complex enough that it couldn’t be done “manually”

• If we wanted to change the assumptions, it would be easy to re-run the data

Explicit Assumptions• Turnover rate does not change

• Voluntary turnover percentage is the same

• “Avoidable” turnover includes other employment, resignation – personal and working hours

• No new hires will retire in the next 5 years

• Retirement – 5% age 55, 4% age 56, 3% age 57 4% age 58-59, 7% age 60-61, 18% age 62, 12% age 63-64, 40% age 65, 25% age 66, 30% age 67, 35% age 68, 40% age 69, 100% age 70+

• Growth – simplified as 2% growth for outpatient year over year, 1.7% growth for inpatient for 2006, then flat

Notes• Data used in the analysis represents a snapshot in time

and may vary from current totals

• The number of openings indicated includes active requisitions only and may not reflect all vacancies

• Growth data is an approximation. More detailed analysis can be done with a variety of growth scenarios.

• Growth for laboratory positions was not included in this project. Analysis shown is based only on turnover data.

• Turnover data covered the period of 5/2005 – 4/2006

• Requisitions shown are as of April 2006.

• All positions at X were included.

Staffing Projections Review

Shown by position or group of similar

positions

Turnover data from HR

SHC

Entire Population

Current

Openings

Current Head

Count

Percentage

openings of

total (Openings

+ Headcount)

Total 460 5506 7.7%

2007 2008 2009 2010 2011 Total Impact

Growth 105 79 81 82 84 431 Turnover

Replace

Turnover 708 718 718 719 719 3582 12.9%

Replace

Retirement 94 25 26 34 38 217

Total

Recruiting

Requirement 907 822 825 835 841 4230

Detailed data tables exist for the following “slices” of the employee data:• Vice Presidents – by name• Department Group – cardio, clinic, etc.• EEO Group – professionals, clerical, technical,

etc.• General Group – admin, diagnostic, etc.• Job Group – by job code in HRIS• Super Group – support services, patient care,

etc.

Overall turnover is high ( > 20%):• Super Group – Mgmt

• Dept Group – Amb Surg Ctr, Audiology, Comm/Gov

Relations, Lab Support, Legal, Nutrition, Occ Health,

Ortho, Planning, Sleep Clinic, Transport

• Job Group – Assistant, Nurse – Exempt, Nurse – Relief,

PA, Rlf Cyto Tech, Rlf Sonographer, Rlf Technician, Rlf

Therapist, Service Rep

• VP Group

“Avoidable” Turnover

• Almost 50% of all terms are “avoidable” in all periods studied – probationary, less than 2 yrs, greater than 2 yrs

• “Avoidable” Turnover is high:– Super Group – Clinical Services– Dept Group – Admin, Amb Surg Ctr, ED, OR, Outreach Lab,

Pharmacy, Sleep Clinic, Transplant– Job Group – Lab Asst, Staff Nurse, NA, Professional, Rlf

Technician, Technician– EEO Group – Office and Clerical, Professionals, and Service

Workers– VP Group

Retirement Statistics are high:• Super Group – General Services• Dept Group – Dietary, Plant Operations,

Social Services• Job Group – Courier, Housekeeper, Mgr -

People• VP Group

Findings• For the current employee headcount, X will need to

recruit 4,230 new hires between 2007 and 2011.

– 10% of the total is the result of growth

– 85% is the result of turnover

– 5% is the result of retirement

• 2007 and 2011 are the years identified with the highest recruiting requirements.

• When viewed in isolation, the turnover rate of 12.9% is not alarming. However the greatest impact on achieving staffing requirements can be accomplished by reducing “avoidable” turnover.

Findings, p.2• 47% of all resignations occur in the first two years of

employment.

• 47% of all voluntary terminations were “avoidable.” More detailed study, tracking and analysis are recommended into causes for seeking other employment.

• Several job categories did not project staff growth (Lab, for example). More detailed projection is recommended.

• Data collection and projection is difficult and internal systems are not integrated (Business Development, Finance, HR).

RecruitmentIdentifying, selecting and

capturing the talent required.

RetentionCreating a culture of sustained

commitment

HR SystemsProviding support processes to

facilitate effective recruitment

•Expand recruiting sources, including school relationships, internships, etc.

•Increase image advertising, media recognition, visibility on selected campuses

•Enhance HR recruiting systems to simplify job applications and to speed up response to applicants

•Enhance employee referral bonuses for selected positions; consider campaign related to specific growth initiatives

•Implement standardized interview and selection process to ensure high-quality hiring. Train managers and monitor.

•Establish retention as a management responsibility, hold managers accountable, set goals, track and reward.

•Conduct analysis of voluntary terminations and identify more specific causes for voluntary quits

•Establish mentoring, support and retention initiatives directed at new hires

• Develop 1-day retention management workshop for all managers.

•Explore use of scholarships or loan payback incentives tied to length of employment.

•Streamline application, interviewing and job offer processes to accelerate hiring of identified candidates.

•Implement applicant and requisition tracking systems. Monitor “time to close,” establish goals, identify causes for failure to achieve.

•Expand employee referral awards

•Evaluate implementation of sign-on bonuses, tiered housing , commute allowances, housing allowances (or housing) for long-distance commuters.

•Increase exit interview and post-termination surveying to determine true reasons for resignations.

Recommendations

High Tech Company

• Problem definition: “Can you interview former employees who have been gone for 6 months to find out why they really left?”

• Global company, 13,000 employees, would need to do interviews globally

• Hard to contact 6 months later, hard to get them to agree to talk, schedule interviews

My Response

• Happy to do the interviews

• Want to first learn what the organization collectively “knows” so that I can ask smarter questions

Data Collection

• Started looking at various sources of internal data:

–HRIS

–Exit data from vendor

–“Great places to work” data

• Difficult to get access to data

• Unwillingness to share data

• Data for HR dept was incomplete

• Worked with internal team:

–2 HR VP’s

–One OE consultant

–One intern

• Team wanted to tell me what the issues were

• I wanted to go where the data took me.

• Assumed there were “hot spots” in different parts of the company, for different reasons.

• Looked at all the variables I could based on the data

• Split by BU, geography, level, job family, etc.

• Prepared report (detailed and high level) for CEO and Staff, and VPs.

Data analysis

Discovery-Based Study• Assumptions

– While there may be some common themes across the Company, there are likely “hot spots.”

– Need to confirm what we already know.

– Some turnover is good – look at desirable and undesirable.

– COMPANY turnover should be better (lower) than the market.

• Questions

– What are the top reasons for leaving?

– Is turnover different among Business Units, Locations, Jobs, Tenure, Age, Ethnicity, Gender?

– What is going on that is not obvious?

High Level Findings• Comparable attrition trends exist between COMPANY and the industry

within the US and internationally• Job market and social trends increasing impact on the employment

dynamic• Better job opportunities is cited as the main reason for leaving in general

and across several cuts of the data• Significant number of employees who leave have under 3 years of tenure

– 14.5 % turnover rate among that demographic (52.5% of total terms) • Employees under the age of 30 are leaving at an overall turnover rate

(14%*) exceeding average• There are hot spots among jobs in various job families; turnover varies by

BU• Sunnyvale and India have highest attrition in tech centers; Turnover in

RTP is better than top quartile in turnover among valued employees (rated 1, 2 or 3)

• V, S, and A have highest attrition in Field Ops sites

Our turnover is close to market 50th percentile, but not in the top quartile.

9.5% 9.2%

7.6%

7.6%

8.7%

10.0% 9.5%

8.8%

9.4%10.4%

9.3%

14.6% 14.2%

12.7% 12.6%13.3%

14.6% 14.3%13.2% 13.1%

14.5%13.7%

7.47%8.22%

9.30%10.11% 10.13%

9.58% 9.21%

9.36%

9.46% 9.79%

10.75%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Q1 2010 Q2 2010 Q3 2010 Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012 Q2 2012 Q3 2012

4.2% 4.2%4.8% 4.9%

6.2%6.9%

7.1%

6.3%6.6%

7.1%6.3%

7.4% 7.2%7.8%

7.8%

9.0%

10.1%

11.1%

9.7% 9.6%

10.6%

9.3%

5.72%

6.37%

7.58%

8.40%

8.46%

8.22% 7.90% 7.68% 7.83%8.30%

9.25%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

External Top Quartile

External 50th Percentile

COMPANY

OV

ERA

LLV

OLU

NTA

RY

- Quarters represent calendar years – Source: Radford

4.7%

3.6%

4.2% 4.1%4.4%

9.0%

10.1%

11.1%

9.7% 9.6%

3.12

3.1

3.13

3.15

3.18

3.06

3.08

3.1

3.12

3.14

3.16

3.18

3.2

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

Q1FY11 Q2FY11 Q3FY11 Q4FY11 Q1FY12Jo

bs

in m

illio

ns

% o

f p

eo

ple

Bay Area Unemployment Rates National Turnover Rate Bay Area Job Gains(in millions)

Trends are pointing to a buyer’s market.

Source: Employment Development Department, Dice.com and Radford

Trends• Escalation for talent predicted in

high tech firms like Facebook, Apple, Google, Twitter and Zynga

• Turnover in high demand occupations predicted to rise by 25%

*ere.net (Recruiting Intelligence)

• In San Jose, there is just one person available for every job posted; ratio 1:1

** Indeed.com job competition trends

• Sourcing passive candidates and social professional networking are top recruiting trends in 2012

*** Linked in Global Recruiting Trends Survey

• According to a 2011 SHRM study 42% of satisfied employees said that they are “likely to look”

Market trends show move to new/cool companies for traditional reasons.

32

5%

11%

17%

23%

0% 5% 10% 15% 20% 25%

Promotion or new title

Flexible work hours

For better compensation

More challenging job roles

Reasons for Leaving in Silicon Valley

Source: Linked in – most common reasons for employees leaving

Source: Dice.com – most common reasons for employees leaving

Source: Forbes – job migration trend

Internal Analysis

Exit interviews yield some high-level trends…

34 COMPANY Confidential - Internal Use Only

Section Q1FY12 Q2 FY12 Q3 FY12 Q4 FY12 Q1 FY13

Company, Culture, & Value 4.15 4.09 3.95 3.98 4.01

Management 4.01 4.05 3.93 3.80 4.00

Position 4.01 4.00 3.81 3.89 3.93

Recognition & Growth 3.74 3.70 3.60 3.61 3.68

Working Condition 4.13 4.13 4.00 4.06 4.08

Compensation 4.09 4.01 3.97 3.93 4.03

Ethics 4.18 4.23 4.07 4.09 4.13

Overall Rating 4.00 3.98 3.86 3.85 3.93

Participants 138 170 133 110 188

35

- Employees rated 1, 2 and 3 = Valued and Voluntary- Source: Exit Check Data (N = 479 Valued Employees)- Bullet Points in descending order of frequency

Other Reasons• COMPANY Strategy and

Processes

• Lack of clarity about strategy

• Due to changes in strategy

• Due to work culture

• Due to bureaucratic processes

• Management Behavior

• Lack of strong management skills

• Due to conflict with manager

• Lack of management support / mentoring

• Natural Progression

• Managed out due to performance

• Had been in the company long enough

… And analysis of qualitative data provides more insight.

COMPANY Confidential - Internal Use Only

Top Reasons Valued Employees Depart

• Better and More Challenging Jobs (25%)

• Career Advancement (12%)

• Better Job Fit / Alternate Domains or Careers (11%)

• Start Ups (6%)

• Personal Reasons – Relocation or Family (8%)

• Work-Life Balance (3%)

• Approached externally (rated as “1”) (3%)

• Further Education (“1s and 2s” in India) (3%)

• Compensation (“1s and 2s”) (3%)

Over half of voluntary turnover is employees with less than 3 years of service.


8.1%

12.6%

10.5% 10.1%

7.8%

6.3% 6.4%

3.8%3.0%

6.3%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

0-1Years

1-2Years

2-3Years

3-5years

5-7Years

7-10Years

10-15Years

15-20Years

20-30Years

30+Years

% H

C a

s o

f FY

13

Q1

Actual Turnover by Tenure

15.7%

24.4%

12.4%

19.0%

15.4%

7.0% 5.6%

0.3% 0.1% 0.3%0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

0-1Years

1-2Years

2-3Years

3-5years

5-7Years

7-10Years

10-15Years

15-20Years

20-30Years

30+Years

% o

f Te

rms

% of Terms by Tenure

Top Reasons <3 yrs.*

• Better and more challenging

job roles (58%)

• Personal reasons (i.e. work-life

balance, relocation) (31%)

• For further education (6%)

• For better compensation (2%)

HRIS data* Voluntary Turnover


We are losing our funnel for the future at a rate exceeding Company Average.


Top Reasons <30 yrs*

N=79 N=247 N=445 N=398 N=179 N=45N=168

• Career Opportunity (35%)

• Personal Reasons (19%)

• Return to School (13%)

• Relocation (8%)

• Compensation (4%)

16.5%

13.1%14.0%

10.3% 9.8%9.0%

14.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

Under 25 25-29 Under 30 30-39 40-49 50-59 Over 60

Act

ual

Att

riti

on

Rat

e

14.0%

11.2%11.9%

9.4%8.4%

7.1%

11.5%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

Under 25 25-29 Under 30 30-39 40-49 50-59 Over 60

Act

ual

Att

riti

on

Rat

e

N=67 N=211 N=404 N=341 N=140 N=37N=144

OV

ERA

LLV

OLU

NTA

RY

HRIS data*Voluntary Turnover


Field Operations

• Better job opportunities

• Compensation

• Work-life balance

G & A Functions

• Work-life balance

• Frequent strategy change

• Conflict with managers


Reasons for valued employees leaving varies by BU…


Customer Advocacy

• Alternate careers or domains

• Better job fit

- Rated 1, 2 and 3 = Valued and Voluntary- Source: Exit Check Data- Bullet points in descending order of frequency

Product Operations


• Start-ups

• More challenging jobs

• Alternate domains

• Career advancement

• Further education


Lessons Learned• Need access to data

• Data needs to be “clean”

• Look beyond what is being asked - what they need may be different than what they want

• Tie the results to the business – ask “so what?”

• Document all assumptions and steps

• Someone, either internal or consultant, needs to be able to do this for your company

www. woolcockconsulting.com

THANK YOU

Portfolio.

Business

Transcript of Portfolio.