Portfolio.

395 views
307 views

Published on

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
395
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Portfolio.

  1. 1. • 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.
  2. 2. • Healthcare organization • High-tech company • Lessons learned
  3. 3. • Problem definition: – “We have an aging workforce problem. Could you please do a 5-year staffing forecast?”
  4. 4. • 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
  5. 5. 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
  6. 6. • 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
  7. 7. 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
  8. 8. 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
  9. 9. 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.
  10. 10. 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
  11. 11. 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.
  12. 12. 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
  13. 13. “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
  14. 14. Retirement Statistics are high: • Super Group – General Services • Dept Group – Dietary, Plant Operations, Social Services • Job Group – Courier, Housekeeper, Mgr - People • VP Group
  15. 15. 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.
  16. 16. 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).
  17. 17. Recruitment Identifying, selecting and capturing the talent required. Retention Creating a culture of sustained commitment HR Systems Providing 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
  18. 18. 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
  19. 19. My Response • Happy to do the interviews • Want to first learn what the organization collectively “knows” so that I can ask smarter questions
  20. 20. Data Collection • Started looking at various sources of internal data: –HRIS –Exit data from vendor –“Great places to work” data
  21. 21. • Difficult to get access to data • Unwillingness to share data • Data for HR dept was incomplete
  22. 22. • Worked with internal team: –2 HR VP’s –One OE consultant –One intern
  23. 23. • 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.
  24. 24. • 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
  25. 25. 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?
  26. 26. 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
  27. 27. 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 OVERALLVOLUNTARY - Quarters represent calendar years – Source: Radford
  28. 28. 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 Q1FY12 Jobsinmillions %ofpeople 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”
  29. 29. 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
  30. 30. Internal Analysis
  31. 31. 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
  32. 32. 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%)
  33. 33. Over half of voluntary turnover is employees with less than 3 years of service. 36 COMPANY Confidential - Internal Use Only 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% 0-1 Years 1-2 Years 2-3 Years 3-5 years 5-7 Years 7-10 Years 10-15 Years 15-20 Years 20-30 Years 30+ Years %HCasofFY13Q1 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% 0-1 Years 1-2 Years 2-3 Years 3-5 years 5-7 Years 7-10 Years 10-15 Years 15-20 Years 20-30 Years 30+ Years %ofTerms % 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 COMPANY Confidential - Internal Use Only
  34. 34. We are losing our funnel for the future at a rate exceeding Company Average. 37 COMPANY Confidential - Internal Use Only 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 ActualAttritionRate 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 ActualAttritionRate N=67 N=211 N=404 N=341 N=140 N=37N=144 OVERALLVOLUNTARY HRIS data *Voluntary Turnover COMPANY Confidential - Internal Use Only
  35. 35. Field Operations • Better job opportunities • Compensation • Work-life balance G & A Functions • Work-life balance • Frequent strategy change • Conflict with managers • Better job opportunities Reasons for valued employees leaving varies by BU… 38 COMPANY Confidential - Internal Use Only 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 • Better job opportunities • Start-ups • More challenging jobs • Alternate domains • Career advancement • Further education COMPANY Confidential - Internal Use Only
  36. 36. 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
  37. 37. www. woolcockconsulting.com THANK YOU

×