Evidence-based and business-wise HRM: The case of strategic workforce planning.

CBCS XIXth Annual Conference of HRM

Dr. Gerard Evers

August 24th, 2016

14.00-16.30 hrs

Willemstad, Curacao

Dr. Gerard Evers

• Econometrician

• Former Professor in Human Capital Valuation

• Director EuroHRM

Tilburgseweg 117

5051 AC Goirle

The Netherlands

gerard@eurohrm.nl

00316 53465225

Amman, Jordania Abruzzo region, Italy

Be Smart, don’t Kiss (Keep it simple, stupid)

Think SMART:

• Specific

• Measurable

• Acceptable

• Realistic

• Time-specific

Simplicity and

intuition are

alibi’s for the

poor minded

Measuring is knowing,

Guessing is missing,

gambling is paying

Why to use data?

5

The agenda for this afternoon

14.00 hrs. Evidence based and business wiseHRM, the steps towards HR Analytics

15.00 hrs. short break

15.15 hrs. Strategic Workforce Planning

16.15 hrs Discussion, questions

16.30 hrs. Closing

Two approaches

1. Internal (from HR perspective): measuring

the (effectiveness of) HR processes. HR is

in the lead.

2. External (from HR perspective): analytics

is about the contribution of HR policies

(a.o.) for the firm outcomes. Management

is in the lead.

10 Golden rules for Internal perspectives - 1

1. Analyse the HR processes and products

on the basis of data, rather than

assumptions.

2. Collect data, make it consistent, try to use

digital techniques (data infrastructure).

3. Describe the processes and the desired

outcomes, both in time and volumes.

4. Measure discrepancies between budgets

and realization.

Internal perspectives - 2

5. From partial to integral: use several sources

and techniques.

6. Try to translate in financial

data as well

(like in Prince 2 applications)

Internal perspectives - 3

7. Make adequate time- and financial

planning.

8. Develop appropriate measurement

techniques and make them objective and

repeatable.

9. Develop a good monitoring system.

10. Define the link to organizational goals

(external approach)

External approach: Metrics & Analytics

HR Metrics reveals the

present human

Machinery;

HR Analytics indicates

the possible futures.

HR metrics

Systematic and data-driven overview of recent trends

and developments in human resources, by means of

key figures, KPI’s, balanced scorecards and

dashboards.

Gerard Evers, Rolf Baarda en

Jelle Dijkstra, 2007

12

13

KPI’s and Balanced scorecards

• Define dominant themes from the organisational strategy

• Translate these themes to HR: whichinstruments could contribute?

• Create a set of KPI’s

• Make a balanced scorecard

What are good KPI’s?

• Easy to understand

• Clear definition, valid

• Efficient to collect (costs)

• Reliable measurement

• Clear relation with strategic issues

• Reasonable frequency over time

• Credibility: Roughly right is better than precisely wrong

15

FINANCIAL

CUSTOMER

INTERNAL/BUSINESS PROCESS

LEARNING AND GROWTH

•Source: Robert S. Kaplan and David P. Norton, The Balanced Scorecard (Boston, MA: Harvard Business School

Press, 1996), 31.

Return of Capital Employed

Customer Loyalty

On-time

delivery

Process Quality Process Cycle Time

Employee Skills

Bron: CapGemini 2011

But, we should do better

Steering by the rear view mirror?

What is more useful:

Rapporting what happened yesterday?

OR

Forecasting what could happen tomorrow?

18

From “LAGGING”: Operational results form the past

To “LEADING”: Steering on future predictors: leadership,

commitment, talent, competencies, culture, imago

And STRATEGIC:Fit between Human Capital,

proces, Customer and Financial results (scorecard)

Shifting focus on measuring

HR Analytics – what and why?

• Definition: Systematic and data-driven analysis and translation of future developments in human resources towards the consequences and business impact.

• Primary goal: higher individual and firmperformance.

HR Analytics

Combining HR data, Customer data and financial data

From HR data to Business Impact

What is the impact of HR-instruments on:

Innovation power? Customer satisfaction? Sales?

‘HR is hitting the wall’

Source: Cascio & Boudreau 2010

Val

ue

ad

de

d

Degree of ‘HR Intelligence’

Benchmarks

Key figures

Scorecards

Link withBusiness data

Surveys

Relations

Causal analyses

Forecasts

Measuring Analytics

Why?Business impact?What if? Simulations.

Exploratory: what

happens?

Explanatory: why

does this happen?

Predictive: what

could happen

tomorrow?

Metrics versus analytics

HR metrics HR Analytics

The figures The observation

This is how it is This is how it will be

Yesterday Future

Facts and data What is it telling us?

Everything Need to know

All instruments The general picture

Information Relations

Collecting data Asking questions

Rapporting Analysing

HR ownership Corporate level

HR metrics and HR-analytics are both part of

a continious organizational process: 4 phases

I. Identifybusiness

opportunitiesand problems

II. Analytics: transform data

into action-oriented

information

III. Translate insights into desired

HR policy, and implement this

IV. Measure, monitor and evaluate theoutcomes

HR METRICS

HR ANALYTICS

24

Does my strategywork?

How much to investin learning &

development?

How to increaseemployee

commitment?

Will our best people stay or

leave?

What HR interventions do

increaseproductivity?

Phase II. Analytics : transform data into

action-oriented information (steps 2-6)1. What is thekey business

question

2. Translate thisinto an anlytic

design

3. Data collection

4. Data preparation

5. Data analysis, translate to

insights

6. Presentation and visualisation

7. Translate intoHR policies

8. Measure theoutcomes

(= metrics)

Example : labour turnover

12%

13%

14%

15%

16%

17%

2010 2011 2012 2013

Turnover

Top 5 talentsChance to

leave

1 Dwight Stewart 85%

2 Marion Young 76%

3 Iris Trump 63%

4 Churandy Martina 52%

5 Tom Peters 41%

HR metrics

HR analytics

HR analytics proces: transforming data to action-info

Problem Data AnalysisReporting,

action

From business question to analyticsquestion

Design research model (analysis tree, hypotheses)

Data sources, inventory, create

Data collecting Data cleaning

and enrichment

Analysis Reviewing

analysis tree and hypotheses

Combining and translating to insights and actions

Appropriatereporting, visualizationof results

Correlation and causality: Does more chocolate

eating leads to more Nobel price winners?

Or should we open more IKEA shops?

Example analysis tree

Poor quality of

services/products

Difficult work

environmentPoor material High sick leave

High work

pressurePoor leadershipLow

commitment

Other targetsLittle MDHigh

turnover

Dissatisfied

customers

Higher demands

from customers

Visualisation: Hoe many dots do

you see?

And now?

33

"A good hockey

player plays where

the puck is. A great

hockey player plays

where the puck is

going to be.“Wayne Gretzky

Conclusions HR Analytics

1. Do not collect KPI’s at random

2. Think also about KPI’s that are more difficult to measure

3. Focus on relation HR and business

4. Do not only look backwards, also use future data

5. Create a proper monitoring system

6. Create a logical design for analysis

7. Complete with a compact presentation (dashboard)

Short break

• Time for coffee and tea

Several books on HR planning

1993: HR3P 1999: strategy 2014: scenarios

Headlines

• WHY– Why SWP ?

• HOW– processes, data

• WHAT– Products, tools, instruments

37http://www.ted.com/talks/simon_sinek_how

_great_leaders_inspire_action?language=n

l

Wake-up call: understanding the importance

What will be important developments in the next years?

• Ageing and labour market consequences

• Vitality, sustainable employability

• Wage costs, budget steering

• Flexibility and income/work security

• Please add your own…

StrategicOrganisation Policy

StrategicHR-Policy

PIOFACH:people, import,

organisation, finance,

automation,communication,

housing.

StrategicHR Planning

(1)

(2)

(3)

(4)

If you don’t know where you are going, any road will take you thereLewis Caroll, “Alice in Wonderland”

41

Now

Future

Formation Actual

workforce

Desired

formation

Vis

ieio

n, m

iss

ion

,

str

ate

gy,

En

vir

on

men

t, e

tc.

K³R

Dyn

am

iek ID

U

Demand

Internal

Expected

workforce

Cocktail of

policies,

instruments and

actions

ScenarioScenario

Gaps

Vacancy

Abundancy

External

Supply

K³RK³R

K³R

Policy discussion

- Quantity

- Quality

- Costs

- Flexibility

K³R =

Scenario

automation,

outsourcing

Recruitment

training,

mobility

flexpool, etc

Ageing

Mobility

School leavers

Business cycle

Etc.

1

4

25

637

8

SWP building stones

SWP = K3R = chess on 4 boards

1. Right size: quantitative (hands)

2. Right shape: qualitative (brains)

3. Right costs: costs (dollars, euro’s)

4. Right agility: flexibility

But it should be

consumable…

SWP, Procrustes and tailor-made

• Many experiences in over 300 organisations. Differences in complexityand in degree of pragmatism. Anythinggoes….

• SWP should not become a Procrustes’ bed.

• SWP is tailor-made work.

Proces

Data

Product

SWP and proces

• Support from line managers and financedepartments.

• Position in Planning & Control cyclus

• Position in HR-cyclus

45

Generic SWP (environment, scenarios, strategy, budgets

Consolidating decentral outcomes and aggregate to organisation level

Projectmanagement (planning, communication, support)

SWP decentral

Translate central to decentral, finetuning, operationalisation

Ce

ntr

al

De

ce

ntr

al

Combination central and decentral

SPP and data

• How can you find, proces and analyse relevant data? Cross-sectional and also longitudinal?

• How can you combine HR data with financial data and other Big Data…

• How to combine quantitative and qualitative data (like fleet test)

SWP and product

• Relation with organisation strategy: scenario’s and positioning

• Present formation, PFM, job title mix

• Fleet test / HR3P

• Models for future labour supply

• Models for future labour demand

• Simulation models with possible variants

How to determine future formation(your future dream team)

At the level of job titles, there are 6 techniques:

• Extrapolation

• Proportional cutting (Dutch: kaasschaaf)

• Elbow-Machiavellism: hawks and doves

• Management opinions

• Via the product-dimension (U-turn)

• Idem, including several scenario’s

Present formation: γνῶθι σεαυτόν (gnothi seauton)

• What do we produce: core tasks and products?

• Who contributes labour for this(primary job titles, not overhead)?

• How do you produce each product, with how much labour (job titlemix)?

Products/Job title Matrix (2015)

product Job title A Job title B Job title C Total # hours

Product 1 7.000 64.000 16.000 87.000

Product 2 28.000 16.000 20.000 64.000

Product 3 35.000 0 4.000 39.000

total 70.000 80.000 40.000 190.000

W

H

A

T

WHO

HOW

Scenario’s: not SWOT but TOWS

• Analyse outside-in

• Strategy: plan A, B, C, D,

instead of “JWWH”

• Future is not predictable, however itis imaginable

What will be the effects on product volumes?

Strategy and positioning

Operational

Excellence

Operational

Excellence

Customer

Intimacy

Customer

Intimacy

Product

Leadership

Product

Leadership

What should be effects for

desired future job title mix?

The U-turn and steering wheels

l

Net hours

per job title

2015

Job title

mix 2015Desired job

title -mix

2018

Products

2015

Products

2018

Net hours per

job title, 2018

3

2

4

5

6

FTE per job

title 2015

Desired FTE per

job title 2018

Present

GNT

New

desired

GNT

18

PJT

2015

7

?????

3

Scenarios

1

2

3

Building stone 4

Information about size and characteristics of the actual workforce

Also its quality, costs and agility

And about its dynamics (Inflow, Throughflow, Outflow)

Quality: the Fleet test

Evers (1993), HR3P, the HR Performance-

Potential-Portfolio approach

The HR3P – matrix

Present performance

potential bad moderate fine excellent

No potential Peter,

Sharita

Susan,

Dick

Mary,

Patricia

Henk & Ingrid

Moderate

potential

Ayla, Pip Irene

Florence

Alice, Chuck

Good

potential

Sven Tommy

Excellent

potential

Jacques Geert-Jan,

Jorieke

56

HR3P

underperformers 31% diesels 44%

question marks 5% stars 20%

Performancelevel

low high

low

high

Potential

57

Building Stone 7: Expected gaps

1. What will be gaps within 3-5 years?

2. Make a distinction between

quantitative gaps, qualitative gaps,

cost gaps and agility gaps.

Example gap analysis

dimension Desired

formation

Expected

workforce

solutions

quantitative 400 fte 500 persons;

350 fte

qualitative High on

entrepeneurship

Moderate level

costs 30 mln 28 mln

flexibility 80 fte temporary 20 fte temporary

Building Stone 8: Solutions

1. SWP is self-denying prophecy

2. Cocktail: several instruments available.

3. Rubik-puzzle

61

With SWP, there is a picture

“Key-players”• Retention plan:

• MD• Compensation, bonus• Training

“To be moved”• Assess potential• Development plans• Mobility plan• Outplacement

“Present core”• Assess potential• Development plans• Mobility plan• Outplacement plan

“Prospects”• Recruitment program• Training• Collaboration with external suppliers“Unused potential”

• Alternative employment• Temporary detachment• Retention policy

“temporary forces”• short term contracts• detached workers fromelsewhere

“Most wanted”• Recruitment• MD/TD• Training

Needed in

future

Now

presentNow

needed

1

2

3

4

5

7

6

Questions…?