U.S. patent application number 13/741128 was filed with the patent office on 2014-01-30 for strategic workforce planning model.
This patent application is currently assigned to OPTIMIZATION TECHNOLOGIES, INC.. The applicant listed for this patent is Optimization Technologies, Inc.. Invention is credited to Jay April, Marco Better, Candace Brinkman, James P. Kelly, Terry Wubbena.
Application Number | 20140032253 13/741128 |
Document ID | / |
Family ID | 43465916 |
Filed Date | 2014-01-30 |
United States Patent
Application |
20140032253 |
Kind Code |
A1 |
April; Jay ; et al. |
January 30, 2014 |
STRATEGIC WORKFORCE PLANNING MODEL
Abstract
Systems, devices, and methods are provided for workforce
planning models. Technologies are described to manage human capital
decisions. Decision making models and related tools are described
that support the development and implementation of workforce
strategies, programs and policies. In one model, resources may be
allocated to specific practices (policies, programs, initiatives,
organizational culture) used to attract and retain valued
employees. Resources may be increased or decreased until the
optimal allocation of resources is found that is most likely to
enable the achievement of specific goals (e.g., attraction,
retention, readiness, and representation).
Inventors: |
April; Jay; (Denver, CO)
; Better; Marco; (Boulder, CO) ; Brinkman;
Candace; (Boulder, CO) ; Kelly; James P.;
(Boulder, CO) ; Wubbena; Terry; (Boulder,
CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Optimization Technologies, Inc.; |
|
|
US |
|
|
Assignee: |
OPTIMIZATION TECHNOLOGIES,
INC.
Boulder
CO
|
Family ID: |
43465916 |
Appl. No.: |
13/741128 |
Filed: |
January 14, 2013 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
12819392 |
Jun 21, 2010 |
8386300 |
|
|
13741128 |
|
|
|
|
61218807 |
Jun 19, 2009 |
|
|
|
Current U.S.
Class: |
705/7.14 ;
705/7.12 |
Current CPC
Class: |
G06Q 10/063112 20130101;
G06Q 10/1053 20130101; G06Q 99/00 20130101; G06Q 10/06
20130101 |
Class at
Publication: |
705/7.14 ;
705/7.12 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A method of workforce planning for an organization, the method
comprising: identifying a plurality of employee attributes;
associating selected attributes of the plurality of employee
attributes with each employee of a plurality of employees of the
organization; identifying a plurality of practices of the
organization; associating each of the plurality of practices with
one or more impact factors, wherein each respective impact factor
represents an impact of the respective practice on employees based
on their associated attributes; and calculating a plurality of
retention probabilities, wherein each retention probability
represents a probability that one or more of the plurality of
employees will remain employed during a time period at the
organization based on the impact factors associated with respective
employees.
2. The method of claim 1, further comprising: simulating a
workforce composition for the organization utilizing the plurality
of retention probabilities for a plurality of time periods.
3. The method of claim 1, further comprising: optimizing selection
of a subset of the plurality of practices for the organization
based at least in part on the calculated retention
probabilities.
4. The method of claim 1, further comprising: identifying for the
time period a plurality of job descriptions, wherein each job
description includes at least one job requirement; and assigning
each employee to a respective job description from the plurality of
job descriptions.
5. The method of claim 4, further comprising: identifying for the
time period at least one of the plurality of job descriptions that
is not assigned at least one respective employee; and identifying a
promotion for at least one of the employees to the at least one not
assigned jobs based on at least one attribute associated with the
employee.
6. The method of claim 1, further comprising: identifying for the
time period a recruitment channel to provide at least one potential
employee for at least one job description that is not assigned to
at least one respective employee; and determining the at least one
potential employee to fill the at least one job description that is
not assigned to at least one respective employee.
7. The method of claim 1, further comprising: determining a change
in at least one of the retention probabilities based on a change in
at least one of the practices of the organization.
8. The method of claim 1, wherein identifying the plurality of
practices of the organization comprises identifying at least a
current practice or a potential practice of the organization.
9. The method of claim 1, wherein associating selected attributes
of the plurality of employee attributes for each employee of a
plurality of employees of the organization comprises associating
selected attributes with a current employee or a potential
employee.
10. A system for workforce planning for an organization, the system
comprising: one or more storage mediums; an employee attribute
module communicatively coupled with at least one of the one or more
storage mediums and configured to: identify a plurality of employee
attributes; and associate selected attributes of the plurality of
employee attributes with each employee of a plurality of employees
of the organization; an employer practices module communicatively
coupled with at least one of the one or more storage mediums and
configured to: identify a plurality of practices of the
organization; and associate each of the plurality of practices with
one or more impact factors, wherein each respective impact factor
represents an impact of the respective practice on employees based
on their associated attributes; and a retention probability module
communicatively coupled with at least one of the one or more
storage mediums and configured to: calculate a plurality of
retention probabilities, wherein each retention probability
represents a probability that one or more of the plurality of
employees will remain employed during a time period at the
organization based on the impact factors associated with respective
employees.
11. The system of claim 10, further comprising: a simulation module
communicatively coupled with at least one of the one or more
storage mediums and configured to simulate a workforce composition
for the organization utilizing the plurality of retention
probabilities for a plurality of time periods.
12. The system of claim 10, further comprising: an optimization
module communicatively coupled with at least one of the one or more
storage mediums and configured to optimize selection of a subset of
the plurality of practices for the organization based at least in
part on the calculated retention probabilities.
13. The system of claim 10, further comprising: a workforce
requirement module communicatively coupled with at least one of the
one or more storage mediums and configured to: identify for the
time period a plurality of job descriptions, wherein each job
description includes at least one job requirement; and assign each
employee to a respective job description from the plurality of job
descriptions.
14. The system of claim 10, further comprising: a promotion module
communicatively coupled with at least one of the one or more
storage mediums and configured to: identify for the time period at
least one of the plurality of job descriptions that is not assigned
at least one respective employee; and identify a promotion for at
least one of the employees to the at least one not assigned jobs
based on at least one attribute associated with the employee.
15. The system of claim 10, further comprising: a recruitment
module communicatively coupled with at least one of the one or more
storage mediums and configured to: identify for the time period a
recruitment channel to provide at least one potential employee for
at least one job description that is not assigned to at least one
respective employee; and determine the at least one potential
employee to fill the at least one job description that is not
assigned to at least one respective employee.
16. The system of claim 10, wherein the employer practices module
configured to identify the plurality of practices of the
organization comprises identifying at least a current practice or a
potential practice of the organization.
17. The system of claim 10, wherein the employee attributes module
configured to associate a subset of the plurality of employee
attributes with an employee of the organization comprises
associating a subset of the plurality of employee attributes with a
current employee or a potential employee.
18. A machine-readable storage medium comprising executable
instructions for modeling a workforce of an organization, the
executable instructions comprising code for: identifying a
plurality of employee attributes; associating selected attributes
of the plurality of employee attributes with each employee of a
plurality of employees of the organization; identifying a plurality
of practices of the organization; associating each of the plurality
of practices with one or more impact factors, wherein each
respective impact factor represents an impact of the respective
practice on employees based on their associated attributes; and
calculating a plurality of retention probabilities, wherein each
retention probability represents a probability that one or more of
the plurality of employees will remain employed during a time
period at the organization based on the impact factors associated
with respective employees.
19. The machine-readable storage medium of claim 18, wherein the
executable instructions further comprise code for: simulating a
workforce composition for the organization utilizing the plurality
of retention probabilities for a plurality of time periods.
20. The machine-readable storage medium of claim 18, wherein the
executable instructions further comprise code for: optimizing
selection of a subset of the plurality of practices for the
organization based at least in part on the calculated retention
probabilities.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a non-provisional patent application
claiming priority benefit of U.S. provisional patent application
Ser. No. 61/218,807, filed on Jun. 19, 2009 and entitled "Strategic
Workforce Planning Model," the entire disclosure of which is herein
incorporated by reference for all purposes.
BACKGROUND
[0002] The present invention relates to novel modeling techniques
in general and, in particular, to workforce planning
techniques.
[0003] Business leaders consistently identify "attracting,
retaining, and developing talent" as a priority, as well as a top
business challenge for the future. They recognize that success
often depends on having the right people, in the right place, at
the right time, and for the right cost. The ability to anticipate
and rapidly respond to changing workforce needs, and to allocate
resources to meet those needs, takes on increased importance in the
dynamic economy of today.
[0004] While talent is a top priority, many organizations find it
challenging to manage their workforce as strategically as they do
their financial and physical assets or their customer requirements.
The pace of change within economies, industries, and organizations
continues to accelerate, while labor markets continue to become
more competitive and more global, and the workforce continues to
become more diverse in terms of its demographics, expectations, and
goals. There is, therefore, a need in the art for novel models and
tools to allow human capital recommendations and decisions to be
made based on data and analytics, instead of relying on anecdotes
and assumptions.
SUMMARY
[0005] Methods, systems, and devices are described for advanced
workforce planning and management tools. Tools are described to
forecast human capital requirements (numbers, skill sets,
locations, timing) given a range of possible business scenarios,
and respond in real-time to changes in the assumptions behind those
scenarios. The impact of various human resource (HR) programs and
practices on the attraction and retention of employees may be
forecast. These impacts may vary based on demographics, job level,
and performance. The impact of turnover and movement may be
modeled, and the tradeoff between readiness (the ability of an
organization to staff its labor requirements in a timely manner)
and cost may be assessed.
[0006] In some embodiments, a method of workforce planning for an
organization is provided. The method may include identifying
multiple employee attributes. Selected attributes of the multiple
employee attributes may be associated with each employee from the
multiple employees of the organization. The method may also
identify multiple practices of the organization. Each of the
multiple practices of the organization may be associated with one
or more impact factors. Each respective impact factor may represent
an impact of the respective practice on employees based on their
associated attributes. Multiple retention probabilities may be
calculated. Each retention probability may represent a probability
that one or more of the multiple employees will remain employed
during a time period at the organization based on the impact
factors associated with respective employees.
[0007] Some embodiments of the method of workforce planning for an
organization may include simulating a workforce composition for the
organization utilizing the multiple retention probabilities for
multiple time periods. Some embodiments of the method of workforce
planning for an organization may include optimizing selection of a
subset of the multiple practices for the organization based at
least in part on the calculated retention probabilities.
[0008] In some embodiments, a system for workforce planning for an
organization is provided. The system may include one or more
storage mediums. The system may include an employee attribute
module communicatively coupled with at least one of the one or more
storage mediums. The employee attribute module may be configured to
identify multiple employee attributes. The employee attribute
module may be configured to associate selected attributes of the
multiple employee attributes with each employee from multiple
employees of the organization. The system may include an employer
practices module communicatively coupled with at least one of the
one or more storage mediums. The employer practices module may be
configured to identify multiple practices of the organization. The
employer practices module may be configured to associate each of
the multiple practices with one or more impact factors. Each
respective impact factor may represent an impact of the respective
practice on employees based on their associated attributes. The
system may include a retention probability module communicatively
coupled with at least one of the one or more storage mediums. The
retention probability module may be configured to calculate
multiple retention probabilities. Each retention probability may
represent a probability that one or more of the multiple employees
will remain employed during a time period at the organization based
on the impact factors associated with respective employees.
[0009] Some embodiments of the system for workforce planning for an
organization may include a simulation module communicatively
coupled with at least one of the one or more storage mediums and
configured to simulate a workforce composition for the organization
utilizing the multiple retention probabilities for multiple time
periods. Some embodiments of the system for workforce planning for
an organization may include an optimization module communicatively
coupled with at least one of the one or more storage mediums and
configured to optimize selection of a subset of the multiple
practices for the organization based at least in part on the
calculated retention probabilities.
[0010] In some embodiments, a machine-readable storage medium
including executable instructions for modeling a workforce of an
organization is provided. The executable instructions may include
code for identifying multiple employee attributes. The executable
instructions may include code for associating selected attributes
of the multiple employee attributes with each employee from the
multiple employees of the organization. The executable instructions
may include code for identifying multiple practices of the
organization. The executable instructions may include code for
associating each of the multiple practices with one or more impact
factors. Each respective impact factor represents an impact of the
respective practice on employees based on their associated
attributes. The executable instructions may include code for
calculating multiple retention probabilities. Each retention
probability may represent a probability that one or more of
multiple employees will remain employed during a time period at the
organization based on the impact factors associated with respective
employees.
[0011] Some embodiments of the machine-readable storage medium
including executable instructions for modeling a workforce of an
organization may include code for simulating a workforce
composition for the organization utilizing the multiple retention
probabilities for multiple time periods. Some embodiments of the
machine-readable storage medium including executable instructions
for modeling a workforce of an organization may include code for
optimizing selection of a subset of the multiple practices for the
organization based at least in part on the calculated retention
probabilities.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] A further understanding of the nature and advantages of the
present invention may be realized by reference to the following
drawings. In the appended figures, similar components or features
may have the same reference label. Further, various components of
the same type may be distinguished by following the reference label
by a dash and a second label that distinguishes among the similar
components. If only the first reference label is used in the
specification, the description is applicable to any one of the
similar components having the same first reference label
irrespective of the second reference label.
[0013] FIG. 1 illustrates a block diagram of a workforce planning
system in accordance with various embodiments.
[0014] FIG. 2 illustrates a block diagram of a workforce planning
system in accordance with various embodiments.
[0015] FIG. 3 illustrates a block diagram of a workforce planning
system in accordance with various embodiments.
[0016] FIG. 4 illustrates a table of workforce requirements for an
organization in accordance with various embodiments.
[0017] FIG. 5 illustrates a mobility probability table for an
organization in accordance with various embodiments.
[0018] FIG. 6 illustrates a graph illustrating a comparison between
different workforce planning scenarios where one or more of the
decisions are varied in accordance with various embodiments.
[0019] FIG. 7 illustrates a graph illustrating a comparison between
different workforce planning scenarios where one or more of the
decisions are varied in accordance with various embodiments.
[0020] FIG. 8 illustrates a graph illustrating a comparison between
different workforce planning scenarios where one or more of the
decisions are varied in accordance with various embodiments.
[0021] FIG. 9 provides a graphical representation of a workforce
simulation process in accordance with various embodiments.
[0022] FIG. 10 provides a table illustrating an example mapping of
employer programs to retention drivers in accordance with various
embodiments.
[0023] FIG. 11 provides an example of an employee description with
employee attributes in accordance with various embodiments.
[0024] FIG. 12 provides a matrix of employer program impacts based
on employee attributes.
[0025] FIG. 13 provides a table of employer program impacts in
accordance with various embodiments.
[0026] FIG. 14 provides a graph showing the results of an example
optimization run of a strategic workforce planning session for an
organization in accordance with various embodiments.
[0027] FIG. 15 provides a best solution for an optimization of a
strategic workforce planning session in accordance with various
embodiments.
[0028] FIG. 16 provides a best solution for an optimization of a
strategic workforce planning session in accordance with various
embodiments.
[0029] FIG. 17 provides a block diagram of a method of workforce
planning for an organization in accordance with various
embodiments.
[0030] FIG. 18 provides a block diagram of a method of workforce
planning for an organization in accordance with various
embodiments.
[0031] FIG. 19 provides a schematic of a device structure that may
be used to implement different embodiments.
DETAILED DESCRIPTION
[0032] Simulation and optimization technologies are described to
manage human capital decisions. Decision making systems, devices,
methods, and software and related tools are set forth to support
the development and implementation of workforce strategies,
programs, and policies. In the systems, devices, methods, and
software described, resources may be allocated to specific
practices (policies, programs, initiatives, organizational culture)
used to attract and retain valued employees. Resources may be
increased or decreased in various simulations until the proper
allocation of resources is identified to enable the achievement of
specific goals (e.g., attraction, retention, readiness, and
representation).
[0033] This description provides example embodiments only, and is
not intended to limit the scope, applicability, or configuration of
the invention. Rather, the ensuing description of the embodiments
will provide those skilled in the art with an enabling description
for implementing embodiments of the invention. Various changes may
be made in the function and arrangement of elements without
departing from the spirit and scope of the invention.
[0034] Thus, various embodiments may omit, substitute, or add
various procedures or components as appropriate. For instance, it
should be appreciated that in alternative embodiments, the methods
may be performed in an order different from that described, and
that various steps may be added, omitted, or combined. Also,
features described with respect to certain embodiments may be
combined in various other embodiments. Different aspects and
elements of the embodiments may be combined in a similar
manner.
[0035] It should also be appreciated that the following systems,
methods, and software may individually or collectively be
components of a larger system, wherein other procedures may take
precedence over or otherwise modify their application. Also, a
number of steps may be required before, after, or concurrently with
the following embodiments.
[0036] Systems, devices, methods, and software are described for
developing strategic workforce forecasts (in terms of numbers,
skills, demographics, locations, timing) linked to business plans
and financial and operational forecasts. The movement of people
into, within, and out of the organization may be modeled, factoring
in employee attributes. Such attributes may include demographics,
skill and performance data, information on the current and
potential practices that impact attraction, retention and movement,
and economic or environmental factors that will impact the business
and/or workforce. The outcome of the process may be made up of a
set of gaps between actual workforce and forecasted workforce
requirements. A decision-making optimization-based model may then
be used to determine the optimal strategy.
[0037] FIG. 1 illustrates aspects of a workforce planning system
100 in accordance with various embodiments. Workforce planning
system 100 may include an employee attribute module 105. Employee
attribute module 105 may identify multiple employee attributes.
Attributes may describe the characteristics of an employee, which
may include, but are not limited to, age, gender, ethnicity, work
experience, education, performance or talent review rating, etc.
Employee attribute module 105 may associate selected employee
attributes with each employee of an employer, which may be referred
to herein also as an organization. The organization may have
multiple employees for which different attributes are associated.
Employee attribute module 105 may be configured to associate
employee attributes with current employees and/or potential
employees. Further aspects of employee attribute module 105 are
described in more detail below.
[0038] Workforce planning system 100 may include employer practices
module 110. Employer practices module 110 may be configured to
identify multiple practices of the organization. Employer practices
may describe different programs, practices, and/or policies that
may impact attraction, movement, and retention of current and/or
potential employees. Employer practices may include, but are not
limited to, education programs, flex-time practices, healthcare
plans, retirement plans, incentive pay plans, compensation plans,
recognition/awards, ombudsman programs, training programs,
mentoring programs, and/or diversity/inclusiveness practices.
Employer practices module 110 may be configured to associate each
of the practices with one or more impact factors. The impact factor
may represent an impact of a respective practice on employees based
on their associated attributes. Employer practices module 110 may
be configured to identify practices of the organization for current
practices and/or potential practices of the organization. Further
aspects of employer practices module 110 are described in more
detail below.
[0039] Workforce planning system 100 may include a retention
probability module 115. Retention probability module 115 may
calculate multiple retention probabilities. Each retention
probability may represent a probability that one of the employees
will remain employed during a time period at the organization based
on the impact factors associated with respective employees. Further
aspects of retention probability module 115 are described in more
detail below.
[0040] FIG. 2 illustrates aspects of a workforce planning system
200 in accordance with various embodiments. Workforce planning
system 200 may include aspects of workforce planning system 100.
For example, workforce planning system 200 may include modules such
as employee attribute module 105-a, described as employee attribute
module 105 of system 100. Workforce planning system 200 may include
modules such as employer practices module 110-a, described as
module 110 of system 100. Workforce planning system 200 may include
modules such as retention probability module 115-a, described as
retention probability module 115 of system 100.
[0041] Workforce planning system 200 may include simulation module
205. Simulation module 205 may be configured to simulate a
workforce composition for the organization utilizing multiple
retention probabilities for multiple time periods. Simulation
module 205 may receive retention probabilities from retention
probability module 115-a. Further aspects of simulation module 205
are described in more detail below.
[0042] Workforce planning system 200 may include optimization
module 210. Optimization module 210 may be configured to optimize
selection of practices for the organization based at least in part
on the calculated retention probabilities. Optimization module 210
may work in conjunction with simulation module 205. Further aspects
of optimization module 210 are described in more detail below.
[0043] FIG. 3 illustrates aspects of a workforce planning system
300 in accordance with various embodiments. Workforce planning
system 300 may include aspects of workforce planning systems 100
and/or 200. For example, workforce planning system 300 may include
retention module 310, which may include modules such as employee
attribute module 105, employer practices module 110, and/or
retention probability module 115. In some embodiments, workforce
planning system 300 may be utilized as part of a simulation module
such as simulation module 205 of system 200.
[0044] Workforce planning system 300 may include workforce
requirement module 305. Workforce requirement module 305 may be
configured to identify job descriptions for one or more time
periods. In some embodiments, each job description may include at
least one job requirement. Workforce requirement module 305 may be
configured to assign each employee of an organization to a
respective job description from the multiple of job descriptions.
Further aspects of workforce requirement module 305 are described
in more detail below.
[0045] Workforce planning system 300 may include promotion and
mobility module 320. Promotion and mobility module 320 may identify
job descriptions that have not been assigned to at least one
employee for one or more time periods. In some embodiments,
promotion and mobility module 320 may also identify a promotion for
at least one of the employees to the at least one not assigned jobs
based on at least one attribute associated with the employee. In
some embodiments, promotion and mobility module 320 may also
identity movement of an employee from one job or job location to
another. Further aspects of promotion and mobility module 320 are
described in more detail below.
[0046] Workforce planning system 300 may include recruitment module
315. Recruitment module 315 may be configured to identify
recruitment channels to provide potential employees for different
job descriptions that have not been assigned to at least one
employee. Recruitment module 315 may be configured to identify
recruitment channels for different time periods. Recruitment module
315 may be configured to determine potential employees to fill job
descriptions that have not been assigned to at least one respective
employee. Further aspects of recruitment module 315 are described
in more detail below.
[0047] Workforce planning system 300 may include externalities
module 325. Externalities module 325 may be configured to identify
externalities such as economic factors that may have an impact on
employee decisions to remain with a company, practices an
organization may adopt, and/or the ability of an organization to
recruit new employees, merely by way of example. Further aspects of
recruitment module 325 are described in more detail below.
[0048] The modules of systems 100, 200, and 300 may include, for
example, one or more server computers, workstations, web servers,
or other suitable computing devices. The modules may be fully
located within a single facility or distributed geographically, in
which case a network may be used to integrate different components.
The modules may be configured to communicate with a data store. The
modules may manage different aspects of the workforce plan
modeling. The functions of each module may also be implemented, in
whole or in part, with instructions embodied in a memory, formatted
to be executed by one or more general or application-specific
processors. Modules may also be implemented in hardware and/or
software.
[0049] Modules such as modules 105, 110, and 115 may be
communicatively coupled with each other. In some embodiments,
modules such as 105, 110, and 115 may also be communicatively
coupled with one or more storage mediums. Similar communication may
also be found with the modules in systems 200 and 300.
[0050] I. The Workforce Planning Models:
[0051] In some embodiments, workforce planning systems, such as
systems 100, 200, and/or 300 and their associated modules may be
utilized to develop and to implement workforce planning models.
Workforce planning models may be based on an agent-based simulation
model. In some embodiments, individual employees are simulated as
"computerized agents" that interact with their environment and
periodically make decisions about their career in the organization
(the agent-based simulation looks at individual employees in the
abstract).
[0052] In some embodiments, employee decisions are defined by the
organization's practices (e.g., policies, programs, initiatives,
work environment) and current and future job opportunities.
Embodiments may consider probabilistic impacts of different
organization practices given specific employee attributes. For
different time periods, embodiments of different workforce planning
systems may model how each employee makes a decision (according to
a probability) of whether s/he stays in the organization for
another period, and how the organization may make decisions (again,
probabilistically) about the assignment of employees to particular
jobs, the promotion and movement of employees, and/or the
recruitment of new hires to fill available positions. Embodiments
of different workforce planning systems may also reflect how the
organization may also make decisions about which practices to
start, enhance, reduce, and discontinue.
[0053] Users may define the optimization objectives, identifying
the goal or goals the model may optimize (typically related to
readiness, cost, diversity representation, etc.), and other key
measures of success. A set of parameters may also be defined that
govern a simulation, including length of the planning horizon, the
practices to be included in the simulation, changes in business
strategy/priorities and environmental factors (e.g., economic
outlook, talent availability, business outlook) that need to be
factored into the simulation, and constraints (e.g., budget
limitations). The output of each simulation may be represented as a
set of metrics that relate to goal achievement.
[0054] A number of different optimization algorithms may be used to
optimize workforce planning. A user interface may be implemented in
software to structure the planning process. This may be delivered
through a web interface deployed through a software-as-a-service
(SAS) sales model.
[0055] In one embodiment, the basic steps in building a model are
as follows, although a number of different steps and combinations
may be used in other embodiments. Embodiments may be implemented
using workforce planning systems such as system 100, 200, and/or
300 and may utilize the different modules of these respective
systems. Some embodiments may utilize some or all of these steps
and may include additional steps as described below. In some
embodiments, building a model may include the following steps: (a)
define workforce requirements; (b) define key attributes most
relevant to categorizing employees (e.g., gender, ethnicity, age,
job level, performance rating, etc.); (c) identify current and
proposed organization practices, such as HR policies, programs and
initiatives designed to influence employee attraction, retention,
and movement within the organization; (d) determine the impact of
each practice such as policy, program, and initiative on employees
with different attributes; (e) define current and potential
recruitment channels and practices; and (f) define assumptions with
respect to promotion and movement within the organization.
[0056] Tools and templates may be provided for data collection,
external data to support model assumptions (e.g., correlation
between a specific practice and the corresponding retention rates
based on demographics), recruiting channel effectiveness in
recruiting employees with specific attributes, guidance in
determining relevant inputs to the model, and seasoned judgment in
the formulation of components of the model which are more
subjective, either by nature or due to the lack of historical data
when the model is first developed.
[0057] A. Define Workforce Requirements
[0058] A forecast of talent requirements given likely business
scenarios may be defined, translating business plans into a
specific workforce profile or staffing plan--number of positions,
types of skills, timing, location, etc.--and identifying those
factors that could change the required profile so that contingency
plans can be developed. Some embodiments may utilize a workforce
requirement module, such as workforce requirement module 305 of
FIG. 3, as part of this process.
[0059] Varying levels of specificity of business assumptions may be
used (e.g., is there a direct relationship between revenue/volume
and headcount requirements? What productivity improvements are
anticipated? Will a change in business direction require different
skills?). It may be appropriate to start out with a relatively
simplistic planning process and build sophistication over time.
[0060] In some embodiments, a workforce requirement module may
define specific job requirements (e.g., knowledge/skills/abilities,
education and experience, certifications). The requirements may be
taken from existing job descriptions or job postings. FIG. 4
illustrates an example table 400 of workforce requirements for an
engineering services company, although this may take a variety of
forms in other embodiments.
[0061] Column 1410 includes the different job categories (i.e., job
families, job types, roles, etc.) to be included in the workforce
planning simulation. Columns 2 through 5 420 include the minimum
job requirements an employee must meet in order to be qualified for
that job category. The number and type of requirements may depend
on each organization, and various combinations may be accommodated.
The precision of the model may, to some extent, depend on the level
of detail in specifying job requirements and employee attributes
that relate to job requirements.
[0062] Column 6 430 may be used to assign a priority to each job
category. This priority may reflect the relative importance of
filling that position, either internally or by recruiting a new
employee. In other words, a job with a higher value may be given
priority over one with a lower value if and when the two jobs are
competing for resources (i.e., budget). Column 7 440 includes the
salary range minimum, average salary, or another proxy for average
hiring rate for each job. The model may use this information to
estimate new hire salaries.
[0063] The remaining columns 450 include the estimated quantity
requirements for each category during upcoming periods. Planning
can be done on a quarterly, biannual, annual, or other basis.
[0064] The workforce requirement model may drive the planning
process, in that readiness will be measured as the extent to which
the defined job requirements are met. Therefore, the model may
drive job assignments, promotions and other internal movement, and
hiring decisions during various simulations.
[0065] B. Define the Attributes Relevant to Categorizing
Employees
[0066] Embodiments may also identify employee attributes to be
considered in the model. Some embodiments may utilize an employee
attribute module, such as employee attribute module 105 of systems
100 and/or 200, or as part of retention module 310 of system 300,
as part of this process. Attributes may describe the
characteristics of an employee, which may include, but are not
limited to, age, gender, ethnicity, work experience, education,
performance or talent review rating, etc. Attribute values may be
used to classify employees for the purpose of assessing the impact
of different HR decisions on different groups of employees. For
instance, employees may be tracked by two attributes: Gender and
Age. Then, within Gender we have two values: Male and Female; and
within Age we have four values: Veterans, Baby Boomers, Generation
X, and Generation Y. As an example, if an organization were to
implement a policy that allows for flex-time, a highly positive
impact may be predicted on the retention rate of Female, Generation
Y employees, whereas we would expect little or no effect on the
retention of Male, Baby Boomers.
[0067] C. Identify Current and Potential Employer Practices
[0068] In some embodiments, a comprehensive inventory of employer
practices currently in place that impact attraction, movement, and
retention may be developed, as well as any proposed modifications
to current practices, and any practices being considered for future
implementation. Some embodiments may utilize an employer practices
module, such as module 110 of systems 100 and 200, or as part of
retention module 310 of system 300, as part of this process. An
interface may be provided that organizes practices into different
user-defined attraction and retention drivers. These drivers
represent key factors that may affect employee decisions to join an
organization or to leave the organization. The drivers may include,
but are not limited to, (a) Compensation, (b) Benefits, (c) Career
Development Opportunities, (d) Work-Life Balance, (e) Manager
Quality, (f) Company Reputation and Performance, (g) Company
Culture and Work Environment, and (h) Job Satisfaction. These
drivers may be modified to reflect any categorization scheme used
by the organization.
[0069] D. Determine the Impact of Each Practice on Employees with
Different Attributes
[0070] Some embodiments may determine the impact of each practice
on an employee's behavior based on relevant employee attributes.
Some embodiments may utilize different
[0071] Modules, such as a retention probability module as seen with
retention probability module 115 of systems 100 and 200, or as part
of retention module 310 of system 300, as part of this process.
Some embodiments may utilize an employee attribute module 105 as
seen with employee attribute module 105 of systems 100 and 200, or
as part of retention module 310 of system 300 and/or employer
practices module 110 as seen with employee practices module 110 of
systems 100 and 200, or as part of retention module 310 of system
300, as part of this process. Historical data, external benchmark
data and anecdotal data, and informed judgment as to the expected
impact of different practices on employees with specific attributes
may be considered. Employee surveys may also be used.
[0072] E. Define Current and Potential Recruitment Channels
[0073] In addition to considering the impact of various employer
practices on current employees, some embodiments may consider the
effectiveness of alternate recruiting channels in bringing
employees into the organization. Some embodiments may utilize a
recruitment module, such as recruitment module 315 of system 300,
as part of this process. For each current and potential future
recruiting channel, the following parameters (or any combination
thereof) may be defined for some embodiments: (1) a probability
distribution of the population in that channel, as defined by key
employee attributes; (2) a cost-per-hire figure for that channel by
job level; (3) an effectiveness factor for that channel by job
level that defines the efficiency of obtaining recruits; and (4) a
maximum number of new hires that can be obtained from that channel,
by job level or any combination of employee attributes.
[0074] The model may be populated with available published data on
common channels (e.g., universities, job sites, etc.), but
parameters related to effectiveness and cost will vary by
organization, so the model will be enhanced by historical,
company-specific data. The probability distribution of the
population in a channel may define the likelihood that a new hire
will have certain desired attributes.
[0075] The cost-per-hire figure for the channel may be set as the
average amount it costs an organization to hire a new employee
utilizing that particular channel. It may include costs and
expenses related to hiring, including, but not limited to, setup
costs (i.e., travel costs to a university, setting up a booth at a
job fair, etc.), advertising costs, recruiting costs (i.e.,
recruiters' time, managers' time in interviews, etc.), agency fees,
employee referral fees, relocation expenses, signing bonuses, etc.
If an organization does not calculate cost-per-hire for each
channel, but has a good estimate of average cost-per-hire by job
level (e.g., hourly, professional, middle management), each
channel's cost-per-hire figure may be derived by multiplying the
cost-per-hire times the effectiveness factor, described below.
[0076] The effectiveness factor may relate to the efficiency of the
channel in yielding qualified candidates for a given job family or
level. It may be multi-dimensional, and can consider such factors
as percentage of jobs filled by this channel, offers as a
percentage of interviews, first-year retention rates, offer
acceptance rate. Effectiveness can be measured in many ways.
[0077] Finally, an estimate of the maximum number of new hires an
organization expects to obtain from each channel may be made for
each job family and/or level, during each period. This information
may be forecast based on historical recruitment data, adjusted to
reflect expected future state, but it may be based simply on the
best judgment of in-house recruiting experts.
[0078] The data in the recruitment channels may be used to simulate
new hires entering the organization, according to alternative
recruitment budget allocations across channels and the probability
distributions of the population associated with each channel. Such
simulation may be done using a simulation module such as simulation
module 205 of system 200, merely by way of example.
[0079] F. Define Assumptions with Respect to Promotion and Movement
within the Organization
[0080] Some embodiments may consider how a workforce planning model
may relate to the mobility of employees within the
organization--promotions, job changes, location changes. Some
embodiments may utilize a promotion and mobility module, such as
promotion and mobility module 320 of system 300, as part of this
process. Attributes associated with each employee may include their
level within the organization, which may be defined either
generically for the entire organization or by defined career paths
by job family. Using historic data on mobility, a probability table
may be developed. This table may predict the likelihood that
employees with various combinations of attributes will move within
the organization during the planning timeframe.
[0081] FIG. 5 shows an example of a mobility probability table 500
for an engineering services company, merely by way of example. In
this example, employees may be described by tenure 510, job level
520, performance rating 530, and personality type 540, and a
movement probability 550 is assigned to each employee with a
distinct combination of employee attributes, as shown in the last
column. Probability 550 may represent the likelihood that an
employee with the attributes shown in the first four columns 510,
520, 530, and 540 may change jobs or locations during the upcoming
period. These data may be used to simulate promotion/advancement of
employees within the organization.
[0082] II. Decision Scenario Testing:
[0083] Once the model has been populated with the data described
above (or any subset thereof), different decision scenarios can be
tested to predict the outcome of various employer or HR decisions.
These decisions may relate to, but not be limited to, the
following. Some embodiments that include decision scenario testing
may utilize an optimization module, such as optimization module 210
of FIG. 2. Decision scenario testing may also utilize a simulation
module, such as simulation module 205 of FIG. 2. Other modules of
systems 100, 200, and/or 300 may also be utilized in different
embodiments.
[0084] Changes in employer practices: An organization may
prioritize the practices it may implement, maintain, change, or
discontinue, and the level of funding for each. One application of
the model is to determine the budget allocation that results in the
highest possible level of readiness while meeting defined
representation goals. Some embodiments may utilize an employer
practices module, such as employer practices module 110 of systems
100 and 200, or as part of retention module 310 of system 300, as
part of this process.
[0085] Allocation of recruitment budget: The model may consider how
budget dollars are allocated across recruitment channels in
simulating movement into the organization. One application of the
model is to determine the budget allocation that will most likely
enable the organization to achieve readiness and
representation/diversity goals.
[0086] Economic/business outlook and other environmental
parameters: Factors such as economic forecasts, the unemployment
rate, financial strength of the organization, demand and supply
gaps for certain skills, etc., affect employee decisions about
staying in a job or seeking other employment opportunities. How
this factor is defined may be unique to each organization,
depending on the factors that are most relevant to an organization
and the degree to which these factors can be based on quantitative
metrics. Some embodiments may utilize an externalities module, such
as externalities module 325 of system 300, as part of this
process.
[0087] FIGS. 6-8 show graphs 600, 700, 800 illustrating example
comparisons between different scenarios where one or more of the
decisions described above are varied. The first scenarios, which
are denoted as the Base scenarios 610, 710, 810 respectively, may
refer to the situation where the organization continues to conduct
business as usual; in other words, no new employer/BR practices are
added or modified, and investment in current recruitment channels
remains the same. The second scenarios, denoted What-if 620, 720,
and 820 respectively, represent the cases where the user has
manually changed certain decisions to add or modify an HR practice,
or to reallocate recruitment investments. The third scenarios,
denoted Optimized 630, 730, and 830 respectively, refer to the
solution found to be the best solution using one or more
optimization algorithms.
[0088] Referring first to FIG. 6, although the starting readiness
level is about 85%, both the base and the what-if scenarios perform
poorly in terms of readiness (reaching levels of 60% and 83% at the
end of Year 3, respectively), while the optimized scenario results
in an increased readiness level to 97% at the end of Year 3.
[0089] Next, referring to FIG. 7, in terms of new hires, after a
small upward adjustment from 131 new hires in Year 1 to 137 new
hires in Year 2, in order to account for initial turnover, the
Optimized 730 scenario becomes stable at 137 new hires in Years 2
and 3. However, since turnover is much higher in the Base 710 and
What-if 720 scenarios, the adjustments are much larger, and the
number of new hires each year is unstable. The Base 710 scenario
requires 137 new hires in Year 1, 133 in Year 2, and 138 in Year 3;
the What-if 720 scenario requires 122 new hires in Year 1, 142 in
Year 2, and 196 in Year 3.
[0090] The composition of turnover may also be analyzed, and with
the correct set of HR programs and practices, the Optimized 730
scenario may improve retention of the right kind of employees,
described by a certain type of attributes. For example, if an
organization wants to increase female representation; then, the
organization may be interested in investing its budget in
practices/programs designed to increase the probability of
retention of female employees, such as a comprehensive healthcare
program. Such a program may also increase the probability that
other types of employees will stay, but its impact on female
employees may be higher. Then, when looking to hire new employees,
it may be much easier to reach the desired levels if turnover of
female employees were lower to begin with. See, for example, FIG.
8, where the trend in female employees is charted for three years.
In Base 810 scenario, the number of female employees decreases
steadily if the organization continues with its current HR programs
as implemented. In What-if 820 scenario, certain programs have been
chosen which are designed to reduce turnover of female employees;
however, it takes two years for the downward trend to be
overturned, because the hurdle that has to be overcome through
hiring is large. This can be explained by considering that, given
budget restrictions, the programs chosen under What-if 820 scenario
do not produce the biggest impact per dollar invested. On the other
hand, Optimized 830 scenario shows an increasing trend in the
number of female employees from the start. This is because, under
this scenario, the investment in HR programs is chosen to produce
the greatest impact in terms of the goal of female retention.
[0091] III. Simulation Process
[0092] FIG. 9 provides a graphical representation 900 of an
embodiment of a workforce simulation process. Some embodiments may
utilize a simulation module, such as simulation module 205 of
system 200, as part of this process. Other modules may also be
utilized, including but not limited to modules as seen in system
300, such as workforce requirement module 305, retention module
310, recruitment module 315, promotion and mobility module 320,
and/or externalities module 325. The simulation process may model
workforce impacts across a defined number of measurement periods
which may be expressed in months, quarters, years, etc. During each
measurement period, the following steps may occur: (a) each
employee makes a decision whether to stay or leave the
organization. This may be decided based on a probabilistic test
which calculates the impact of the factors identified above on an
employee based on their unique combination of attributes; (b) once
all employees have made a decision, employees who remain in the
organization are assigned to available jobs, based on the match
between employee attributes and job requirements; (c) remaining
jobs are filled by employees who have a high probability of
mobility/promotion and attributes which match the requirements of
the target job; and (d) new employees are recruited from the
appropriate recruitment channels to fill open jobs, as long as the
budget allows for the additional recruitment.
[0093] In FIG. 9, circles represent employees and rectangles
represent jobs. Employees may be described as executives 940,
middle management 950, or non-managerial 960, merely by way of
example. FIG. 9 also show organization policies 970, such as HR
programs, policies, and initiatives. In this example, the employee
decision and job assignment process is done every year, for three
years. FIG. 9 also shows recruitment channels 980, such as
recruitment pools. For this example, the initial workforce is
composed of two executives, three middle managers and four
non-managerial employees. However, during the first year period
910, one executive and one non-managerial employee decide to leave.
The remaining employees are assigned to available jobs. In
addition, one middle manager is promoted into an executive level
job and one non-manager is promoted into a middle manager level
job, as depicted by the solid up arrows in the first year. Finally,
a new employee is hired to fill an available non-managerial
position.
[0094] During Year 2 period 920, one executive, one middle manager,
and one non-manager are separated; one non-manager is promoted into
a middle manager job; and two new employees are hired. During this
year, an additional non-managerial job is created, but remains
unfilled due to lack of budget.
[0095] During Year 3 period 930, there are no promotions; a new
middle management job and a new non-management job are created,
requiring five new employees to be hired.
[0096] IV. Retention Probabilities
[0097] FIG. 10 is a table 1000 illustrating an example mapping of
programs to retention drivers. An "X" in a matrix cell means that
the program is linked to a driver. The table shows a sample matrix.
Note that in most cases all options of a particular program are
linked to the same driver(s). Table 1000 includes different
possible employer practices such as education programs 1010,
flexible workplace policies 1020, healthcare policies 1030, and
retirement policies 1040. Table 1000 also shows how different
employer practices may be linked to different drivers 1050, such as
base pay, healthcare plan, career development, work/life balance,
manager quality, company reputation, pay for performance,
retirement plan, and/or company culture.
[0098] Embodiments may use a variety of different modules as part
of the following processes. For example, some embodiments may
utilize an employee attribute module, such as employee attribute
module 105 of systems 100 and/or 200, as part of this process. Some
embodiments may utilize an employer practices module, such as
employer practices module 110 of systems 100 and/or 200, as part of
this process. Some embodiments may utilize a retention probability
module, such as retention probability module 115 of systems 100
and/or 200, and/or retention module 310 of system 300, as part of
this process.
[0099] Employees may be described by a set of attributes. An
example of an employee description is shown in the table 1100 of
FIG. 11. Some embodiments may utilize an employee attribute module,
such as module 105 of systems 100 and/or 200, as part of this
process. FIG. 11 shows general attributes 1110 and specific
attributes 1120 for the employee description.
[0100] A matrix of practice impacts may be created based on
employee attributes. FIG. 12 is an example of such a matrix shown
in a table format 1200, with impacts coded as follows: -3=highly
negative, -1=negative, 0 or blank=neutral (no impact), 1=positive,
3=highly positive. FIG. 12 also shows different potential practices
of the organization, such as education programs 1210, flexible
workplace practices 1220, healthcare plans 1230, and retirement
plans 1240. FIG. 12 furthermore shows employee attributes 1250,
such as ethnicity, gender, age, tenure, job level, performance
rating, dependents, and personality type. Some embodiments may
utilize an employer practices module, such as module 110 of systems
100 and/or 200, as part of this process.
[0101] The probability of retention for a particular employee,
P(r), may computed as:
P(r)=.DELTA.P(r)+Base, Eq. 1
where .DELTA.P(r) is the change in retention probability from the
implementation of a set of programs, and Base is the base (or
current) retention probability for the employee. The Base may be
obtained from historical records. Some embodiments may utilize a
retention probability module, such as retention probability module
115 of systems 100 and/or 200, and/or retention module 310 of
system 300, as part of this process.
[0102] .DELTA.P(r) may be calculated from the impact of the
programs. The impact may be calculated as:
Impact=.beta.[.alpha..sub.1S(d.sub.1)+.alpha..sub.2S(d.sub.2)+ . .
. +.alpha..sub.nS(d.sub.n)], Eq. 2
where .alpha.(i) is the industry-wide impact weight of driver i,
S(d.sub.i) is the impact score of driver i for the employee, and
.beta. is a normalizing constant.
[0103] The industry-wide impact weights may represent the relative
importance of each driver, and may be obtained from existing survey
data. For example, in one embodiment, the industry-wide impact
weights may be as follows:
.alpha.(Base Pay)=0.79, .alpha.(Healthcare)=0.31, .alpha.(Career
Devel)=0.27, .alpha.(Work/Life)=0.26, .alpha.(Manager)=0.50,
.alpha.(Company Reputation)=0.18, .alpha.(Pay for
Performance)=0.31, .alpha.(Retirement)=0.25,
.alpha.(Culture)=0.13
[0104] An employee impact score for each driver may be set as the
score, given the employee's attributes, that produces the absolute
maximum impact. For example, for the employee described by FIG. 11,
the program impacts would be as shown in the table 1300 of FIG. 13.
FIG. 13 also shows different potential practices of the
organization, such as education programs 1310, flexible workplace
practices 1320, healthcare plans 1330, and retirement plans 1340.
FIG. 13 furthermore shows employee attributes 1350, such as
ethnicity, gender, age, tenure, job level, performance rating,
dependents, and personality type.
[0105] The calculation of the final score for each program may
be:
TABLE-US-00001 if (abs(Min)>=Max) Final Score = Min; else Final
Score = Max;
meaning that a negative score dominates a positive score of equal
magnitude.
[0106] Once the scores for each individual program have been
obtained, they may be related back to the drivers. Looking at the
driver-program matrix shown in table 1000 of FIG. 10, the
"Work/Life Balance" driver is linked to Education Program and
Flexible Workplace programs. Therefore, the employee's final score
for the "Work/Life Balance" driver may be set to the maximum impact
of the linked program scores for those program options that are
implemented.
[0107] For this example, assume that the implemented program
options are No Tuition Reimbursement within Education Programs, and
Flexible Start Time within the Flexible Workplace program. This
employee described by FIG. 11 would have a score of -1 for
Education Programs and 3 for Flexible Workplace, resulting in an
overall score for the "Work/Life Balance" driver of,
S(d.sub.worklife)=3. Again, the maximum absolute score is selected
where ties favor negative scores.
[0108] The normalizing constant, .beta., may be calculated as
follows. Using the highest positive score for each driver (in this
case 3) in the equation:
B=.alpha..sub.1S(d.sub.1)+.alpha..sub.2S(d.sub.2)+ . . .
+.alpha..sub.nS(d.sub.n) Eq. 3
For the example, this would be:
B=0.79(3)+0.31(3)+0.27(3)+0.26(3)+0.50(3)+0.18(3)+0.31(3)+0.25(3)+0.13(3-
)=9
Then, in order to find .beta., the inverse of B is taken:
.beta.=1/B Eq. 4
and again,
Impact=.beta.[.alpha..sub.1S(d.sub.1)+.alpha..sub.2S(d.sub.2)+ . .
. +.alpha..sub.nS(d.sub.n)]
[0109] However, since it may not be desirable for the retention
probability to vary too much (or go beyond 100% or below 0), its
impact may be limited. Once the impact score is calculated, the
change .DELTA.P(r) in the retention probability may be calculated
as:
If the Impact score is positive, then
.DELTA.P(r)=(1-Base)/p*Impact; Eq. 5
If the impact score is negative, then
.DELTA.P(r)=(Base)q*Impact Eq. 6
where p and q are scalar parameters, limiting the change in
retention probability to no more than 1/q and 1/p of the current
difference between Base and 0 or 100%, respectively. The new
retention probability for the employee may then be calculated as
P(r)=.DELTA.P(r)+Base.
[0110] Modeling all possible combinations of practices, recruitment
budget allocations, and environmental parameters would be
computationally challenging, to say the least Therefore,
optimization algorithms may be used to find the best solutions to
simulation problems efficiently. This enables the user to focus on
evaluating a limited number of potential solutions that
optimization technology has concluded will most likely yield the
best results. Some embodiments may utilize an optimization module,
such as optimization module 210 of system 200 as part of this
process and that which follows below.
[0111] The graph 1400 of FIG. 14 shows the results of an example
optimization run of a strategic workforce planning session for an
engineering services firm. The performance curve 1410 represents
the readiness level, and each dot on the performance curve
represents an improving solution (set of selected programs) in
terms of readiness.
[0112] The identified goals for the optimization were to maximize
readiness on a three-year planning horizon, while making sure that,
at the end of the three years, non-white and female employees would
represent at least 30% of the total workforce. In addition, the
company imposed a $4M annual recruitment budget, a $10M annual
retention budget, a $100M annual compensation budget, and a total
annual HR budget (recruitment+retention +compensation) of
$105M.
[0113] Possible best solutions may be shown in tables 1500, 1600 in
FIGS. 15 and 16. FIG. 15 shows both general employer practices or
programs 1510 and specific employer practices or programs 1520,
while FIG. 16 shows recruitment channels 1610 and budget allocation
1620 for each channel. If the program options marked "YES" in table
1500 were implemented, and there were a $4M annual recruitment
budget as depicted in table 1600, then the model predicts a
readiness level of 96.3% at the end of three years. The total
investment in personnel costs and expenses is $94.01M, of which
$3.27M is spent in recruitment of new hires and $90.73M is spent in
compensation, benefits, and other retention programs.
[0114] If this particular solution is simulated to obtain more
details, women are expected to grow from 24.7% of the workforce to
39.8%, minorities from 25.5% to 43.5%, and that the age composition
of the workforce varies from 35.6% to 40.2% in Generation Y, 23.8%
to 42.6% in Generation X, and 40.6% to 25.2% in Baby Boomers.
Average annual turnover is 6.7%, total new hires are 39.4%, and
total separations are 19.4%.
[0115] This could be further drilled down within each job level to
view trends in workforce composition, performance ratings, etc.
which may aid in proactive decision-making.
[0116] Different methods may be utilized to model workforce
composition that may utilize simulations and optimization in some
cases as discussed above. For example, FIG. 17 provides a block
diagram of a method 1700 of workforce planning for an organization
in accordance with various embodiments. Method 1700 may be
implemented with systems 100, 200, and/or 300 of FIG. 1, FIG. 2,
and FIG. 3 respectively. At block 1705, multiple employee
attributes may be identified. At block 1710, selected attributes of
the multiple employee attributes may be associated with each
employee of the organization; the organization may have multiple
employees. The employees of the organization may be current
employees and/or potential employees of the organization. At block
1715, multiple practices of the organization may be identified.
Practices of the organization may be current practices and/or
potential practices of the organization. At block 1720, each of the
practices may be associated with one or more impact factors. Each
respective impact factor may represent an impact of the respective
practice on employees based on their associated attributes. At
block 1725, multiple retention probabilities may be calculated.
Each retention probability may represent a probability that one or
more of the employees will remain employed during a time period at
the organization based on the impact factors associated with
respective employees.
[0117] FIG. 18 provides a block diagram of method 1800 of workforce
planning for an organization in accordance with various
embodiments. Method 1800 may be implemented with systems 100, 200,
and/or 300 of FIG. 1, FIG. 2, and FIG. 3 respectively. Method 1800
may include aspects of method 1700, such as the steps performed
blocks 1705, 1710, 1715, 1720, and 1725.
[0118] At block 1810, workforce composition for the organization
may be simulated utilizing the multiple retention probabilities for
multiple time periods. After a simulation for a given time period,
information regarding retained employees may be utilized for a
subsequent time period, providing the information back to blocks
1705-1725. At block 1820, selection practices for the organization
may be optimized based at least in part on the calculated retention
probabilities. In some embodiments, information from optimization
block 1820 may be fed back into simulation block 1820 as part of a
simulation-optimization process. Budgets can be used to constrain
hiring and practice optimization.
[0119] The simulations that may occur at block 1810 may also take
into account other information besides retention probabilities. For
example, at block 1830, multiple job descriptions may be identified
for a given time period. Each job description may include at least
one job requirement. At block 1835, each employee may be assigned
to a respective job description from the multiple job descriptions.
At block 1840, one or more job descriptions that have not been
assigned to at least one respective employee may be identified. At
block 1845, one or more promotions may be identified for at least
one of the employees to at least one non-assigned job based on at
least one attribute associated with the employee. This information
regarding unassigned jobs and/or job promotions may be provided to
block 1810 as part of a simulation process.
[0120] At block 1850, a recruitment channel to provide at least one
potential employee for at least one job description that is not
assigned to at least one respective employee may be identified for
a given time period. At block 1855, at least one potential employee
may be determined to fill at least one job description that is not
assigned to at least one respective employee. This information may
be provided to block 1810 as a part of a simulation process.
[0121] Some embodiments of a method of workforce planning may
include determining a change in at least one of the retention
probabilities based on a change in at least one of the employer
practices or employee attributes
[0122] Methods 1700 and 1800, may be implemented on modules such as
those seen in systems 100, 200, and/or 300, and may also be
implemented with different device structures.
[0123] For example, a device structure 1900 may be used to
implement the functionality described, which may be illustrated
with the schematic diagram of FIG. 19. Structure 1900 may be used,
for example, to implement method 1700 of FIG. 17 and/or method 1800
of FIG. 18. Structure 1900 may be used also to implement the
modules and different aspects of systems 100, 200, and/or 300 of
FIG. 1, FIG. 2, and FIG. 3 respectively. This drawing broadly
illustrates how individual system elements of a device may be
implemented, whether in a separated or more integrated manner. The
example structure is shown made up of hardware elements that are
electrically coupled via bus 1905, including processor(s) 1910
(which may further comprise a DSP or special-purpose processor),
storage device(s) 1915, input device(s) 1920, and output device(s)
1925. The storage device(s) 1915 may be a machine-readable storage
media reader connected to any machine-readable storage medium, the
combination comprehensively representing remote, local, fixed, or
removable memory, storage devices, or other storage media for
temporarily or more permanently containing computer-readable
information. The communications systems interface 1945 may
interface to a wired, wireless, or other type of interfacing
connection that permits data to be exchanged with other devices.
The communications system(s) 1945 may permit data to be exchanged
with a network.
[0124] The structure 1900 may also include additional software
elements, shown as being currently located within working memory
1930, including an operating system 1935 and other code 1940, such
as programs or applications designed to implement methods of the
invention. It will be apparent to those skilled in the art that
substantial variations may be used in accordance with specific
requirements. For example, customized hardware might also be used,
or particular elements might be implemented in hardware, software
(including portable software, such as applets), or both.
[0125] It should be noted that the methods, systems, and devices
discussed above are intended merely to be examples. It must be
stressed that various embodiments may omit, substitute, or add
various procedures or components as appropriate. For instance, it
should be appreciated that, in alternative embodiments, the methods
may be performed in an order different from that described, and
that various steps may be added, omitted, or combined. Also,
features described with respect to certain embodiments may be
combined in various other embodiments. Different aspects and
elements of the embodiments may be combined in a similar manner.
Also, it should be emphasized that technology evolves and, thus,
many of the elements are examples and should not be interpreted to
limit the scope of the invention.
[0126] Specific details are given in the description to provide a
thorough understanding of the embodiments. However, it will be
understood by one of ordinary skill in the art that the embodiments
may be practiced without these specific details. For example,
well-known circuits, processes, algorithms, structures, and
techniques have been shown without unnecessary detail in order to
avoid obscuring the embodiments.
[0127] Also, it is noted that the embodiments may be described as a
process which is depicted as a flow diagram or block diagram.
Although each may describe the operations as a sequential process,
many of the operations can be performed in parallel or
concurrently. In addition, the order of the operations may be
rearranged. A process may have additional steps not included in the
figure.
[0128] Moreover, as disclosed herein, the term "memory" may
represent one or more devices for storing data, including read-only
memory (ROM), random access memory (RAM), magnetic RAM, core
memory, magnetic disk storage mediums, optical storage mediums,
flash memory devices, or other computer-readable mediums for
storing information. The term "computer-readable medium" includes,
but is not limited to, portable or fixed storage devices, optical
storage devices, wireless channels, a sim card, other smart cards,
and various other media capable of storing, containing, or carrying
instructions or data.
[0129] Furthermore, embodiments may be implemented by hardware,
software, firmware, middleware, microcode, hardware description
languages, or any combination thereof. When implemented in
software, firmware, middleware, or microcode, the program code or
code segments to perform the necessary tasks may be stored in a
computer-readable medium such as a storage medium. Processors may
perform the necessary tasks.
[0130] Having described several embodiments, it will be recognized
by those of skill in the art that various modifications,
alternative constructions, and equivalents may be used without
departing from the spirit of the invention. For example, the above
elements may merely be a component of a larger system, wherein
other rules may take precedence over or otherwise modify the
application of the invention. Also, a number of steps may be
undertaken before, during, or after the above elements are
considered. Accordingly, the above description should not be taken
as limiting the scope of the invention.
* * * * *