POMS 2011 Full Paper
-
Upload
jennifer-ryan -
Category
Documents
-
view
214 -
download
0
Transcript of POMS 2011 Full Paper
-
8/4/2019 POMS 2011 Full Paper
1/14
-
8/4/2019 POMS 2011 Full Paper
2/14
-
8/4/2019 POMS 2011 Full Paper
3/14
3
complexities of a professional service environment, such as the varying nature of customer
requirements, the different types and levels of skills available, the question of whether the
work experience obtained in one industry sector can transfer to other sectors, the level of
cross-training and flexibility employees possess and the speed at which employees learn and
forget skills. Finally, the workforce planning process must be completed within the context of
the organization's objective, such as profit maximization, subject to a set of operational
constraints.
While the concept of workforce planning is not new, it is suggested that the discipline
has been in decline since the 1970's (Cappelli, 2009). Motivated by calls to action from a
number of leading academics and business practitioners (e.g., Dietrich, 2006; Dietrich and
Harrison, 2006; Cappelli, 2009), the area of resource allocation and human capital
management in professional services has emerged as a significant research opportunity in
recent years. Due to the increasing economic importance of professional services and the
projected employment growth in the sector, there is a requirement for new systems to control
the workforce assignment process which take account of the unique characteristics of the
"white collar" work carried out in professional service firms (Hopp, Iravani and Liu, 2009).
To achieve this, these companies are starting to focus on "Talent Analytics'', which involves
the use of detailed analytical models, rather than a reliance on "gut instinct'' as a method of
improving their competitive advantage, by analyzing employee ("talent'') data in order to
maximize productivity (Davenport, Harris and Shapiro, 2010).
We develop a comprehensive workforce planning model for professional service
organizations to enable these organizations to optimize the allocation of their skilled
personnel to client projects and to provide strategic and practical insights into different
workforce planning policies through the numerical analysis of that model. The remainder of
this paper is structured as follows: In the literature review section, we provide an overview of
-
8/4/2019 POMS 2011 Full Paper
4/14
4
the salient elements from service operations and workforce planning literature, and identify
the gap which we believe our research will address. We then describe the model using the
information gathered during semi-structured interviews with executives in professional
service firms. The experimental data section outlines the creation of the test firms and the
experiments conducted. We conclude with a discussion of the results and suggestions for
future work.
2.0 Literature Review
Professional service firms have become an increasingly important industry group. Drucker
(1999) has described the shift away from capital-intensive, manufacturing based industry,
towards a knowledge-intensive industry. Goodale, Kuratko and Hornsby (2008) report that
employment in professional and business services in the United States has increased by over
50% since 1990. Within the services sector, "professional and related occupations" and
"management, business and financial occupations" will rank as the first and third fastest
growing occupation categories between 2004 and 2014 (Hopp et al., 2009). The importance
and impact of large professional service firms is identified by Greenwood, Morris, Fairclough
and Boussebaa (2010), who argue that such firms are critical corporate players in the 21st
century as they "sell expertise" and deliver customized solutions to the world's largest
corporations and governments.
The category professional service has been classified as the activities undertaken by
groups such as business consultants, engineers, doctors, lawyers, accountants, which all share
common characteristics of a high degree of customer interaction, customization and labor
intensity (Schmenner, 1986). In general, service processes can be classified by three main
types: professional, service shop and mass service (Silvestro, Fitzgerald, Johnston and Voss,
1992). Using this general classification of services, a significant portion of the literature onworkforce planning related to services falls within the mass services and service shop
-
8/4/2019 POMS 2011 Full Paper
5/14
5
segments, where employee tasks are considered to be homogeneous or where there is a very
small number of customer types. Workforce planning research in the professional service
sector is seen to lag behind manufacturing and other service sectors (Dietrich, 2006). Much of
the existing research on workforce planning has focused on manufacturing-based supply
chains (e.g., Azmat, Hurlimann and Widmer, 2004; Celano, Costa and Fichera, 2008)or onthe mass services category, with an emphasis on tactical decision-making activities such as
the development of schedules and rosters for service employees and the assignment of
employees to specific tasks within those rosters. For instance, the transportation services
literature focuses on the airline sector, with crew scheduling, pairing and rostering of
particular interest (e.g., AhmadBeygi, Cohn and Weir, 2009; Kohl and Karisch, 2004).
Similarly, the nurse scheduling and rostering literature describes the generation of optimal
rosters, subject to personnel preferences and regulatory constraints (e.g., Cheang, Lim and
Rodrigues, 2003).
The key characteristic of the professional services segment is that it consists of highly
unique and customized activities that are heavily dependent on human resources. In addition,
the unit of sales in professional service firms is typically a project contract (Dietrich, 2006).
Projects by their nature are unique activities with specified time parameters, requiring human
and non-human resources (Gray and Larson, 2010). Most professional service organizations
operate as multi-project systems (Engwall and Jerbrant, 2003), each with a portfolio of
customer projects having different start dates, durations and end dates. This environment
requires such organizations to develop an adequate human resource allocation or capacity
management process that optimizes use of organizational resources (Hendriks, Voeten and
Kroep, 1999). Similar to the mass service and service shop literature, existing workforce
planning research within the professional service sector deals with the tactical short term
perspective of allocation and scheduling. This approach has been addressed in research on
-
8/4/2019 POMS 2011 Full Paper
6/14
6
engineering consulting (e.g., Brennan and Orwig, 2000; Brennan, 2006), financial auditing
(e.g., Dodin and Elimam, 1998) and hospital residents (e.g., Franz and Miller, 1993; Sherali,
Ramahi and Saifee, 2002; Topaloglu, 2009; Ovchinnikov and Milner, 2008).
Recently published work from the IBM Watson Research organization (Gresh,
Connors, Fasano and Wittrock, 2007) and Hewlett Packard's Palo Alto Research Laboratories
(Santos, Zhang, Gonzalez and Jain, 2009) has led to the development of specific workforce
planning tools to assist with the assignment of professional service employees to tasks and
the generation of service staffing plans. These decision-making tools were developed to solve
a specific problem within the IBM and Hewlett Packard organizations, but apart from Huang,
Lee, Song and Eck (2009) they provide little insight into the impact of strategic decisions
made by professional service organizations, such as workforce size, skill mix and cross-
training policies, worker flexibility, employee departure and hiring rates and the ability of
employees to learn new skills at different rates. The focus of the research presented in the
current paper is to analyze the impact of these strategic decisions on the workforce planning
process in professional service firms, thereby addressing a gap the literature, which to date
has focussed on the development of staff planning and scheduling tools.
3.0 Model Development and Formulation
A comprehensive mixed integer linear programming model of the workforce planning
process for engineering professional service organizations will now be described, with the
objective of optimally matching professional service employees to client projects in order to
maximize the profitability of the firm. The workforce planning/allocation problem can be
considered a sub-problem of the broader assignment problem (AP), the classic version of
which is well described by Kuhn (2005).
-
8/4/2019 POMS 2011 Full Paper
7/14
7
3.1 Model Description
Informed by data gathered during semi-structured interviews with senior executives in
several large professional services firms, our mixed integer programming model captures the
complexities of the workforce planning process in this sector. In professional service firms,
the workforce planning process must capture all of the attributes of the firm's resources (i.e.,
employees) and optimally assign these resources to satisfy customer demand, represented by
projects, while meeting the overall objective of the firm, such as profit maximization. In
developing the model, we capture the various attributes of both employees and projects. Each
employee is described in terms of skills possessed, grade or rank in the organizational
hierarchy, availability in each planning time period, sub-sector or line of business
specialization, overhead costs, revenue and geographical location. Each customer project is
described according to its start date, duration, skills and number of hours each skill is
required in each time period, nature of the contract (fixed price or billable hours) and
geographical location.
The workforce planning process is subject to a number of operational constraints,
which we identified during the interviews with the firms. Examples of these constraints
include the maximum number of customer projects to which any employee can be
simultaneously assigned, the maximum number of employees that can be assigned to any one
project, the requirement for multi-skilled employees to use all of their skills at various stages
over the planning horizon, and the requirement that employees achieve high utilization rates
over the planning horizon.
3.2 Experimental Data
In order to validate our model, we created sets of test data for four different firms, the
parameters for which were informed by the case study interviews with the professional
-
8/4/2019 POMS 2011 Full Paper
8/14
8
service firms mentioned previously. A number of initial simplifying assumptions were made.
The firms each operate a single line of business, but customers and employees can be located
in different geographical locations. Each employee has multiple skill types and is fully
available, so training and vacation time are not considered. Projects can require multiple
skills, with the time required for each skill varying over the project lifecycle. Overhead cost
is assigned based on the employee level in the hierarchy, but project revenue can vary by
consultant.
3.3 Experimental Results
Based on the test firms that were developed, experiments were conducted to analyze the
impact of various factors, such as employee skill profile, the structure of a firm's project
portfolio, limits on the number of concurrent projects an employee can be assigned to, the
role of employee cross-training, separation, hiring and organizational design, on the optimal
solution. A commercial optimization software package (Xpress-MP from Fair Isaac
Corporation) on a DELL M6400 laptop was used to conduct the experiments. The results are
presented in terms of key business metrics, such as project completion rate, net profit, and
employee utilization.
We first note that the base model can be used to identify where there are shortages
and excess resources in the firm. When analyzed in terms of sets of skill shortages for
customer projects, this is a very useful input to skill cross training analysis. For example,
when medium to long term demand indicates that certain skill sets are likely to become
redundant, it is possible to use the model to target those employees with redundant skills for
cross training in skills that are projected to be in short supply.
We next provide an overview of our main results to date. First, our results indicate
that, as expected, increasing the number of skills possessed by employees across the entire
-
8/4/2019 POMS 2011 Full Paper
9/14
9
firm results in a higher number of projects being completed. There is, however, no marginal
benefit obtained from increasing the number of skills beyond three or four per employee.
We also find that firms will experience a reduction in profit of up to 10% if they take
the short term view of trying to maximize employee utilization. This provides a significant
insight into the conflict between the overall objective of the firm, which is to maximize
profit, and the targets set by workforce planning managers with the goal of "keeping
everyone as busy as possible".
We also found that the optimal number of concurrent projects to which every
employee can be assigned is generally greater than one, an observation which conflicts with
the industry practice of assigning junior staff to just one project at a time. The results indicate
no marginal benefit in assigning employees to more than three projects simultaneously.
The issue of separation, whereby employees leave the firm, and new replacement
employees are hired, was also evaluated. It was found that if the firm can manage this
separation rate at a detailed level (i.e., at each employee grade) rather than at an aggregate
organizational level, then a reactive ("wait and see") policy with a short hiring lead time
provides the best results for the firm. In other words, rather than trying to predict in advance
which employees will leave at some point in the future and hiring new employees on this
basis before separation actually occurs, the firm is better off waiting until an employee leaves
and then replacing that skill as quickly as possible with a newly hired employee. However,
for this approach to be beneficial, the firm must be able to reduce the hiring lead time to a
level that is less than that generally experienced in the industry.
Large professional service firms frequently operate as multi-national organizations,
with employees and customer projects based in several different geographical locations.
These firms must determine the best organizational design with regard to locating employees
-
8/4/2019 POMS 2011 Full Paper
10/14
10
and skills in different locations and determine the best policy with regard to assigning
employees to projects in locations other than their base location. As expected, the results
from the organizational design scenarios indicate that a completely flexible location policy
delivers the best results for the firm. In addition, we find that the largest marginal benefit
comes from increasing the location flexibility of the lowest grades of employees. The
practical implementation of such a policy may prove difficult as it would require the majority
of employees to agree to be potentially assigned to customer projects away from their base
locations.
4.0 Conclusions and Future Work
In professional service organizations, activities are generally complex and highly customized,
with customer demand generally captured as projects. The task of workforce planning in
these firms is further complicated by the large number of human labor attributes that must be
captured in the resource planning process, including indicators of employee skills, knowledge
and teamwork. In addition, both the customer demand and employee profiles tend to be
dynamic, with project requirements changing over time and employee skill sets expanding as
project activities are performed. Given the limitations of the existing literature and the
complexities of the workforce planning problem, this paper addresses an area not previously
considered in the workforce planning literature. Specifically, we develop a comprehensive
resource planning model for professional service firms that captures the real issues
encountered by such organizations as they attempt to optimize the allocation of their skilled
personnel to client projects. In addition, the model presents a unique opportunity to bring
positive economic impact to a broad base of companies in the professional services sector by
providing strategic and practical insights into the workforce planning process, which can
assist capacity and human resource management in making better informed decisions.
-
8/4/2019 POMS 2011 Full Paper
11/14
11
Opportunities for further research include using the model to develop the optimum
start date schedule for every project, in the event that the proposed start dates of projects
cannot be met due to lack of available skills. Another potential application is to analyze the
impact of long term forecast accuracy on the workforce planning process. For example,
projects that are due to start in the immediate future may proceed with 100% certainty, but
those in the pipeline one or two years away may only have a 25%-50% chance of proceeding,
but still need to be factored in the long term workforce planning process of the firm.
Acknowledgements
The authors wish to acknowledge the financial support received for this research from the
IBM PhD Fellowship Program 2010-2011.
References
AhmadBeygi, S., Cohn, A. and Weir, M. (2009), "An integer programming approach to
generating airline crew pairings", Computers and Operations Research, 36(4), 1284-1298.
Azmat, C. S., Hurlimann, T. and Widmer, M. (2004), "Mixed integer programming to
schedule a single-shift workforce under annualized hours", Annals of Operations Research,
128(1-4), 199-215.
Brennan, L. L. (2006), "Operations management for engineering consulting firms: A case
study",Journal of Management in Engineering, 22(3), 98-107.
Brennan, L. L. and Orwig, R. A. (2000), "A tale of two heuristics: Conflicting work
allocation approaches in engineering consulting", Engineering Management Journal, 12(3),
18-25.
-
8/4/2019 POMS 2011 Full Paper
12/14
12
Cappelli, P. (2009), "A supply chain approach to workforce planning", Organizational
Dynamics, 38(1), 8-15.
Celano, G., Costa, A. and Fichera, S. (2008), "Scheduling of unrelated parallel manufacturing
cells with limited human resources", International Journal of Production Research, 46(2),
405-427.
Cheang, B., Li, H., Lim, A. and Rodrigues, B. (2003), "Nurse rostering problems - a
bibliographic survey",European Journal of Operational Research, 151(3), 447-460.
Davenport, T. H., Harris, J. and Shapiro, J. (2010), "Competing on talent analytics", Harvard
Business Review, 88(10), 52-58.
Dietrich, B. (2006), "Resource planning for business services", Communications of the ACM,
49(7), 62-64.
Dietrich, B. and Harrison, T. (2006), "Serving the services - the emerging science of service
management opens opportunities for operations research and management science", OR/MS
Today, 33(3).
Dodin, B. and Elimam, A. A. (1998), "Audit scheduling with overlapping activities and
sequence dependent setup costs", European Journal of Operational Research, 104(1), 262-
264.
-
8/4/2019 POMS 2011 Full Paper
13/14
13
Drucker, P. (1999), "Knowledge-worker productivity: The biggest challenge", California
Management Review, 41(2), 79-107.
Engwall, M. and Jerbrant, A. (2003), "The resource allocation syndrome: The prime
challenge of multi-project management?", International Journal of Project Management,
21(6), 403-409.
Franz, L. S. and Miller, J. L. (1993), "Scheduling medical residents to rotations - solving the
large-scale multi-period staff assignment problem", Operations Research, 41(2), 269-279.
Gray, C. and Larson, E. (2010),Project Management - The Managerial Process, 5th
edition,
McGraw Hill.
Gresh, D., Connors, D., Fasano, J. and Wittrock, R. (2007), "Applying supply chain
optimization techniques to workforce planning problems", IBM Journal of Research and
Development, 51(3-4), 251-261.
Hendriks, M. H. A., Voeten, B. and Kroep, L. (1999), "Human resource allocation in a multi-
project R & D environment: Resource capacity allocation and project portfolio planning in
practice",International Journal of Project Management, 17(3), 181-188.
Hopp, W. J., Iravani, S. M. R. and Liu, F. (2009), "Managing white-collar work: An
operations-oriented survey",Production and Operations Management, 18(1), 1-32.
-
8/4/2019 POMS 2011 Full Paper
14/14
14
Huang, H.-C., Lee, L.-H., Song, H. and Eck, B. T. (2009), "SimMan: a simulation model for
workforce capacity planning", Computers and Operations Research, 36(8), 2490-2497.
Kohl, N. and Karisch, S. E. (2004), "Airline crew rostering: Problem types, modeling, and
optimization",Annals of Operations Research, 127(1-4), 223-257.
Kuhn, H.W. (2005), "The Hungarian Method for the Assignment Problem", Naval Research
Logistics, 52(1), 7-21.
Ovchinnikov, A. and Milner, J. (2008), "Spreadsheet model helps to assign medical residents
at the University of Vermont's College of Medicine",Interfaces, 38(4), 311-323.
Santos, C. A., Zhang, A., Gonzalez, M. T. and Jain, S. (2009), "Workforce planning and
scheduling for the HP IT services business", 4th Multidisciplinary International Scheduling
Conference: Theory and Applications (MISTA).
Schmenner, R. W. (1986), "How can service businesses survive and prosper?", Sloan
Management Review, 27(3), 21-32.
Sherali, H. D., Ramahi, M. H. and Saifee, Q. J. (2002), "Hospital resident scheduling
problem",Production Planning and Control, 13(2), 220-233.
Silvestro, R., Fitzgerald, L., Johnston, R. and Voss, C. (1992), "Towards a classification of
service processes",International Journal of Service Industry Management, 3(3), 62-75.