POMS 2011 Full Paper

8/4/2019 POMS 2011 Full Paper

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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


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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


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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


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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).


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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


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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


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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


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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.


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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.

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