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The Use of Temporary Workers as a Response to Pressure in Service Operations
Luis E. López • Carlos M. Fernández
Noviembre, 2015
CEN 1411
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Documento. Escrito por Luis E. López, facultad de INCAE y Carlos M. Fernández, investigador de INCAE. Este trabajo busca estimular la reflexión sobre marcos conceptuales novedosos, posibles alternativas de abordaje de problemas y sugerencias para la eventual puesta en marcha de políticas públicas, proyectos de inversión regionales, nacionales o sectoriales y de estrategias empresariales. No pretende prescribir modelos o políticas, ni se hacen responsables el o los autores ni el Centro Latinoamericano de Competitividad y Desarrollo Sostenible del INCAE de una incorrecta interpretación de su contenido, ni de buenas o malas prácticas administrativas, gerenciales o de gestión pública. El objetivo ulterior es elevar el nivel de discusión y análisis sobre la competitividad y el desarrollo sostenibles en la región centroamericana. El contenido es responsabilidad, bajo los términos de lo anterior, de CLACDS y no necesariamente de los socios contribuyentes del proyecto. Noviembre, 2015.
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The Use of Temporary Workers as a Response
to Pressure in Service Operations Luis E. López • Carlos M. Fernández
INCAE Business School, Alajuela, Costa Rica
INCAE Business School, Alajuela, Costa Rica
[email protected] • [email protected]
This paper examines the effects of organizational responses to work pressure when capacity is
managed through an active and aggressive use of temporary employment. The article develops a
comprehensive model to describe the organizational structure associated to dealing with work
pressure in a service company that has temporary workers. The paper reports the elicitation pro-
cess used for discovering model structure, describes policies for matching supply with demand in
the service system, shows the responses to work pressure when the company uses temporary
workers to adjust capacity, and uses the model to understand the organizational effects of tempo-
rary worker usage on the organization. Results suggest that companies that use temporary work-
ers may be myopically responding to short-term gains associated with “temping,” forgoing the
larger long-term benefits provided by a more stable workforce.
(Temporary workers; Service Organizations; System Dynamics)
1. Introduction
Service firms’ impossibility to inventory
makes supply-demand mismatches difficult to
manage. Services respond to the work pressure
that comes from supply-demand variations with
different mechanisms. In the short term they may
use overtime or let workers go (when dealing with
the unbalance from the supply side); or they can
resort to price mechanisms or some other scheme
to limit the flow of incoming work (when dealing
with the unbalance from the demand side). In the
longer term, firms may deal with supply-demand
mismatches by increasing or decreasing capacity.
Increasing capacity requires time because ser-
vice firms need to assess potential capacity short-
falls and go to the labor market to procure the nec-
essary resources to close the capacity gap. The
process is lengthy. Firms advertise personnel
needs, recruit potential candidates, choose candi-
dates, make job offers, and see if anyone accepts
the job. Because of these delays, organizations
seek mechanisms to make outright service capac-
ity adjustments more flexible. One such mecha-
nism that has traditionally been a popular choice
for managing supply-demand mismatches in ser-
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
vice organizations is the use of temporary em-
ployment agencies to maintain readily available
personnel capacity that can be added or subtracted
to the company’s workforce on short order.
Firms, particularly service firms, choose
“temping” as a strategy for several reasons. Ac-
cording to Bryson (2013) temping is used to: a)
save money by cutting costs; b) address fluctua-
tions in demand, and c) address shortages of peo-
ple or skills for a given demand. More recently,
the use of temporary workers is becoming en-
trenched, as employers do not “temp” just to deal
with temporary demand imbalances, but have
started to convert their very long-term workforce
into bands of temporary workers. The popular
press has echoed this tendency, and many have
heralded the not-too-distant rise of the “perma-
nent temp economy”, in which jobs are not occu-
pied by permanent workers formally affiliated to
organizations, but by temporary workers, or
“perma-temps” (see for instance Greenhouse,
2014; Smith, 2014, White, 2014).
However, although the use of temporary work-
ers is growing, its organizational consequences
have scarcely been explored, and extant literature
reports controversial results regarding the effects
of temping upon firms and workers. Some authors
argue that temping may have positive effects upon
firm performance and workers’ well-being. For
example, Lane, Mikelson, Sharkey and Wissoker
(2003) argue that temporary jobs are just an inter-
mediate stage on toward full time permanent
work. Similarly, advantages to staffing compa-
nies are reported in terms of flexibility and access
to talent with apparently no downside (Amiti and
Wei 2005). Moreover, temporary agency work
may be a vehicle for firms to evaluate a potential
employee without incurring the cost of actually
offering a permanent position to the worker (Jahn
and Rosholm, 2014). Arvanitis (2005) explored
the effect of labor flexibility at the firm level,
founding mixed effects of these arrangements on
firm innovation: no effect on sales per employee
or introduction of process innovations, but an ef-
fect associated to the introduction of product in-
novation. Bryson (2013) finds that the use of tem-
porary agency workers associates positively with
financial performance of firms in the British pri-
vate sector, but it is not associated with value
added per employee or with the perceptions of
workers about how intensively they have to work.
In contrast, other authors argue that temporary
job arrangements are just cost-cutting mecha-
nisms that strip workers of their rights. Parker
(1994), for instance, argues that the human re-
sources method based on the ‘temping’ that is cur-
rently much in vogue in the United States, is a sys-
tem that has been created at the expense of work-
ers’ skills, training, and labor rights. Similarly,
Hatton (2013) asserts that “temping” by US em-
ployers has simply become “a way to reduce ben-
efits and cut wages,” and Ford and Slater (2006),
exploring the often advanced theory that temping
contributes to quality in the workforce, find no ev-
idence of an association between work in tempo-
rary employment agencies and the emergence of
specialized and sophisticated skills related to the
knowledge economy. These authors find that
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
temporary agency work reflects considerations to
reduce labor costs, and that temporary agency
work results in low levels of commitment and
high levels of job anxiety. Hatton (2014) argues
that employers use temporary agency work as a
mechanism to discourage the formation and
strengthening of labor unions, and Autor and
Houseman (2010) find that temping may reduce
individual subsequent earnings and employment
outcomes.
In all, the literature on temporary work reports
mixed effects upon firm performance—for in-
stance Valverde et al. (2000) report a positive ef-
fect, Angeles Diaz-Mayans et al. (2004) report
negative effects, and Roca-Puig et al. (2015) find
a curvilinear relationship—and overwhelmingly,
negative effects upon the individuals. Hence, the
effect upon the firm is unclear. Temping renders
companies with organizational structures that are
different and may be unique. Different mixes or
proportions of temporary workers and permanent
workers may have different effects upon different
types of operations. Temporary workers tend to
have shorter tenures, and they are formally affili-
ated to the temporary work agency, not to the
company they are actually working for.
Even if temporary workers were, in terms of
skills and personal characteristics, similar to per-
manent contract workers, government authorities
in certain countries impose restrictions and regu-
lations upon their contract structures. Because
firms use temping as a cost-cutting device, to, for
instance, avoid liabilities associated to severance
pay, vacation time, health and other benefits and,
possibly, to circumvent paying higher salaries,
governments may limit the unrestricted use of
temporary employment by enforcing time limits
to continuous employment under a temporary sta-
tus in the same organization. Often, as a result,
temporary employment contracts are terminated
in order to dodge such limits, thus infusing com-
panies with crews who have inherently shorter
average tenures, that is employees who rotate
more, not by will but by fiat.
Such shorter organizational tenures may result
in systems that exhibit chronically low productiv-
ity levels because of prolonged delays and the
presence of substantial groups of workers with
high turnover rates. Moreover, the use of temps
requires an organizational effort to actively man-
age costly transactions, such as hiring and firing.
Finally, for a given learning rate, the use of temps
with curtailed tenures may result in a persistently
low productivity levels—compared to what it
could have been without the temps—which must
be compensated with larger long-run staffing lev-
els. Should this be the case, the added long-run
cost of having more unproductive people perform
a task that could be done by fewer more produc-
tive people must be compensated somehow, either
by cutting wages, increasing utilization, or some
other measure.
This paper explores the effects of temping upon
overall productivity for a typical service firm by
examining the effects of organizational responses
to work pressure when capacity is managed
through an active and aggressive use of temporary
employment. Results suggest that companies may
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
be myopically responding to short term gains as-
sociated to “temping,” forgoing larger long-term
benefits associated with a more stable workforce.
This paper is organized as follows: First, we de-
velop a formal model that integrates the organiza-
tional structure of a typical service firm that em-
ploys temporary workers to match supply with de-
mand (§2). Thereafter we test and validate the
model empirically in the context of a case study
in a service organization whose managers wanted
to understand the dynamics of resource manage-
ment and worker turnover (§3). Then, we use the
model to understand the implications of temping
(§4) and to generate some policy recommenda-
tions (§5). Finally, we discuss the implications of
our findings for the service industry and identify
future research areas (§6).
2. Model Structure
This section presents a model of a typical logistics
firm that uses a temporary workforce to adjust its
capacity to meet demand. The model allows us to
analyze the effectiveness of using “temping” to
improve firm’s productivity. Each model equa-
tion is supported by relevant theory and substan-
tive empirical evidence.
Figure 1 shows the four-sector model. The pro-
cessing and shipment sector tracks incoming
work as it is processed and shipped by the service
firm. The work imbalance sector includes worker
productivity as affected by overtime and fatigue.
The capacity sector models the service firm
Figure 1 Model Overview
capacity to process orders. This sector provides
details related to learning and workforce turnover.
Finally, the capacity adjustment sector models the
policies that set temporal and permanent staffing
levels.
Processing and Shipment. The processing
and shipment sector tracks the incoming customer
workflow. A backlog (B) accumulates incoming
work (𝑠𝑜) until it is processed. The incoming work
rate is exogenous, and, by contract, the company
should clear daily work arrivals within the same
day. The backlog is reduced by the order comple-
tion rate (𝑠𝑐) as follows:
(𝑑/𝑑𝑡)𝐵 = 𝑠0 − 𝑠𝑐 (1)
The order completion rate (𝑠𝑐) is the firm’s ef-
fective capacity (c) adjusted by the number of
shifts employees work (s). The backlog (B) limits
the order completion rate when there is excess ca-
pacity:
𝑠𝑐 = min(𝑐 ∙ 𝑠, 𝐵) (2)
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
Work imbalance. The simultaneity constraint
imposed by the same day processing requirement
makes it difficult to balance supply and demand.
Such difficulties are exacerbated by system de-
lays and incoming work variability. Work imbal-
ance (𝜔) measures the mismatch between service
demand and capacity, and is defined as the gap
between pending work and effective capacity as a
fraction of current capacity,
𝜔 = (𝐵 − 𝑐)/𝑐 (3)
Work imbalance also describes the relative
workload. If the backlog is large compared to the
current capacity and no outright capacity increase
occurs, the employees must respond either by ad-
justing the time allocated to each order or by
working overtime. Oliva and Sterman (2001)
show service workers “cutting corners” as a short
term reaction to increases in work pressure, with
a consequent degradation of service quality. Oliva
and Sterman (2001) argue that work imbalances
can be partially absorbed by workers dedicating a
smaller amount of time to each order. The study
here does not model such behavior for two rea-
sons: First, the service firm we studied, a logistics
company, did not keep track of processing times
at the order level, and, thus, we did not have any
available data to estimate the effect of work pres-
sure on the allotted time per order. Second, this
effect plays a less relevant role in our context, as
employees doing manual labor in our empirical
setting had less leeway to reduce the amount of
time they invested in each order. Hence, to deal
with work imbalances, employees at the firm
could only increase their order completion rate by
working overtime. In our model, employees ad-
just the fractional number of shifts (s) they work
in response to work imbalances (w). Such re-
sponse is nonlinear and is limited by the maxi-
mum shifts an employee could work,
𝑠 = 𝑓𝑠(𝜔) (4)
𝑓(0) = 1, 𝑓(∞) = 𝑠𝑚𝑎𝑥 , 0 ≤ 𝑓′ ≤ 1 (4)
Fatigue from extended periods of working
overtime eventually hampers some of the produc-
tivity gains attained from working longer hours
(Homer 1985, Thomas 1993). The model captures
fatigue (𝐹) by exponentially smoothing the inten-
sity of the workload—represented by the work
shifts (s)—over the average time required for fa-
tigue to set in (𝜏𝑓).
(𝑑/𝑑𝑡)𝐹 = (𝑠 − 𝐹)/𝜏𝑓 (5)
Fatigue (F) affects productivity. Such effect is
often modeled as a decreasing nonlinear function
that reduces effective service capacity when ser-
vice personnel are tired (Oliva and Sterman,
2001). Our model takes into consideration this ef-
fect through Equation (6), where 𝑓 is the effect of
fatigue on productivity.
𝑓 = 𝑓𝑓𝑝(𝐹) (6)
𝑓(𝐹 ≤ 1) = 1, 𝑓′ ≤ 0, 𝑓′′ > 0 (6)
Working beyond a normal shift for an extended
period also impacts the average employee tenure
(Mobley 1992, Weisberg 1994). Similar to Equa-
tion (6), Equation (7) captures the effect of fatigue
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
on employee turnover (𝑎𝑓; Equations (22) and
(23)),
𝑎𝑓 =𝑓𝑓𝑡(𝐹) (7)
𝑓(𝐹 ≤ 𝑥) = 1, 𝑓(∞) = 0, 𝑓′ ≤ 0 (7)
Capacity. The capacity sector includes the
firm’s order processing capabilities, employee
learning, and labor force turnover. Effective ca-
pacity (c) is determined by multiplying the effec-
tive labor force (𝐿𝑒𝑓𝑓; Equation (10)) by the aver-
age number of orders that an experienced worker
can process per unit of time (𝜌), and adjusting the
result by the impact of fatigue on effectiveness (f),
𝑐 = 𝐿𝑒𝑓𝑓 ∙ 𝜌 ∙ 𝑓 (8)
Learning-by-doing is well documented in a
wide range of settings including manufacturing
and service organizations (Lopez and Suñé 2013,
Argote and Epple 1990, Darr et al. 1995). Our
model accounts for employee learning through an
experience chain (Jarmain, 1963). New employ-
ees enter the company with only a fraction (𝜀) of
the productivity of experienced employees. These
employees progressively increase their productiv-
ity by training, learning from others, and gaining
experience. As in Oliva and Sterman (2001), each
new employee imposes training and mentoring
loads upon existing employees, who must devote
some of their available time to such activities.
Hence, while training, each new employee re-
duces the productivity of extant experienced em-
ployees by a fraction (𝜂). Labor (L) is split in two
groups: experienced personnel (𝐿𝑒) and rookies
(𝐿𝑟). The mix of the two groups and their produc-
tivities determines the effective labor force
(𝐿𝑒𝑓𝑓)—the number of full-time-equivalent expe-
rienced personnel—which in turn affects service
capacity (Equation (8)). Our model constrains the
effective labor force (𝐿𝑒𝑓𝑓) to be nonnegative.
The restriction of no negativity is necessary to ac-
count for scenarios where rookies require super-
vision beyond their initial effectiveness (𝜂 ≫ 𝜀)
and they exceed the total experienced personnel
(𝐿𝑟 ≫ 𝐿𝑒),
𝐿 = 𝐿𝑒 + 𝐿𝑟 (9)
𝐿𝑒𝑓𝑓 = max(0, 𝐿𝑒 + 𝐿𝑟(𝜀 − 𝜂)) (10)
0 ≤ 𝜀 ≤ 1, 𝜂 ≥ 0 (10)
Additionally, the labor force is divided into two
other groups: permanent (𝐿𝑝) and temporary em-
ployees (𝐿𝑡). Temporary employees have prede-
termined contracts with fixed employment work-
ing periods beyond which they cannot continue
working in the firm. The time that the workers
can remain in the company is given by 𝜏𝑡 (Equa-
tion (23)), while permanent employees may re-
main indefinitely in the firm. Of course, these
contracts accelerate the temporary employees’
turnover (𝑙𝑎𝑡; Equation (21)). In addition, only
temporary workers are hired and laid off as a di-
rect consequence of supply-demand mismatches.
Strictly speaking, the service firm does not hire
new permanent employees. Instead, when the
contracts of some of the temporary employees ex-
pire, the company “rehires” these employees as
part of its permanent workforce. Such formulation
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
accounts for the often purported idea that tempo-
rary work is a conduit for permanent work. Fur-
thermore, permanent employees are never laid off
as a consequence of supply-demand mismatches,
and only exit the system due to nominal turnover
(𝑙𝑎𝑝; Equation (20)). Consequently, the tempo-
rary workforce (𝐿𝑡) consists of both rookies (𝐿𝑟)
and experienced temporary personnel (𝐿𝑒𝑡), while
the permanent workforce (𝐿𝑝) is conformed only
by permanent experienced personnel (𝐿𝑒𝑝).
𝐿𝑒 = 𝐿𝑒𝑡 + 𝐿𝑒𝑝 (11)
𝐿𝑝 = 𝐿𝑒𝑝 (12)
𝐿𝑡 = 𝐿𝑟 + 𝐿𝑒𝑡 (13)
Equations (14) – (23) represent the movement
of employees through the experience chain and
on-the-job learning. The hiring rate (𝑙ℎ) increases
the stock of rookies (𝐿𝑟), which in turn is de-
creased when employees gain experience (𝑙𝑒) or
are laid off (𝑙𝑟𝑙). The stock of temporary experi-
enced personnel (𝐿𝑒𝑡) increases as rookies gain
experience (𝑙𝑒), and diminishes according to con-
tract expirations (𝑙𝑎𝑡) and layoffs (𝑙𝑒𝑙). The stock
of permanent experienced personnel (𝐿𝑒𝑝) is aug-
mented as experienced temporary workers are re-
hired as part of the permanent workforce (𝑙𝑟), and
is reduced by turnover (𝑙𝑎𝑝). The experience rate
(𝑙𝑒) captures the transition from rookies to expe-
rienced. Cumulative experience is modeled
through a first order process whereby rookies de-
velop full productivity over an average training
period (𝜏𝑒), (Oliva and Sterman, 2001),
(𝑑/𝑑𝑡)𝐿𝑟 = 𝑙ℎ − 𝑙𝑒 − 𝑙𝑟𝑙 (14)
(𝑑/𝑑𝑡)𝐿𝑒𝑡 = 𝑙𝑒 − 𝑙𝑎𝑡 − 𝑙𝑒𝑙 (15)
(𝑑/𝑑𝑡)𝐿𝑒𝑝 = 𝑙𝑟 − 𝑙𝑎𝑝 (16)
𝑙𝑒 = 𝐿𝑟/𝜏𝑒 (17)
New recruitments do not occur immediately.
The rate at which the firm hires new employees
(𝑙ℎ) is determined by the accumulated vacant job
positions at the firm (𝐿𝑣) and the time it takes to
contact the temping agency and wait for the new
personnel to arrive (𝜏ℎ). The vacant job positions
represent the hiring orders (𝑙𝑜) that the firm has
issued to the temping agency but that have not
been filled yet,
𝑙ℎ = 𝐿𝑣/𝜏ℎ, (24)
(𝑑/𝑑𝑡)𝐿𝑣[𝑥] = 𝑙𝑜 − 𝑙ℎ (25)
When companies are under periods of very low
labor intensity, they lay off part of their workforce
to compensate for underused capacity. We as-
sume that companies under pressure to dismiss
part of their personnel would rather lay off rook-
ies than experienced personnel, because experi-
enced personnel are more productive and expen-
sive to dismiss. However, if layoff orders exceed
the total number of rookies, service firms may be
forced to dismiss some of their experienced em-
ployees. The number of rookies dismissed (𝑙𝑟𝑙) is
the minimum between the stock of rookies (𝐿𝑟)
and the total layoff orders (𝑙𝑙). The number of
temporary experienced employees dismissed (𝑙𝑒𝑙)
is equal to the minimum between the stock of ex-
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
perienced temporary workers (𝐿𝑒𝑡) and the num-
ber of layoff orders not covered by the rookies
dismissed,
𝑙𝑟𝑙 = min(𝐿𝑟 , 𝑙𝑙) (18)
𝑙𝑒𝑙 = min(𝐿𝑒 , 𝑙𝑙 − 𝑙𝑟𝑙) (19)
Employees may also leave the company be-
cause of exogenous reasons, or in the case of tem-
porary employees, because their contracts ex-
pired. We modeled the turnover from the perma-
nent and temporary experienced personnel as an
exponential function. The average times for turn-
over are𝜏𝑎𝑝 and 𝜏𝑎𝑡 for permanent and temporary
experienced employees respectively. We ignore
the rookies’ turnover because of the relatively
short learning period in comparison with the av-
erage tenure of permanent employees. Moreover,
we found in the labor historical records that rook-
ies rarely leave the firm before their short-lived
contracts expire, unless the company lays them
off. Hence, the time for turnover of temporary
employees (𝜏𝑎𝑡) is determined by the span of their
contracts (𝜏𝑡). For permanent employees, on the
other hand, turnover depends on external and in-
ternal factors to the firm, including circumstances
such as the competitiveness of the labor market,
the economy, and other characteristics specific to
the workers (Mobley 1992). We considered all
these elements as exogenous and captured them in
a nominal turnover time (𝜏𝑎∗ ). Fatigue, however,
is modeled endogenously and may lead to more
turnover (Equations (22) and (23)):
𝑙𝑎𝑝 = 𝐿𝑒𝑝/𝜏𝑎𝑝 (20)
𝑙𝑎𝑡 = 𝐿𝑒𝑡/𝜏𝑎𝑡 (21)
𝜏𝑎𝑝 = 𝜏𝑎∗ ∙ 𝑎𝑓 (22)
𝜏𝑎𝑡 = 𝜏𝑡 ∙ 𝑎𝑓 (23)
Capacity Adjustment. The capacity adjust-
ment sector models the firm’s policies to increase
and decrease its staffing levels. When service
firms adjust their capacity, they do so by changing
their permanent workforce, and in the case of ser-
vice firms that use temping, also by adjusting their
temporary workforce. This type of service firms
tend to aim for a temporary to permanent em-
ployee ratio (temp-perm ratio;𝜓). This ratio can
be interpreted as the proportion of variable capac-
ity that the firm wants to have. Fluctuations in ser-
vice demand may lead to service firms adjusting
their temporary capacity accordingly, which im-
plicates that companies need to constantly strug-
gle to correct imbalances between temporary and
permanent employees to achieve their temp-perm
ratio (𝜓). The target number of temporary em-
ployees (𝐿𝑡∗) is the number of permanent employ-
ees (𝐿𝑝) multiplied by the temp-perm ratio (𝜓).
Similarly, the target number of permanent em-
ployees (𝐿𝑝∗ ) is the number of temporary employ-
ees (𝐿𝑡) divided by the temp-perm ratio (𝜓). The
differences between the targets and the actual per-
sonnel are defined as gaps in temporary and per-
manent employees (𝑔𝑡 and𝑔𝑝respectively).
𝐿𝑡∗ = 𝐿𝑝 ∙ 𝜓 (26)
𝐿𝑝∗ = 𝐿𝑡/𝜓 (27)
𝑔𝑝 = 𝐿𝑝∗ − 𝐿𝑝 (28)
𝑔𝑡 = 𝐿𝑡∗ − 𝐿𝑡 (29)
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
Service firms use these targets as guidelines to
adjust their capacity, but labor adjustments are ul-
timately driven by mismatches between demand
and capacity. These mismatches are perceived by
management as changes in the average fractional
number of shifts that the personnel work. An in-
crease in the extra hours paid or a constant trend
of underutilization are the usual ways in which
management perceives such changes. In an effort
to minimize costs, management constantly tries to
maximize utilization and minimize the extra
hours paid by adjusting the firm’s labor. Thus,
changes in management’s perception of work
shifts (S) have a direct effect on the pressure to
hire o layoff personnel (p; Equation (31)). It takes
time for management to perceive changes in the
work shifts (s). We assume that management’s
perception of changes in the average work shift
(S) occurs after a delay (𝜏𝑝𝑠) that represents the
time it takes to measure, report, and evaluate ex-
cessive variations in costs due to extra hours paid
or underutilization.
(𝑑/𝑑𝑡)𝐼 = (𝑖 − 𝐼)/𝜏𝑝𝑠 (30)
Changes in management’s perception of work
shifts have a direct impact on the firm’s pressure
to hire or layoff personnel. Service firms adjust
their workforce when they perceive that their ca-
pacity utilization rate goes above or below their
target utilization rate (𝜃). Personnel pressure (p)
is defined as the gap between management’s per-
ceived work shifts (S) and the target utilization
rate (𝜃) as a fraction of the target.
𝑝 = (𝑆 − 𝜃)/𝜃 (31)
Personnel pressure can also be interpreted as
management’s perception of the required relative
change on the firms’ personnel to reach its target
utilization rate. Consequently, the desired adjust-
ment in labor (𝑙∗) is the personnel pressure (p)
multiplied by the total labor (L). Although we
acknowledge that investment in service capacity
is frequently limited by financial restrictions, we
ignore such constraints in the hiring policies of
our model to favor simplification. However,
given that the purpose of this study is to under-
stand the effects of temping on the productivity of
the firm, we do take into consideration the re-
striction that the firm’s gap in temporary employ-
ees (𝑔𝑡) imposes over the desired adjustment in
labor (𝑙∗). While managers are willing to increase
their temporary workforce (𝐿𝑡) above their target
(𝐿𝑡∗) in order to meet demand, they refrain from
decreasing their temporary labor too much for
fear of not being able to react to future peaks in
demand. Hence, the desired adjustment in labor is
always equal to or greater than the gap in tempo-
rary employees (𝑔𝑡).
𝑙∗ = max(𝑝 ∙ 𝐿, 𝑔𝑡) (3)
The desired adjustment in labor (𝑙∗) is the num-
ber of employees that the firm wants to hire or lay
off. A positive desired adjustment represents the
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
number of hiring requests1 (𝑙𝑜∗), while a negative
adjustment represents the number of layoff orders
(𝑙𝑙∗),
𝑙𝑜∗ = max(0, 𝑙∗) (33)
𝑙𝑙∗ = max(0,−𝑙∗) (34)
It is assumed that hiring requests and layoff or-
ders are processed after a delay (𝜏𝑜and𝜏𝑙 respec-
tively). The delay to process hiring requests rep-
resents the time it takes to approve new hires,
while the delay to process layoff orders represents
the time it takes to determine which employees to
dismiss. The total number of hiring requests (𝑙𝑜∗)
divided by the hiring request delay (𝜏𝑜) deter-
mines the hiring orders (𝑙𝑜). Similarly, the total
number of layoff orders (𝑙𝑙∗) divided by the layoff
delay (𝜏𝑙) determines the layoff rate (𝑙𝑙), which in
turn determines the layoff rate of rookies and ex-
perienced employees (Equations (18) and (19)).
𝑙𝑜 = 𝑙𝑜∗/𝜏𝑜 (35)
𝑙𝑙 = 𝑙𝑙∗/𝜏𝑙 (36)
Service firms may also adjust their permanent
workforce to achieve their target temp-perm ratio
(𝜓). When the contracts of temporary workers ex-
pire, service firms may rehire some of these em-
ployees as permanent workers. The rehiring rate
(𝑙𝑟) is defined as the gap between the firm’s per-
manent personnel goal (G) and its actual perma-
nent personnel (𝐿𝑝) adjusted by exponential
smoothing with time constant (𝜏𝑔). The rehiring
1 In our fieldwork, we discovered that management did
not take into consideration the rookies’ learning curve in
rate is also limited by the number of temporary
employees whose contract expired (𝑙𝑎𝑡), and is
constrained to be nonnegative to control for cases
where the permanent personnel goal is below the
actual permanent personnel.
𝑙𝑟 = max(0,min(𝑙𝑎𝑡, (𝐺 − 𝐿𝑝)/𝜏𝑔)) (37)
The permanent personnel goal (G) depends on
the firm’s target number of permanent employees
(𝐿𝑝∗ ). We found that the firm started with a low
permanent personnel goal, but as the system be-
came more stable, its goal gradually increased un-
til it reached the target number of permanent per-
sonnel. We also found that the service firm under
study only offered permanent jobs in periods of
low personnel pressure. The explanation is that
managers tend to withhold permanent job offer-
ings in peak hiring periods to avoid slowing the
hiring process of temporary workers to increase
capacity. Hence, increments in the permanent per-
sonnel goal only occurred in periods of negative
personnel pressure (p). Furthermore, we never
saw decreases in the goal, thus we constrained
changes in the goal to be nonnegative,
(𝑑/𝑑𝑡)𝐺 = max(0,min(𝐿𝑝∗ − 𝐺,−𝑝 ∙ 𝐺) (38)
3. Empirical Tests and Validation
Our model is based on relationships which, indi-
vidually, have extensively been studied in the
past. However, most of the available documenta-
tion is aspect-specific, and few studies have taken
their staffing policies, hence we use labor (L) instead of ef-
fective labor (𝐿𝑒𝑓𝑓) to calculate hiring and lay off orders.
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
into consideration the simultaneous interactions
between these relationships. Oliva and Sterman
(2001) built a system dynamics model of a retail
banking operation that incorporated interactions
between fatigue, turnover, productivity, learning
time, and quality, but the study focuses on the ef-
fects of work intensity on quality while exploring
the effect of long learning curves and temporary
labor on the productivity of the firm is not fully
addressed.
The study here uses data from a specific service
context—a cross docking operation—to statisti-
cally estimate model parameters and the interac-
tion of temping policies with the individual rela-
tionships in the model. We also consider this as a
step towards evaluating whether the individual re-
lationships described in the model exist concur-
rently in an ample number of service contexts, and
if their interactions are accurate in reflecting ob-
served behaviors of service contexts, a necessary
measure to test and build confidence in the model
as a whole (Naylor and Finger 1967, van Horn
1971). To test the model, we compare the simu-
lated results against the historical data to evaluate
the degree to which the model quantitatively re-
flects the observed behavior. We also run simula-
tions of scenarios not encountered at the research
site and use sensitivity analysis to add robustness
to our conclusions.
3.1 The Research Setting
This paper originates in the interest of a service
company to understand how to better manage the
allocation of human resources in its operations.
The firm is a logistics service company special-
ized in cross-docking operations. Our data comes
from the main hub of the company, called the
Cross Docking Distribution Center (CDDC),
from which most of the logistics operations are
coordinated. Work arrives at the CDDC by in-
bound trucks with materials and packages that
need to be distributed to several clients. The pack-
ages are unloaded, screened, sorted and reloaded
into outbound trucks that deliver them to the cus-
tomers. The firm was organized along customer-
specific projects. These projects were, upon esti-
mating future demand, staffed using a combina-
tion of permanent and temporary workers. Con-
tracts were drawn for a given expected incoming
order rate, and the service firm’s earnings would
be contingent on compliance with certain agreed-
upon standards, measured by on-time delivery or
fill rate.
To elicit the model structure related to the issue
of resource allocation, we conducted a workshop
with executives. These data was used to deter-
mine the system’s feedback structure and to de-
velop a simulation game based upon a preliminary
structure. The simulation game was thereupon
used in a learning laboratory with the executives
to see if the feedback structure was representative
of actual day to day project dynamics. We used
these observational data, as well as qualitative in-
formation from interviews with executives, to
specify the decision rules of managers. Addition-
ally we collected information on several key op-
erational metrics related to four projects, which
were ongoing at the time of the study, and starting
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
one month after the time of project inception. This
information was used to statistically estimate
most model parameters. We also used qualitative
information from interviews and documentation
of previous studies to estimate parameters when
data were not available. The next subsection
shows an example of how we estimated a crucial
decision rule—the adjustment of the service
firm’s temporary workforce. The other subsec-
tions show the sources for parameter estimation
and the model’s fit to historical data.
3.2 Partial Model Estimation
Our hypothesis (Equation (32)) is that the desired
adjustment in labor (𝑙∗) depends on the firm’s gap
in temporary employees (𝑔𝑡), its current capacity
(L), and the pressure to adjust its personnel (p).
High levels of pressure cause the company to in-
crease its capacity to meet demand, while low lev-
els of pressure cause the company to lay off em-
ployees to increase utilization, as long as the de-
sired labor adjustment is not less than the gap in
temporary employees. Pressure to adjust person-
nel is not explicitly observable; hence to test our
hypothesis, we estimated the parameters influenc-
ing personnel pressure to evaluate its effect on the
desired labor adjustment. In particular, we esti-
mated the firm’s target utilization rate (𝜃) and the
time it takes management to perceive changes in
work intensity (𝜏𝑝𝑖). Since the company did not
keep record of differences in efficiency between
rookies and experienced personnel, we also esti-
mated the time it takes for rookies to become ex-
perienced (𝜏𝑒), and the efficiency of a rookie rel-
ative to an experienced employee (𝜀). The estima-
tion process required us to calculate the parame-
ters that minimized the sum of squared errors be-
tween simulated and actual temporary employees
(𝐿𝑡 and TL respectively) given the structure of the
model. The simulation was driven by data for the
actual permanent employees (PE) and the pro-
cessed orders (PO):
𝑀𝑖𝑛𝜌, 𝜏𝑒 , 𝜀
∑(𝐿𝑡(𝑡) − 𝑇𝐿(𝑡))2𝑛
𝑡=1
subject to
𝐿𝑒𝑝(𝑡) = 𝑃𝐸(𝑡) (16′)
𝑠𝑓(𝑡) = 𝑃𝑂(𝑡) (2′)
𝑠(𝑡) = 𝑠𝑓(𝑡)/𝑐(𝑡) (4′)
The data for the processed orders (PO), the actual
number of permanent employees (PE), and the ac-
tual number of temporary employees (TL) was
collected from the CDDC’s daily operational met-
rics reports from February 2014 to August 2014.
The results for the parameter estimation are
shown in Table 1. These estimates were corrobo-
rated by anecdotal evidence, by interviewing and
consulting project executives, by carefully ob-
serving the executives playing the simulation—
fitted with such parameters—and by discussing
their reasonableness in the subsequent debrief.
Figure 2 presents the model’s fit to historical data.
We use Theil’s inequality statistics to show the
composition of the mean square error in terms of
unequal means (bias), unequal variances, and im-
perfect correlation (Theil 1971).
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
Table 1 Estimates for the Adjustment of Labor
Estimate
𝜃 0.80
𝜏𝑝𝑠 3.49
𝜏𝑒 83.7
𝜀 0.66
The low bias and unequal variation represent a
lack of systematic errors (Sterman 1984). The
most notable outcome of this estimation is the dif-
ference in productivity between rookies and expe-
rienced employees. New employees are approxi-
mately only 66% as efficient as experienced
workers, and it takes about 84 working days for
them to become fully proficient at their jobs.
Since the difference in productivity between new
and experienced employees is temporary, most
service firms, and the one explored here was no
exception, pay the same wage to employees re-
gardless of whether they have already surpassed
the learning curve or not. Consequently, compa-
nies pay more to rookies to do the same amount
of work that experienced employees could have
done in less time. In the past, this has not been a
major problem, since new employees are rela-
tively more expensive only for a limited period.
The low bias and unequal variation represent a
lack of systematic errors (Sterman 1984). The
most notable outcome of this estimation is the dif-
ference in productivity between rookies and expe-
rienced employees. Rookies are approximately
only 66% as efficient as experienced workers, and
it takes about 84 working days for them to become
fully proficient at their jobs. Since the difference
in productivity between new and experienced
Figure 2 Temporary Employees (Partial Model
Estimation)
employees is temporary, most service firms, and
the one explored here was no exception, pay the
same wage to employees regardless of whether
they have already surpassed the learning curve or
not. Hence, firms pay more to rookies to do the
same amount of work that experienced workers
could have done in less time. In the past, this has
not been a major problem, since new workers are
relatively more expensive only for a limited pe-
riod. However, firms that manage capacity
through an aggressive use of temporary employ-
ment might find themselves constantly replacing
their personnel, and thus having a more expensive
workforce due to the firm becoming embedded in
a perpetual learning curve.
Summary Statistics for Historical Fit—Temp E.
n = 171
𝑅2 0.797
Mean Absolute Percent Error 3.7%
Root Mean Square Error 7.6
Theil’s Inequality Statistics
Bias 0.001
Unequal Variation 0.002
Unequal Covariation 0.997
Personal Temporal
220
200
180
160
140
0 20 40 60 80 100 120 140 160
Time (Day)
Simulated Actual
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
3.3 Estimation Summary
Table 2 lists all parameters, their values, and
sources. To estimate them, we used similar meth-
odologies to the one discussed in the previous
subsection. We used data series of processed or-
ders to estimate work pressure, work shifts and
worker productivity (Equations (3), (4) and (8)).
Several parameters of the service capacity sector
(Equations (8)-(19)) were estimated using data se-
ries of the number of temporary and permanent
employees. These data series were also used to es-
timate management’s perception of work shifts
and labor adjustment (Equations (30)-(34), (37),
and (38)). Turnover parameters were estimated
using Kaplan-Meir Log Rank tests for tenures of
permanent and temporary employees (Equations
(20)-(23)). Delays for hiring, delays for pro-
cessing hiring requests, and delays for laying off
employees were estimated using the average of
the distribution of delays (Equations (24), (25),
(35) and (36)).
All the initial stocks were set based on historical
data, except for two stocks: management’s per-
ception of work intensity and the permanent per-
sonnel goal. These two stocks were estimated to
fit past data on temporary employees and perma-
nent employees respectively. The time it takes for
an experienced employee to mentor and coach a
Table 2 Parameters and Sources for Service Model
Parameter Value Source
Capacity
𝜌 Productivity per worker 22.0 orders/day Estimated to fit past data on processed orders
𝜀 Relative effectiveness of rookies 0.68 dimensionless Estimated to fit past data on temp. employees
𝜂 Fraction of experienced personnel for training 1/15 dimensionless Set based on interviews
𝜏𝑒 Time for employees to become experienced 83.7 day Estimated to fit past data on temp. employees
𝜏𝑎𝑡 Time for attrition of temporary employees 23.3 day Set based on survival analysis of emp. tenure
𝜏𝑎𝑝 Time for attrition of permanent employees 396.4 day Set based on survival analysis of emp. tenure
𝜏ℎ Hiring delay 10.0 day Set based on the distribution of hiring delays
Capacity adjustment
𝜏𝑝𝑠 Time to adjust perceived work shifts 3.8 day Estimated to fit past data on temp. employees
𝜃 Target utilization rate 0.80 dimensionless Estimated to fit past data on temp. employees
𝜓 Target temporary-to-permanent-employee ratio 1.3 dimensionless Set based on historical data
𝜏𝑙 Layoff delay 5.0 day Set based on distribution of layoff delays
𝜏𝑜 Hiring requests delay 10.0 day Set based on distribution of hiring delays
𝜏𝑔 Time to adjust permanent personnel to goal 15.9 day Estimated to fit past data on perm. employees
Work imbalance
𝑓𝑠 Effect of work imbalance on work shifts 𝑒0.66𝜔 dimensionless Estimated to fit past data on processed orders
𝜏𝑓 Time for fatigue to set in 15 day Previous studies
𝑓𝑓𝑝 Effect of fatigue on productivity 1.4-0.4*F dimensionless Previous studies
𝑓𝑓𝑡 Effect of fatigue on turnover 1.3-0.3*F dimensionless Previous studies
Initial conditions
B Backlog 0.00 orders Set based on historical data
F Employee’s fatigue 1.00 dimensionless Set based on historical data
S Perception of work shifts 1.25 dimensionless Estimated to fit past data on temp. employees
𝐿𝑟 Rookies 35 employees Set based on historical data
𝐿𝑒𝑡 Experienced temporary employees 124 employees Set based on historical data
𝐿𝑒𝑝 Experienced permanent employees 102 employees Set based on historical data
𝐿𝑣 Job openings to be filled 0 employees Set based on historical data
G Permanent personnel goal 83.5 employees Estimated to fit past data on perm. employees
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
new worker (𝜂) was estimated based on observa-
tions and managerial opinions due to lack of
available data.
Including nonlinear functions and initial condi-
tions, the model has 25 parameters in total. We
estimated 14 of them statistically and set another
7 according to their historical values. We also set
one of the parameters based on the interviews we
conducted. Lastly, since we had no data available
for the three parameters related to fatigue and its
effects, we set these parameters based on esti-
mates available in past studies (Kossoris, 1947;
Weisberg, 1994).
3.4 Historical Fit of the Model
The elicitation of the model structure from the ob-
served decision rules and the accuracy with which
the model is capable of replicating the historical
data series support the structural validity of the
model (Barlas, 1989, Forrester and Senge, 1980).
Moreover, the simulation game we used in the
learning laboratory with the executives confirmed
that the estimated decision rules reflect manage-
ment’s behavior relative to the existing incentive
system (Morecroft, 1985). To test the model’s
ability to replicate historical behavior, we
Figure 3 Comparisons of Simulated and Actual Data.
Temporary Employees
220
200
180
160
140
0 20 40 60 80 100 120 140 160
Time (Day)
Simulated Actual
Permanent Employees
140
125
110
95
80
0 20 40 60 80 100 120 140 160
Time (Day)
Simulated Actual
Total Labor
310
295
280
265
250
0 20 40 60 80 100 120 140 160
Time (Day)
Per
sonas
Simulated Actual
Processed Orders
60,000
45,000
30,000
15,000
0
0 20 40 60 80 100 120 140 160
Time (Day)
Simulated Actual
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
simulated the full model driven by only one exog-
enous data series: demand. Then, we evaluated
and compared the model results against four vari-
ables that had time series available. The compari-
son between the simulated and the actual data are
shown in Figure 3.
The low mean absolute percent error (MAPE)
between the series generated by the model and the
historical data—less than 5.6% for all series (Ta-
ble 3)—vouches for the accuracy of the model.
Also, the low bias and unequal variation showed
as part of Theil’s Inequality Statistics demonstrate
that there are no systematic errors present in the
generated series. This is substantiated by the high
𝑅2 for most of the comparisons. The lower 𝑅2 of
the total labor series is a result of the high-fre-
quency noise in demand. We acknowledge that
the model is not convenient to make predictions
of random daily events, but it is capable of de-
scribing the overall behavior of the system varia-
bles over time. Finally, the exceptional similarity
between the simulated and actual data series of
the orders processed occurs because the firm was
bound by contract to process all incoming work
each day. The firm was capable of preventing ca-
pacity shortfalls by having the employees work-
ing overtime and keeping a relatively low target
utilization rate.
The simulation starts 1 month after the start of
the project and runs for 8 months. The time unit
being used for the simulation is working days.
Since employees do not work on weekends, each
month has approximately 21 working days. De-
mand remains stationary throughout the simula-
tion period, as evidenced in Processed Orders in
Figure 3. Nonetheless, the CDDC is forced to
ramp up its staff during the first two months due
to a significant labor shortage. During this period,
the firm is forced to deal with work imbalances by
having employees work additional shifts. Eventu-
ally, the CDDC is able to reduce overtime and in-
crease its effective capacity after aggressively hir-
ing temporary personnel. By the beginning of the
second month (day 24), the hiring rate is reduced
and there is no longer a labor deficit. At the start
of the third month (day 49), regardless of the sta-
tionary nature of demand, there is excessive ca-
pacity. Management did the staff ramp up, and did
not take into consideration the large proportion of
inexperienced workers that required training.
Once the hiring rate diminishes, the training and
Table 3 Historical Fit February 2014-August 2014
Theil’s Inequality Statistics
MAPE Bias Unequal
Variation
Unequal
Covariation R2 N
Temporary employees 2.9 0.000 0.005 0.995 0.871 170
Permanent employees 2.5 0.039 0.028 0.933 0.955 170
Total labor 1.7 0.011 0.008 0.981 0.710 170
Processed orders 5.6 0.003 0.048 0.949 0.920 170
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The Use of Temporary Workers as Response to Pressure in Service Operations
mentoring workload disappears. New employees
also become experienced, and thus more produc-
tive, increasing the effective capacity. Although
management changes its perception of work
shifts, the delays in the system are long enough to
cause excessive capacity, utilization to drop, and
management to issue mass layoffs.
4. Analysis
The early labor shortage and the consequently
work imbalance in the historical case are very
convenient to analyze the effects of temping upon
the overall productivity of service firms, and pro-
vide a robust validation of the model. To begin
with, the good fit between the simulation and the
historical data bolsters our confidence in the ac-
curacy of the proposed model. In addition, the
simulation under historical conditions suggests a
significant use of inexperienced personnel to sup-
ply demand as a regular basis—shown in the rel-
atively large rookies to experienced personnel ra-
tio (Figure 4)—which hampers labor efficiency.
However, this negative effect on labor efficiency
occurs when the CDDC is initiating operations
and the firm is forced to hire new inexperienced
personnel in order to meet demand. To test if
temping is detrimental for the overall productivity
of the firm, we must demonstrate that the use of
inexperienced personnel to meet demand prevails
during normal operations and not only during the
transient conditions the CDDC faces at the begin-
ning of the simulation. Furthermore, we have to
show that temping is a worse alternative than
Figure 4 Rookies-to-experienced-personnel ratio
some other, like a completely permanent work-
force. To eliminate any bias towards the transitory
effects of an early labor shortage, we tested the
model in stochastic equilibrium. We also com-
pared thee different temping strategies to evaluate
temping relative to other alternatives. The strate-
gies were evaluated using performance metrics
for the overall productivity of the firm.
4.1 Labor strategies
We compared three strategies to evaluate the effi-
ciency of using temping to supply demand. The
first strategy emphasizes a moderate use of tem-
porary workers, and it is the same that the CDDC
used to supply demand in the historical data. We
call this the mixed labor strategy (MLS). In this
strategy, temporary experienced workers remain
in the system for only 23.3 workdays in average,
but the firm may choose to rehire some of them
after their contracts expire, converting them into
permanent employees. After being rehired, work-
ers stay in the firm for 396.4 workdays in average.
The second and third strategy emphasize two
extremes: full temporary labor and full permanent
labor. The second strategy is called the temporary
Rookies/Experienced
1.2
1
0.8
0.6
0.4
0 20 40 60 80 100 120 140 160
Time (Day)
Rookies/Experienced Emp.
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
labor strategy (TLS) and is one in which there are
no permanent employees, and temporary workers
cannot be rehired. In this strategy, the average
tenure of all experienced employees is 23.3 work-
days. On the other hand, the third strategy, called
the permanent labor strategy (PLS), is one in
which we turn temporary workers into permanent
workers by removing the contract expiration re-
striction, effectively increasing their average ten-
ure to that of workers in the permanent roster
(419.7 workdays), thus turning all workers into
permanent workers for all practical purposes. To
test these two strategies, we did some minor mod-
ifications to the dynamics of the original model.
First, considering there is no longer a target temp-
perm ratio, the desired labor adjustment is no
longer limited by the number of temporary or per-
manent employees (Equation (32′)). In addition,
since it is no longer necessary to distinguish be-
tween temporary and permanent workers, the
stock of permanent employees is set to 0 (Equa-
tion (12′)).
𝑙∗ = 𝑝 ∙ 𝐿 (32′)
𝐿𝑝 = 0 (12′)
The three models were initialized in equilib-
rium with the same characteristics the CDDC had
after the transitory labor shortage period and the
subsequent capacity overshoot. Then, we con-
ducted 500 simulations over 400 workdays for
each of the models. Stochastic variations in de-
mand were introduced in day 40. Demand was
modeled as an independent random variable with
no growth or decline over time. We used histori-
cal data to calculate its mean, variance, and auto-
correlation spectra. The three strategies were
compared using the four performance metrics
summarized in Table 4: total recruitments, total
layoffs, overtime, and accumulated labor days.
These metrics were used to measure perfor-
mance after day 40. Since all metrics are related
to costs, the lower the metrics, the more effective
a strategy is. Total recruitments and total layoffs
refer to the number of workers that were hired and
laid off during the evaluation period. Keep in
mind that MLS and TLS only hire or lay off tem-
porary employees, while PLS only hires or lays
off permanent employees. Furthermore, layoffs
do not include employees that leave the company
because they no longer want to stay (permanent
labor turnover) or because their contract expired
(temporary labor turnover). Overtime refers to the
total number of person-days that were paid as ex-
tra hours. Finally, accumulated labor days refer to
Table 4 Performance metrics of labor strategies
Permanent labor strategy Mixed labor strategy Temporary labor strategy
Mean 95% Conf. Interval Mean 95% Conf. Interval Mean 95% Conf. Interval
Total recruitments 637 633 642 763 635 641 1,370 1,364 1,375
Total layoffs 445 441 449 128 34 36 301 296 306
Overtime (person-days) 3,003 2,970 3,036 1,917 1,693 1,773 6,927 6,881 6,974
Accumulated labor days 93,219 93,003 93,435 118,413 117,915 118,911 112,312 112,063 112,561
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
the total number of person-days paid by the firm
without including the overtime.
As shown in Table 4, in spite of the stationary
demand and the equilibrium conditions, results
persistently showed a dramatic difference be-
tween the strategies’ performances. Furthermore,
Figure 5, which displays the strategies’ perfor-
mance metrics as a percentage of the largest value
in each metric, shows that none of the three strat-
egies is superior to the others in all aspects. How-
ever, TLS is outperformed by at least one of the
other two strategies in all metrics.
We attribute TLS’ underperformance to the
perpetual learning curve it faces. In this strategy,
the short length of the workers’ contracts forces
them to leave the company shortly after they have
become experienced. In order to compensate for
the lack of experienced personnel, the firm is
forced to hire additional rookies. However, be-
cause rookies are less efficient than experienced
personnel, the company has to hire more than one
rookie to do the same amount of work that a single
experienced worker could have done. Conse-
quently, when the firm requires its employees to
work overtime, it has to pay for additional hours
than if it had had an experienced labor force. Sim-
ilarly, TLS requires more labor days than PLS be-
cause there are more inactive employees in peri-
ods of low demand. Moreover, the firm needs to
constantly hire new employees to compensate for
the high turnover, which explains the high num-
ber of recruitments in this strategy. Nonetheless,
TLS is superior to PLS in terms of the total num-
ber of layoffs. The shorter tenure of the temporary
Figure 5 Metrics as a percentage of max value
employees makes it easier for the firm to reduce
its labor force in periods of low demand without
having to lay off employees.
However, at first glance is difficult to evaluate
if PLS is better than a strategy that mixes tempo-
rary and permanent employees, such as the strat-
egy used by the CDDC in the historical data. In
fact, the MLS is superior to the PLS in terms of
the total number of layoffs and the overtime met-
ric. We attribute this seeming superiority to the
target temp-perm ratio that the firm maintains. In
the MLS strategy, the temp-perm ratio limitation
of the desired labor adjustment works similarly to
a minimum capacity restriction, causing capacity
never to fall below a certain level. Therefore, the
firm stops laying off employees if it has reached
its target temp-perm ratio, even if its utilization
rate is still below its target. Hence, this strategy
causes much less layoffs than the other ones. Ad-
ditionally, this strategy reduces the firm’s depend-
ency on overtime to meet sudden upward changes
in demand. In contrast to the other strategies,
where the labor force is drastically reduced in pe-
riods of low demand, the MLS provides the firm
0%
20%
40%
60%
80%
100%
Hirings Layoffs Overtime Labor days
PLS MLS TLS
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
with extra capacity to compensate sudden rises in
demand. However, this strategy has a major
downside. Once the firm has reached its minimum
capacity, it is unable to increase its utilization
rate, which translates to more paid labor days. The
other strategies, on the other hand, allow the firm
to freely adjust its labor force in order to reach its
target utilization rate and reduce the idle time.
4.2 Total costs
To assess which strategy is better to minimize
costs, it is not enough to analyze each perfor-
mance metric individually. Therefore, we con-
verted the four performance metrics into a single
value that could be used to compare the cost of all
strategies: person-workdays. We recognize that
there are other qualitative aspects beyond these
metrics that may add to the costs of each strategy,
such as the lack of commitment of temporary em-
ployees, but we do not include them in our com-
parison because we had not empirical evidence to
model them, and therefore, we could not simulate
them.
Since in our case study extra hours were twice
as expensive as regular hours, we converted the
overtime metric by multiplying it by 2. We could
not estimate the hiring metric cost using empirical
evidence because we did not have data available
to measure the financial burden of recruiting,
screening, testing and hiring. However, we found
that temping agencies charge between 10% and
20% of first year salary as a fee for permanent
placements. Hence, to convert the hiring metric of
PLS, we multiplied it by 52 (the number of labor
days in 20% of a work year). In addition, based
on the average tenure of laid off employees in
PLS, each layoff was estimated to cost the equiv-
alent of about 31 workdays, which represents the
payment of severance and accrued vacations. As
for MLS and TLS, we assume that the hiring and
lay off metrics have no influence on their costs,
since temping agencies absorb these costs when
offering temporary placements. However, temp-
ing agencies charge companies a monthly fee
based on the salary of the temporary workers they
place. Although these fees vary according to the
industry sector, they are usually between 25% and
75%. We assume a temping fee of 50%, which is
common for the industrial sector. Consequently,
the labor days of temporary employees were mul-
tiplied by 1.5, while the labor days of permanent
employees were multiplied by 1.2 to account for
social securities and benefits. Finally, to calculate
the final cost of each strategy in terms of person-
workdays, we added up all of their converted met-
rics (Table 5).
As Table 5 shows, the least expensive strategy
is PLS. Although this strategy incurs in hiring and
layoff costs, and it requires the firm to pay for
more overtime than MLS, these aspects are com-
pensated by the lower labor days and the absence
Table 5 Cost in person-workdays per strategy
PLS MLS TLS
Hiring 33,148 - -
Layoffs 13,784 - -
Overtime 6,006 3,835 13,855
Labor days 111,863 163,983 168,468
Total cost 164,801 167,818 182,323
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
of temping fees. Furthermore, the TLS is the most
expensive of all strategies. In fact, even though
TLS has fewer accumulated labor days than MLS,
it is still the most costly of all strategies in this
rubric due to the temping fees.
5. Policy Analysis
In this section, we analyze how the two most ef-
fective strategies discussed in the previous sec-
tion—PLS and a MLS—perform using different
staffing policies. Moreover, we evaluate their per-
formance on settings with divergent learning
curves. The parameters for all simulations are the
same as above, and all simulations were run from
initial equilibrium with variations in demand in-
troduced as indicated in §4.1.
5.1 Combinations of parameters
In §4.1 we identified that the target temp-perm ra-
tio plays a significant role in the effectivity of
MLS, as it works similarly to a minimum capacity
restriction that helps to reduce overtime and the
number of layoffs. To make MLS and PLS com-
parable in this aspect, we added a minimum ca-
pacity restriction (m) to the desired labor adjust-
ment in the PLS model:
𝑙∗ = max(𝑝 ∙ 𝐿,𝑚 − 𝐿) (32′)
Then, we used sensitivity analysis to test the ef-
fectivity of staffing policies. We conducted over
10,000 simulations with each of the two models
(MLS and PLS), and started each of the simula-
tion runs with random configurations of the pa-
rameters that have a direct effect on staffing deci-
sions: target utilization rate, minimum capacity
restriction, and target temp-perm ratio. Since the
target temp-perm ratio is only present in MLS,
random configurations of this parameter were
only introduced in this model. Similarly, we in-
troduced random values for the minimum capac-
ity restriction only for the PLS model. In addition,
all simulations had random values for the target
utilization rate and were initialized in equilibrium
according to the randomly generated values of the
staffing decision parameters. The results of these
simulations were used to evaluate the impact of
these parameters on the cost of the strategies (Fig-
ure 6). The randomly generated values were lim-
ited to the range that is shown in the axes of the
charts shown in Figure 6.
As shown in Figure 6, the optimum configura-
tion parameters for MLS are a low target temp-
perm ratio (between 0 and 0.43) and a high target
utilization rate (between 97% and 120%). How-
ever, even with the right configuration of staffing
decision parameters, MLS is more costly than
PLS. Approximately 4% of the PLS simulations
had a lower cost than the minimum cost in all the
MLS simulations. Results suggest that the best al-
ternative to minimize costs is a full permanent la-
bor strategy coupled with a high target utilization
rate (between 97% and 120%) and an intermedi-
ate minimum capacity restriction (between 153
and 227), at least when considering the perfor-
mance metrics defined in §4.1 and their respective
estimated costs. However, to evaluate if this state-
ment holds true in contexts with different learning
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
Figure 6 Policy costs by strategy and staffing decision parameters
curves, it is necessary to test how these policies
and strategies fare in settings with different pa-
rameters for the efficiency of newly hired em-
ployees and the time it takes for employees to be-
come fully experienced.
5.1 Learning contexts
In this subsection we explore if there are settings
in which MLS could prove to be superior to PLS,
in particular in environments where new employ-
ees are quite efficient from the moment they are
hired and where learning occurs fast. The models
of both strategies are initialized in equilibrium
and with a target utilization rate of 100%. Simi-
larly to the previous subsection, we initialize the
minimum personnel and the target temp-perm ra-
tio with random values. However, we limit the
randomly generated values to the ranges defined
in the previous subsection (from 0 to 0.43 in the
case of temp-perm ratio, and from 153 to 227 in
the case of the minimum capacity restriction). We
then conducted over 10,000 simulations with the
PLS and MLS models in three learning contexts:
rapid learning, normal learning, and slow learn-
ing. Afterwards, we compared the optimum PLS
and MLS results with each other (Table 6).
As shown in Table 6, even in contexts where there
is extremely fast learning, MLS is not a better al-
ternative than PLS. However, the opposite is true
in some settings: MLS becomes more expensive
than PLS as the learning curve becomes steeper.
Even when the firm operates using a TLS with an
optimum target temp-perm ratio, this is not
enough to compensate for the temping fees and
the costs of having a less experienced labor force.
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LÓPEZ AND FERNÁNDEZ
The Use of Temporary Workers as Response to Pressure in Service Operations
Table 5 Strategies’ performance in different learning contexts
Fast learning Normal learning Slow learning
Time to become experienced 5 workdays 3 months (63 workdays) 6 months (126 workdays)
Relative rookie efficiency 80% 50% 30%
MLS optimum avg. person-workdays 113,467 129,593 157,121
Optimum target temp-perm ratio 0.19 0.11 0.03
PLS optimum avg. person-workdays 113,490 127,362 149,205
Optimum minimum personnel 205 226 264
T-Test p-value 0.932 0.000 0.000
Gap in costs No statistical difference 2,231 (1.75%) 7,919 (5%)
6. Implications
Although we cannot provide a definitive answer
to whether all organizations should or should not
embrace a “temping” strategy, our findings indi-
cate that there are contexts bounded by certain
combinations of parameters in which such strat-
egy may not be sound. Evidently, our results are
not deterministic for all industries and contexts,
and there are other qualitative factors that may
come into play to tip the balance towards one
strategy or the other. Still, the process we fol-
lowed to develop the model in close concurrence
with company executives gives us confidence in
that the structure we developed represents well
the actual dynamic structure of the system. In ad-
dition, the fact that our study is heavily grounded
in empirical data and that our model behavior
closely resembles the historical data series serves
as a validation that our model is operating within
reasonable boundaries. Hence, at least in the con-
text of logistics, temping seems to be a subopti-
mum resource strategy. Reductions in layoff costs
and potential decreases in overtime are not
enough to compensate the higher staffing level
with much less overall experience. Our findings
show that, even in contexts of fast learning, a full
permanent labor force is superior, and that in fact
the company would behave myopically if it chose
a resource strategy based on the use of temporary
workers.
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