The Use of Temporary Workers as a Response to Pressure in...

The Use of Temporary Workers as a Response to Pressure in Service Operations

Luis E. López • Carlos M. Fernández

Noviembre, 2015

CEN 1411

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.

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-

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



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



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)



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



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



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-



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)



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



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.



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



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).



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



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



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



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



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.



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



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



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



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



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.



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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