ArticlePDF Available

A comparative analysis of learning curves: Implications for new technology implementation management

Authors:

Abstract and Figures

New technology implementation projects are notoriously over time and budget resulting in significant financial and strategic organizational consequences. Some argue that inadequate planning and management, misspecification of requirements, team capabilities and learning contribute to cost and schedule over runs. In this paper we examine how learning curve theory could inform better management of new technology implementation projects. Our research makes four important contributions: (1) It presents a comparative analysis of learning curves and proposes how they can be used to help ERP implementation planning and management. (2) Based on empirical data from four ERP implementation projects, it provides illustrations of how managers can apply the curves in different project situations. (3) It provides a theoretical basis for empirical studies of learning and ERP (and other IT) implementations in different organizational settings. (4) It provides empirical justification for the development of learning curve theory in IT implementation.
Content may be subject to copyright.
Decision Support
A comparative analysis of learning curves: Implications for new technology
implementation management
Malgorzata Plaza
a
, Ojelanki K. Ngwenyama
a,b,*
, Katrin Rohlf
c
a
Institute for Innovation and Technology Management, Ryerson University, 55 Dundas Street West, 9 Floor, Room 3-089, Toronto, Ontario, Canada M5B 2K3
b
Aarhus School of Business, Aarhus University, Denmark
c
Department of Mathematics Ryerson University, Toronto, Canada
article info
Article history:
Received 8 September 2007
Accepted 5 January 2009
Available online xxxx
Keywords:
Project management
Information systems implementation
Learning curve models
Organization learning
abstract
New technology implementation projects are notoriously over time and budget resulting in significant
financial and strategic organizational consequences. Some argue that inadequate planning and manage-
ment, misspecification of requirements, team capabilities and learning contribute to cost and schedule
over runs. In this paper we examine how learning curve theory could inform better management of
new technology implementation projects. Our research makes four important contributions: (1) It pre-
sents a comparative analysis of learning curves and proposes how they can be used to help ERP imple-
mentation planning and management. (2) Based on empirical data from four ERP implementation
projects, it provides illustrations of how managers can apply the curves in different project situations.
(3) It provides a theoretical basis for empirical studies of learning and ERP (and other IT) implementations
in different organizational settings. (4) It provides empirical justification for the development of learning
curve theory in IT implementation.
Crown Copyright Ó2009 Published by Elsevier B.V. All rights reserved.
1. Introduction
Cost and schedule overruns during new technology implemen-
tation projects are well documented in academic and practitioner
journals. Recent studies (Robbins-Gioia, 2002; The Standish Group,
2004) find that more than 70% of ERP implementations are over
schedule and budget. Much of the literature aimed at ‘fixing’ the
ERP implementation problem focuses on prescribing ‘best practice’
for successful ERP implementation (Davenport, 1998; Markus
et al., 2000; Besson and Rowe, 2001). Some of the prescriptions
encourage project managers to: (a) adopt standard business pro-
cesses to fit with the ERP software (Markus et al., 2000; Palanisw-
amy and Frank, 2000; Sumner, 2000); (b) avoid customizing the
software (Parr and Shanks, 2000; Mabert et al., 2001; Murray
and Coffin, 2001); and (c) provide appropriate user training (Bingi
et al., 1999; Holland and Light, 1999; Al-Mudimigh et al., 2001).
Other researchers offer frameworks to assist managers in defining
and analyzing critical success factors (Akkermans et al., 1999; Bingi
et al., 1999; Holland and Light, 1999; Nah et al., 2001), and project
risk factors (Sumner, 2000). Still others suggest the building of in-
tra-organizational coalitions to support ERP implementation pro-
jects (Pozzebon and Pinsonneault, 2005). However, there is now
growing acknowledgment that team training/learning and im-
proved project management methods are important to ERP imple-
mentation success (Bingi et al., 1999; Holland and Light, 1999;
Kumar and van Hillegersberg, 2000; Willcocks, 2000; Hong and
Kim, 2002; Verville and Halingten, 2002).
Recent research has identified a relationship between project
team capabilities and IT implementation effectiveness. For exam-
ple, Chatzoglou and Macaulay (1996) found that project team capa-
bilities are the single most important factor affecting the timelines
of software implementation projects. A recent study by Karlsen
and Gottschalk (2003) also found that knowledge transfer is signif-
icantly related to IT project success. Dixon (2000) found that
knowledge transfer among teams enables problem solving on IT
projects. Several researchers have also pointed out that inaccurate
predictions of team learning performance often lead to conse-
quences of project creep and cost escalation (Keil et al., 1995; Chat-
zoglou and Macaulay, 1996; Callaway, 1999; Barry et al., 2002;
Depledge, 2003; Wiegers, 2003; Wallace and Keil, 2004). ERP
implementations projects are especially vulnerable schedule over-
runs because: (1) ERP software is complex and, in most cases, the
managers of organizations implementing these systems often have
little or no prior experience with them; (2) the scale of the imple-
mentation is often larger in scope than any previous IT implemen-
tation in the organization; (3) the organization’s own IT specialists
0377-2217/$ - see front matter Crown Copyright Ó2009 Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.ejor.2009.01.010
*Corresponding author. Address: Institute for Innovation and Technology
Management, Ryerson University, 55 Dundas Street West, 9 Floor, Room 3-089,
Toronto, Ontario, Canada M5B 2K3. Tel.: +1 416 979 5000/4203; fax: +1 416 979
5249.
E-mail addresses: mplaza@ryerson.ca (M. Plaza), ojelanki@ryerson.ca (O.K.
Ngwenyama), krohlf@ryerson.ca (K. Rohlf).
European Journal of Operational Research xxx (2009) xxx–xxx
Contents lists available at ScienceDirect
European Journal of Operational Research
journal homepage: www.elsevier.com/locate/ejor
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
often have little knowledge of the ERP system and project must be
augmented by specialized external consultants; (4) the schedule
for completion of the ERP implementation is often tight due to
competitive pressures on the organization. Seen from this perspec-
tive, the management project team learning capabilities is an
important dimension of effective project management. Unfortu-
nately, although it is accepted that the team’s learning rate and
capabilities influence its performance and productivity, little re-
search has been conducted on this issue (Barry et al., 2002).
In this paper we investigate the potential of learning curves as
a method for planning and management of ERP implementation
projects. This paper extends the work of Ngwenyama et al.
(2007) who proposed a learning curve model for managing orga-
nizational software upgrades cycles. We focus on three key ques-
tions: (1) How can managers achieve effective project team
learning that is so critical to the success of ERP implementation?
(2) How can managers develop viable project schedules that take
into account the learning curve of the project team? and (3) How
can managers identify and dynamically adjust project plans and
resources in response to contingencies during ERP implementa-
tion. Our discussion of these issues unfolds as: in Section 2we
discuss the linkage between learning and project performance,
and the challenges of managing ERP projects. In Section 3we dis-
cuss the basic and assumptions of two learning curve models, the
S-curve and the simplified exponential curve. In Section 4we
present a comparative analysis of the implications of using both
curves using empirical data from four ERP implementation cases
from three companies. Finally in Section 5we conclude with
implications for new technology implementation theory and
practice.
2. The learning and performance relationship
The relationship between learning and project performance was
first formally defined by Wright (1936). Since then, several
researchers have investigated and modeled this relationship. To-
will (1985) studied on-the-job (or industrial) learning and devel-
oped a set of parameters for measuring learning and productivity
gains in industrial projects. Terwiesch and Bohn (2001) investi-
gated the cost and benefits of learning during ramp-up production
of new products. Their primary interests were the cost of speeding-
up learning and experimentation, and the likelihood and cost of
product defects as teams progressed to full production.
Although on the surface it might appear that new product man-
ufacturing and ERP implementation projects are different, they
share important features. In both cases the project teams must
learn new production methods and work with designs of end prod-
ucts of which they may have little knowledge. In both cases the
teams need to learn from experience in spite of whatever initial
training they may receive. Further, the cost of product defects
could be quite significant and threatening to the economic viability
of the company. From this perspective, ERP implementation pro-
jects can be viewed as limited-time endeavors that can be planned
and managed similarly to some new product manufacturing pro-
jects. One key difference is that the ERP project team is cross-dis-
ciplinary, comprising technical and business experts from various
organizational functions, as well as external consultants hired to
support the endeavor (Davenport, 1998;Boyer, 2001; Baccarini
et al., 2004). Team members of different backgrounds, business
disciplines and interests must learn and work together if the
implementation project is to succeed (Fedorowicz et al., 1992;
Edmondson et al., 2003). Careful selection of the team members,
a good project plan, high quality training in the ERP software and
implementation practices along with strong top management sup-
port are also essential to success (Davenport, 1998; Boyer, 2001;
Baccarini et al., 2004).
An important factor in successful ERP implementation projects
is early team learning. New implementation teams require signifi-
cant initial classroom training on the ERP software followed by
experiential learning on configuring the software in a sandbox set-
ting (Robey et al., 2002). And since team members play different
roles on the project, they need to develop cross-functional skills
(Boyer, 2001; Rocheleau, 2006). Further, many issues challenge
team learning and performance during implementation. Software
features may not be well understood; some members leave the
team and new ones fill their places (Chambers, 2004; Pendharkar
and Subramanian, 2006). A wide range of hygiene factors, such
as boredom, fatigue, distractions, can also impede individual and
team learning and performance (Eason, 1988; Adler and Clark,
1991). Thus a key problem for project managers is to understand
the rate of project team learning in order to effectively plan and de-
velop the project schedule. Predicting the impact of team learning
in complex projects is essential to effective project management
but it can be quite challenging (Eden et al., 1998; Ellis and Shpiel-
berg, 2003).
3. Modeling learning and performance
Since Wright, various learning curve models have emerged, but
the two main curves used for cost estimation and productivity
assessment are the S-curve and the exponential progress curve
1
(Yelle, 1979; Towill, 1985; Argote and Epple, 1990; Pananiswami
and Bishop, 1991; Teplitz, 1991; Badiru, 1992; Mosheiov and Sidney,
2003). The S-curve conceptualizes performance improvement as
function of practice, with the most dramatic improvements taking
place at the beginning of the learning process. According to Arrow
(1962), knowledge increases with experience and the process of
learning is a product of experience. In an S-curve, either time or a
cumulative output can be chosen as an independent variable and
influences of experience carry-over, cessation of learning and a start
up effect are considered (Howell, 1990). Interestingly, an exponen-
tial curve is the one most commonly used to track performance in
technology related projects (Butler, 1988; Teplitz, 1991; Teplitz
and Amor, 1993; Jovanovic et al., 1995; Jackson, 1998; Chambers,
2004; Dardan et al., 2006; Pendharkar and Subramanian, 2006).
The logistic curve is similar to the S-curve but ignores the start up
effect. The rationale for using an exponential model is that it ade-
quately describes the performance improvement during the experi-
ential learning phase. The logistic model is robust and has a wide
range of applicability. Since in the logistic model output is related
to asymptotic performance, the curve coefficient, k, relates changes
in performance over time to the performance threshold (when learn-
ing is complete) (Yelle, 1979; Edgington and Chen, 2002). In the next
section we examine the relationship between the logistic model and
the S-curve model, and discuss the range of applications of the logis-
tic model and illustrate how it can be used as an analytical tool to
assist project managers in analyzing learning and productivity issues
and their implications for IS project scheduling and duration.
3.1. Basic learning curve concepts
As stated earlier, learning curve models used on technology and
IS projects are often called progress curves or progress functions
(Malerba, 1992). Progress curves model practice and performance,
where practice is represented by units of time (or the number of
times a predefined output is delivered), and performance is mea-
sured as a rate, in which a predefined output is produced (Fedo-
rowicz et al., 1992). In classical form, progress curves depict a
1
Space limitation prevents a detailed discussion of learning curve models; the
interested reader can refer to Badiru (1992) and Yelle (1979).
2M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
rapid increase of early performance then a decline and diminish-
ment to 0. James (1984) reports that variations in performance rel-
ative to practice and experience track the S-curve more closely
when hygiene factors that affect performance and learning such
as boredom, fatigue and distractions are considered.
For our purposes of modeling and analyzing learning and per-
formance in ERP implementation projects we define an initial team
training exercise and a two phase implementation cycle, compris-
ing configuration and testing/conversion phases (Markus et al.,
2000). Assuming that ERP implementation follows a progress
curve, we can model the relationship between the progress curve
coefficient kand performance. In this case we define performance
as the rate of completion of a task; for example: (1) the number of
sessions configured by a single team member in a unit of time, or
(2) the number of transactions completed by a single team mem-
ber in a unit of time. We now define the basic terms and concepts
we use in modeling the logistic and Scurves:
Definition of model terms
P
l
(t), P
s
(t) progress functions as functions of time, t
p
t
performance threshold (constant performance function)
represents the optimal performance of the fully trained
and integrated team operating at their peak performance
levels
T
0
planned project duration is the expected time for comple-
tion of the implementation. It is estimated in weeks,
months or fractions of the above depending on which units
is project duration measured in. It is calculated based on
the assumption that performance remains predetermined
and constant during the progression of the project
T
l
,T
s
project durations, calculated in the same units as used for
the planned project duration. They are estimated based on
the assumption that performance on the project is repre-
sented by either P
l
(t)orP
s
(t)
D
T
l
,
D
T
s
extensions of the planned project duration. They are calcu-
lated as the difference between Tand T
0
kprogress curve coefficient
T
0k
knowledge absorption capacity coefficient
mTeam’s initial performance level measured at the begin-
ning of the project and scaled by p
t
D
0
(t),
D
(t) the difference between the logistics and S-curves mea-
sured as the difference in areas underneath those curves
at time tscaled by p
t
3.2. Basic assumptions
We now outline three basic assumptions upon which our anal-
ysis is based; there are planned project duration,team performance
levels and forgetting. Planned or expected project duration is a crit-
ical project parameter that is usually underestimated by project
managers due to uncertainty about (a) the team’s performance
capability and (b) the amount of work required to configure, test
and install the ERP system. Project duration is often optimistically
estimated with an input from the software vendor, who is often
biased by their interest in selling the software. Assumption 1 be-
low establishes a key link between planned project duration and
the progress curve models.
Assumption 1. If the performance of each team member is at p
t
at
all times during the IS project, then the total time required for the
implementation will be equal to planned duration T
0
.
This assumption implies a fully trained and integrated project
team capable of peak performance. Such a team is seldom available
except from the software vendor or specialized consultants who
have gained competence from repeated implementation of the
software in other firms. In practice, however, most ERP implemen-
tation projects will require time extensions (defined as
D
T) beyond
the planned duration. Therefore we will relax Assumption 1 to P
s
(t)
when project team performance follows the S-curve and P
l
(t) when
it follows the simplified logistic model. Team performance will al-
ways fall below the performance threshold p
t
, where tis time
elapsed on the project.
Fig. 1 illustrates the performance outcomes when Assumption 1
is strong and relaxed. When Assumption 1 is valid (strong) the to-
tal amount of work required to complete the project is represented
by the area defined as R
T
0
p
t
dt. When Assumption 1 is relaxed
implementation time is extended by
D
T
l
when logistic curve is
used, and
D
T
s
when S-curve is utilized. However, the total work re-
quired remains unchanged as illustrated by the two integrals
R
T
l
0
P
l
ðtÞdt ¼AreaðLogistics curveÞ;
R
T
s
0
P
s
ðtÞdt ¼AreaðScurveÞ:
(
3.2.1. Factors affecting team performance
Several factors affect team performance including team mem-
bers’ knowledge, practice and experience gained on implementa-
tion projects. These performance changes are measured (the
coefficient k), cumulatively in terms of reduction of time required
to complete various implementation tasks. Other factors influenc-
ing performance improvements during ERP implementations are:
(1) team cohesion resulting from storming, forming and norming
into the performing stage (Gray and Larson, 2000), (2) internal
team members learning the new ERP software, and (3) external
team members (consultants) learning the business processes of
the organization (Davenport, 1998). Thus for this analysis we make
the following assumption:
Assumption 2. We estimate the progress curve coefficient k
(changes in team performance levels) from the average team
member’s performance and adjust for the team environment.
A key factor contributing to forgetting is the time lag between
training and task execution. This is a situation that does not nor-
mally exist in ERP implementations. Team members are trained
just before the start of the project and the training includes an
experiential component in a sandbox. Further, ERP implementation
projects are carried out under conditions conducive to learning and
performance such as: (1) executive level support and involvement;
(2) expert consulting support and guidance; (3) project schedules
no longer than 18 months; and (4) performance incentives (Daven-
port, 1998; Boyer, 2001; Baccarini et al., 2004; Dong, 2004). Condi-
tions which (Fedorowicz et al., 1992) argue, promote learning and
overcome boredom, fatigue and forgetting. Thus in our analysis we
ignore performance decay due to forgetting:
0
Pl, P s
t
Performance Threshold
Progress Function Pl: Logistic curve
Progress Function Ps: S-curve
Tl
T0
Δ
T
Training
Configuration/ Testing/ Go live
Ts
Δ
T
Fig. 1. Illustration of the model parameters.
M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx 3
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
Assumption 3. The impact of forgetting is not considered in either
model.
3.3. Formal description of the progress curve models
We define a progress function Pfor the average project team
member, with kas the progress curve coefficient, and p
t
the perfor-
mance threshold. The performance growth rate is thus the deriva-
tive
dP
s
dt
expressed by the S-curve equation (3.1)
dP
s
dt ¼kP
s
1P
s
p
t

:ð3:1Þ
The solution to the Eq. (3.1) is
P
s
ðtÞ¼ p
t
1þAe
kt
;ð3:2Þ
where A¼
p
t
p
0
p
0
¼
p
t
p
0
1¼m1 and m¼
p
t
p
0
represents the project
team’s initial performance (i.e. p
0
) measured relative to the perfor-
mance threshold p
t
.
Eq. (3.2) can be further transformed into dimensionless pro-
gress function
P
s
ðtÞ
p
t
¼1
1þAe
kt
:ð3:2
0
Þ
Performance growth rate can also be expressed as the logistics
equation
dP
l
dt ¼kðp
t
P
l
Þð3:3Þ
the solution to which
P
l0
ðtÞ¼p
t
ð1e
kt
Þif P
l
ð0Þ¼0;
P
l
ðtÞ¼p
t
ð1
m1
m
e
kt
Þif P
l
ð0Þ¼p
0
(ð3:4Þ
is again transformed into dimensionless form as
P
l0
ðtÞ
p
t
¼1e
kt
if P
l
ð0Þ¼0;ð3:4:1
0
Þ
P
l
ðtÞ
p
t
¼1m1
me
kt
if P
l
ð0Þ¼p
0
:ð3:4:2
0
Þ
The difference between the logistic and S-curves, is defined by the
difference in areas underneath those curves at time tscaled by p
t
(cf. Fig. 1) will be represented as either
D
0
(t) when the initial con-
dition P
l
(0) = 0 is applied to the logistic curve (3.4.1)
0
,or
D
(t) when
the initial condition P
l
(0) = p
0
is used.
Theorem 1. for proof see Appendix A.When the team reaches an
initial level of performance p
0
¼
p
t
e
before the commencement of the
project (m equals to constant e) it makes no difference which curve is
used to track performance since both the S-curve and logistic curve
become identical in relatively short time.
Theorem 1 explain why both curves have been used success-
fully to track performance on ERP projects when team performance
reaches the level of p
0
equal or close to 30% of the performance
threshold prior to the commencement of the project. We will
now define Theorem 2 as an extension of Theorem 1 to situations
where the project team did not reach the critical level of perfor-
mance before the commencement of the project. The difference be-
tween the logistics and S-curves measured as the difference in
areas underneath those curves at time tscaled by p
t
(Fig. 2) will
be represented as
D
0
(t) (Eq. (A.2)
0
).
Theorem 2. for proof see Appendix B.When m >e (i.e. p
0
does not
reach 30% of performance threshold), j
D
0
(t)jdepends only on the
coefficient k and parameter m ¼
p
t
p
0
and will reach its maximum as
when implementation time approaches infinity t ?1or as imple-
mentation time equal to critical time t
*
(Eq. (3.5)).
t
¼1
kln p
t
2p
0
p
t
p
0

:ð3:5Þ
The reader should take note that when the team’s initial perfor-
mance is low (i.e. p
0
does not exceed 30% of performance thresh-
old), then the difference between the logistic and S-curves
D
0
(t)
is calculated for the initial condition of P
l
(0) = 0. Fig. 3 provides a
graphic illustration of Theorem 2. On the left plot
D
0
(t) is a func-
tion of very short project duration (depicted in weeks) and on
the right plot a longer project duration (depicted in months). It
0
Pl, Ps
t
Performance Threshold p t
Progress Functions Ps:
S-curve Progress Function Pl, Pl(0)=0
Logistic curve
Fig. 2. The difference between the logistic (initial condition of 0) and S-curves
measured as the difference in areas underneath those curves at time tfor cases
when m=eor m>e.
k=0.8
0
0.5
1
1.5
2
2.5
1
Implementation time t [months]
Δ0(t)
m=3
m=4
m=10
m=14
k=0.8
m=3
m=4
m=10
m=14
0.5
0
Δ0(t)
-0.2 19171513119753
Fig. 3. The difference between the logistic and S-curves as a function of time: measured for short (plot on the left) and long (plot on the right) implementations.
4M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
can be seen from the plots that j
D
0
(t)jreaches its maximum in less
than 6 weeks and becomes smaller as mbecoming larger.
Fig. 4 illustrates the maximum value of j
D
0
(t)jas a function of m
following Theorem 2 when mis dimensionless and
D
0
(t) is ex-
pressed in units of measure used to evaluate project duration in
relation to productivity threshold p
t
. We can assume mP1 since
the levels of initial performance can never exceed the performance
threshold.
Two important observations related to Theorem 2 should be
noted:
1.
D
0
(t) will reach minimum if p
0
remains close to 30% of the per-
formance threshold (mremains close to constant e), which cor-
responds again to situation discussed in relation to Theorem 1.
2. When m> 3 (initial performance is in the lower range) the dif-
ference between the area under the curves will increase with m.
The decision on which curve should be used to track perfor-
mance changes must be made based on other factors such as
team dynamics, amount of consultants, in-house training incor-
porated into the implementation schedule, familiarity with
other team members, consultants, etc.
D
0
(t) will be relatively
larger than if m was close to 3 for both short implementations
(project with an aggressive schedules and implementation
times at 3–4 months) and longer implementations (Fig. 3).
We will discuss the selection of the functional form of the pro-
gress curve for the case, where initial performance is low
(m= 10) in more details in Sections 3 and 4.
We will now define Theorem 3, to include situations where the
project team exceeded the critical level of performance before the
commencement of the project. The difference between the logistics
and S-curves measured as the difference in areas underneath those
curves at time tscaled by p
t
(Fig. 5) will now be represented as
D
(t)
(Eq. (A.2)
0
).
Theorem 3. When m <e (i.e. p
0
is larger than 30% of performance
threshold), it makes no difference which curve is used to track
performance since both the S-curve and logistic curve become
identical in relatively short time.
The reader should note that for those high levels of initial per-
formance we use the initial condition of P
l
(0) = p
0
to calculate the
difference between the logistics and S-curves, and the second
equation (3.4) represents the logistic curve.
D
(t) is calculated from
Eq. (A.2)
0
.Theorem 3 is depicted in Fig. 6, in which
D
(t) is plotted as
a function of time for various sets parameters: kand m.
Following from Theorem 3 and Fig. 6, the S-curve becomes clo-
ser to the logistic curve if the project team shows higher levels of
initial performance (depicted as plots for m= 1.1 and 1.5). If team’s
initial performance level is very high (m< 2) then the situation on
the ERP project will be similar to the other industrial projects (in a
sense that very little if at all initial training will be provided) and
the progress curve will represent performance increases due to
team cohesion and integrated work patterns.
3.4. Context for applying the models
We will now discuss the context for application of the progress
functions. In each project situation the team has the capacity to
learn and accomplish a finite amount of work within fixed time
period. We call this the knowledge absorption capacity coefficient
T
0k
. The relationship between planned duration T
0
, the progress
curve coefficient k and the knowledge absorption coefficient is de-
fined in (4.1)
T
0k
¼kT
0
:ð4:1Þ
Combining the progress coefficient and the project duration into a
single variable T
0k
enables critical analysis of the project situation
as Eq. (4.1) also defines the tradeoff between project duration and
Fig. 4. The maximum value of the difference between the logistic and S-curves as a function of m.
0
Pl, Ps
t
Performance Threshold p t
Progress Function Ps:
S-curve
Progress Function Pl, Pl(0)=p0
Logistic curve
Fig. 5. The difference between the logistic (initial condition of p
0
) and S-curves
measured as the difference in areas underneath those curves at time tfor cases
when m<e.
M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx 5
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
learning. We can analyze the impact of learning on duration (slower
learning extends the project duration and faster learning shortens
project duration). Further, since kis expressed in [1/‘‘] unit of mea-
sure used for project duration”, T
0k
is dimensionless and can repre-
sent planned project duration, if kremains fixed.
For the purpose of illustrating the application of the models we
define four categories of IS implementation projects (cf. Table 1).
Implementation projects that fit categories I and II can be modeled
using either the S-curve or logistic curve and to these Theorem 1 is
applicable. When the project team is given intensive initial in-
house training (team bonding/cohesion has occurred) and medium
to high support from external consultants is available, the project
startup effect can be minimized and the logistic curve applicable.
However, if the team members received training at different loca-
tions and team bonding/cohesion was not achieved the startup ef-
fect could be considerable and the S-curve will better represent
performance changes.
Projects in category III are those in which the team has very lit-
tle knowledge or experience in the software they are implementing
and only medium external consulting support is available. The ini-
tial performance of the category III project team is also much less
that 30% of the performance threshold. In such situations Theorem
2is applicable, and we would suggest the logistic equation with
the initial condition P
l
(0) = 0, rather than an S-curve. Fig. 7 below
presents the implications of using the logistic curve in this situa-
tion. We compare the difference between the logistic and S-curves
for different levels of initial team performance (m= 1.5, 3, 6, 10,
and 14). In Fig. 7
D
(t) is scaled by T
0
and plotted as a function of
kT
0
to accommodate the combined effect of project duration and
the progress curve coefficient.
2
In can be seen that when m=3
the plot representing the difference between the two curves re-
mains relatively small for all values of kT
0
. For cases where m<3,
a logistic curve with initial condition of P
l
(0) = 0 will not be used.
If m> 3, the increased values of
D
(t) scaled by T
0
are present only
for very low values of kT
0
(implementation times less than 3
months) and even then they are below the level of 0.4 of the per-
formance threshold.
For implementations projects within Category IV it is assumed
that the project team has a high level of competence obtained from
prior experience implementing the same software elsewhere. Con-
sequently, the team members need no training before the com-
mencement of the project and require minimal consulting
support during the implementation. In this situation Theorem 3
is applicable and either the logistic curve with initial condition
P
l
(0) = p
0
or the S-curve can be used. Fig. 8 below presents the
implications of using the logistic curve in this situation. We com-
pare the difference between the logistic and S-curves for different
levels of initial team performance (m= 1.1, 1.5, 2, 3 and 10). In
Fig. 8
D
(t) is scaled by T
0
and plotted as a function of kT
0
to accom-
modate the combined effect of project duration and the progress
curve coefficient.
Fig. 6. The difference between the logistic and S-curves as a function of time: plotted for various sets of parameters kand m.
Table 1
Four categories of IS projects.
Project categories Level of initial performance p
0
Amount of initial training prior to implementation
a
Amount of consulting support during the implementation
b
I Moderate (mclose to 3) High Very high
II Moderate (mclose to 3) Medium Low
III Very low (m> 4) Low Medium
IV Very high (m< 2) None Very low
a
The following criteria were used for classification: ‘‘low” – 2 weeks or less, ‘‘medium” – more than 2 weeks up to 3 weeks, ‘‘high” – more than 3 weeks.
b
The classification was based on the ratio of consultants to team members: ‘‘low” – 0.25 or less, ‘‘medium” – more than 0.25 but less than 0.5, ‘‘high” – 0.5 and higher but
less than 1, ‘‘very high” – 1 and higher.
Fig. 7. Comparison of logistic and S-curves for Category III projects.
2
Note that D0ðtÞ
T0in Figs. 5 and 6 is dimensionless, expressed in the units of measure
scaled by performance threshold p
t
.
6M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
4. The case studies
The data used to illustrate our learning curve analysis was col-
lected from four ERP implementation projects in three companies.
One of the authors was a participant observer on three of the pro-
jects and conducted post-implementation data collection on the
fourth. All three companies are located in Canada. They vary in
size, age and industry. Due to confidential agreements we cannot
disclose the names of the companies in our study; we refer to them
as Company A, B or C. Company A is manufacturer and processor of
vegetable oils and meals. It was established in 1992, has 750
employees and achieves approximately CD$100 million in total
sales, 50% of which is from foreign exports. Company B is a mem-
ber based organization providing services in standards and certifi-
cations. It was established over 80 years ago and has total annual
sales of CD$150 million. Company C is a global manufacturer of
spice ingredients, established in 1919. It is privately owned and
has 2500 employees. It supplies spices to various players (super-
market chains, restaurants, and the food industry). The common
thread in each of these companies is that they all implemented
some type of ERP system and fit the requirements for our analysis.
Table 2 provides a summary of basic data about the implementa-
tion projects. Complete data including project schedules, budgets
and team characteristics and learning activities were collected
for each of Projects 1, 2, 3 and 4.
4.1. Details of the projects
Project 1 concerns the implementation of several modules of the
SAP ERP software over a nine month period. The project team was
composed of 20 employees drawn from the company and 20 exter-
nal consultants. Each team member received 15 days of in-class
training at various SAP training centers on the various modules
that were scheduled to be implemented. They reached a level of
performance of 30% before the commencement of the implementa-
tion (m=3). Following our previous discussion we place this
implementation project in Category I (Table 1). The following
observations were made during the implementation: (1) The ratio
of internal staff to consultants during the project was 1:1. The pro-
ject team had not bonded, so a start up effect was observed during
the early stages of the project. (2) Even though extensive consult-
ing support was provided, the team operated just below its
performance threshold the first four months of the project. Using
the S-curve we were able to estimate kclose to 0.8 (see Table 3).
(3) To meet the planned implementation schedule excessive over-
time was required close to the go live week, and additional post
implementation phase work was required. We estimated that
approximately five additional weeks (
D
Tobserved = 1.25 months),
would have been added to the initial project schedule had the
manager been able accurately predict the performance of the team.
Project 2 concerns the implementation of BAAN ERP software
with a Supply Chain Management extension. The project lasted
one year (T
0
= 12 months). Members of Project Team received in-
house training, during which some level team cohesion was
accomplished. A few members attended advanced level classes at
various locations. The team reached a 30% level of performance be-
fore the commencement of the implementation (m= 3) and fully
bonded, so the start up effect was not observed. We classify this
implementation project as Category II (cf. Table 1). The following
observations were made during the implementation: (1) The con-
sultant to staff ratio during the project was 1:4 ratio. (2) After a lit-
tle over four months, the team was operating close to their
performance threshold. Using the logistic curve we were able to
estimate kas close to 0.8 (cf. Table 3). (3) However, the implemen-
tation schedule was extended by five additional weeks (
D
Tob-
served = 1.25 months).
Project 3 concerns the first implementation of the BAAN ERP
software in one location of Company A. Subsequently, the system
was rolled out to three other factories in Ontario and then to facto-
ries in Alberta and Quebec. Project 3 took one year (T
0
=12
months). A few members received limited training from the soft-
ware provider at various locations. We estimated that the team
reached a 15% level of performance before the commencement of
the project (m= 10). The project team received extensive in-house
training during the early stages of the project and some support
from external consultants. A start up effect was not observed,
and we believe that this was because the team was selected from
members of middle management who already had a very good
Table 2
Comparison of the progress curves for the four projects.
Project and team
characteristics
Project 1
(Company B)
Project 2 (Company C) Project 3 (Company A) Project 4 (Company A)
Project duration [months] 9 12 12 9
Participant observation of
project
7 months 9 months Post implementation data collection 9 months
Modules implemented MM, FI, CO,
Project
Distribution, manufacturing, finance,
constraint based planning
Distribution, process, manufacturing
excluding MPS and MRP, finance
Cost accounting, finance,
distribution, manufacturing,
Team size 20 12 8 8
Team avg. IT experience
(prior to the project)
Over 2 years 1–2 years 1–2 years 3–4 years
Team avg. ERP experience
(prior to the project)
1 month 1 month None 2 years
No. Consultants 20 3 2–4 No full time consultants
Consultants avg. experience 1 year 1–2 years 3 years N/A
No. Team training days (per
member)
15–20 days 15 days 10 days None
Was the implementation
completed
Yes Yes Yes Yes
Fig. 8. Comparison of logistic and S-curves for Category IV projects.
M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx 7
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
working relationship and were also able to further bond during the
in-house training. We classified this project as Category III (cf. Ta-
ble 1). During the project consultant to staff ratio remained at 1:4
ratio. Extensive consulting support was given only during the early
stages of the project to reinforce training. By the sixth month of the
project, the team was operating very close to their performance
threshold. Using the logistic curve we were able to estimate kas
close to 0.7 (cf. Table 3). While the implementation schedule was
not extended, excessive overtime was required close to the go-live
week and additional post implementation work was required. We
estimate that had the manger accurately predicted team perfor-
mance approximately six more weeks (
D
Tobserved = 1.5 months),
would have been added to the project schedule.
Project 4 concerns the implementation of a heavily customized
version of the BAAN ERP software in Company A. This project
was completed in nine months (T
0
= 9 months), with the team
reaching a very high level of performance (m= 1.5) before the com-
mencement of the project. This is due to the fact that several of the
team members had participated in the previous implementation.
However, although the majority of the team already worked to-
gether, the start up effect was observed. We believe that this was
due to the fact that no in-house training was provided and team
did not have a chance to bond before starting the project. We clas-
sify this project as Category IV (cf. Table 1). Consulting support was
very minimal and only given on as needed basis to answer team
members’ questions. The team demonstrated a very steep progress
curve and was operating close to their performance threshold dur-
ing the third month of the project. Using the S-curve we were able
to estimate kas close to 0.9 (cf. Table 3). Extension to the imple-
mentation schedule was not required and post implementation
support was estimated as two additional weeks for minor error
correction and data clean up (
D
Tobserved = 0.5 months).
4.2. Discussion of the analysis
The additional time or work required beyond the initial project
duration was registered at the end of each project and then con-
verted to months of extension to the original project duration de-
picted as ‘‘
D
Tobserved” in Table 4. The required extensions were
also calculated from equations presented in Appendix C, which
were derived from the three progress curves. The results are de-
picted in section ‘‘
D
Tcalculated” in columns P
l0
,P
l
and P
s
for two
logistic curves and the S-curve correspondingly. In the construction
of Table 4 we used one month as a duration unit of measure thus,
‘‘
D
Tobserved”, ‘‘
D
Tcalculated” and T
0
are depicted in months. We
present the values of extensions to original project duration (de-
rived from the progress curves) scaled by the values observed on
the projects in section ‘‘
D
Tcalculated/
D
Tobserved”. We also scaled
the total time required to complete the four projects when pro-
gress is tracked by the three progress curves by the total time ob-
served. We present the results in section ‘‘Tcalculated/Tobserved”.
The middle line for each project represents the actual parame-
ters as registered or calculated from Eqs. (C.2–C.5). The top and
bottom lines are presented to show how sensitive the calculations
are in regards misjudgment of k. The columns in grey present the
results of using the ‘‘incorrect” progress curve following the argu-
ment from our previous discussion. We summarized the analysis of
the results depicted in Table 4 into the following observations:
1. For the first two projects the results of ‘‘
D
Tcalculated/
D
T
observed” based on S-curve are similar to the results based on
the logistic curve with the initial conditions of P
l
(0) = 0 and very
different from the results based on the logistic curve with the
initial conditions of P
l
(0) = p
0
. The results remain within ±13%
Table 3
Parameters and progress curve coefficients used in our calculations.
Project Parameters Equation for
calculations of
progress curve
coefficient k
Progress
curve
coefficient
k
mN
0
of months
after which
performance
reached
Team
performance
levels
1 3 4 0.97 (3.4.2)
0
0.8
2 3 4.5 0.97 (3.4.1)
0
0.8
3 10 5 0.97 (3.4.1)
0
0.7
4 1.5 2.5 0.97 (3.4.2)
0
0.9
Table 4
Comparison of the progress curves for the four projects.
Project
Parameters
ΔT calculated
ΔT calculated/
ΔT observed
T calculated/
T observed
ΔT
obser-
ved/
T0
ΔT calculated/T0
[%]
PROJECT
T0 k M
Δ
T
observed
Pl0 P
l P
s P
l0 P
l P
s P
l0 P
l P
s [%] Pl0 P
l P
s
0.7 1.42 0.95 1.57 1.14 0.76 1.26 1.02 0.97 1.03 15.8 10.6 17.4
0.8 1.25 0.83 1.38 1.00 0.66 1.10 1.00 0.96 1.01 13.9 9.2 15.3
1
9
0.9
3 1.25
1.1 0.75 1.23 0.88 0.60 0.98 0.99 0.95 1.00
13.9
12.2 8.3 13.7
0.7 1.45 0.94 1.56 1.16 0.75 1.25 1.02 0.98 1.02 12.1 7.8 13.0
0.8 1.22 0.84 1.38 0.98 0.67 1.10 1.00 0.97 1.01 10.2 7.0 11.5
2
12
0.9
3 1.25
1.1 0.74 1.25 0.88 0.59 1.00 0.99 0.96 1.00
10.4
9.2 6.2 10.4
0.6 1.66 1.5 3.85 1.11 1.00 2.57 1.01 1.00 1.17 13.8 12.5 32.1
0.7 1.45 1.28 3.3 0.97 0.85 2.20 1.00 0.98 1.13 12.1 10.7 27.5
3
12
0.8
10 1.5
1.25 1.15 2.85 0.83 0.77
1.90 0.98 0.97 1.10
12.5
10.4 9.6 23.8
0.8 1.25 0.42 0.51 2.50 0.84 1.02 1.08 0.99 1.00 13.9 4.7 5.7
0.9 1.12 0.38 0.46 2.24 0.76 0.92 1.07 0.99 1.00 12.4 4.2 5.1
4
9
0.99
1.5 0.5
1.05 0.35 0.43 2.10 0.70 0.86 1.06 0.98 0.99
5.6
11.7 3.9 4.8
8M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
tolerance levels if kvaries between [0.7 and 0.9] and are within
[98%, 110%] of the observed extension to the planned project
duration. The observations are in good agreement with and con-
firm the trend depicted in Theorem 1.
2. For the third project the results of ‘‘
D
Tcalculated/
D
Tobserved”
are very similar for both logistic curves and very different from
the results based on the S-curve. The results remain within
±14% tolerance levels if kvaries between [0.6 and 0.8] and are
within [85%, 97%] of the observed extension to the planned pro-
ject duration. Although the calculated values of extension to ini-
tial durations are below the results observed, the results
presented in Table 4 confirm the trend depicted in Theorem 2.
We would like to point out that the results are very close to
what was actually observed if a logistic curve with the initial
conditions of P
l
(0) = 0 is used for the calculation of
D
T.
3. For the fourth project the results of ‘‘
D
Tcalculated/
D
T
observed” based on S-curve are similar to the results based on
the logistic curve with the initial conditions of P
l
(0) = p
0
and
very different from the results based on the logistic curve with
the initial conditions of P
l
(0) = 0. The results remain within
±16% tolerance levels if kvaries between [0.8 and 0.99] and
are within [76%, 92%] of the observed extension to the planned
project duration. Although the calculated values of extension to
initial durations are again below the results observed, the
results presented in Table 4 confirm the trend depicted in The-
orem 3. We would like to point out that the results are again
very close to what was actually observed if a logistic curve with
the initial conditions of P
l
(0) = p
0
is used for calculation of
D
T,as
predicted in our previous discussion.
5. Conclusions
Researchers have identified learning and knowledge transfer in
ERP implementation projects as a significant problem that requires
attention (Bingi et al., 1999; Markus et al., 2000; Robey et al.,
2002). However, there has been limited research on modeling
and understanding this problem. This research makes four contri-
butions to understanding and managing the learning and knowl-
edge transfer problem in ERP implementation: (1) It presents a
comparative analysis of learning curves and proposes how they
can be used to help ERP implementation planning and manage-
ment. (2) Based on empirical data from 4 ERP implementation pro-
jects, it provides illustrations of how managers can apply the
curves in different project situations. (3) It provides a theoretical
basis for empirical studies of learning and ERP (and other IT)
implementations in different organizational settings. (4) It pro-
vides empirical justification for the development of learning curve
theory in IT implementation.
In this paper we presented an analysis of the general logistic
and S-curves and suggest that these curves can be utilized in differ-
ent situations of IT implementation. To illustrate how these curves
can be utilized to improve planning and management of ERP pro-
jects, we presented and analysis using empirical data from four
ERP implementation projects in three companies. Based on theo-
retical explication and our observations, we conclude that a logistic
curve is a good approximation of the performance changes on a
majority of ERP implementations. The logistic curve with the initial
condition of P
l
(0) = 0 can be used on ERP implementation projects
where the levels of initial performance reaches 30% (i.e. m is equal
or greater than 3). The start up effect can be neglected especially if
the team goes through the early stages of team integration during
the in-house training and is guided by either the instructor or a
group of very experienced consultants. The start up effect was,
however, observed in a team that achieved the level of initial per-
formance close to 30% but members received individual training
and therefore integration was not accomplished during training
phase of the project. Although it was demonstrated that both
curves will yield similar results for estimation of total work re-
quired on the project an S-curve should be used in such case.
We also conclude that in cases where team members already
are at a high initial level of performance due to either previous
experience with the system or intensive training, both the logistic
curve with the initial condition of P
l
(0) = p
0
and S-curve can be
effectively used to track performance. However, when the start
up effect is observed (or expected), the S-curve is a better model
than logistic curve for tracking team performance. This observation
can be explained by the effect of team dynamics, which slows
down the performance during the early stages of the project. When
team dynamics are not managed by early ERP training and team
integration strategies high levels of initial performance will not
be achieved and the start up effect will be observed. In this situa-
tion the logistic curve with the initial condition of P
l
(0) = 0 can be
used to track team performance, and is especially effective in situ-
ations where team is at a relatively low initial performance level in
relation to the performance threshold.
As we have shown in our analysis, it is important for ERP imple-
mentation managers to understand the impact of the start up ef-
fect on project duration. Our analysis offers managers direct
insight into both the impact of the start up effect and the impact
of intensive in-house training on project planning and manage-
ment. Our models are easy to implement on ordinary spread pro-
grams and as such are immediately useful to managers. Our
research also provides a basis for empirical studies in ERP imple-
mentation to define learning curve effects in different implementa-
tion situations. Such studies can help the development of better
management techniques that could lead to improvements in the
cost performance of ERP systems.
Appendix A
In this Appendix we perform a comparative analysis between
the progress functions P
s
and P
l
used in (3.2) and (3.4) respectively.
In particular, we compare the areas underneath the two curves for
tP0 and discuss the difference for large times. Defining
D
(t)as
the difference in area underneath the two curves at time tscaled
by p
t
, we have
D
ðtÞ 1
p
t
Z
t
0
½P
l
ðsÞP
s
ðsÞds
¼1
p
t
Z
t
0
p
t
ð1e
ks
Þ p
t
1þAe
ks

ds:ðA:1Þ
Evaluation of the integral and rearranging gives
D
0
ðtÞ¼
1
k
½lnðAþ1Þ1
þ
1
k
e
kt
1
k
lnðAe
kt
þ1Þ
D
ðtÞ¼
1
k
½lnðAþ1Þ
m1
m
þ
1
k
m1
m
e
kt
1
k
lnðAe
kt
þ1Þ
8
>
>
>
>
<
>
>
>
>
:
if P
l
ð0Þ¼0;
P
l
ð0Þ¼p
0
:ðA:2Þ
Since according to (3.2) A=m1, (A.2) can be rewritten into (A.2
0
)
D
0
ðtÞ¼
1
k
½ln m1þ
1
k
e
kt
1
k
ln½ðm1Þe
kt
þ1;
D
ðtÞ¼
1
k
½ln m
m1
m
þ
1
k
m1
m
e
kt
1
k
ln½ðm1Þe
kt
þ1:
(ðA:2
0
Þ
From this we see that as t?1,
D
has a well-defined limit, and the
value of this asymptote is
Llim
t!1
D
ðtÞ¼
1
k
½ln m1¼
1
k
ln
p
t
p
0
1
hi
1
k
ln m
m1
m

¼
1
k
ln
p
t
p
0
p
t
p
0
p
t
hi
8
>
<
>
:
if P
l
ð0Þ¼0;
P
l
ð0Þ¼p
0
:
ðA:3Þ
M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx 9
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
The sign of this limit will depend on the ratio of the productivity
threshold to the initial productivity. In cases when we compare
the logistic curve under the initial condition of P
l
(0) = 0 (condition
most commonly used to represent the situation when initial level
of performance is low, or p
0
60.33*p
t
) to the S-curve, we see that
if the productivity threshold is exactly ep
0
, then both curves become
identical in a relatively short time and it makes no difference which
curve is used for analysis, as depicted in (A.4).
L>0ifp
t
>ep
0
;
L¼0ifp
t
¼ep
0
;and
L<0ifp
t
<ep
0
:
8
>
<
>
:
ðA:4Þ
Appendix B
Here we compute the maximum value of j
D
(t)jwhere
D
(t) is the
difference in the area underneath the two curves at time tscaled
by p
t
, whose form is given in (A.2). We will focus again on the sit-
uation when the initial level of performance is low, which is repre-
sented by the logistic curve under the initial condition of P
l
(0) = 0
(first equation in (A.2)).
Since
D
(t) is a continuous function,
D
(0) = 0 and Llim
t!1
D
ðtÞ
is positive, negative or zero depending on the value of
p
t
p
0
(recall
(A.4)) but finite, it follows that the maximum value of j
D
(t)jwill
occur either at a critical point for
D
(t)orast?1. Differentiating
D
(t) gives
D
0
ðtÞ¼ e
kt
Ae
kt
þ1ðAAe
kt
1ÞðB:1Þ
from which it follows that
D
0
(t) < 0 for all time if p
t
62p
0
, and has a
local minimum (found by setting the derivative equal to zero and
solving for t)at
t
¼1
kln p
t
2p
0
p
t
p
0

;ðB:2Þ
if and only if p
t
>2p
0
. Note that we used the definition for Agiven
(3.2) to rewrite this result.
It follows that the maximum value of j
D
(t)jis given by
M¼max j
D
ðtÞj ¼ jLjif p
0
P
p
t
2
;
maxfj
D
ðt
Þj;jLjg if p
0
<
p
t
2
:
ðB:3Þ
Here t
*
is given in (B.2),
D
(t) is given in (A.2) and Lis given in (A.4).
Appendix C
In order for the total work required for system implementation
to be the same if the progress curve is represented by either of the
progress functions: P
s
(Eq. (3.2),P
l0
(1st equation from (3.4)), P
l
(2nd equation from (3.4)) or constant performance function p
t
,
we require
Z
T
0
0
p
t
dt ¼R
T
0
p
t
ð1e
kt
Þdt
R
T
0
p
t
1
m1
m
e
kt

dt
R
T
0
p
t
1þðm1Þe
kt
dt
8
>
>
<
>
>
:
where
P
l0
ð0Þ¼0;
P
l
ð0Þ¼p
0
;
P
s
ð0Þ¼p
0
;
ðC:1Þ
which can be integrated and divided by p
t
to give
kT þe
kt
¼kT
0
þ1 if logistic curve and P
l0
ð0Þ¼0;ðC:2Þ
kT þm1
me
kt
¼kT
0
þm1
mif logistic curve and P
l
ð0Þ¼p
0
;
ðC:3Þ
kT þlnððm1Þe
kt
þ1Þ¼kT
0
þln mif S-curve:ðC:4Þ
We solved equations (C.2–C4) numerically for the given choice of
parameter values depicted in Table 2. The required extension to
the original project duration
D
Tcan be then calculated from Eq.
(C.5), where Tis the numerical solution of either (C.2), (C.3), or
(C.4) depending on whether one of the logistic curves or S-curve
represents the progress function and T
0
is the planned project dura-
tion calculated from the constant performance function p
t
.
D
T¼TT
0
:ðC:5Þ
References
Adler, P.S., Clark, K.B., 1991. Behind the learning curve: A sketch of the learning
process. Management Science 37 (3), 267–288.
Akkermans, H., Bogerd, P., Vos, B., 1999. Virtuous and vicious cycles on the road
towards international Supply Chain Management. International Journal of
Operations and Production Management 19 (5/6), 565–581.
Al-Mudimigh, A., Zairi, M., Al-Mashari, M., 2001. ERP software implementation: An
integrative framework. European Journal of Information Systems 10, 216–226.
Argote, L., Epple, D., 1990. Learning curves in manufacturing. Science 247, 920–924.
Arrow, K.J., 1962. The implications of learning by doing. The Review of Economic
Studies 29, 155–173.
Baccarini, D., Salm, G., Love, P., 2004. Management of risks in information
technology projects. Industrial Management and Data Systems 104 (3/4),
286–295.
Badiru, A.B., 1992. Computational survey of univariate and multivariate learning
curve models. IEEE Transactions on Engineering Management 39 (2), 176–198.
Barry, E., Mukhopadhyay, T., Slaughter, S., 2002. Software Project Duration and
Effort: An Empirical Study Information Technology and Management 3.
Besson, P., Rowe, F., 2001. ERP project dynamics and enacted dialogue: Perceived
understanding, perceived leeway, and the nature of task-related conflicts. The
DATA BASE for Advances in Information Systems 32 (47–66).
Bingi, P., Sharma, M.K., Godla, J.K., 1999. Critical issues affecting an ERP
implementation. Information Systems Management 16 (3), 7–14.
Boyer, D., 2001. ERP implementation: Managing the final preparation and go-live
stages. Government Finance Review 17(6), 41–44. Retrieved on July 20, 2006.
<http://www.gfoa.org/services/dfl/trg/ERP-Implementation.pdf>.
Butler, J.E., 1988. Theories of technological innovations as useful tools for corporate
strategy. Strategic Management Journal 9 (1), 15–29.
Callaway, E., 1999. Enterprise Resource Planning: Integrating Applications and
Business Processes Across the Enterprise. Computer Technology Research
Corporation, Charleston, South Carolina.
Chambers, C., 2004. Technological advancement, learning and the adoption of new
technology. European Journal of Operational Research 152 (1), 226–247.
Chatzoglou, P.D., Macaulay, L.A., 1996. A review of existing models for project
planning and estimation and the need for a new approach. International Journal
of Project Management 14 (3), 173–183.
Dardan, S., Busch, D., Sward, D., 2006. An application of the learning curve and the
nonconstant-growth dividend model: IT investment valuations at Intel
Corporation. Decision Support System 41 (4), 688–697.
Davenport, T.H., 1998. Putting the enterprise into enterprise system. Harvard
Business Review 76 (4), 121–131.
Depledge, G., 2003. Escalation in information systems development projects: The
roles of problem recognition and cognitive bias. Ph.D. Dissertation. University of
Western Ontario.
Dixon, N.M., 2000. Common Knowledge. Harvard Business School Press, Boston.
Dong, L., 2004. Management influence on information systems (IS) implementation
effectiveness. Ph.D. Dissertation. University of Western Ontario.
Eason, K., 1988. Information Technology and Organizational Change. Taylor &
Francis, New York.
Eden, C., Williams, T., Ackermann, F., 1998. Dismantling the learning curve: The role
of disruptions on the planning of development projects. International Journal of
Project Management 16 (3), 131–138.
Edgington, T.M., Chen, A.N.K., 2002. An economic benefit model for knowledge
creation. In: Proceedings of Twenty-Third International Conference on
Information Systems.
Edmondson, A.C., Winslow, A.B., Bohmer, R., Pisano Gary, P., 2003. Learning how
and learning what: Effects of tacit and codified knowledge on performance
improvement following technology adoption. Decision Sciences 34 (2).
Ellis, S., Shpielberg, N., 2003. Organizational learning mechanisms and managers at
perceived uncertainty. Human Relations 56 (10), 1233–1254.
Fedorowicz, J., Oz, E., Berger, P.D., 1992. A learning curve analysis of expert systems
use. Decision Sciences 23 (4).
Gray, C., Larson, E., 2000. Project Management – The Managerial Process. McGraw-
Hill Higher Education Press, Boston, Massachusetts.
Holland, C., Light, B., 1999. Critical success factors model for ERP implementation.
IEEE Software 16 (3), 30–36.
Hong, K.-K., Kim, Y.-G., 2002. The critical success factors for ERP implementation:
An organizational fit perspective. Information and Management 40 (1), 25–40.
Howell, S.D., 1990. Parameter instability in learning curve models: Invited
comments on papers by Towill and by Sharp and Price. International Journal
of Forecasting 6 (4), 541.
10 M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
Jackson, D., 1998. Technological Change, The Learning Curve and Profitability.
Northampton, Mass (distributed by American International Distribution
Corporation, Williston, Vt).
James, R., 1984. The use of learning curves. Journal of European Industrial Training 8
(7), 13–16.
Jovanovic, B., Nyarko, Y., Griliches, Z., 1995. A Bayesian learning model fitted to a
variety of empirical learning curves. Brookings Papers on Economic Activity,
247–305.
Karlsen, J.T., Gottschalk, P., 2003. An empirical evaluation of knowledge transfer
mechanisms for it projects. The Journal of Computer Information Systems 44
(1), 112–119.
Keil, M., Mixon, R., Saarinen, T., Tuunainen, V., 1995. Understanding runaway
information technology projects: Results from an international research
program based on escalation theory. Journal of Management Information
Systems 11 (3), 65–85.
Kumar, K., van Hillegersberg, J., 2000. Enterprise resource planning: Introduction.
Communications of the ACM 43 (4), 22–26.
Mabert, V.A., Soni, A., Venkataraman, M.A., 2001. Enterprise resource planning:
Measuring value. Production and Inventory Management 42 (3–4), 46–51.
Malerba, F., 1992. Learning by firms and incremental technical change. The
Economic Journal 102 (413), 845–859.
Markus, L.M., Axline, S., Petrie, D., Tanis, C., 2000a. Learning from adopters’
experiences with ERP: Problems encountered and success achieved. Journal of
Information Technology 15, 245–265.
Markus, M.L., Tannis, C., Zmud, R.W., 2000b. The Enterprise Systems Experience:
From Adoption to Success. Framing the Domains of IT Management: Projecting
the Future Through the Past. Pinnaflex Education Resources, Inc., Cincinnati,
OH.
Mosheiov, G., Sidney, J., 2003. Scheduling with general job-dependent learning
curves. European Journal of Operational Research 147 (3), 665–670.
Murray, M., Coffin, G., 2001. A case study analysis of factors for success in ERP
systems implementations. In: Seventh Americas Conference on Information
Systems, Boston, Massachusetts.
Nah, F.F., Lau, J.L., Kuang, J., 2001. Critical factors of successful implementation of
Enterprise Systems. Business Process Management Journal 7 (3), 285–296.
Ngwenyama, O., Guergachi, A., McLaren, T., 2007. Using the learning curve to
maximize IT productivity: A decision analysis model for timing software
upgrades. International Journal of Production Economics 105 (2), 524–
536.
Palaniswamy, R., Frank, T., 2000. Enhancing manufacturing performance with ERP
systems. Information systems management 17 (3), 43–55.
Pananiswami, S., Bishop, R., 1991. Behavioral implications of the learning curve for
production capacity analysis. International Journal of Production Economics 24
(1–2), 157–163.
Parr, A.N., Shanks, G., 2000. A model of ERP project implementation. Journal of
Information Technology 15 (4), 289–303.
Pendharkar, P., Subramanian, G., in press. An empirical study of ICASE learning
curves and probability bounds for software development effort. European
Journal of Operational Research. Corrected Proof, Available online 26 May 2006.
Pozzebon, M., Pinsonneault, A., 2005. Global-local negotiations for implementing
configurable packages: The power of initial organizational decisions. Journal of
Strategic Information Systems 14, 121–145.
Robbins-Gioia, 2002. Biotechnology Survey Reveals Gaps in Project Management.
Robbins Gioia Press.
Robey, D., Ross, J.W., Boudreau, M.C., 2002. Learning to implement enterprise
systems: An exploratory study of the dialectics of changes. Journal of Strategic
Information Systems 19 (1), 17–46.
Rocheleau, B., 2006. Information Technology, Training, and Organizational Learning.
Public Management Information Systems. Idea Group Publishing. Chapter 7.
Sumner, M., 2000. Risk factors in enterprise-wide/ERP projects. Journal of
Information Technology 15 (4), 317–327.
Teplitz, C.J., 1991. The Learning Curve Desk Book: A Reference Guide to Theory,
Calculations, and Applications. Quorum Books, New York.
Teplitz, C.J., Amor, J.-P., 1993. Improving CPM’s accuracy using learning curves.
Project Management Journal 24 (4), 15.
Terwiesch, C., Bohn, R., 2001. Learning and process improvement during production
ramp-up. International Journal of Production Economics 70 (1), 1–19.
The Standish Group, I., 2004. CHAOS Demographics and Project Resolution.
Towill, D.R., 1985. The use of learning curve models for prediction of batch
production performance. International Journal of Operations and Production
Management 5 (2), 13.
Verville, J., Halingten, A., 2002. An investigation of the decision process for selecting
ERP software: The case of ESC. Management Decision 40 (3), 206–217.
Wallace, L., Keil, M., 2004. Software project risks and their effect on outcomes.
Communications of the ACM 47 (4).
Wiegers, K., 2003. Software Requirements. Redmond, Washington.
Willcocks, L., 2000. Enterprise resource planning: The role of the CIO and it function
in ERP. Communications of ACM 43 (4), 32–38.
Wright, T., 1936. Factors affecting the cost of airplanes. Journal of Aeronautical
Science 3 (122–128).
Yelle, L.E., 1979. The learning curve: Historical review and comprehensive survey.
Decision Sciences 10 (2).
M. Plaza et al. / European Journal of Operational Research xxx (2009) xxx–xxx 11
ARTICLE IN PRESS
Please cite this article in press as: Plaza, M., et al. A comparative analysis of learning curves: Implications for new technology ... European
Journal of Operational Research (2009), doi:10.1016/j.ejor.2009.01.010
... Sun et al. (2015) measured performance at each stage using CSF weighted KPI scores and identified remedial actions if the performance was below expectation. Plaza and Rohlf (2008) and Plaza et al. (2010) defined performance as the rate of competition of a task (the number of modules configured, or the number of transactions completed), and modelled the performance as a mathematical function that is dependent on time, training and learning. Sun et al. (2005) developed a quantitative evaluation of overall ERP implementation performance in terms of utilisation of the ERP system's capabilities, and the organisation's functionality requirement that is met by the ERP system. ...
... For example, Plaza and Rohlf (2008) investigated how the training, learning and performance of the project team can minimise ERP project consultancy costs, and developed an analytical model to predict the project completion date. Based on their 2008 work, Plaza et al. (2010) presented a comparative analysis of two types of learning curves and illustrated how they can be applied in four ERP implementation projects. Although Plaza and Rohlf's work enhanced the traditionally qualitative ERP research by developing quantitative models, their work has certain limitations: 1) only one CSF, project team progress, is addressed; 2) the analytical models developed are only tested in the context of the case study organisations and not validated by statistical analysis; 3) analytical models cannot provide dynamic views on the ERP implementation project processes. ...
... The progress curve presented in Fig. 1 is a logistic curve, being similar to the S-Curve but ignoring the start-up effect in the project planning stage. The logistic model is noted for its robustness and is frequently used to predict and model the performance of an ERP project team (Plaza and Rohlf, 2008;Plaza et al., 2010), to measure project complexity (Dao et al., 2020), and to analyse productivity changes and financial implications of the introduction of new technology (Dardan et al., 2006). Plaza and Rohlf (2008) demonstrated that, for a project team working on a CSF, progress follows a logistic curve. ...
Article
Full-text available
This research examines how a Constrained Nonlinear programming model for ERP implementation (CNL_ERP) can facilitate Small and medium sized enterprises (SMEs) to deploy resources to address the Critical Success Factors (CSFs) in the pre-implementation phase, and to invest in them during implementation to increase the probability that the implementation will be successful. Applications of CNL_ERP in three case studies demonstrate that the average ERP implementation outcomes outperform the observed results. Using the Generalised Reduced Gradient Method, we developed an ERP implementation strategy realising resource allocation to CSFs. The strategy provides a rich picture of where to concentrate effort in the initial, intermediate and final phases, and is very helpful in enabling an SME to understand the progress of an ERP project and the resources needed. In case there are changes in resources (such as budget, team performance), the model enables SMEs to rank CSFs, and to adjust resources allocations accordingly to achieve the best ERP implementation performance.
... A contribution of Wright (1936) entitled "Factors affecting the cost of airplanes" was the first examination of the learning phenomenon in the context of industrial production. Since then, there have been many publications discussing methodological enhancements in learning curve theory (Towill, D. R. 1985;de Jong 1957;Jaber and El Saadany 2011;Carlson 1973) or applying learning curve models to empirical cases in operations management (Terwiesch and E. Bohn 2001;Grosse, E. H. and Glock 2013;Gunawan 2009;Nembhard and Osothsilp 2001) and technology implementation management (Plaza, Ngwenyama, and Rohlf 2010). Literature reviews examining learning curves (Stroieke, Fogliatto, and Anzanello 2013;Anzanello and Abstract: Order picking has been identified as the most labour-intensive, as well as costly activity within warehouse logistics and is experiencing significant changes due to new technologies in the forms of artificial intelligence (AI) and automation. ...
... A contribution of Wright (1936) entitled "Factors affecting the cost of airplanes" was the first examination of the learning phenomenon in the context of industrial production. Since then, there have been many publications discussing methodological enhancements in learning curve theory (Towill, D. R. 1985;de Jong 1957;Jaber and El Saadany 2011;Carlson 1973) or applying learning curve models to empirical cases in operations management (Terwiesch and E. Bohn 2001;Grosse, E. H. and Glock 2013;Gunawan 2009;Nembhard and Osothsilp 2001) and technology implementation management (Plaza, Ngwenyama, and Rohlf 2010). Literature reviews examining learning curves (Stroieke, Fogliatto, and Anzanello 2013; Anzanello and Smart and efficient: Learning curves in manual and human-robot order picking systems Dominic Loske*. ...
... A contribution of Wright (1936) entitled "Factors affecting the cost of airplanes" was the first examination of the learning phenomenon in the context of industrial production. Since then, there have been many publications discussing methodological enhancements in learning curve theory (Towill, D. R. 1985;de Jong 1957;Jaber and El Saadany 2011;Carlson 1973) or applying learning curve models to empirical cases in operations management (Terwiesch and E. Bohn 2001;Grosse, E. H. and Glock 2013;Gunawan 2009;Nembhard and Osothsilp 2001) and technology implementation management (Plaza, Ngwenyama, and Rohlf 2010). Literature reviews examining learning curves (Stroieke, Fogliatto, and Anzanello 2013; Anzanello and ...
Article
Full-text available
Order picking has been identified as the most labour-intensive, as well as costly activity within warehouse logistics and is experiencing significant changes due to new technologies in the forms of artificial intelligence (AI) and automation. One fundamental question concerns the employees learning progress in human-robot picking systems compared to existing manual technologies. Therefore, this paper presents an empirical analysis of learning curves in manual pick-by-voice (n=30 pickers) and semi-automated (n=20 pickers) order picking. Aspiring to measure the individual learning progress without a priori assumptions, this publication is the first to apply Data Envelopment Analysis and examine order pickers learning curves in real application scenarios. The findings indicate that automating human work accelerates the individual learning progress in human-robot picking systems.
... A contribution of Wright (1936) entitled "Factors affecting the cost of airplanes" was the first examination of the learning phenomenon in the context of industrial production. Since then, there have been many publications discussing methodological enhancements in learning curve theory (Towill, D. R. 1985;de Jong 1957;Jaber and El Saadany 2011;Carlson 1973) or applying learning curve models to empirical cases in operations management (Terwiesch and E. Bohn 2001;Grosse, E. H. and Glock 2013;Gunawan 2009;Nembhard and Osothsilp 2001) and technology implementation management (Plaza, Ngwenyama, and Rohlf 2010). Literature reviews examining learning curves (Stroieke, Fogliatto, and Anzanello 2013;Anzanello and Abstract: Order picking has been identified as the most labour-intensive, as well as costly activity within warehouse logistics and is experiencing significant changes due to new technologies in the forms of artificial intelligence (AI) and automation. ...
... A contribution of Wright (1936) entitled "Factors affecting the cost of airplanes" was the first examination of the learning phenomenon in the context of industrial production. Since then, there have been many publications discussing methodological enhancements in learning curve theory (Towill, D. R. 1985;de Jong 1957;Jaber and El Saadany 2011;Carlson 1973) or applying learning curve models to empirical cases in operations management (Terwiesch and E. Bohn 2001;Grosse, E. H. and Glock 2013;Gunawan 2009;Nembhard and Osothsilp 2001) and technology implementation management (Plaza, Ngwenyama, and Rohlf 2010). Literature reviews examining learning curves (Stroieke, Fogliatto, and Anzanello 2013; Anzanello and Smart and efficient: Learning curves in manual and human-robot order picking systems Dominic Loske*. ...
... A contribution of Wright (1936) entitled "Factors affecting the cost of airplanes" was the first examination of the learning phenomenon in the context of industrial production. Since then, there have been many publications discussing methodological enhancements in learning curve theory (Towill, D. R. 1985;de Jong 1957;Jaber and El Saadany 2011;Carlson 1973) or applying learning curve models to empirical cases in operations management (Terwiesch and E. Bohn 2001;Grosse, E. H. and Glock 2013;Gunawan 2009;Nembhard and Osothsilp 2001) and technology implementation management (Plaza, Ngwenyama, and Rohlf 2010). Literature reviews examining learning curves (Stroieke, Fogliatto, and Anzanello 2013; Anzanello and ...
Conference Paper
Full-text available
Order picking has been identified as the most labour-intensive, as well as costly activity within warehouse logistics and is experiencing significant changes due to new technologies in the forms of artificial intelligence (AI) and automation. One fundamental question concerns the employees learning progress in human-robot picking systems compared to existing manual technologies. Therefore, this paper presents an empirical analysis of learning curves in manual pick-by-voice (n=30 pickers) and semi-automated (n=20 pickers) order picking. Aspiring to measure the individual learning progress without a priori assumptions, this publication is the first to apply Data Envelopment Analysis and examine order pickers learning curves in real application scenarios. The findings indicate that automating human work accelerates the individual learning progress in human-robot picking systems.
... [15] incorporated the learning effect of setup costs into an inventory replenishment system. [25] examined how the learning curve theory could inform better management of new technology implementation projects. [33] studied the impacts of the supply-side cost learning effect on dynamic pricing strategies and the channel efficiency in a decentralized supply chain. ...
Article
For minimizing purchase cost, a buying firm would switch to suppliers with providing more favorable prices. This paper investigates the optimal switching decision of a buyer that may switch to an entrant supplier with production learning ability (which is regarded as a private information) under a principal-agent framework. The results obtained show that the switching cost and the learning effect have significant impacts on the buyer's switching decision. Only when the fixed component of the switching cost is relatively low, the buyer can be better off from a partial switching strategy; otherwise, the buyer should take an all-or-nothing switching strategy or no switching strategy. As the learning ability of the entrant supplier increases, the buyer prefers to make more switching. Finally, a benefit-sharing contract is proposed to evaluate the performance of the principal-agent contract, and we demonstrate that the principal-agent contract almost completely dominates the benefit-sharing contract.
... Boone et al., [32] conducted an empirical study based on seven years of project data collected from an architectural engineering firm. The analysis showed that professional services display learning curves, which have been used to estimate the costs of consulting and technology implementation [33]. ...
Article
Cellular phones have gained popularity in emerging and developing economies over the last two decades. In this study, we show that the phenomenon of falling prices over time is applicable to the diffusion of prepaid mobile phones. This article extends the logistic diffusion model to accommodate disadopters of the service category. Goodness of fit of the extended model is found superior to the original version, which does not consider disadoption. The empirical findings show that for the 12 countries considered in the analysis, the diffusion speeds and the disadoption rates are influenced by one marketing factor and three socioeconomic factors.
... Technology implementation management has been a critical challenge in organizations due to frequent cost and schedule overruns (Plaza et al. [5]). Implementation includes activities ranging from the decision to adopt to the incorporation of the technology in the routines of the adopter, or its abandonment. ...
Conference Paper
Cyber-physical systems (CPS) are a new generation of systems that integrate computation and physical processes interacting with humans in different ways. Integrated networks of computers, sensors and similar technologies monitor and control the physical processes, reporting relevant data to planners and decision-makers, and vice versa. By means of case research, this paper analyzes the implementation of cyber-physical systems aiming at lead-time reduction in two manufacturing contexts, namely footwear and natural cork stoppers. The results of this research contribute to literature and practice with a conceptual framework for the implementation of cyber-physical systems and the discussion of the challenges of implementing this technology.
... Those two options are discussed in this presentation. We explore the available training assessment approaches and methods and close the gap in the extant literature by offering an analytical decision model as the research contribution by [6][7][8][9]. ...
Presentation
Full-text available
The construction firms experience continuous decline in the supply of qualified workers, which is one of the reasons why labor productivity has fallen in the industry [1]. Training seems to be a proper solution; since it delivers the various skills to the candidates seeking employment and therefore carries a promise to increase the overall productivity of a Canadian workforce. Training is expensive and takes time. HR department must project the value and future benefits of various training programs in order to fully understand the impact of training strategies on the labour requirements of the organization [2]. Note that the "linear" performance changes represent a permanent shift in performance while "non-linear" are typically caused by learning effects and team integration [3, 4]. Non-linear changes also impact the quality [5]. Research emphasizes the importance of training and offers a variety of assessment methods to measure its outcomes. However, the available methods do not include the impact of an employee's progress curve on his/her performance. As a result, the management cannot compare the two most common options, which are: (1) allowing an employee to "learn on the job", or (2) sending an employee to an external partner for a formal training. Those two options are discussed in this presentation. We explore the available training assessment approaches and methods and close the gap in the extant literature by offering an analytical decision model as the research contribution by [6-9]. The model is illustrated with a real case study of a Construction Project. The Cost/Benefit analysis is conducted for a Project Manager (PM), who earns a Project Manager Professional (PMP) certification through a professional training.
Article
Responding rapidly to customer needs is one of the main targets of industrial organizations that want to survive in the current market competition. This objective can be attained through robust planning. Workforce productivity is considered one of the important entities in production planning. However, it has a dynamic nature, i.e. the productivity growths thanks to on-job training or learning phenomenon. Considering this fact in manufacturing planning enhances the robustness of the developed plans. The present paper presents a mathematical model for medium-range production planning that is used to find the optimal aggregate production plan. The model aims to optimize the total production costs while respecting most of the operational constraints and considering the process of organizational learning. The presented model is constructed relying on the real industrial practices; the outcome is a mixed-integer linear program. The model was validated and checked using real data collected from an Egyptian factory that produces electric motors for home appliances. The proposed mathematical model was optimally solved using “ILOG-CPLEX 12.6”. By comparing the results obtained versus that of the method adopted in the factory, a cost reduction of 6.3% is achieved for the presented data set. A set of managerial aspects are concluded after the model analysis. Moreover, the impact of using detailed learning rates on the production cost is discussed.
Article
Studies reported in extant literature confirm the importance of the considering learning curve effect in a project context. However, a study that incorporates the learning curve effect in an integrated procurement and project schedule has not been yet reported in the literature. The primary contribution of the present study is to address this research gap. It develops a learning curve based integrated procurement and project scheduling Mixed Integer Programming (MIP) model for a shipbuilding project involved in manufacturing of multiple sister ships. The present study makes its second contribution in the MIP model. It provides an innovative way to model decision variables corresponding to order quantity by defining order batching scenarios. This approach reduces the size of search space, thereby reducing the computational complexity of solution approach. Its third contribution is conceptualization of two new parameters, schedule compression percentage and batching degree, to analyze the effect of the learning curve on project schedule and the tradeoff between transportation cost and inventory holding cost.
Article
Full-text available
This paper presents empirical research aimed at identifying the impacts of knowledge transfer mechanisms on information technology (IT) projects: Serial, near, far, strategic and expert knowledge transfer mechanisms are evaluated. The various dimensions of IT project success include project performance, project outcome, system implementation, benefits for the client organization, and benefits for the stakeholders. A survey was conducted in Norway to collect data on knowledge transfer mechanisms and project success; research results indicate that IT project success is significantly related to serial, strategic and expert knowledge transfer mechanisms.
Article
Full-text available
The present study examined the relations between perceived environmental/technological uncertainty among managers and intensity of use of organizational learning mechanisms. Confirming the research hypotheses, negative relations were found between the intensity of use of each of the five factors of organizational learning mechanisms (formal learning processes, information dissemination, training, information gathering, information storage and retrieval) and perceived environmental/technological uncertainty. These correlations were higher in the organizations that function under uncertain as opposed to certain environments. Finally, when perceived uncertainty was regressed on the five factors of organizational learning mechanisms, information gathering came out with a positive regression weight, that is, when organizational learning mechanisms like information dissemination, training or information storage and retrieval are held constant, information gathering is positively related to uncertainty.
Article
This article reports on a recently completed study of enterprise resource planning (ERP) system implementations. The study measured the attainment of business and project budget goals to determine success and value. A questionnaire focusing on ERP system implementation and performance was sent to U.S.-based manufacturing firms that had implemented ERP. A total of 75 responses are used to provide insight into the benefits firms have gained and the factors that influence ERP system implementation.
Article
Towill, and Sharp and Price, have confirmed that the Time Constant/Wiltshire learning curve model is relevant to wider aspects of learning, and over longer time horizons, than it was designed for. However in many contexts the model shows paradoxical properties; it is non-robust in estimation, due to instability in its parameter estimates, but robustly good at forecasting, whether the parameters are unstable or not. Researchable causes and estimation remedies are suggested, but it is also pointed out that this model should not converge in all circumstances, and failure to converge can be informative.
Article
Problems with the implementation of ERP systems are well documented. Although companies spend millions on ERP packages and the implementation process, there is extensive evidence that they experience considerable problems, particularly during the actual implementation project. This paper presents a project phase model (PPM) of ERP implementation projects that is a synthesis of existing ERP implementation process models and focuses on the implementation project. Two case studies of ERP implementation within the same organization, one unsuccessful and a later successful one, are reported and analysed in order to determine which critical success factors (CSFs) are necessary within each phase of the PPM. The CSFs are drawn from an earlier stage of this research and from recent literature. The PPM is used as a ‘lens’ for understanding ERP implementation projects, by highlighting the differences between the two cases. We then offer an explanation for these differences, focusing particularly on the successful case. Firstly, the organizational learning that occurred during the unsuccessful project and the associated early appointment of an experienced ‘champion’ with clearly defined responsibilities were critical to the successful project. Secondly, organizations implementing ERP systems should partition large projects into several smaller, simpler projects identified here as ‘vanilla’ implementations. The PPM, together with associated CSFs, provides guidance for practitioners when planning ERP implementation projects and also provides researchers with a foundation for further empirical research.Journal of Information Technology 2000 15, 289–303. doi:10.1080/02683960010009051
Article
Information technology (IT) projects can fail for any number of reasons, and can result in considerable financial losses for the organizations that undertake them. One pattern of failure that has been observed but seldom studied is the runaway project that takes on a life of its own. Such projects exhibit characteristics that are consistent with the broader phenomenon known as escalating commitment to a failing course of action. Several theories have been offered to explain this phenomenon, including self-justification theory and the so-called sunk cost effect which can be explained by prospect theory. This paper discusses the results of a series of experiments designed to test whether the phenomenon of escalating commitment could be observed in an IT context. Multiple experiments conducted within and across cultures suggest that a high level of sunk cost may influence decision makers to escalate their commitment to an IT project In addition to discussing this and other findings from an ongoing stream of research, the paper focuses on the challenges faced in carrying out the experiments.