MANAGING ENGINEERING CHANGES ALONG WITH
NEW PRODUCT DEVELOPMENT
Weilin Li
L. C. Smith College of Engineering & Computer Science
Syracuse University, 223 Link Hall, Syracuse, NY 13244, U.S.A.
Keywords: Engineering Change Management, New Product Development, Process Modeling, Discrete-Event
Simulation.
Abstract: This proposed research is to develop a process model for managing Engineering Changes (ECs) while other
New Product Development (NPD) activities are being carried out in a company. The discrete-event
simulation model incorporates Engineering Change Management (ECM) into an NPD environment by
allowing ECs to compete for limited resources with regular NPD activities. The goal is to examine how the
size and frequency of NPD as well as ECM, NPD process structure (in terms of overlapping and department
interaction), and the policies one organization employs (such as resource using priority and project
cancellation policy) affect lead time and productivity of both NPD and ECM. Decision-making suggestions
for minimum EC impact are drawn from an overall enterprise system level perspective based on the
simulation results.
1 PROBLEM DEFINITION
New product development is defined as the complete
process from idea generation, product design, to
detail manufacturing until bringing a product to
market. It is the whole process beginning with the
perception of a market opportunity and ending in
production. There are several important
characteristics of NPD. First, a product design and
development company usually launches certain
number of new products according to a relatively
strict schedule. Second, although scarce engineering
capacity has always been a huge problem faced by
most organizations, the resources provided for NPD
projects are relatively placed and fixed firmly. That
is to say, there are always certain amounts of
resources to be dedicated to each NPD project. Third,
though the NPD process tends to become more and
more complex attributable to the increasing volume
of information involved, it has some repeatable
structure due to fact that design is something of an
art but with many consistent patterns (Browning,
2007).
Engineering change management, on the other hand,
is defined as a collection of procedures, tools, and
guidelines for handling modifications and changes to
a product that has been released to the market
(Terwiesch and Loch, 1999; Bhuiyan 2006). Unlike
the iterations within NPD process, engineering
change is the rework after production. It occurs in
far more random pattern compared with regular
NPD projects. The amount of time and effort
required for each ECM also varies from case to case.
As an industry norm, ECM usually doesn’t have its
own specified resources. It shares the same pool of
engineering capacity with NPD projects. That is to
say, NPD and ECM activities normally compete for
limited resources available.
ECM is a major competitive component in product
design and development process that should not be
neglected. It plays a critical role in finally realizing
actual profits from new product development efforts.
Companies benefit from ECM by correcting design
faults; solving safety or functionality problems;
providing better customers’ satisfaction; reflecting
technology improvements. However, on the other
hand, ECM consumes considerable amount of
resource, which in turns affects the lead time and
productivity of regular NPD projects significantly. It
also accounts for high EC costs with regards to
manufacturing tool costs, engineering rework,
inventory obsolescence, and possible downstream
EC propagation. (Loch, 1999; Balakrishnan, 1996).
193
Li W. (2009).
MANAGING ENGINEERING CHANGES ALONG WITH NEW PRODUCT DEVELOPMENT.
In Proceedings of the 11th International Conference on Enterprise Information Systems, pages 193-200
DOI: 10.5220/0002193901930200
Copyright
c
 SciTePress

2 RESEARCH QUESTIONS
The objective of this research is to fully model the
ECM process within a multi-project environment to
provide insightful decision-making suggestions for
companies regarding how engineering changes
should be implemented with minimal adverse effects
on normal NPD activities. To be more specific, this
research intends to answer the following questions.
1) How important is ECM for a firm that is
engaged in developing new products?
2) What are the key contributors to long lead
times in NPD in relation with ECM? And
vice versa.
3) How will the occurrence of an ECM
influence regular NPD activities? Within
which activity during which phase in the
NPD process will the impact be the most
tremendous?
4) What are the key contributors to low
production rates in ECM in relation with
NPD? And vice versa.
5) What is an optimal way of allocating
limited resources between NPD and ECM?
6) Is there a generic guideline for incomplete
NPD/ ECM cancellation when engineering
capacity is overloaded?
3 METHODOLOGY
This research focuses on the “flow of information”
standpoint of an NPD process (Krishinan, Eqqinger,
and Whitney 1997). From this information
processing point of view, an NPD project can be
treated as evolving product information that travels
through time (total development cycle time) and
space (all the departments involved), seizing and
releasing engineering capacity. However, we are not
interested in the way how the input information of
an NPD activity from its previous one evolves
gradually into the eventual output information, but
the separated points in time when entities arriving
and leaving an NPD activity and change of the state
of system. By doing this, we can check the duration
of each NPD/ECM activity and resource utilization.
Also, the repeatable nature of an NPD process
structure provides validation for decomposing an
NPD process into successive design and
development phases, each enclosing several
sequentially repeated activities. Nevertheless, NPD
is also an iterative process rather than a purely linear
one with unforeseen uncertainty and ambiguity
(Terwiesch and Loch 1999). This feature can be
represented by both NPD iteration and variation of
activity duration.
Among various kinds of mathematical and computer
models, a stochastic discrete-event simulation are
adopted as the modeling approach for the following
reasons: 1) it is more suitable to represent such a
complex and dynamic system; 2) allows for more
detailed analysis; 3) matches the nature of problem
well as discussed above; 4) several sophisticated
software packages available.
4 STATE OF THE ART
While NPD is an area that attracts lots of
investigations by huge amounts of researchers over
the past decades, ECM, particularly, how ECM
affects general NPD activities and vice versa, is
overlooked in the past.
The review of papers until 1995 was done by Wright.
(Wright, 1997) The author categorized the EC
related papers into two main topics, computer-based
“tools” for the analysis of EC problems and
“methods” to reduce the impact of ECs on
manufacturing and inventory control. We can find
that most of the publications in that time period
predominantly focused on the EC control
mechanisms and systems. An important observation
by Wright is that understanding of the positive effect
EC can provide for product improvement and
enhanced market performance is long omitted by EC
research.
Terwiesch and Loch presented a process-based view
of ECM. (Terwiesch and Loch, 1999) They showed
by an industrial case study that a complicated and
congested administrative support process is one of
the root causes of long lead time and high cost.
Based on the field study, they identified five key
contributors to lengthy ECO lead time: complex
ECO approval process, scarce capacity and
congestions, setups and batching, snowballing
changes, and organizational issues.
In another paper they wrote, an analytical
framework that explains the extreme ratio between
theoretical processing time and actual lead time was
developed. (Loch and Terwiesch, 1999) They
showed how congestion and batching influence
engineering processes at a more detailed level.
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Based on the processing network framework, they
suggested improvement strategies such as flexible
work times, the grouping of several tasks, workload
batching, the pooling of resources, and the reduction
of setup times.
Krishnan presented a model-based framework to
manage the overlapping of coupled product
development activities. (Krishnan, 1997) The author
studied the overlapping problem based on two
properties, upstream information evolution and
downstream iteration sensitivity, of the information
exchanged between product design phases. The
mathematical model and conceptual framework of
the overlapped process were illustrated with
industrial examples to provide managerial insights.
The most related work to this research is done by
Bhuiyan and her co-workers. They built a stochastic
computer model to examine how overlapping and
functional interaction affect the performance
measures of development time and effort under
varying conditions of uncertainty. (Bhuiyan, 2004) It
is the first comprehensive model using a discrete
event simulation for the entire NPD process by
taking into account functional interaction at different
values of overlapping under different uncertainty
conditions. Development effort was also introduced,
in the form of total person-days for a project, as a
measure of NPD performance that was neglected by
earlier researchers. A number of conclusions were
drawn from the model, however, their model
assumed an unlimited amount of resources, which is
unrealistic in practice.
Bhuiyan’s research group has also expanded this
framework to compare two methods for managing
Engineering Change Requests (ECRs): immediate
individual processing as issued and batch processing
after accumulation. (Bhuiyan, 2006) They evaluated
the effects of the methods in terms of development
time and effort. The model they developed, though,
has a couple of limitations: (i) the research scope
only on immediate or batch processing is too
simplified compared with a large amount of ECM
problems; (ii) treating all ECRs similarly is
acceptable only for comparative analysis. Despite of
these limitations, Bhuiyan’s model is the only study
on ECM using the discrete-event simulation. Thus it
inspired our model.
Another important work is the comprehensive
heuristic for a stochastic, resource-constrained
project scheduling problem in an iterative project
network. (Cho and Eppinger 2005) Their model
uses a parallel discrete event simulation
methodology to compute the distribution of lead
time of engineering design processes for project
scheduling analysis. Many important characteristics
of complex design process, such as overlapping,
iteration of tasks, rework concurrency, task priority,
are incorporated in this model. Design Structure
Matrix (DSM) is employed to capture the
information flows between tasks.
Browning presented a thorough literature survey on
the topic of activity network-based models for NPD
project management. (Browning, 2007) The paper is
based upon four major categories: visualization,
planning, execution and control, and project
development. And he highlighted five research
directions for future study: activity interactions,
global process improvements, process models as an
organizing structure for knowledge management,
modeling in cases of uncertainty and ambiguity, and
determining the optimum amount of process
prescription and structure for an innovative project.
Insufficient resource allocation always remains as
one of the important questions faced by NPD
organizations. At the same time, discovery of major
problems is so often identified in final stages of the
development cycle, which will require significant
additional resources from other projects, especially
those ones still in early phases, thus further
detriment the problem of dysfunctional resource
allocation. Black and Repenning propose a
simulation framework to analyze different policies
organizations may adopt for earlier problem
resolution, better quality and performance in a multi-
projects environment. This paper concludes with the
following two main insights: 1) the importance of
realistic schedules and appropriate amounts of
resources at the early phases of NPD projects; 2) a
strict and inflexible version of cancellation policy
offers the highest potential to produce effective
improvement in NPD projects.
5 STATE OF THE RESEARCH
In this part, the first model version is introduced by
the illustration of both model screenshots and word
explanation. Arena simulation package is used for
the project.
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195

5.1 Model Configuration
Figure 1: 3 phase and 3 activity framework for NP.
Figure 2 : Model overview of the NPD part.
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Figure 3: Model overview of the ECM part.
5.2 Preliminary Results
For the model described above, we analyzed the
influence of resource constraint, resource using
priority, overlapping, NPD departmental interaction,
ECM effort, on both NPD and ECM lead time and
productivity under different NPD and ECM arrival
rates. Three levels of NPD and ECM arrival rates are
combined in pairs according to their value. That is,
high NPD arrival rate is studied with high ECM
arrival rate, and low NPD arrival rate with low ECM
arrival rate.
Partial results are presented in this poster due to
space limitation. The following two charts show the
impacts of overlapping, NPD departmental
interaction, and ECM effort on NPD Total Time and
Productivity under resource constraint of 60 units
from each department.
The NPD and ECM model framework introduced
above address several issues that earlier models
didn't. In this model, we capture important new
product design and development characteristics such
as iteration and overlapping of NPD process,
interaction among different functional areas,
resource constraints and its using priority. We also
take into account the size of NPD projects and ECRs
in terms of their arrival rates and processing effort.
From the simulation results, a number of
conclusions can be drawn:
Figure 4: Simulation Results.
1) ECM is an important aspect to the success of an
NPD project. On one hand, it solves safety or critical
functionality problems of a product. And it reflects
customer requirements or technology developments.
On the other hand, it also consumes a considerable
amount of product development resources which in
turns affects the lead time and productivity of
regular NPD activities significantly.
MANAGING ENGINEERING CHANGES ALONG WITH NEW PRODUCT DEVELOPMENT
197

2) While each of the six model variables,
overlapping, NPD departmental interaction, ECM
effort, resource constraints, arrival rate, and resource
using priority, affects the overall lead time and
productivity of both NPD and ECM by some extent,
the effect of resource constraints is most significant.
3) This model addresses decision-making
suggestions for firms under different organization
environment and resource constraint condition.
Specifically, when the resource capacity is limited, a
medium level of overlapping and high departmental
interaction is suggested to optimize system resource
utilization.
6 FUTURE WORK
6.1 Model Extention
There are several aspects of this model that need
further investigation.
1) The assumption that one EC is confined in one
NPD activity is not always true. An EC that requires
rework in a design activity may propagate to other
activities in design or production phase. Future study
should include engineering change propagation as
one feature of the ECM process.
2) In the current model, probabilities for feedback
iterations are assigned to an NPD project. However,
when a new product project needs to go back to
earlier NPD activities for a rework, subsequent
activities need to be followed again no matter how
many times these activities are repeated. In other
words, an NPD entity has to go through again all the
downstream activities after being sent back to the
iteration starting point. Feed-forward flexibility and
learning effects for iteration need to be considered in
future work.
3) In this model, it is assumed that NPD and ECM
share the same pool of resources with using priority.
I could let NPD and ECM have their own dedicated
resources. Or, NPD and ECM still use the same pool
of resources. But ECM requests for outsourcing
when resources are not available. In this case,
different utility costs can be set for using resources
within a department, cross departments, and for
outsourcing.
4) Besides lead time and productivity, other critical
criteria such as resource utilization, total cost, and
customer satisfaction, can be adopted to review and
evaluate the impact of ECM throughout NPD
process.
5) As we can see from the preliminary running
results of the first model version, production of NPD
and ECM keeps to be less than 1 and is far less when
the resource level is low (with a number of 60 per
department). Black found out from her policy
analysis model that the policy cancellation of the
work that falls behind schedule well in advance of
its launch date can ensure consistently high
performance and recovering productive capability.
(Black and Repenning 2001) Effective cancellation
of incomplete NPD/ECM is also one direction of
this research.
6.2 Model Validation and Verification
1) Use output analysis as the first step of model
validation, and check to see if the simulation output
is reasonable.
2) Comparison of this model and related studies
provides another way of validation.
3) Apply the correlated inspection approach. That
is, compare real-world observation and simulation
output with historical system input data. For
example, given input parameter from industry
(actual observed inter-arrival time of NPD projects
and EC Requests; actual observed activity duration
in different NPD phases; etc.), we can determine the
accuracy of the model by comparing the model
output data and the inspection from company).
6.3 Model Validation and Verification
1) Use animation to enhance the credibility of this
model.
2) Run the model under simplifying assumptions
for which its true characteristics are known, and then
gradually add details into the simulation project.
3) Run the model under a variety of settings of the
input parameters, especially in those extreme
conditions, and check to see if the output is
reasonable.
6.4 Experimental Design
From the preliminary running of the first model
version, we have already got some ideas about
which model variables, such as inter-arrival rates of
NPD and ECM, NPD departmental interaction,
ECM effort, are likely to be important. However,
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carefully designed experiments should be carried out
for efficient experimentation in determining which
factors are most important and joint effect of the
factors on a response as well. Table 1 shows
possible model factors and responses.
6.5 Data Collection from Industry
Some of the parameter setting and input data for the
first model version are hypotheses based on relevant
results from similar studies or the modeler’s
experience. These may be obsolete due to time
concerns but still realistic when this simulation study
is initiated. We can replace them by real inspection
from industry in later stages of this research.
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