52 ISE Magazine | www.iise.org/ISEmagazine
Inside IISE Journals
research
Studying flow time in assembly
processes with collaborative robots
Collaborative robot systems in manufacturing enterprises pro-
vide an opportunity for humans and robots to work jointly
on shared tasks. In such systems, collaborative robots, often
referred to as cobots, and human operators occupy the same
workspace and interact to mutually accomplish an operation.
Using cobots, the dexterity and cognitive skills of human
operators and the repeatability and payload capacity of robots
can be integrated effectively to carry out operations with high
flexibility and agility, enhanced safety and reduced cost. Such
a feature has led to an increasing trend to use collaborative ro-
bots in manufacturing environments, where the cobot market
share has grown rapidly and is becoming an important part of
smart manufacturing and factory automation, particularly for
small- and medium-sized manufacturing enterprises.
In their paper, “Flow Time in a Human-Robot Collab-
orative Assembly Process: Performance Evaluation, System
Properties and a Case Study,” Ya-Jun Zhang from Southeast
University, China; Ningjian Huang from General Motors Re-
search and Development Center; Robert Radwin from Uni-
versity of Wisconsin-Madison; Zheng Wang
from Southeast University; and Jingshan Li
from Tsinghua University, China, introduce
an analytical method to study the flow time
in collaborate assembly systems with cobots,
which consist of independent preparation pro-
cesses of human operators and robots and a
joint process of collaborations.
In addition to providing evaluation formulas
for the flow time of assembly systems with col-
laborative robots, system properties are inves-
This month we highlight two articles in IISE Transactions. The first article looks into the issue of how the use of
collaborative robots, also known as cobots, affect the flow time of an assembly process while working alongside human
operators. The authors derived evaluation formulas, investigated the properties of the analytic study and identified the
bottleneck tasks for continuous improvement. The authors demonstrated the applicability of their method in a front panel
assembly process at an automotive assembly plant. The second article asks the question of how reliable supposedly
highly reliable consumer products really are. The authors set out to develop a data-driven method that estimates the mean-
time-to-failure using degradation data from those highly reliable products. Using two datasets on lithium-ion batteries,
the authors demonstrate that the proposed estimation method is reasonably accurate, yet flexible and made few restrictive
assumptions. These articles will appear in the March 2022 issue of IISE Transactions (Volume 54, No. 3).
Ya-Jun Zhang Ningjian Huang
An illustration showing the role of cobots on an
auto assembly line.
Robert Radwin Zheng Wang
Jingshan Li
February 2022 | ISE Magazine 53
tigated to optimize task allocation and to identify bottleneck
tasks for continuous improvement in collaboration processes.
Through a case study of a front panel assembly process at an
automotive assembly plant, where a cobot loads the panel and
two human operators help position it and finish the assembly
work, the authors demonstrate the applicability of the method.
Similar scenarios can be widely encountered in other collab-
orative assembly environments, such as such as pick-and-place,
joining, screwing, material handling and inspection.
This work provides production engineers and managers a
quantitative tool to design and analyze the performance of as-
sembly systems with collaborative robots. It also provides the
foundation for further integrating with ergonomics measures,
such as the strain index and fatigue analysis, and for optimizing
production control and scheduling strategies.
CONTACT: Jingshan Li; jsli@tsinghua.edu.cn; (8610) 6278-7546; Depart-
ment of Industrial Engineering, Tsinghua University, Beijing 100084, China
How reliable are supposedly
highly reliable consumer products?
With improvements in technologies, most high-tech products
have become more reliable. Popular consumer products such
as commercial light-emitting diode (LED) lamps and lithium-
ion batteries used in electric cars and cellphones are supposed
to have lifetimes that last from three to 10 years. Since it is
quite infeasible for these products to undergo a life test that
lasts for that long, how can manufacturers provide an accurate
estimate of the mean lifetime and the reliability of those reli-
able products with few field failure data?
Utilizing degradation data that measures the reduction
in product performance is one of the possible solutions. For
example, the light output of an LED lamp and the battery
capacity of a lithium-ion battery are possible degradation mea-
surements. Using appropriate statistical models and analytical
techniques with the degradation data, accurate reliability in-
formation of highly reliable products can be obtained.
In their paper, “Evaluation of Mean-Time-To-Failure Based
on Nonlinear Degradation Data with Applications,” Lochana
K. Palayangoda from PepsiCo Inc., Ronald W. Butler, Hon
Keung Tony Ng from Southern Methodist University, Fang-
fang Yang from Sun Yat-sen University and Kwok-Leung
Tsui from Virginia Polytechnic Institute and State University
have developed flexible and
robust statistical procedures to
estimate the mean lifetime of
highly reliable products when
the degradation measurements
are either linearly or nonlin-
early related to time.
The authors proposed sto-
chastic models and processes
based on various assumptions
to model the degradation data. Moreover, to reduce the risk
of making incorrect assumptions, a data-driven approach is
proposed to select the most appropriate model. The proposed
models and methods are evaluated using two data sets on lith-
ium-ion batteries for estimating the mean lifetime of batteries.
The proposed methodologies are shown to be effective under
different scenarios via both Monte Carlo simulation and real
data illustrations.
The proposed statistical procedures require less restrictive
assumptions and can be flexibly applied to different kinds of
degradation data to provide accurate estimates of mean life-
times for highly reliable products.
CONTACT: Hon Keung Tony Ng; ngh@mail.smu.edu; (214) 768-2465; Depart-
ment of Statistical Science, Southern Methodist University, Dallas, TX 75275
This month we highlight two articles from The
Engineering Economist (Volume 66, No. 4). The first
article investigates the voluntary and simultaneous
investment referred to as “bandwagon investment
and the circumstances that attract rational investors
to do so. The authors modeled the investment
problem as an option exercise game that integrates
game theory and real options. The second article
addresses internal rates of return and shareholder
value creation, in which the author demonstrates that
such well-known rates as the return on investment
and the return on equity provide precise information
on economic profitability and give rise to correct
investment decision criteria.
Ronald W. Butler Hon Keung Tony Ng
Fangfang Yang Kwok-Leung Tsui
Lochana K. Palayangoda
54 ISE Magazine | www.iise.org/ISEmagazine
research
Bandwagon investment equilibrium
of investment timing games
Under stochastic and competitive environments, irreversible
investment timing is an essential yet delicate strategic decision.
Although the investment timing decision is complicated, it is
evident that excessive industry capacity caused by simultane-
ous investment is undesirable to every investor. However, con-
current investments are frequently observed, and some of the
simultaneous investments are even voluntary.
Kihyung Kim and Abhijit V. Deshmukh, authors of the
article, “Technical note: Bandwagon Investment Equilib-
rium of Investment Timing Games,” call the voluntary and
simultaneous investment “bandwagon investment.” Is simul-
taneous investment unreasonable? Under what circumstanc-
es are rational investors on the investment bandwagon? This
research answers these questions.
To find the answers, the article modeled the investment
problem as an option exercise game that integrates game
theory and real options. This research contributes to the
literature in option exercise games by elucidating closed-
loop equilibrium where firms voluntarily invest at the same
time. The results showed that investors are on the investment
bandwagon when they expand their current capacities and
the second mover’s additional profit rate exceeds a threshold
value. Otherwise, investors invest sequentially. This result
explains the frequently observed investment herd effect.
Their work provides interesting implications about the
impact of the COVID-19 pandemic on capital investment.
For an investment opportunity that is expected to be profit-
able but risky, bandwagon investment is more likely to hap-
pen as the uncertainty about the investment return increases
with other conditions remaining the same. On the other
hand, increasing the discount rate reduces the incentive to be
on the investment bandwagon.
Therefore, the uncertainty increased by the COVID-19
pandemic can result in overcapacity caused by bandwagon
investment, but the ination, which is affected by the stimu-
lus aid and aggressive fiscal policy of governments, makes
sequential investment rather than bandwagon investment
more likely.
CONTACT: Kihyung Kim, assistant teaching professor; kimkihy@missouri.
edu; Department of Management, Trulaske College of Business, University
of Missouri, Columbia, MO 65211
Internal rates of return and
shareholder value creation
Industrial practitioners often use accounting rates of return
for assessing a project’s or firms economic efficiency and
for investment decision-making. However, they are warned
by accounting and finance scholars that it is not possible to
correctly infer economic profitability from an accounting
rate.
Building upon his recently conceived accounting-and-
nance engineering system, described in “Investment De-
cisions and the Logic of Valuation” (Springer Nature 2020),
Carlo Alberto Magni overturns these scholars’ arguments in
the paper “Internal Rates of Return and Shareholder Value
Creation.” He demonstrates that such well-known rates as
the return on investment (ROI) and the return on equity
(ROE) provide precise information on economic profit-
ability and give rise to correct investment decision criteria.
Specifically, considering a project or a firm, the average ROI
(ratio of total after-tax operating profits to total capital in-
vested) captures the project’s or firms economic profitability,
when compared with the weighted average cost of capital
(WACC), suitably adjusted with the project’s market-to-
book ratio.
Analogously, the average ROE (ratio of total net income
to total equity) captures shareholder value creation and mea-
sures the efficiency of the shareholders’ investment, when
compared with the cost of equity, suitably adjusted with the
corresponding market value-to-book ratio.
As opposed to the traditional internal-rate-of-return ap-
proach, this accounting approach offers practitioners several
favorable features where the average ROI and ROE:
Are intuitive and simple to calculate.
Exist and are unique.
Are explicitly based on the forecasts of revenues and costs
made by the analyst.
May be employed with time-varying costs of capital.
Are genuinely internal metrics, in the sense that their cal-
culation and their financial nature (investment rate versus
nancing rate) do not depend on the cost of capital.
Are based on the accounting metrics that are universally
employed in any firm for measuring periodic perfor-
mance (and, as such, they link periodic performance with
multiperiod performance).
Are net-present-value consistent and therefore link ac-
counting with finance for engineering decision-making.
Furthermore, the average ROI is equal to the book-value-
Kihyung Kim Abhijit V. Deshmukh
February 2022 | ISE Magazine 55
weighted mean of the av-
erage ROE and the (after-
tax) average ROD (return
on debt), and the project’s
cost of capital is the book-
value-weighted mean of
the equity cost of capital
and the debt’s cost of capi-
tal. Finally, the net present
value is the product of in-
vestment scale (total capital
invested) and the economic
efficiency (average ROI less
the adjusted WACC). This enables one to draw managerial in-
sights and recommend possible managerial actions to augment
the project’s value added, e.g., change the project’s scale or the
scale of financing or some operating variables, etc.
CONTACT: Carlo Alberto Magni, full professor of engineering econom-
ics; magni@unimo.it; University of Modena and Reggio Emilia, School of
Doctorate E4E (Engineering for Economics – Economics for Engineering),
Modena, Italy
Yu Ding is the Mike and Sugar Barnes Professor of Industrial
and Systems Engineering at Texas A&M University and associate
director for Research Engagement at the Texas A&M Institute of
Data Science. He is editor-in-chief of IISE Transactions and a
fellow of IISE.
Heather Nachtmann is the Earl J. and Lillian P. Dyess Endowed
Chair in Engineering and a professor of industrial engineering at the
University of Arkansas. She is editor-in-chief of The Engineering
Economist and a fellow of IISE.
Carlo Alberto Magni
IIISE Transactions (link.iise.org/iisetransactions) is IISE’s flagship
research journal and is published monthly. It aims to foster
exchange among researchers and practitioners in the industrial
engineering community by publishing papers that are grounded
in science and mathematics and motivated by engineering
applications.
The Engineering Economist (link.iise.org/engineeringeconomist) is a
quarterly refereed journal published jointly by IISE and the American
Society of Engineering Education. Devoted to issues of capital
investment, its topics include economic decision analysis, capital
investment analysis, research and development decisions, cost
estimating and accounting, and public policy analysis.
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