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 inﬂation, 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 ﬁrm’s 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-

ﬁnance 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 ﬁrm’s 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

ﬁnancing 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.

To subscribe, call (800) 494-0460 or (770) 449-0460.

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