2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
184
MC06
102A-MCC
Text Analytics for Quality Management
Sponsored: Data Mining
Sponsored Session
Chair: Alan Abrahams, Virginia Tech, Pamplin 1007, Blacksburg, VA,
24061, United States,
1 - Human Intelligence In Keyword Query Formulation: Comparing
The Recall Performance Of Computer- And Human-generated
Smoke Word Lists
Richard Gruss, Pamplin College of Business, Virginia Tech,
Blacksburg, VA, United States,
Alan Abrahams, Siriporn Srisawas
Are humans better at producing safety concern keyword searches than
computers? 72 subjects annotated 263 reviews of over-the-counter medicines,
half of which contained known safety concerns. Subjects then compiled a list of
words and phrases that might be effective filter terms in finding reviews with
safety concerns. Subjects then collaborated in groups of 2, 3 or 4 to assemble a
group list. Lists were scored on how many of the known safety concerns they
were able to recall in a separate test set. Preliminary results indicate that human-
generated lists frequently outperform computer-generated lists.
2 - Let’S Not Get Too Sentimental: A Critical Analysis Of Sentiment
Analysis For Quality Surveillance
Nohel Zaman, Virginia Tech, Blacksburg, VA, 24060,
United States, , Alan Abrahams,
Richard James Gruss, Siriporn Srisawas
Our study will be beneficial for quality management (QM) professionals analyzing
unstructured user-generated-content in social media. The goal is to determine
whether and to what extent negative sentiment and defect existence are
associated, in different products across multiple industries. With product defects
being expensive, this paper could help manufacturers more rapidly discover
defects. This paper aims to assess which sentiment and non-sentiment scoring
methods are most effective at finding product defects in each industry, and which
methods generalize well across industries.
3 - Online Reviews To Revenue: Contributory Factors And
Moderating Effects In The Airline Industry
Zachary Davis, Virginia Tech,
Alan S. Abrahams, Lara Z Khansa
In this study, online reviews of airlines are examined with respect to company
revenue. Though previous studies have examined online reviews’ effect on
product sales, the relationship has not been elicited in service industries, including
the airline industry. Using passengers’ reviews of the 7 airline groups with the
highest revenue we consider the impact of these reviews on the quarterly
revenue of each of these companies. Compared with other major industries, the
airline industry has the highest defect rate in their online reviews. Our findings
suggest that online reviews do have an impact on company revenue, however
this impact is dampened by offline maneuvers.
4 - Identifying Product Defects From User Complaints:
A Probabilistic Defect Model
Alan Wang, Associate Professor, Virginia Tech, 2070 Pamplin Hall,
Virginia Tech, Blacksburg, VA, 24060, United States,
Xuan Zhang, Zhilei Qiao, Weiguo Fan,
Edward A Fox
Discovering potential product defects from large amounts of user complaints is a
challenging task. In this research, we develop a probabilistic defect model (PDM)
that identifies the most critical product issues and corresponding product
attributes (e.g., product model-year, defective components, symptoms, etc.),
simultaneously. We conduct comprehensive evaluations to ensure the quality of
discovered information. Our research has significant managerial implications for
managers, manufacturers, and policy makers.
MC07
102B-MCC
Data Analytics in Renewable Energy
Sponsored: Data Mining
Sponsored Session
Chair: Zijun Zhang, City University of Hong Kong, 83 Tat Chee Avenue,
Kowloon Tong, Hong Kong,
1 - Exploratory Data Analytics For Temporal Lighting Energy Usage In
Commercial Buildings
Mingyang Li, University of South Florida, Tampa, FL, United
States, Kara C Heuer, Dina Villalba-Sanchez,
Zhe Song
Lighting energy usage accounts for a major proportion of building energy
consumption. A better understanding of lighting energy data will facilitate
building energy management. Conventional studies mainly focused on aggregate-
level energy usage, ignoring the temporal usage patterns featured in stochasticity,
nonlinearity and intermittency. In this study, an exploratory data analytics
approach is proposed to extract, cluster and visualize temporal patterns of lighting
energy usage in commercial buildings. A real data study is further provided to
illustrate the proposed work and demonstrate its validity.
2 - Characterization Of Air Traffic Network Using Ads-b Data
Lishuai Li, City University of Hong Kong, P6606, AC1,
Tat Chee Avenue, Kowloon, Hong Kong,
Pan Ren
Airspace capacity has been credited as a major factor for air traffic congestion and
flight delays. However, few studies provided measures of airspace capacity and
efficiency for a large air traffic network. This research aims at evaluating whether
airspace capacity is a significant factor in relation to recent air traffic delays in
China. We developed a novel method to characterize flow patterns in the airspace
and construct an air traffic network using cluster analysis on historical flight
trajectories. Findings will be useful in evaluating the efficiency and robustness of
an air traffic network in relation to its actual operation and management.
3 - Image-based Wind Turbine Blade Surface Crack Detection
And Analysis
Zijun Zhang, City University of Hong Kong,
Long Wang
A data-driven framework for automatically detecting wind turbine (WT) blade
surface cracks based on images taken by unmanned aerial vehicles (UAVs) is
proposed in this paper. Haar-like features are applied to depict crack regions and
train a cascading classifier. The computational results demonstrate that the
proposed framework can successfully provide the number of cracks and locate
them in original images.
MC08
103A-MCC
Innovation in Product and Service Development
Invited: Business Model Innovation
Invited Session
Chair: Morvarid Rahmani, Georgia Institute of Technology, Atlanta,
GA, United States,
1 - An Economic Analysis Of Customer Codesign
Sreekumar R Bhaskaran, Southern Methodist University,
, Amit Basu
A key barrier to companies successfully engaging customers in the design of new
products is customers fearing that they will be forced to pay much more for the
custom products they help design. We show how a firm can motivate its
customers to engage in co-design through its product line choices. The effect of
market and firm characteristics on the value of engaging customers in the co-
design process is also examined. In addition, we analyze the effects of (a)
information asymmetry about the firm’s co-design capability, and (b) competition,
on the firm’s decisions regarding co-design.
2 - Allocating Customer Control In Service Processes
Ioannis Bellos, George Mason University, ,
Stylianos Kavadias
In most services customers actively participate in the service deliver process. In
practice we observe services that require varying degrees of customer
involvement. Motivated by this, we develop an analytical model to determine
which parts of a service process should be performed by the service provider and
which parts should be delegated to the customers.
3 - Sourcing Innovation: Private And Public Feedback In Contests
Jurgen Mihm, Insead, Fontainebleau, France,
Jochen Schlapp
Contests, in which contestants compete for a prize offered by a contest holder,
have become a popular way to source innovation. Despite great interest from the
academic community, many important managerial aspects of contests have
received very little formal inquiry. The most important of these is feedback from
the contest holder to the contestants while the contest unfolds. This paper sets out
to establish a comprehensive understanding of how to give feedback in a contest
by answering the questions of when to give feedback and when not to give
feedback and which type of feedback to give, public (which all solvers can
observe) or private (which only the concerned party can observe).
MC06