Matching Items (20)

Consumer Review Variation by Product Type - A Multi-method Analysis

Description

In the past decade, online shopping mode has been recognized and accepted by more and
more people. Over 200 million people were online shoppers in the United States. Convenient,
options,

In the past decade, online shopping mode has been recognized and accepted by more and
more people. Over 200 million people were online shoppers in the United States. Convenient,
options, and better prices compared to traditional shopping mode attract more people to choose
the products online. Consumer’s feedback presented as online reviews on products after the
purchase has become one of the most important factors influencing whether other consumers will
purchase products. For merchants, by studying the behavioral differences of these online
consumers when evaluating products, they can help them to understand product characteristics
and their customers to improve online marketing strategies. This article explores the differences
in the types of utilitarian and hedonic products and behavioral changes in customer opinions,
which involves 22 different categories of products from Amazon.com and customer reviews for
analysis through a variety of technical and research methods.

Contributors

Agent

Created

Date Created
  • 2020-05

148263-Thumbnail Image.png

Development of Automated Data-Collecting Processes for Current Factory Production Systems: An Investigation to Validate Computer Vision Model Outputs

Description

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Contributors

Agent

Created

Date Created
  • 2021-05

156053-Thumbnail Image.png

A Data Mining Approach to Modeling Customer Preference: A Case Study of Intel Corporation

Description

Understanding customer preference is crucial for new product planning and marketing decisions. This thesis explores how historical data can be leveraged to understand and predict customer preference. This thesis presents

Understanding customer preference is crucial for new product planning and marketing decisions. This thesis explores how historical data can be leveraged to understand and predict customer preference. This thesis presents a decision support framework that provides a holistic view on customer preference by following a two-phase procedure. Phase-1 uses cluster analysis to create product profiles based on which customer profiles are derived. Phase-2 then delves deep into each of the customer profiles and investigates causality behind their preference using Bayesian networks. This thesis illustrates the working of the framework using the case of Intel Corporation, world’s largest semiconductor manufacturing company.

Contributors

Agent

Created

Date Created
  • 2017

154566-Thumbnail Image.png

Reliability based design optimization of systems with dynamic failure probabilities of components

Description

This research is to address the design optimization of systems for a specified reliability level, considering the dynamic nature of component failure rates. In case of designing a mechanical system

This research is to address the design optimization of systems for a specified reliability level, considering the dynamic nature of component failure rates. In case of designing a mechanical system (especially a load-sharing system), the failure of one component will lead to increase in probability of failure of remaining components. Many engineering systems like aircrafts, automobiles, and construction bridges will experience this phenomenon.

In order to design these systems, the Reliability-Based Design Optimization framework using Sequential Optimization and Reliability Assessment (SORA) method is developed. The dynamic nature of component failure probability is considered in the system reliability model. The Stress-Strength Interference (SSI) theory is used to build the limit state functions of components and the First Order Reliability Method (FORM) lies at the heart of reliability assessment. Also, in situations where the user needs to determine the optimum number of components and reduce component redundancy, this method can be used to optimally allocate the required number of components to carry the system load. The main advantage of this method is that the computational efficiency is high and also any optimization and reliability assessment technique can be incorporated. Different cases of numerical examples are provided to validate the methodology.

Contributors

Agent

Created

Date Created
  • 2016

156625-Thumbnail Image.png

Performance Analysis of a Double Crane with Finite Interoperational Buffer Capacity with Multiple Fidelity Simulations

Description

With trends of globalization on rise, predominant of the trades happen by sea, and experts have predicted an increase in trade volumes over the next few years. With increasing trade

With trends of globalization on rise, predominant of the trades happen by sea, and experts have predicted an increase in trade volumes over the next few years. With increasing trade volumes, container ships’ upsizing is being carried out to meet the demand. But the problem with container ships’ upsizing is that the sea port terminals must be equipped adequately to improve the turnaround time otherwise the container ships’ upsizing would not yield the anticipated benefits. This thesis focus on a special type of a double automated crane set-up, with a finite interoperational buffer capacity. The buffer is placed in between the cranes, and the idea behind this research is to analyze the performance of the crane operations when this technology is adopted. This thesis proposes the approximation of this complex system, thereby addressing the computational time issue and allowing to efficiently analyze the performance of the system. The approach to model this system has been carried out in two phases. The first phase consists of the development of discrete event simulation model to make the system evolve over time. The challenges of this model are its high processing time which consists of performing large number of experimental runs, thus laying the foundation for the development of the analytical model of the system, and with respect to analytical modeling, a continuous time markov process approach has been adopted. Further, to improve the efficiency of the analytical model, a state aggregation approach is proposed. Thus, this thesis would give an insight on the outcomes of the two approaches and the behavior of the error space, and the performance of the models for the varying buffer capacities would reflect the scope of improvement in these kinds of operational set up.

Contributors

Agent

Created

Date Created
  • 2018

157596-Thumbnail Image.png

IT-enabled monitoring in the gig economy

Description

Two-sided online platforms are typically plagued by hidden information (adverse selection) and hidden actions (moral hazard), limiting market efficiency. Under the context of the increasingly popular online labor contracting platforms,

Two-sided online platforms are typically plagued by hidden information (adverse selection) and hidden actions (moral hazard), limiting market efficiency. Under the context of the increasingly popular online labor contracting platforms, this dissertation investigates whether and how IT-enabled monitoring systems can mitigate moral hazard and reshape the labor demand and supply by providing detailed information about workers’ effort. In the first chapter, I propose and demonstrate that monitoring records can substitute for reputation signals such that they attract more qualified inexperienced workers to enter the marketplace. Specifically, only the effort-related reputation information is substituted by monitoring but the capability-related reputation information. In line with this, monitoring can lower the entry barrier for inexperienced workers on platforms. In the second chapter, I investigate if there is home bias for local workers when employers make the hiring decisions. I further show the existence of home bias from employers and it is primarily driven by statistical inference instead of personal “taste”. In the last chapter, I examine if females tend to have a stronger avoidance of monitoring than males. With the combination of the observational data and experimental data, I find that there is a gender difference in avoidance of monitoring and the introduction of the monitoring system increases the gender wage gap due to genders differences in such willingness-to-pay for the avoidance of monitoring. These three studies jointly contribute to the literature on the online platforms, gig economy and agency theory by elucidating the critical role of IT-enabled monitoring.

Contributors

Agent

Created

Date Created
  • 2019

156910-Thumbnail Image.png

Exploring the Mechanisms of Information Sharing

Description

Online product ratings offer consumers information about products. In this dissertation, I explore how the design of the rating system impacts consumers’ sharing behavior and how different players are affected

Online product ratings offer consumers information about products. In this dissertation, I explore how the design of the rating system impacts consumers’ sharing behavior and how different players are affected by rating mechanisms. The first two chapters investigate how consumers choose to share their experiences of different attributes, how their preferences are reflected in numerical ratings and textual reviews, whether and how multi-dimensional rating systems affect consumer satisfaction through product ratings, and whether and how multi-dimensional rating systems affect the interplay between numerical ratings and textual reviews. The identification strategy of the observational study hinges on a natural experiment on TripAdvisor when the website reengineered its rating system from single-dimensional to multi-dimensional in January 2009. Rating data on the same set of restaurants from Yelp, were used to identify the causal effect using a difference-in-difference approach. Text mining skills were deployed to identify potential topics from textual reviews when consumers didn’t provide dimensional ratings in both SD and MD systems. Results show that ratings in a single-dimensional rating system have a downward trend and a higher dispersion, whereas ratings in a multi-dimensional rating system are significantly higher and convergent. Textual reviews in MDR are in greater width and depth than textual reviews in SDR. The third chapter tries to uncover how the introduction of monetary incentives would influence different players in the online e-commerce market in the short term and in the long run. These three studies together contribute to the understanding of rating system/mechanism designs and different players in the online market.

Contributors

Agent

Created

Date Created
  • 2018

158541-Thumbnail Image.png

Queueing Network Models for Performance Evaluation of Dynamic Multi-Product Manufacturing Systems

Description

Modern manufacturing systems are part of a complex supply chain where customer preferences are constantly evolving. The rapidly evolving market demands manufacturing organizations to be increasingly agile and flexible. Medium

Modern manufacturing systems are part of a complex supply chain where customer preferences are constantly evolving. The rapidly evolving market demands manufacturing organizations to be increasingly agile and flexible. Medium term capacity planning for manufacturing systems employ queueing network models based on stationary demand assumptions. However, these stationary demand assumptions are not very practical for rapidly evolving supply chains. Nonstationary demand processes provide a reasonable framework to capture the time-varying nature of modern markets. The analysis of queues and queueing networks with time-varying parameters is mathematically intractable. In this dissertation, heuristics which draw upon existing steady state queueing results are proposed to provide computationally efficient approximations for dynamic multi-product manufacturing systems modeled as time-varying queueing networks with multiple customer classes (product types). This dissertation addresses the problem of performance evaluation of such manufacturing systems.

This dissertation considers the two key aspects of dynamic multi-product manufacturing systems - namely, performance evaluation and optimal server resource allocation. First, the performance evaluation of systems with infinite queueing room and a first-come first-serve service paradigm is considered. Second, systems with finite queueing room and priorities between product types are considered. Finally, the optimal server allocation problem is addressed in the context of dynamic multi-product manufacturing systems. The performance estimates developed in the earlier part of the dissertation are leveraged in a simulated annealing algorithm framework to obtain server resource allocations.

Contributors

Agent

Created

Date Created
  • 2020

148215-Thumbnail Image.png

Development of Automated Data-Collecting Processes for Current Factory Production Systems: An Investigation to Validate Computer Vision Model Outputs

Description

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Contributors

Agent

Created

Date Created
  • 2021-05

148216-Thumbnail Image.png

Development of Automated Data-Collecting Processes for Current Factory Production Systems: An Investigation to Validate Computer Vision Model Outputs

Description

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Contributors

Agent

Created

Date Created
  • 2021-05