Doctor of Philosophy, University of Toledo, 2011, Manufacturing and Technology Management

Several operations management researchers have considered the role of knowledge integration (KI) activities in coping with uncertain environments and improving organizational performance. The previous research focused on investigating and defining KI conceptually and ignored the need to investigate and define KI operationally. Therefore, there are doubts about how to develop effective KI capability and implement it at the organizational level. This study provides detailed explanations and guidelines for researchers and practitioners about KI, its antecedents and consequences. This research meets the needs of manufacturing management practitioners and researchers by providing an operational definition of how to integrate internal and external knowledge to manage environmental uncertainty and enhance a firm's overall performance. This research is built on an integrated perspective of operations management and knowledge management using a number of theories that include Knowledge Based View (KBV), Dynamic Capability (DC), and Contingency Theory (CT). This integrated perspective helps clarify how the implementation of internal integration and external integration practices can lead to effective development of knowledge integration capability. This study offers an in-depth understanding of knowledge integration (KI) capability and its related activities. Hypotheses of this research are developed on the relationships among the antecedents of KI (internal integration and external integration), knowledge integration capability, and the consequences of KI capability (mass customization, operational performance and the firm‘s performance). The study provides five significant contributions to manufacturing management research. First, it explores the antecedents (or the facilitators) that an organization should implement before developing a successful KI capability. Second, it provides an operational definition of KI that was not available in the manufacturing literature. (open full item for complete abstract)

Committee: Mark Vonderembse PhD (Committee Co-Chair); T.S Ragu-Nathan PhD (Committee Co-Chair); Stephen Callaway PhD (Committee Member); Iryna Pentina PhD (Committee Member); Mohammad Elahinia PhD (Committee Member) Subjects: Business Administration; Management

Doctor of Philosophy, The Ohio State University, 2004, Business Administration

The relationship between lean manufacturing management practices and business financial performance is examined through the use of empirical surveys and archival accounting data from Compustat and stock return data from CRSP. A sample frame of small to medium sized discrete product and process manufacturing companies reporting participation in only one four-digit SIC was identified as the sample frame. The five-year (1998-2002) financial performance for these companies was analyzed at the operations and business levels using a median z-score comparing median firm performance with the median performance of a matched portfolio of firms. Operations measures included asset and employee productivity, gross margin ratio and two measures of aggregate cycle time. Business measures included return on equity (ROE), sales growth, and stock return. A web-based survey was used to collect data on seven lean practices including just-in-time production management, statistical process control, total productive maintenance, group technology, employee involvement, supplier communication, and customer involvement. Forty-two responding firms were classified as being either lean or non-lean based on a cluster analysis of factor scores. The results demonstrated that lean practices act as a synergistic, mutually supportive set rather than linearly additive individual practices in affecting operations financial performance. Lean classification was associated with better total and cash-to-cash cycle times, but was not related to either better or worse asset or employee productivity. Lean firms also tended to have narrower grow margins than non-lean firms. With respect to business level performance, lean firms tend to have better ROE, but no relationship was found with respect to either stock return or sales growth. Of all the lean practices tested only employee involvement demonstrated a significant relationship to business level performance. Firms with high ROE tend to have high employee in (open full item for complete abstract)

Committee: Peter Ward (Advisor) Subjects:

PhD, University of Cincinnati, 2022, Engineering and Applied Science: Mechanical Engineering

Prognostics and Health Management (PHM) has extended its frontiers to more pervasive applications for failure detection, process monitoring, and predictive maintenance in the increasingly complicated manufacturing environment. Meanwhile, as Internet of Things (IoT) technologies are developed rapidly, the research for PHM is facing non-negligible challenges in several aspects. The advancement in the volume, velocity, and variety of the manufacturing data demands improved analytics of PHM solutions. The mass of the manufacturing data demands more efficient selection strategy to exclude the incorrect and useless information. Also, in the industrial environment, the high-dimensional data is usually collected from various sensor recordings with changes and drifts, which constitute the fundamental properties of the stream data. The advanced PHM techniques are required to be capable to capture and track the coming information within the high-dimensional data continuously and adaptively. To deal with the challenges and research gaps, this research proposes a scalable methodology for discrete time series prediction based on industrial high-dimensional data. First, a reference-based fusion strategy is proposed and employed to combine the valuable knowledge from the historical data, to reduce data dimensionality and to exclude the information which is not helpful for further analysis. Second, a state modeling strategy is designed to fuse both the reference data selected by the previous strategy and the past time series data. Also, it formulates an efficient and accurate function to depict the relationship between the predictor and the target. Finally, a Bayesian filter is designed to deal with the strong non-linearity, to propagate in high-dimensional space and to learn the new knowledge continuously in the stream data without losing the properties of the historical data. Finally, three cases from different industrial environments are implemented to justify the feasibility, ef (open full item for complete abstract)

Committee: Jay Lee Ph.D. (Committee Member); David Siegel Ph.D. (Committee Member); Jing Shi Ph.D. (Committee Member); Jay Kim Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2021, Engineering

The planar and rigid nature of silicon-based electronics limit their reliability and integration into the next generation of electronics, like the Internet of Things (IoT) and wearable sensors. Unconventional electronics integrated with soft materials typically exhibit thermally limited performance due to low interfacial conductance and poor substrate thermal conductivity. To combat these issues, graphite nanoplatelets (GNPs) were used to increase the thermal conductivity of a flexible polydimethylsiloxane (PDMS) substrate by creating a percolating network of high thermal conductivity filler, increasing the substrate conductivity from 0.2 W-m-1K-1 to upwards of 1.8 W-m-1K-1, more than 9 times enhancement. This substrate material retained other useful properties including rheological behavior necessary for additive manufacturing, high temperature stability (upwards of 300C), flexibility (4 MPa compression modulus) and strong adhesion to device materials. This work is the first to demonstrate the direct transfer of the thinned AlGaN/GaN high electron mobility transistors (HEMTs) to the flexible polymeric nanocomposite substrate without an adhesive layer. The devices transferred to the PDMS composite substrates exhibited significantly lower self-heating temperatures experimentally (e.g., delta T = 24C at 30 mW) than those on PDMS when operated at comparable powers (15-50 mW), validating computational model results. These lower operating temperatures directly facilitate the operation of the devices at higher saturation currents and powers. The higher thermal conductivity of the PDMS composite substrate promotes heat conduction away from the device channel and effectively behaves as a flexible heat sink, which contributing to the high operating powers of 6 W-mm-1, especially compared to conventional flexible electronic substrates with low thermal conductivities (i.e. PDMS with no fillers). Additionally, the reduction of device temperatures at target operating powers resu (open full item for complete abstract)

Committee: Christopher Muratore PhD (Advisor) Subjects: Engineering; Materials Science

Doctor of Philosophy (PhD), Ohio University, 2019, Industrial and Systems Engineering (Engineering and Technology)

As a strategic decision in the supply chain design, the manufacturing system design impacts the quality, flexibility and the profitability of the entire supply chain. The global supply chain network confronts challenges such as the uncertain market demand and the global trade tariffs. The main goal of this dissertation is to design global supply chain under the stochastic demand considering manufacturing operations and the impact of tariffs. The methodology consists of eight steps. First, the local manufacturer, located in USA, groups the similar products into families to save time, effort and cost. Second, as a clustering approach, the p-median model is studied and then modified to identify families considering the minimum average family similarity. Third, the manufacturer decides the best design for the manufacturing operations, in this step, the classical-cellular manufacturing system is designed under the stochastic market demand. Fourth, the expected revenues generated by the cells open for product families, considering the expected sales and selling prices, are determined. Besides, the expected manufacturing costs including the labor, machine, material and shortage costs are determined as well. Eventually, the expected profits are calculated and the optimal number of cells is identified based on the highest profits generated. Although, the optimal design of the manufacturing system generates higher profits, the demand may not be fully covered.Fifth, based on the optimal design obtained in the third step, the optimal expected profits of the product, based on the scenarios of restrictions on their demand coverage probabilities, are determined by a proposed mathematical model. In this step, if there is no restriction, the maximum profits are made when only one product is produced and sold. This is due to that it has the lowest processing time among all products. When restrictions are applied on the demand coverage, other products are produced and sold; however, l (open full item for complete abstract)

Committee: Gürsel Süer (Advisor); Khurrum Bhutta (Committee Member); Gary Weckman (Committee Member); Tao Yuan (Committee Member); Ashley Metcalf (Committee Member) Subjects: Business Administration; Industrial Engineering; Management; Systems Science

Doctor of Philosophy, University of Toledo, 2018, Manufacturing and Technology Management

Today's small-market and mid-market sized manufacturers, competitively face increasing pressure to capture, integrate, operationalize, and manage diverse sources of digitized data. Many have made significant investments in data technologies with the objective to improve on organization performance yet not all have realized demonstrable benefits that create organization value. One simple question arises, do business-analytics make a difference on company performance in today's information intensive environment? The research purpose, to explore this question by looking through the lens of data-centric pressure placed on management driving the invested use of data-technologies; how these drivers impact on management influence to adopt a digitized organization mindset, effecting data practices, shaping key processes and strategies and leading to capabilities growth that impact on performance and culture. The terms `Big Data' and `Small Data' are two of the most prolific used phrases in today's world when discussing business analytics and the value data provides on organization performance. Big Data, being strategic to organization decision-making, and Small Data, operational; is captured from a host of internal and external sources. Studying how leveraging business-analytics into organizational value is of research benefit to both academic and practioner audiences alike. The research on `Big and Small Data, and business analytics' is both varied and deep and originating from a host of academic and non-academic sources; however, few empirical studies deeply examine the phenomena as experienced in the manufacturing environment. Exploring the pressures managers face in adopting data-centric managing beliefs, applied practices, understanding key value-creating process strategy mechanisms impacting on the organization, thus provides generalizable insights contributing to the pool of knowledge on the importance of data-technology investments impacting on organizational cul (open full item for complete abstract)

Committee: Paul Hong (Committee Chair); Thomas Sharkey (Committee Member); Wallace Steven (Committee Member); Cheng An Chung (Committee Member) Subjects: Information Systems; Information Technology; Management; Organization Theory; Organizational Behavior

Master of Science (MS), Ohio University, 2017, Industrial and Systems Engineering (Engineering and Technology)

This thesis aims to bridge the gap of designing cost effective and flexible vinyl windows Supply Chain. It is a case study- based on the problems observed during an internship by the thesis author. From the strategic and tactical levels of Supply Chain- manufacturing facility locations and suppliers have been selected based on budget restriction and annual equivalent worth analysis; number of manufacturing and assembly cells have been determined and their capacities are determined based on skilled workforce availability, production rate of Seru-assembly design, and work-hour availability. From operational level- required number of Seru cells have been determined and flexible line-Seru conversion has been performed. Performance of the conversion has been analyzed using Discrete Event Simulation and statistical analysis.

Committee: Gursel A. Suer PhD (Advisor); Dusan N. Sormaz PhD (Committee Member); Tao Yuan PhD (Committee Member); Ana Rosado Feger PhD (Committee Member) Subjects: Industrial Engineering; Management; Systems Design

Doctor of Philosophy (PhD), Ohio University, 2017, Mechanical and Systems Engineering (Engineering and Technology)

Demand uncertainty has been an important obstacle in production systems and supply chains in recent decades. This uncertainty in demand has stimulated many researchers and practitioners to find new ways--such as combining Make to Stock (MTS) and Make to Order (MTO) manufacturing systems--to handle the uncertainty problem. In this research, applying the hybrid MTS/MTO system is developed in three different manufacturing frameworks. The first framework considers a production line, which produces semi-finished products based on a Make To Stock (MTS) strategy until a specific process is finished, and operates based on a Make To Order (MTO) strategy after this process. Two scenarios are studied in the first model: 1- In the first Scenario, the production line applies the MTO strategy after the OPP, which leads to an idleness cost when there is no order in the system. 2- In the second Scenario, the production line either applies the MTO strategy or the MTS strategy after the OPP, based on the presence of an order for semi-finished products in the line. This second scenario comprises the holding cost of completed products on MTS strategy, but does not have an idle cost. The second framework considers the demand uncertainties in two consecutive echelons of a supply chain--unlike most of the field research--which has only focused on the final customer's demand uncertainty. In order to decrease the operating costs of a manufacturer, a model is proposed to use hybrid manufacturing in two different levels of a supply chain with two echelons of manufacturers. The output of the presented model is the quantity of semi-finished products ordered by the decoupling point upstream manufacturer. The third problem studies a multi-product, two-echelon supply chain within a newsvendor framework, in which semi-finished products are produced by a supplier and customized according to specific customer orders. The focus of this paper is to investigate a situation where the manufacture (open full item for complete abstract)

Committee: Gary Weckman (Advisor); William Young (Committee Member); Dale Masel (Committee Member); Benjamin Sperry (Committee Member); Yong Wang (Committee Member) Subjects: Business Costs; Industrial Engineering; Management; Marketing

PhD, University of Cincinnati, 2016, Engineering and Applied Science: Mechanical Engineering

The research in the area of Prognostics and Health Management (PHM) mainly focuses on detecting and predicting the unexpected events that can adversely affect the performance and productivity of machines and processes. The ultimate goal of PHM is to provide actionable information for achieving near-zero breakdown and unplanned downtime of machinery and processes. For implementing PHM in real-world applications, scientists face major challenges such as the complexity of machines or processes, their dynamic operating regimes, and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using sensors are often related to the costs associated with them and the inaccessibility of critical locations within machines or processes. This PhD dissertation, which focuses on developing minimal-sensing techniques for rotating machinery and manufacturing process applications, aims to address the issue of data insufficiency by proposing alternative solutions capable of dealing with complex operating regimes and manufacturing processes while relying on minimal data sources. The framework and methodologies presented here, aim to provide future researchers in academia and industry with a foundation that can be built upon for achieving accurate results while investing minimal resources. First, a survey on the PHM methods for dealing with complex systems and their sensing requirements for various industrial applications is conducted. There is a very wide range of applications for PHM systems and so the development of a general scheme that can potentially cover the whole range of PHM applications is one of the greatest challenges in this field of research. To tackle this challenge, this dissertation focuses on two groups of applications which can potentially cover a wide range in PHM area: manufacturing processes and rotating machinery. A fault detection and diagnosis framework for multistage manufacturing processes based on product qu (open full item for complete abstract)

Committee: Jay Lee Ph.D. (Committee Chair); Ryan J. Harbour Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Jing Shi Ph.D. (Committee Member) Subjects: Mechanics

Doctor of Philosophy (PhD), Ohio University, 2016, Mechanical and Systems Engineering (Engineering and Technology)

Global supply chain decisions, such as facility location, manufacturing system design, resource allocation, and distribution center location are long-term strategic decisions in nature and involve many uncertainties. Traditionally, a hierarchical approach is used design supply chain networks and manufacturing systems. First, the location of the facilities are determined, and then the manufacturing systems are designed at the selected locations. In this dissertation, a multi-stage supply chain network model is developed where locations of the plants and inner manufacturing system design are determined simultaneously for labor-intensive manufacturing companies. This dissertation aims to develop a decision making framework to integrate manufacturing systems and supply chain network design decisions considering optimal operator assignment and layered cellular manufacturing in mind. The industry studied is fashion jewelry manufacturing where labor cost is one of the major cost factors. Hence, optimizing the number of workers required for each operation, cell, and plant is critical for the cost efficiency of the entire supply chain. The optimal number of operators are determined for each manufacturing process, and then the optimal cell sizes are found for each manpower level using a heuristic procedure. The optimal number of manufacturing cells required to cover the uncertain demand is determined with mathematical modeling, and the designed layered cellular manufacturing systems for manufacturing stages are evaluated using Arena simulation models. The results of these models and methods are used as inputs while finding the optimal locations of the plants and allocating the optimal number of cells, workers, and machines for each selected plant. Different supply chain design alternatives considering various factors such as the shortest lead times, minimum capacity allocations, and multiple shifts are also studied.

Committee: Gursel A. Suer Ph.D. (Advisor) Subjects: Industrial Engineering; Operations Research

Doctor of Philosophy (PhD), Ohio University, 2016, Industrial and Systems Engineering (Engineering and Technology)

This dissertation aims to fulfill the gap of designing the supply chain system as a whole and looking at overall design across the supply chain of the company in the long term rather than short term. This dissertation is inspired from the window manufacturer which manufactures and distributes vinyl windows to meet new construction and replacement/remodeling sector demand. In this dissertation, complementary analytical models are discussed to determine efficient way to design a supply chain network. Mainly, design aspect and operational aspect of a supply chain system are considered. In the design aspect, number of manufacturing facilities, location/allocation decisions are determined. Then, the number of distribution centers, location and allocation decisions are made. Continuing with that, manufacturing configuration of each individual manufacturing facility is designed in detail and analyzed. In the proposed layered cellular manufacturing system design, based on the demand and processing requirements, products are grouped into product families and assigned to dedicated, shared and remainder cells. In the operational aspect, based on the designed manufacturing system, cell loading and product sequencing are performed. Moreover, vehicle routing system is designed to reach out the end customers in the supply chain system. All in all, this dissertation is unique in the sense of covering different levels of supply chain planning and decisions with nested approaches of facilities location, manufacturing system design, network design and vehicle routing design. New mathematical models and various new heuristic approaches are proposed to design a supply chain system in the presence of high-volume and low-volume windows demand.

Committee: Gürsel A. Süer PhD (Advisor); Faizul Huq PhD (Committee Member); M. Khurrum Bhutta PhD (Committee Member); Dale Masel PhD (Committee Member); Diana Schwerha PhD (Committee Member) Subjects: Engineering; Industrial Engineering; Operations Research

Doctor of Philosophy, The Ohio State University, 2003, Business Administration

Supply Chain Management (SCM) offers the possibility of increased customer service while minimizing costs. Before choosing what type of supply chain strategy to pursue, a firm must first evaluate the type of supply chain(s) in which it participates. The type of functional strategies chosen should complement the type of supply chain(s) in which the firm is a member. Certain manufacturing and logistics strategies are more appropriate given the characteristics of the supply chain. This thesis explores the relationships among supply chain characteristics, logistics and manufacturing strategies, and firm performance. In addition, this study proposes an alternative logistics strategy framework using the competitive priorities of cost, quality, delivery and flexibility. Multivariate analysis of variance (MANOVA) was used to test the hypotheses. Results indicate limited support for the notion that successful firms participating in agile supply chains choose to emphasize different logistics and manufacturing strategies than less successful firms in agile supply chains. The same holds true for firms participating in lean supply chains. Results from the logistics strategy factor analysis demonstrated that the proposed framework is not only a feasible one, but one that is effective at describing the logistics strategy.

Committee: Martha Cooper (Advisor) Subjects: Business Administration, General

Doctor of Philosophy in Manufacturing and Technology Management, University of Toledo, 2013, Manufacturing and Technology Management

As global pressures to address climate change intensify, the costs of natural resources increase, public health and safety concerns grow, and diverse consumption patterns emerge, sustainability has become critical for competing in international markets (Epstein, 2008; Lubin and Esty, 2010; Wu and Pagell, 2011). The goal of sustainability is grounded in the concept of the triple bottom line, which indicates that balancing objectives related to profits, the planet, and people is essential for corporations as they grow and compete in the global economy (Kleindorfer et al., 2005). Taking advantage of a broad and systemic approach to addressing sustainability issues, researchers increasingly acknowledge that linking sustainability with the supply chain is a crucial step for operations management (Hall, 2000; Zhu and Sarkis, 2004; Koplin et al., 2007; Matos and Hall, 2007). Despite a growing number of studies on sustainability from the point of view of the supply chain (Linton et al., 2007; Carter and Rogers, 2009; Pagell and Wu, 2009; Pullman et al., 2009), few researchers have developed an empirically based integrative research framework grounded in relevant theories. In particular, the literature lacks research that empirically examines the nomological network of sustainable supply chain encompassing drivers, strategy, practices, and performance outcomes with consideration for all three dimensions of sustainability (economic, environmental, and social performance) (Elkington, 1994, 1997; Kleindorfer et al., 2005; Seuring and Muller, 2008). Drawing from the theoretical lenses of institutional theory (DiMaggio and Powell, 1983), strategic choice theory (Child, 1972), strategic orientation (Venkatraman, 1989), and the resource-based view of firms (Barney, 1991), this dissertation presents a framework, by taking a holistic view, of a sustainable supply chain aimed at explaining the relationships between the antecedents, strategic orientation, supply chain practices, an (open full item for complete abstract)

Committee: Mark Vonderembse PhD (Committee Co-Chair); Sachin Modi PhD (Committee Co-Chair); Paul Hong PhD (Committee Member); Stanford Westjohn PhD (Committee Member); Dwight Haase PhD (Committee Member) Subjects: Alternative Energy; Environmental Management; Environmental Studies; Sustainability