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- Member of: ASU Electronic Theses and Dissertations
Description
Most advanced economies have evolved into service economies with the majority of their activity and jobs being in the service sector. The manufacturing sector is also going through a similar shift towards services. Manufacturers are increasingly complementing their products with new services in order to satisfy a broader array of customer needs and increase the value of their offerings. This shift has offered significant opportunities to the sector and the success of major firms such as IBM, Caterpillar, and Rolls-Royce in competing through services has been remarkable.
Despite the increased importance of services in the manufacturing sector, the academic literature is yet to investigate the many questions that arise under this new manufacturing paradigm. Perhaps for the same reason study of servitization is listed as a research priority in recent publications both in the field of service operations management and in the field services marketing. This dissertation covers three essays aimed at disentangling multiple aspects of the role of services in the manufacturing sector. The literature on the drivers and implications of transition towards services in manufacturing firms is limited. The three studies in this dissertation aim at shedding light on this issue.
Specifically, the first essay looks at the innovation benefits of service transactions with customers. This paper demonstrate the value of services in getting manufacturers closer to customers and allowing them glean useful information from their service interactions. The second essay investigates the antecedents of service strategy adoption. We suggest that the extant diversification theory does not fully explain servitization and this phenomenon represents a unique type of diversification, which is likely driven by different factors. Through econometric analysis of financial data over a 27-year period, this study explores characteristics of product, firm resources, competition, and industry that encourage adoption of service strategies in manufacturing sector. Finally, the third essay takes a deeper dive and focuses on dealerships, as service centers, in the automobile industry. It investigates the role of dealerships in the success of automakers and explores dealership traits that are critical for market success of an automobile brand.
Despite the increased importance of services in the manufacturing sector, the academic literature is yet to investigate the many questions that arise under this new manufacturing paradigm. Perhaps for the same reason study of servitization is listed as a research priority in recent publications both in the field of service operations management and in the field services marketing. This dissertation covers three essays aimed at disentangling multiple aspects of the role of services in the manufacturing sector. The literature on the drivers and implications of transition towards services in manufacturing firms is limited. The three studies in this dissertation aim at shedding light on this issue.
Specifically, the first essay looks at the innovation benefits of service transactions with customers. This paper demonstrate the value of services in getting manufacturers closer to customers and allowing them glean useful information from their service interactions. The second essay investigates the antecedents of service strategy adoption. We suggest that the extant diversification theory does not fully explain servitization and this phenomenon represents a unique type of diversification, which is likely driven by different factors. Through econometric analysis of financial data over a 27-year period, this study explores characteristics of product, firm resources, competition, and industry that encourage adoption of service strategies in manufacturing sector. Finally, the third essay takes a deeper dive and focuses on dealerships, as service centers, in the automobile industry. It investigates the role of dealerships in the success of automakers and explores dealership traits that are critical for market success of an automobile brand.
ContributorsGolara, Sina (Author) / Dooley, Kevin J (Thesis advisor) / Rogers, Dale (Committee member) / Kull, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this dissertation research, I expand the definition of the supply network to include the buying firm’s competitors. Just as one buyer-supplier relationship impacts all other relationships within the network, the presence of competitor-supplier relationships must also impact the focal buying firm. Therefore, the concept of a “competitive network” made up of a focal firm, its competitors and all of their combined suppliers is introduced. Utilizing a unique longitudinal dataset, this research explores how the organic structural changes within the new, many-to-many supply network impact firm performance. The investigation begins by studying the change in number of suppliers used by global auto manufacturers between 2004 and 2013. Following the Great Recession of 2008-09, firms have been growing the number of suppliers at more than twice the rate they had been reducing suppliers just a few years prior. The second phase of research explores the structural changes to the network resulting from this explosive growth in the number of suppliers. The final investigation explores a different flow – financial flow -- and evaluates its association with firm performance. Overall, this dissertation research demonstrates the value of aggregating individual supply networks into a macro-network defined as the competitive network. From this view, no one firm is able to control the structure of the network and the change in structure directly impacts firm performance. A new metric is introduced which addresses the subtle changes in buyer-supplier relationships and relates significantly to firm performance. The analyses expand the body of knowledge through the use of longitudinal datasets and uncovers otherwise overlooked dynamics existing within supply networks over the past decade.
ContributorsHuff, Jerry (Author) / Fowler, John (Thesis advisor) / Rogers, Dale (Committee member) / Carter, Craig (Committee member) / Arizona State University (Publisher)
Created2016
Description
The Cognitive Decision Support (CDS) model is proposed. The model is widely applicable and scales to realistic, complex decision problems based on adaptive learning. The utility of a decision is discussed and four types of decisions associated with CDS model are identified. The CDS model is designed to learn decision utilities. Data enrichment is introduced to promote the effectiveness of learning. Grouping is introduced for large-scale decision learning. Introspection and adjustment are presented for adaptive learning. Triage recommendation is incorporated to indicate the trustworthiness of suggested decisions.
The CDS model and methodologies are integrated into an architecture using concepts from cognitive computing. The proposed architecture is implemented with an example use case to inventory management.
Reinforcement learning (RL) is discussed as an alternative, generalized adaptive learning engine for the CDS system to handle the complexity of many problems with unknown environments. An adaptive state dimension with context that can increase with newly available information is discussed. Several enhanced components for RL which are critical for complex use cases are integrated. Deep Q networks are embedded with the adaptive learning methodologies and applied to an example supply chain management problem on capacity planning.
A new approach using Ito stochastic processes is proposed as a more generalized method to generate non-stationary demands in various patterns that can be used in decision problems. The proposed method generates demands with varying non-stationary patterns, including trend, cyclical, seasonal, and irregular patterns. Conventional approaches are identified as special cases of the proposed method. Demands are illustrated in realistic settings for various decision models. Various statistical criteria are applied to filter the generated demands. The method is applied to a real-world example.
The CDS model and methodologies are integrated into an architecture using concepts from cognitive computing. The proposed architecture is implemented with an example use case to inventory management.
Reinforcement learning (RL) is discussed as an alternative, generalized adaptive learning engine for the CDS system to handle the complexity of many problems with unknown environments. An adaptive state dimension with context that can increase with newly available information is discussed. Several enhanced components for RL which are critical for complex use cases are integrated. Deep Q networks are embedded with the adaptive learning methodologies and applied to an example supply chain management problem on capacity planning.
A new approach using Ito stochastic processes is proposed as a more generalized method to generate non-stationary demands in various patterns that can be used in decision problems. The proposed method generates demands with varying non-stationary patterns, including trend, cyclical, seasonal, and irregular patterns. Conventional approaches are identified as special cases of the proposed method. Demands are illustrated in realistic settings for various decision models. Various statistical criteria are applied to filter the generated demands. The method is applied to a real-world example.
ContributorsKee, Seho (Author) / Runger, George C. (Thesis advisor) / Escobedo, Adolfo (Committee member) / Gel, Esma (Committee member) / Janakiram, Mani (Committee member) / Rogers, Dale (Committee member) / Arizona State University (Publisher)
Created2020