S-Space College of Business Administration/Business School (경영대학/대학원) Dept. of Business Administration (경영학과) Theses (Ph.D. / Sc.D._경영학과)
An integrated model for supply network resilience: capabilities, exchange relationship, and network attributes
공급네트워크 복원력에 대한 통합 모델: 역량, 교환 관계 및 네트워크 속성
- 경영대학 경영학과
- Issue Date
- 서울대학교 대학원
- Supply chain management; SC resilience; SC capabilities; SC exchange relationship; supply network; supply network resilience
- 학위논문 (박사)-- 서울대학교 대학원 : 경영대학 경영학과, 2018. 2. 박상욱.
- The supply chain management (SCM) activities and its performance become vulnerable due to sudden disruptive events in the business process. Specifically, among three phases (sense, respond, recover) supply chain (SC) experience under disruption, we are interested in post-event recovery activities. For example, after the supply disruption, firms must transfer equipment and switch production to alternative or new suppliers utilizing network capability and flexibility. Such recovery activities are termed as resilience activities or a term, SC resilience. The primary objective of this thesis is to thoroughly investigate all the important attributes related to SC resilience, and to propose a comprehensive scheme to show the level of resilience among multiple firms from a network perspective. This thesis considers three problems in a sequential manner so that the critical issues fostering SC resilience can be practically resolved: (1) to determine the critical attributes for SC resilience
(2) to present a network-based structure for managing the levels of resilience
and (3) to propose comprehensive network resilience model for both deterministic and probabilistic situations.
This thesis first elicits important resilience attributes, among which a number of determinant attributes are critical for supply chain sustainability. The resilience capabilities introduced in the existing literature are systematically investigated and classified, based on a value hierarchy. A survey study is then conducted in order to validate the important exchange relationship attributes and supply chain capabilities. Second, a graphical representation is proposed to visualize the resilience relationship in a network formation. A node here represents a partner firm’s resilience capability in the supply network and the network value consists of the positional value of the firm. We then adopt an outranking methodology, concordance discordance approach, to provide a process to identify the improvement priority order. Finally, a total network resilience model is proposed to handle resilience levels and interrelationships of the firms simultaneously. The proposed model is also extended to serve as a probabilistic model, along with a number of sensitivity studies, to improve its applicability.
The study may contribute theoretically to the literature as follows: First, this thesis isolated four key determinant attributes of supply chain resilience through a comprehensive analysis of existing capabilities. The impact of the four attributes on resilience has been verified with a survey study. Second, the interrelationships of the firms have been expressed using leader-member exchange theory. Through the survey analysis, it was found that leader member exchange affects supply chain resilience significantly. Third, a bicriterion network resilience model using resilience and network value has been proposed, along with an ordering approach. The network representation visualizes not only all the levels of resilience of the firms but also their influences within the network structure. Fourth, a total network resilience (TNR) model is developed, through which one can handle both resilience and interrelations among the firms. The model is applicable to both deterministic and probabilistic cases.
Investigating the impact of supply chain capabilities, exchange relationship, and network attributes on supply network resilience offers a fertile avenue for future research. From supply chain perspective, it is recommended that future studies explore the causal relationships among SC capabilities and SC resilience based on different phases of a disruption (i.e., pre-, during-, and post-disruption). One can also investigate the relational behavior based on divergence or crossvergence contexts for more comprehensive analysis. Another possible research direction is to utilize our proposed TNR model in considering triadic relationship and diverse network structural properties. With a further effort on elaboration, we believe that the research results may prove to be a solid basis for network based research in the area of supply chain management.