• Journal of Distribution Science
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• v.18 no.11
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• pp.91-103
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• 2020

The recent Covid-19 outbreak has caused severe disruption of the global supply chain, which tests firms' ability to survive and build resilience. The concept of adaptive supply chain management (A-SCM) has never been tested against a severe supply chain disruption, such as a pandemic. Purpose: The aim of this study is to examine how firms in Indonesia develop resilience through the implementation of components of adaptive supply chain management, namely risk management, resource reconfiguration and supply chain flexibility, in order to survive severe supply chain disruption. Research design, data and methodology: A qualitative method and PLS-SEM were used to analyze 120 data collected from Indonesian manufacturing firms in various industries. Results: The findings show that risk management, resource reconfiguration, and supply chain flexibility are important components that make up A-SCM. However, only risk management contributes to help build firm resilience in the presence of severe supply chain disruption. Conclusions: The components of A-SCM have been empirically tested. The implication is that managers should carefully use RM to prepare firms for different scenarios to develop contingency strategies. This research contributes to the supply chain management body of knowledge in the context of pandemic-level disruption and broadens the dynamic capabilities perspective.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.67-89
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• 2007

This study addresses manufacturers' ability to influence their supply chain network in order to adapt to their competitive environment. From the perspective of a manufacture, the supply chain comprises a network of suppliers and customers, and is theoretically viewed as a complex adaptive system. This study considers the following questions: (1) How can adaptability of supply chain network be operationally defined? (2) How does adaptability of supply chain network lead to combinative capabilities? (3) What is the influence of adaptability of supply chain network on business performance? Drawing on literature streams in supply chain management, operations strategy, organizational change and learning, and complexity theory, this study develops and tests the constructs and operational measures of adaptability of supply chain network and model the nomological set of relationships among constructs that form the basis of our theory. This study then develops and tests a model describing the outcomes of adaptability of supply chain network and its influence on combinative capability and business performance. Empirical results of this study show that adaptability supply chain network directly and positively affects combinative capability. Further, this study finds that adaptability of supply chain network does not impact business performance directly, but rather is mediated through combinative capability, which provides the requisite variety for firms to survive and thrive in dynamic environments.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.655-669
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• 2019

Supplier evaluation is of great significance in green supply chain management. Influenced by factors such as economic globalization, sustainable development, a holistic index framework is difficult to establish in green supply chain. Furthermore, the initial index values of candidate suppliers are often characterized by uncertainty and incompleteness and the index weight is variable. To solve these problems, an index framework is established after comprehensive consideration of the major factors. Then an adaptive weight D-S theory model is put forward, and a fuzzy-rough-sets-AHP method is proposed to solve the adaptive weight in the index framework. The case study and the comparison with TOPSIS show that the adaptive weight D-S theory model in this paper is feasible and effective.

• IE interfaces
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• v.18 no.3
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• pp.343-349
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.27-40
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• 2006

Today's customer demands in supply chains tend to change quickly, variously even in a short time Interval. The uncertainties of customer demands make it difficult for supply chains to achieve efficient inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. Un this paper, we propose an adaptive vendor managed inventory (VMI) model for a two-echelon supply chain with non-stationary customer demands using the action-reward learning method. The Purpose of this model is to decrease the inventory cost adaptively. The control Parameter, a compensation factor, is designed to adaptively change as customer demand pattern changes. A simulation-based experiment was performed to compare the performance of the adaptive VMI model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.658-663
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.323-330
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• 2014

Various inventory control theories have tried to modelling and analyzing supply chains by using quantitative methods and characterization of optimal control policies. However, despite of various efforts in this research filed, the existing models cannot afford to be applied to the realistic problems. The most unrealistic assumption for these models is customer demand. Most of previous researches assume that the customer demand is stationary with a known distribution, whereas, in reality, the customer demand is not known a priori and changes over time. In this paper, we propose a reinforcement learning based adaptive echelon base-stock inventory control policy for a multi-stage, serial supply chain with non-stationary customer demand under the service level constraint. Using various simulation experiments, we prove that the proposed inventory control policy can meet the target service level quite well under various experimental environments.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.344-351
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• 2015

Recently, an ERP (Enterprise resource Planning) system has been becoming an essential S/W tool for companies to manage their business processes and manufacturing resources. As the information exchange becomes more complex, not only corporate companies but also small- and mid- sized enterprises (SMEs) are required to build an ERP system. However, for small- and middle- sized companies, the adoption of ERP systems becomes challenging due to high cost and long installation time of the system. This paper presents a novel concept of an adaptive ERP system incorporating the ontology structure of the business supply chain information. The proposed ERP installation methodology is illustrated with an example of a door-trim manufacturing company in the automotive supply chain.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.109-123
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• 2003

The relationship between suppliers and customers is treated importantly not only in the traditional business-to-business (BtoB) commerce but also in today's Internet environments. On the one hand, most of Internet-based BtoB commerce services like customer-centric e-procurement, supplier-centric e-sales or intermediary-centric e-marketplace focus mainly on the selection of partners according to bidding, auction, etc. This point may result in the problem of overlooking the relationships between suppliers and customers. To overcome this problem in this paper, an intelligent agents-based supply chain operation architecture is proposed and appraised considering the relationship and its adaptation.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.35-50
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• 2000

Diverse requirements of users on web-based model management force a system agent to develop user-adaptive building a model in reality and providing an adequate solution method of the model. The relationship between models is important knowledge for the agent to effectively build a new model to adaptively adjust an existing model under a problem and to efficiently connect the new model into an adequate solution method. Since the generating process of the inter-model relationship is more difficult than the building a new model however the process mostly depends on the knowledge of operation research experts. Without the adequate scheme of the inter-model relationship the burden of the management for the agent increases rapidly and the quality of the services may worsen. This study shows that meta-knowledge generated from relationship between models is important for the user to build a model in reality and to acquire the solver appropriate to the model. The relationship that consists of common and exclusive objects between models can be represented by frames. The system under development to implement the idea includes user-adaptive ability which identifies a model through forward chaining method and searches the solver appropriate to the model by using the meta knowledge. We illustrate the meta knowledge with an applied delivery system in supply chain management.