• 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.

• 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.

• 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.

• Journal of Sensor Science and Technology
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• v.20 no.4
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• pp.254-259
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• 2011

In this paper, a current stimulator circuit with adaptive supply regulator for retinal prosthesis is proposed. In current stimulation systems, the stimulating circuits with wide voltage swing range are needed due to the high impedance of the retina cell and microelectrodes. Thus, previous researches adopt the high voltage architecture to obtain the enough operating range. The high voltage architecture, however, could increase the power consumption and can damage the retina cells. The proposed circuit provides the adaptively regulated supply voltage by measuring the difference between desired stimulation current and the actual stimulation current. The proposed circuit can achieve the extended range of the allowable cell impedance, improved accuracy of the stimulation current, and higher biosafety.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1650-1656
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• 2004

In this paper, a multi-adaptive filter is proposed for removing EOG and the 60 Hz power supply noise from EEG measured in the frontal lobe and the feedback output control method is implemented for biofeedback. The multi-adaptive filter has been implemented on the TMS320C6711 DSP system and the feedback output control algorithm has been realized by calculating the ratio of alpha wave on the TMS320C31 DSP system with real time performance. Through the experiment using the implemented multi-adaptive filter and feedback output controller, we demonstrate that the proposed adaptive filter effectively removes EOG and the 60 Hz power supply noise from the measured EEG in the frontal lobe and the feedback algorithm controls the level of stimulation by the ratio of the alpha wave.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.79-89
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• 2017

The Optimization of a Closed-Loop Supply Chain (CLSC) Model Using an Adaptive Hybrid Genetic Algorithm (AHGA) Approach is Considered in this Paper. With Forward and Reverse Logistics as an Integrated Logistics Concept, The CLSC Model is Consisted of Various Facilities Such as Part Supplier, Product Manufacturer, Collection Center, Recovery Center, etc. A Mathematical Model and the AHGA Approach are Used for Representing and Implementing the CLSC Model, Respectively. Several Conventional Approaches Including the AHGA Approach are Used for Comparing their Performances in Numerical Experiment.

• 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.

• 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.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.1
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• pp.79-86
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• 2013

We describe a neural stimulator front-end with arbitrary stimulation waveform generator and adaptive supply regulator (ASR) for visual prosthesis. Each pixel circuit generates arbitrary current waveform with 5 bit programmable amplitude. The ASR provides the internal supply voltage regulated to the minimum required voltage for stimulation. The prototype is implemented in $0.35{\mu}m$ CMOS with HV option and occupies $2.94mm^2$ including I/Os.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.2
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• pp.106-119
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• 2005

Uncertainties inherent in customer demand patterns make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. In this paper, we propose two intelligent adaptive inventory control models for a supply chain consisting of one supplier and multiple retailers, with the assumption of information sharing. The inventory control parameters of the supplier and retailers are order placement time to an outside source and reorder points in terms of inventory position, respectively. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a stationary statistical distribution is not necessary in these models. Instead, using a reinforcement learning technique, the control parameters are designed to adaptively change as customer demand patterns change. A simulation based experiment was performed to compare the performance of the inventory control models.