A Multi-Objective Closed-Loop Supply Chain Planning Model With Uncertainty

Due to the topics such as the environmental issues, the governments’ legislation, natural resource limitations have attracted attention, the research of closed-loop supply chain is increasingly important. Effectively integrated management of a closed-loop supply chain can be a challenge for companies. Companies not only have to meet the environmental regulations, but also have to sustain high-quality supply chain operations as a means to stay competitive advantages and the profit capability. This study proposes a multi-objective mixed integer programming model for an integrated closed-loop supply chain network to maximize the profit, the amicable production level and the quality level. To our knowledge, this proposed model is the first effort to take economic factors, environmental factors, quality factors and uncertain parameters into account simultaneously, and can be a reference for supporting effectively integrated management of a closed-loop supply chain network

A Model on Energy Power Management of Air-Recirculation in a Clean Room

With the rapid development of intelligent networking technology, fan filter units (FFUs) in cleanrooms can be networked as a cluster to form multiple low-pollution minienvironments. Therefore, FFU cluster management for different process cleanliness requirements and energy savings targets is a critical topic. This study uses particle swarm optimization with a metaheuristics algorithm to collect, calculate, and dynamically adjust air recirculation at a production site and plan FFU clusters for a cleanroom by integrating the FFU number, airflow speed, size, and other characteristics to ensure the quality of production and achieve energy savings targets

The Impact Of Internet Of Things Technologies On Supply Chain Performance: The Mediating Role Of Competitive

Recent advancement of Internet of Things (IoT) technologies has invoked tremendous attention from both academics and industries. The emerging IoT technologies not only serve as possible new tools for enterprise operation, but also trigger impacts in the management arena such as supply chain management (SCM). This study investigates the role of competitive strategy underlying the link between IoT technologies and supply chain performance. By referring to the resource→strategy→performance model, this study builds a research framework in which three strategic positions of firms— low cost, differentiation and market focus—mediate the effect of IoT technologies on supply chain performance. Empirical survey and analysis of enterprise data are conducted to test the hypotheses. The test results support the mediation effects of competitive strategies. Research contributions and managerial implications are elaborated in the conclusions

A Shared Information-Based Petri Net Model for Service Parts Planning

A considerable amount of electronic products are returned after sales, especially in such an economic downturn situation. After repair and refurbishment, the used products can be returned into the markets which fulfill the forward supply chains into a close loop. In this paper, we consider the service parts planning in the beginning of product rolling plan together with the sales through quantities to minimize the inventory level in the period of product lifecycle. A Petri Net is used to model a simple closed-loop supply chain with shared sales information. PUSH and PULL inventory policies are used in this research. Finally, it is investigated how a third party service provider uses this mechanism to improve the accuracy of inventory planning

A Scenario Analysis of Wearable Interface Technology Foresight

Although the importance and value of wearable interface have gradually being realized, wearable interface related technologies and the priority of adopting these technologies have so far not been clearly recognized. To fill this gap, this paper focuses on the technology planning strategy of organizations that have an interest in developing or adopting wearable interface related technologies. Based on the scenario analysis approach, a technology planning strategy is proposed. In this analysis, thirty wearable interface technologies are classified into six categories, and the importance and risk factors of these categories are then evaluated under two possible scenarios. The main research findings include the discovery that most brain based wearable interface technologies are rated high to medium importance and high risk in all scenarios, and that scenario changes will have less impact on voice based as well as gesture based wearable interface technologies. These results provide a reference for organizations and vendors interested in adopting or developing wearable interface technologies

Tacit Knowledge Acquisition in Organizations

This study explores the factors affecting tacit knowledge acquisition (TKA) by knowledge seekers/receivers in organizations. It proposes the theoretical basis for an integrated model for the antecedents of TKA from the socio-cultural and psychological factors of knowledge seekers in organizations. The theoretical model was applied to a sample of 168 employees from the research and development units of a semi-conductor company in Taiwan. The research results show that both social interaction and learning orientation are determinants of TKA and the mediators. Agreeableness and learning need are drivers of TKA through social interaction. Learning orientation acts as the mediator between absorptive capability and TKA as well as between learning need and TKA. However, conscientiousness is a suppressor of TKA

Prediction of financial distress with text mining and hidden Markov model

The financial distress of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss. It is important to establish an effective early warning system for prediction of financial distress. Financial news contains a lot of unstructured text data about the financial status of the business. Therefore, this paper takes into account the unstructured text data to the early warning system for sentimental analysis. In the section of financial indicators, this study uses the seven major categories of financial ratios in the ZETA model of Altman (2000). We use logistic regression and random forest to establish our model. However, the weakness of ZETA model is that the prediction accuracy will be greatly dropped over two years. This study introduces a hidden Markov model to improve the long-term prediction accuracy of the model. This paper provides a hybrid method which integrates text mining and hidden Markov model for prediction of financial distress for listed companies in Taiwan