Purpose -There have been many studies on topics related to supplier relationship management (SRM), namely purchasing strategy, supplier selection and development, and collaboration with suppliers. However, these studies have not suggested a solution based on an integrative concept, as they focus only on domain-specific problems. To overcome this limitation, the purpose of this present paper is to suggest a framework for an integrative SRM system by analyzing comprehensive approaches to overall SRM functions. Design/methodology/approach -The paper reviewed and analyzed studies related to SRM from an integrative viewpoint, proposed a framework for an integrative SRM system, and performed a case study based on the analytical hierarchy process with a field survey. Findings -The paper expects that the proposed framework can play a major role in enhancing the efficiency and effectiveness of SRM by adopting an integrative concept because the functions of SRM are highly interrelated. Furthermore, it verifies the applicability of the framework via a case study.Research limitations/implications -Not many approaches are available for organizing evaluators or evaluation sheets, which are used for selecting criteria, providing weight, and evaluating supplier performance, that can secure objectivity of the evaluation; thus, when applying this framework to the industry, special consideration is needed. Practical implications -The proposed framework allows the purchasing members to scrutinize key features of SRM before and during the SRM system operation. Originality/value -The paper provides useful knowledge about the role of SRM systems by holistically approaching SRM-related processes while suggesting criteria and recommendations to a purchasing manager.

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Abstract:The so-called smart factory is a novel paradigm that is rapidly gaining ground in scenarios for factories of the future. Many manufacturing companies try to raise the level of smartness by considering a number of aspects related to the smart factory. However, there is a lack of field-oriented systematic research to help them fit the interest of industry for promoting interest and diffusion of smart factory. Moreover, it is still difficult to assess whether the vision of the future factory that incorporates information and communication technologies is implemented. Therefore, in this study, we propose a smartness assessment framework for smart factories which is based on the concept of operation management so as to be easy to make manufacturing companies to understand and apply. The framework is composed of evaluation criteria and sets the weightings of the criteria using analytic network processes. From a case study based on 20 small and medium-sized manufacturing enterprises, the effectiveness of the proposed framework has been verified.

As markers for visual sensor networks have become larger, interest in the optimal camera placement problem has continued to increase. The most featured solution for the optimal camera placement problem is based on binary integer programming (BIP). Due to the NP-hard characteristic of the optimal camera placement problem, however, it is difficult to find a solution for a complex, real-world problem using BIP. Many approximation algorithms have been developed to solve this problem. In this paper, a two-phase algorithm is proposed as an approximation algorithm based on BIP that can solve the optimal camera placement problem for a placement space larger than in current studies. This study solves the problem in three-dimensional space for a real-world structure.

Growing competition among manufacturing businesses and the advent of the Fourth Industrial Revolution has meant that many countries are conducting various research projects to understand how to introduce and populate smart factories. Smart factories are expected to provide a way of solving the manufacturing industries’ complex problems, to take a role in breakthroughs in factories and to carry on a sustainable business. Smart factories are currently in the introduction stage, so we should follow up on the majorities and check their tendencies. However, smart-factory research is an interdisciplinary field that should be studied by researchers with diverse backgrounds in various domains. Thus, studying the past and present overall research trends of smart factory studies is required for their successful introduction and sustainable research. In this study, we explored the research trends of smart factories in both international and specifically Korean research, as an example of a nation case, to determine the major research directions. We determined trends using latent semantic analysis, which is a known topic-modeling technique, and analyzed the trends with regression-based methods. As a result, we could read the clear trends by analyzing existing studies related to smart factories. In addition, it is possible to compare research trends in Korea and international research trends for the commonly appeared topics, such as ‘ICT’ (Information and Communications Technology) and ‘R&D (Research and Development)/Technology Innovation’. We expect that the quantitative analysis results and suggestions presented in this study can be used to formulate strategies for the future diffusion of smart factories.

This paper proposes a comprehensive production performance measurement framework and illustrates the method to evaluate the performance and guide practitioners to make further improvement. The development comprises four steps. (1) Performance indicators derived from business excellence models are enumerated to provide the performance model: 74 indicators, which can be classified in terms of their characteristics, are identified in six criteria. (2) A multiple criteria decision-making approach based on the analytic hierarchical and network processes, which determine the weights of the criteria and indicators, is applied. In addition, this study introduced additional formulas to derive the final performance values. (3) A performance measurement framework that integrates the measurement and result analysis processes is implemented. (4) The proposed framework is verified through a case study. The results of the case study show that the proposed framework identifies the gaps and discrepancies among the management levels, enabling the determination of means for continuous improvement.

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In manufacturing companies, it is vital to manage their manufacturing processes in order to ensure high quality of products and manufacturing consistency. Because so-called smart factories interconnect machines and acquire processing data, the business process management (BPM) approach can enrich the capability of manufacturing operation management. In this paper, we propose BPM-based similarity measures for manufacturing processes and apply them to the processes of a real factory. In addition to the structural similarity of the existing studies, we suggest a production-related operation similarity. Our contribution is considered on the assumption that a manufacturing company adopts the BPM approach and it operates a variety of manufacturing process models. The similarity measures enable the company to automatically search and reutilize models or parts of models within a repository of manufacturing process models.