• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.789-799
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• 2018

This article presents machine learning based approach on Big data to analyzing time series data for anomaly detection in such industrial complex system. Long Short-Term Memory (LSTM) network have been demonstrated to be improved version of RNN and have become a useful aid for many tasks. This LSTM based model learn the higher level temporal features as well as temporal pattern, then such predictor is used to prediction stage to estimate future data. The prediction error is the difference between predicted output made by predictor and actual in-coming values. An error-distribution estimation model is built using a Gaussian distribution to calculate the anomaly in the score of the observation. In this manner, we move from the concept of a single anomaly to the idea of the collective anomaly. This work can assist the monitoring and management of Smart Factory in minimizing failure and improving manufacturing quality.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.83-93
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• 2006

This paper is wireless location awareness technology using RFID. We investigates the Location of the received Signal strength reported by RF Analyses of the data are performed to understand the underlying features of location fingerprints. The system is performed factors the extreme environmental Emit signal, which consists of a unique 5000 Terminals. The Location Service have become very popular in many service industries, purchasing and distribution logistics, industry, manufacturing companies and a parking place. The Technically optimal Solution would be the storage of Intelligence information in the most common form of electronic data-carrying device in use in everyday life is the smart card based upon a contact field (telephone smart card, bank cards). The method of an indoor positioning experiment system is compared using measured Location data and a charge of service. The result of research showed the following: first, to check out the mechanism between benefit of system installation and operation of Active RFID. Second, it contributed on indoor wireless location intelligence system efficiency.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.2
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• pp.87-93
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• 2018

This Study Propose A New Approach To Impliment A Intelligent Robot Control Based on Voice Recognition For Smart Factory Automation Since human usually communicate each other by voices, it is very convenient if voice is used to command humanoid robots or the other type robot system. A lot of researches has been performed about voice recognition systems for this purpose. Hidden Markov Model is a robust statistical methodology for efficient voice recognition in noise environments. It has being tested in a wide range of applications. A prediction approach traditionally applied for the text compression and coding, Prediction by Partial Matching which is a finite-context statistical modeling technique and can predict the next characters based on the context, has shown a great potential in developing novel solutions to several language modeling problems in speech recognition. It was illustrated the reliability of voice recognition by experiments for humanoid robot with 26 joints as the purpose of application to the manufacturing process.

• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.169-174
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• 2018

Today, the world has reached 'Industry 4.0'. Industry 4.0 has high uncertainty in various aspects because it is based on building a smart chain where the various elements that make up the industry can communicate with each other. Based on the above facts, based on the researches of the previous researchers, we have searched for the countermeasures of small and medium sized manufacturing companies in Korea in order to minimize the negative aspects of establishing the basic concepts and functioning of Industry 4.0. As a result, efforts to accurately identify and address the uncertainties of Industry 4.0 in a variety of ways will help to drive business growth and economic growth in the country through smart factories, which are at the heart of Industry 4.0.

• Journal of Practical Engineering Education
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• v.9 no.1
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• pp.49-56
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• 2017

Recently, due to Industry 4.0, there is a great interest in smart factory for productivity improvement and customer satisfaction in manufacturing industry, and construction is also actively pursued by government support. In particular, data integration and fieldbus communication technology to build an efficient production system are essential. Fieldbus is an open control system that is not tied to a specific vendor system and has various advantages such as compatibility with other products, accuracy of data transmission, and remote diagnosis. However, there are no educational equipment for training field buses, training courses and examples for practical training, and there are many limitations in improving the practical skills needed for building smart factories in the industrial field. Therefore, this study develops PLC based fieldbus education equipment and training course based on previous research results that selected PLC and communication technology suitable for domestic industry field for practical fieldbus training and develops the training program of Ethernet IP, Profibus DP, Modbus, CC-Link, and DeviceNet. In addition, it is confirmed that the control and remote diagnosis of distributed field devices are possible by data collection and monitoring.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.229-236
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• 2019

IOT middleware is required to play a pivotal role in interpreting, managing, and controlling data information of Internet devices (sensors, etc.). In particular, the root industry has different process flows for different industries, and there are various data processing requirements for each company. Therefore, a general purpose IOT middleware is needed to accommodate this. The IOT middleware structure proposed by this paper is a plug-in that can be used as an engine part for middleware basic processes such as communication, data collection, processing and service linkage, We propose a flexible and effective smart process for root industry. In addition, we propose a method to strengthen prevention and security against tampering, deodorization, etc. through encryption of network data between middleware plug - in and related service layer. We propose a system that will be developed as an IOT middleware platform that is specialized in the root industry so that it can be extended in various network protocols such as MQTT, COAP, XAMP.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.9-16
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• 2019

Companies that want to improve their manufacturing processes have recently introduced the smart factory, which is particularly noticeable. The ultimate goal is to maximize the area of the smart factory that performs the process of the production facility completely with minimal manual control and to minimize errors of reasoning. This research is a part of a project for unmanned production, management, packaging, and delivery management and the detection of the start point of rebars to perform the automatic calibration of the rollers through the tracking of the automated facilities of unmanned production. It must meet the requirement to accurately track the position from the start point to the end point. In order to improve the tracking performance, it is important to set the accurate start point. However, the probability of tracking errors is high depending on environments such as illumination and dust through the conventional time-based detection method. In this paper, we propose a starting point detection method using the average brightness change of high speed IR camera to reduce the errors according to the environments, As a result, its performance is improved by more than 15%.

• Smart Media Journal
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• v.9 no.4
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• pp.109-117
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• 2020

Recently, with the advent of the 4th industrial revolution, digital twins are emerging as an important technology that connects and integrates physical systems and cyber systems. In this article, we analyzed the major requirements of digital twins required for the construction of digital twins of power equipments in the energy field, focusing on the industry 4.0-based Asset Administration Shell(AAS). However, since not so many studies have been conducted yet on a common platform or demonstration model for implementing digital twins both domestically and internationally, digital twin requirements are analyzed with the consideration of digital twinning of power equipment in the energy field. Also, we suggested necessary procedures and specific functions of AAS to establish a smart energy digital twin in the future by analyzing the core requirements necessary for the construction and designing the AAS design for specific power equipment.

• Design & Manufacturing
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• v.16 no.1
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• pp.55-61
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• 2022

As meters become digital and smart, energy data such as electricity, gas, heat, and water can be accurately and efficiently measured with a smart meter, providing consumers with data on energy used, so that real-time demand response and energy management services can be utilized. Although it is developing from a simple metering system to a smart metering industry to create a high value-added industry fused with ICT, illegal counterfeiting of electronic meters is causing problems in intelligent crimes such as manipulation and hacking of SW. The meter not only allows forgery of the meter data through arbitrary manipulation of the SW, but also leaves a fatal error in the metering performance, so that the OIML requires the validation of the SW from the authorized institution. In order to solve this problem, a quantitative confirmation device was developed in order to eradicate the act of cheating the fuel oil quantity through encoder pulse operation and program modulation, etc. In order to prevent the act of deceiving the lubricator, a device capable of checking pulse forgery was developed, manufactured, and verified. In addition, the performance of the device was verified by conducting an experiment on the meter being used in the actual field. It is judged that the developed quantitative confirmation device can be applied to other flow meters other than lubricators, and in this case, accurate measurement can be induced.