The Smart Factory and Its Risks

Herrmann, Frank

doi:10.3390/systems6040038

Cited by 44 publications

(22 citation statements)

References 18 publications

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“…In conclusion, the level quality of IT employees has an important role to develop the quality inspection and inventory management. It is also implied that machine and automation system were manipulated by highly-skilled IT employees [6]. The ICT data collecting and sharing in factory and capacity of analyzing with advance techniques are enabled the advance application of technologies.…”

Section: Discussionmentioning

confidence: 99%

“…Smart factory is a core of Industrial 4.0 revolution, which the machines and systems integrate by technology such as ICT, IoT, and cloud computing to enhance the automation steps in manufacturing processes [6]. In this study, smart factory is determined facilitating the automated, flexible and efficient production of the products and services [7].…”

Section: Literature Reviewmentioning

confidence: 99%

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The Impact of IT Knowledge Capability and Big Data and Analytics on Firm’s Industry 4.0 Capability

Chumnumporn

Jeenanunta

Komolavanij³

et al. 2020

Innovation Aviation &Amp;amp; Aerospace Industry - International Conference 2020

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Smart factory is a fully-integrated of firm’s facilities (i.e., sensors, smart machines, and robots) and information system architecture (i.e., IoT, ICT, and cloud computing) to enable high degree of automation in manufacturing processes. IT knowledge capability is the IT knowledge organization that how employees understand IT knowledge in different dimensions, i.e., general management, product design, production planning, data analysis, information security, and automation system. Since the system of smart factory depends on the massive of data collecting (big data) and the firm’s advance analyzing approach (analytics). The big data in manufacturing include the data from production planning, quality control, procurement, inventory control, human resource management (HRM), and delivery. The purpose of this study is to examine the role of IT knowledge capability and big data and analytics on the degree of smart factory. Survey data from 141 Thai manufacturing firms from the list of the ministry of industry and industrial zones were collected during March–April 2019. The multiple regression result shows that both IT knowledge capability and big data and analytics have a positive impact on the degree of smart factory. In addition, we use a firm’s age and firm’s size (based on the number of employees and total asset) as control variables. The results show that firm’s size have a positive effect on hypothesis model.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

The Impact of IT Knowledge Capability and Big Data and Analytics on Firm’s Industry 4.0 Capability

Chumnumporn

Jeenanunta

Komolavanij³

et al. 2020

Innovation Aviation &Amp;amp; Aerospace Industry - International Conference 2020

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“…In addition, the expected number of visits by an item to each process can be calculated in a similar manner to (3) and (15). Let E up,trad and E down,trad denote the expected visit counts for an upstream process and a downstream process under the traditional rework policy, respectively.…”

Section: Rework Policy Decision Procedures Based On Total Cost Ratiomentioning

confidence: 99%

“…Such knowledge and patterns can enable the manufacturing processes to operate in a more intelligent way, and the intelligent manufacturing process is an important aspect of the smart factory [14]. Nevertheless, many companies still rely on manufacturing processes that operate in traditional ways, even though modern ICT infrastructures for collecting data from their shopfloors are established [15]. For example, the data gathered by IoT devices are sometimes used only to calculate some simple statistics, such as averages or counts [16].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Rework Process Management System under Smart Factory Environment

Kim

2020

Sustainability

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Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is based on intelligent rework policy, which provides a preventive rework procedure for items with latent defects. Such items can be detected before quality tests by applying conventional classification techniques. Moreover, training sets for the classification algorithms can be collected by using modern information and communications technology (ICT) infrastructures. Items with latent defects are not allowed to proceed to the following processes under intelligent rework policy. Instead, they are returned to the preceding processes for rework in order to avoid unnecessary losses on the shopfloor. Consequently, the proposed system helps to achieve a sustainable manufacturing system. Nevertheless, misclassification by the classification model can degrade the performance of intelligent rework policy. Therefore, the i-RPM system is designed to compare rework policies based on classification accuracy and choose the best one of them. For illustration, we applied the i-RPM system to the rework procedure of a steel manufacturer located in Busan, South Korea, and our experiment results revealed that the cost reduction effect of the intelligent rework policy is affected by several input parameters.

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“…Para alcançar este objetivo deve-se chegar a um novo nível de maturidade fabril, por meio de métodos como auto-otimização, autoconfiguração, autodiagnóstico, conhecimento e inteligência dos colaboradores. Estas características constituem o conceito de Smart Factory [7], [8], [9].…”

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