Mahsa Fekrisari and Jussi Kantola
This paper aims to identify potential barriers to Industry 4.0 adoption for manufacturers and examine the changes that must be made to production processes to implement Industry…
Abstract
Purpose
This paper aims to identify potential barriers to Industry 4.0 adoption for manufacturers and examine the changes that must be made to production processes to implement Industry 4.0 successfully. It aims to develop technology by assisting with the successful implementation of Industry 4.0 in the manufacturing process by using smart system techniques.
Design/methodology/approach
Multiple case studies are used in this paper by using the smart system and Matlab, and semi-structured interviews are used to collect qualitative data.
Findings
Standardization, management support, skills, and costs have been cited as challenges for most businesses. Most businesses struggle with data interoperability. Complexity, information security, scalability, and network externalities provide challenges for some businesses. Environmental concerns are less likely to affect businesses with higher degrees of maturity. Additionally, it enables the Technical Director’s expertise to participate in the measurement using ambiguous input and output using language phrases. The outcomes of the numerous tests conducted on the approaches are extensively studied in the provided method.
Originality/value
In this research, a multiple-case study aims to carry out a thorough investigation of the issue in its actual setting.
Details
Keywords
Mahsa Fekri Sari and Soroush Avakh Darestani
The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production…
Abstract
Purpose
The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production process. In the existing method, measuring OEE is based on three main elements consisting availability, performance and quality. The purpose of this paper is to evaluate the recognized metrics of production: OEE and overall line effectiveness (OLE) by using smart systems techniques.
Design/methodology/approach
In this paper, to improve the calculative methods and productivity with three methods: measuring OEE using Mamdani fuzzy inference systems (FIS), measuring OEE using Sugeno FIS, and measuring OLE using FIS and artificial neural networks (ANNs) are proposed.
Findings
The proposed methodologies aim to decrease some weaknesses of OEE and OLE methods by exploiting intelligent system techniques, such as FIS and ANNs. In particular, this research will solve the following issues that occur in manual and automatic data gathering. This technique is an effective way of measuring OEE and OLE with regard to different weights of losses as well as difference in the weight of the machines. In addition, it allows the operator’s knowledge to take a part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.
Originality/value
In relation to other methodologies, it allows the operator’s knowledge to take part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.