Today, a new era of manufacturing innovation is introduced as Smart Manufacturing Systems (SMS) or Industry 4.0. Many studies have discussed the different characteristics and technologies associated with SMS, however, little attention has been devoted to study the development process when establishing new SMS. The study's objective is to propose a development framework that increases the adoption and awareness of Industry 4.0 among manufacturers and aids decision-makers in designing better SMS capabilities. The framework consists of three phases, iterative process of application modelling; evaluation to ensure optimal configuration and adoption; and finally implementation. The proposed framework is hoped to assist the industries' management in planning for the adoption of technology, in establishing SMS or assessing the need in existing ones. Indirectly, more industry will gain the benefits as a support for their initiatives to transform into Industry 4.0.
Numerous studies have been conducted to reveal the importance of Smart Manufacturing Systems (SMS) or Industry 4.0, but very few studies have been made to answer the question on "how to establish a new SMS" taking into account the required efficiency, reliability, cost-effectiveness, and sustainability that requires pre-implementation planning and assessment. Besides, the discussion on the challenges of SMS adoption is very limited in the literature studies. In particular, the recent configuration models proposed by literature overlooked the pivotal role of robots in any SMS project. Therefore, a clear and concise development framework is needed to provide a better understanding of the development process of a new SMS, which leads to higher adoption of this new technology. To do so, the main objective of this study is to propose a development methodology framework that enables stakeholders to build better SMS capabilities while enhancing the adoption awareness of industry 4.0 among manufacturers. The framework consists of four phases, system and robots' configuration, smart system components, smart system integration, and evaluation and selection. This study supports the realization of Industry 4.0, particularly in Malaysia. Currently, Malaysia is behind other ASEAN countries like Indonesia and Singapore as the highest growth country in the digital economy. The proposed methodology is expected to support different industries in the adoption of the technology in building a new SMS or evaluating an existing one.
Several research works have addressed the different aspects and technologies associated with Smart Manufacturing Systems (SMS); however, the evaluation challenges while establishing a new SMS that requires pre-implementation planning and assessment have given little attention. To overcome this limitation, this paper formulates an evaluation framework by identifying apparent evaluation factors to measure the effectiveness of a particular SMS configuration before implementation. Three factors from the literature studies have been used as inputs to control the final output of the configuration modal. Compositions were manipulated based on how factors affected the manufacturing cost justification in multiple setups. Different configurations were analyzed based on the trained Fuzzy Logic model by configurations and based on the trained Fuzzy Logic model using MATLAB’s Fuzzy Logic Designer tool. Results obtained from the evaluation performed by various configuration experiments were later presented to actual field engineers from the manufacturing industry to evaluate the satisfaction level of the evaluation framework. The result showed that this proposed configuration model has a satisfactory rate of 83.7%, as this was achieved by significant feedback from field engineers. This study has significantly facilitated the identification of influential factors and the measured relationship of the factors in the formulated configurations, enabling the best configuration approach to be identified. Therefore, it can be concluded that a visualized and measured configuration system can influence decision-making in the manufacturing industry, thus allowing manufacturers to stay competitive by making well-versed decisions proactively. Exclusively, this research has staged a framework for the industry to follow suit and adapt for future research work related to the SMS field.
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