Due to significant requirement of energy, water, material, and other resources, the manufacturing industries significantly impact environmental, economic, and social dimensions of sustainability (triple bottom-line). In response, today's research is focused on finding solution towards sustainable manufacturing. In this regard, sustainability assessment is an essential strategy. In the past, a variety of tools was developed to evaluate the environmental dimension. Because of this fact, previous review studies were grounded mostly on tools for green manufacturing. Unlike previous review articles, this study was aimed to review and analyze the emerging sustainability assessment methodologies (published from 2010 to 2020) for manufacturing while considering the triple bottom-line concept of sustainability. In this way, the paper presents a decade review on this topic, starting from 2010 as the guidelines for the social dimension became available in 2009. This paper has analyzed various methods and explored recent progress patterns. First, this study critically reviewed the methods and then analyzed their different integrating tools, sustainability dimensions, nature of indicators, difficulty levels, assessment boundaries, etc. The review showed that life cycle assessment and analytic hierarchy process-based approaches were most commonly used as integrating tools. Comparatively, still, environmental dimension was more commonly considered than economic and social dimensions by most of the reviewed methods. From indicators' viewpoint, most of the studied tools were based on limited number of indicators, having no relative weights and validation from the experts. To overcome these challenges, future research directions were outlined to make these methods more inclusive and reliable. Along with putting more focus on economic and social dimensions, there is a need to employ weighted, validated, and applicable indicators in sustainability assessment methods for manufacturing.
This study explores the determinants of the export performance of Indonesia's low-, medium-, and high-technology manufacturing industries by focusing on the role of raw-material imports and technical efficiency. Micro firm-level data from 2010-2015 were utilized for the analysis in this study. The stochastic frontier analysis was employed to measure technical inefficiency and to determine its effect on export performance. Our findings indicate that in all categories of industry technical efficiency, raw materials imports, foreign direct investment (FDI), location, firm size, labour productivity, and concentration of industries were significant determinants of export performance. While high efficiency increases exports in low- and medium-technology firms, exports decrease in firms with high efficiency accompanied by high imports, FDI, size, and labour productivity. Furthermore, in high-technology industries, efficiency reduces exports and again increases them when mediated by a concentration of industries and location. The empirical strategy also supports the positive effect of imports on export performance in both industries, which also aligns with decreased exports in firms with high imports accompanied by high FDI, efficiency, labour productivity, the concentration of industries, and size. To this end, the study has implications for low-, medium-, and high-technology manufacturing that are mainly concerned with increasing exports.
A monitoring procedure was introduced for process variability in a multivariate setting based on individual observations which was a combination of (i) robust high breakdown point approach in the set-up stage to determine the reference sample and (ii) the use of Wilks chart in the mass production stage. This setting is what the Malaysian manufacturing industry is currently lacking in, especially when a robust approach must be used. The advantage of this procedure was revealed by using the case of a female shrouded connector production process in a Malaysian industry. Moreover, this procedure could also be used in any process quality monitoring and for any industry. A recommendation for quality practitioners was also addressed.
Introduction. Demographic information is one of the key parameters that organizations utilize to modify their practices in order to respond to the existing risk within work environments. The present article aims to assess the level of safety climate factors as well as to evaluate the influence of personal factors on safety climate in two different-sized industries. Methods. A total of 216 employees in two large and three small and medium-sized chemical manufacturing industries responded to a questionnaire. Descriptive statistics were used to measure the safety climate level; a two-independent-sample Mann-Whitney U test and a Kruskal-Wallis test were run to compare the difference in safety climate scores among different demographic variables. Results. The lower level of safety climate in small and medium-sized industries revealed lower understanding and performance of management and non-management with regard to safety climate compared to the large industries. Additionally, significant mean differences on some safety climate factors among demographic variables were detected in both sizes of industries, emphasizing the important role of the employees' demographic variables on the plants' safety climate. Conclusion. Improving organization-level and group-level safety climates is recommended to improve employees' level of safety climate and control their personal factors.
This study aims to analyze the development trend of the manufacturing industry transformation and upgrading in the Guangdong-Hong Kong-Macao Greater Bay Area (2008-2018). On the basis of synergetics, the order parameter method of factor analysis is used to study these factors. The results show that: (1) There are five slow variable factors, such as intelligent manufacturing industry, technological innovation, scale agglomeration, market demand, and fixed asset investment, which are important power sources of the transformation and upgrading of the manufacturing industry in Greater Bay Area. The development of these factors is relatively mature, and they cooperate with each other. (2) Similar to a fast variable of manufacturing development ecology, green development is an important coordinating factor in removing bottlenecks. Finally, suggestions for the development of the transformation and upgrading of the manufacturing industry are put forward.
The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts' uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.
Preventive maintenance (PM) planning becomes a crucial issue in the real world of the manufacturing process. It is important in the manufacturing industry to maintain the optimum level of production and minimize its investments. Thus, this paper focuses on multiple jobs with a single production line by considering stochastic machine breakdown time. The aim of this paper is to propose a good integration of production and PM schedule that will minimize total completion time. In this study, a hybrid method, which is a genetic algorithm (GA), is used with the Monte Carlo simulation (MCS) technique to deal with the uncertain behavior of machine breakdown time. A deterministic model is adopted and tested under different levels of complexity. Its performance is evaluated based on the value of average completion time. The result clearly shows that the proposed integrated production with PM schedule can reduce the average completion time by 11.68% compared to the production scheduling with machine breakdown time.
The Industrial Revolution 4.0 began with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories. A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating production with process management, service distribution, and customized product requirement. A big challenge to the smart factory is to ensure that its network security can counteract with any cyber attacks such as botnet and Distributed Denial of Service, They are recognized to cause serious interruption in production, and consequently economic losses for company producers. Among many security solutions, botnet detection using honeypot has shown to be effective in some investigation studies. It is a method of detecting botnet attackers by intentionally creating a resource within the network with the purpose of closely monitoring and acquiring botnet attacking behaviors. For the first time, a proposed model of botnet detection was experimented by combing honeypot with machine learning to classify botnet attacks. A mimicking smart factory environment was created on IoT device hardware configuration. Experimental results showed that the model performance gave a high accuracy of above 96%, with very fast time taken of just 0.1 ms and false positive rate at 0.24127 using random forest algorithm with Weka machine learning program. Hence, the honeypot combined machine learning model in this study was proved to be highly feasible to apply in the security network of smart factory to detect botnet attacks.
This study analyzes the determinants of workplace injuries across 44 four-digit manufacturing industries in Malaysia from 1993 to 2008 through the business cycle and structural approaches. The results of fixed-effects estimations revealed that workplace injuries in Malaysian manufacturing sector were negatively influenced by firm size and positively influenced by business cycle. Consistent with the findings of previous studies in other countries, the empirical evidence of this study supports the pro-cyclical behavior of injury rates in manufacturing industries towards business cycle. The analysis demonstrates that both structural and cyclical variation effects are important determinants of workplace injuries in Malaysia.
Cement is a vital material used in the construction of concrete buildings. World annual cement demand is increasing rapidly along with the improvement in infrastructure development. However, cement manufacturing industries are facing challenges in reducing the environmental impacts of cement production. To resolve this issue, a suitable methodology is crucial to ensure the selected processes are effective and efficient and at the same time environmentally friendly. Different technologies and equipment have potential to produce variations in operational effectiveness, environmental impacts, and manufacturing costs in cement manufacturing industries. Therefore, this work aims to present the sustainability assessment of cement plants by taking into consideration of environmental, social, and economic impacts. Three cement production plants located in Western Indonesian are used as case studies where social impact and environmental impact are evaluated via life cycle assessment (LCA) model. This model is integrated with analytic hierarchy process (AHP), a multi-criteria decision analysis tool in selecting the most sustainable cement manufacturing plant.
The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review's main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel's feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.
Malaysia’s palm oil industry is growing in complexity and successively to succeed on the global level by accounts for about 36% of the word production of palm oil [1]. But, Occupational Health and Safety (OHS) issues are still problematic areas that need to be addressed by all parties concerned in this industry. In the olden days, unlike construction or manufacturing industry, palm oil industry was green in OHS management system. However, due to stringent in the legislative enforcement in the past few years, it has lead some of the plantation companies to develop OHS management system, which are based on Occupational Health and Safety Assessment Series (OHSAS), towards corporate sustainability. Sustainability is not about paying lip-service to the latest corporate buzzword; neither is it about superficially meeting minimum requirements for the sake of compliance. Rather, sustainability is a core value that lies at the heart of the companies’ business conduct. In practical terms, this means strive to operate with due consideration for the interest of all stakeholders by making the health and safety of all workers a priority. This paper describes the certification of OHSAS 18001 and MS 1722 in Genting Plantations Berhad (GENP) prove the commitment to sustainability by forming guiding principle on safety management. Further, this paper also demonstrates that the implementation of safety management can help to reduce the accident rate, especially fatal accident.
Aluminium titanate (AT) (Al2TiO5) is a promising engineering material because of its low thermal expansion coefficient, excellent thermal shock resistance, good refractoriness and non-wetting with most metals. Functionally graded material (FGM) is generally a particulate composite with continuously varying volume fractions. FGMs are alternative materials for dental implants, building materials and ballistic protection. It has been of great interest to future engines, internal combustion engines, metal cutting and other high temperature engineering application. There has been a demand for an adequate disc brake that requires less maintenance in the automotive manufacturing industry. FGM, the next evolution of layered structure, consists of graded compositions that are dispersed across the ceramic which produces a gradual improvement in the properties across the ceramic at a steady pace.
According to annual reports from the Social Security Organization (SOCSO), between years 2009 and 2011,
metal industry has the highest reported number of accidents compared to the other manufacturing industry in small
and medium enterprises (SMEs). Therefore, the aim of this study was to investigate the actual causes of problems that
lead to the accidents involving metal industries within SMEs. In this study, a checklist through site visits was used to
collect the data. The overall results revealed that the main causes of accidents are; organization failure, human factor,
machine failure and surrounding environments.
Background: Technological diversity management in the manufacturing of advanced medical devices is
essential. The manufacturing industries of medical devices should act in accordance with the technical
guidelines and regulations in order to ensure best practices with the use of devices in hospitals
Aim: To explore safety hazards, cost implications, and social and ethical standards to be considered during
the manufacturing of advanced medical devices
Subject and Methods: Aqualitative descriptive study was used. There was no targeted sample in the current
study whereby secondary data were used to explore the research topic. Secondary sources were obtained
from databases including EBSCOHOST, PubMed, ProQuest, Science Direct, and Google Scholar. Peerreviewed
articles, journals, books, conference proceedings, and other web publications were used to gather
relevant data.
Results: The current study indicated that the technological diversity management of advanced medical
devices is associated with safety hazards like security threats, integrity problems, and medical errors. The
study also showed that high cost of standardizations, supply, and purchase of advanced medical devices is a
huge burden faced by the manufacturers andusers. The study showed that the regulation of the medical
devices, certification, and post-market surveillanceare essential social and ethical considerations during the
manufacturing process of the new medical devices.
Conclusion: The current study explored the technological diversity of advanced medical devices. It is
evident in the current study that technology diversity of medical devices is associated with safety hazards
and cost implications. The study disclosed that taking into account social and ethical issues aid in
manufacturing safe and high quality medical devices.
Solid-state recycling, which involves the direct recycling of scrap metal into bulk material using severe plastic deformation, has emerged as a potential alternative to the conventional remelting and recycling techniques. Hot press forging has been identified as a sustainable direct recycling technique that has fewer steps and maintains excellent material performance. An experimental investigation was conducted to explore the hardness and density of a recycled aluminum-based metal matrix composite by varying operating temperature and holding time. A mixture of recycled aluminum, AA6061, and aluminum oxide were simultaneously heated to 430, 480, and 530 °C and forged for 60, 90, and 120 min. We found a positive increase in microhardness and density for all composites. The hardness increased approximately 33.85%, while density improved by about 15.25% whenever the temperature or the holding time were increased. Based on qualitative analysis, the composite endures substantial plastic deformation due to the presence of hardness properties due to the aluminum oxide embedded in the aluminum matrix. These increases were significantly affected by the operating temperature; the holding time also had a subordinate role in enhancing the metal matrix composite properties. Furthermore, in an effort to curb the shortage of primary resources, this study reviewed the promising performance of secondary resources produced by using recycled aluminum and aluminum oxide as the base matrix and reinforcement constituent, respectively. This study is an outline for machining practitioners and the manufacturing industry to help increase industry sustainability with the aim of preserving the Earth for our community in the future.
Traditional robotic work cell design and programming are considered inefficient and outdated in current industrial and market demands. In this research, virtual reality (VR) technology is used to improve human-robot interface, whereby complicated commands or programming knowledge is not required. The proposed solution, known as VR-based Programming of a Robotic Work Cell (VR-Rocell), consists of two sub-programmes, which are VR-Robotic Work Cell Layout (VR-RoWL) and VR-based Robot Teaching System (VR-RoT). VR-RoWL is developed to assign the layout design for an industrial robotic work cell, whereby VR-RoT is developed to overcome safety issues and lack of trained personnel in robot programming. Simple and user-friendly interfaces are designed for inexperienced users to generate robot commands without damaging the robot or interrupting the production line. The user is able to attempt numerous times to attain an optimum solution. A case study is conducted in the Robotics Laboratory to assemble an electronics casing and it is found that the output models are compatible with commercial software without loss of information. Furthermore, the generated KUKA commands are workable when loaded into a commercial simulator. The operation of the actual robotic work cell shows that the errors may be due to the dynamics of the KUKA robot rather than the accuracy of the generated programme. Therefore, it is concluded that the virtual reality based solution approach can be implemented in an industrial robotic work cell.
The research aims to address the physically loading task and quality and productivity problems in the brazing of coils of air-handler units. Eight operators participated in two intervention studies conducted in a factory in Malaysia to compare the status quo brazing with (1) the use of a new twin-brazing torch that replaced the single-brazing gun and (2) brazing in a sitting position. The outcome measures are related to quality, productivity, monetary costs, body postures and symptoms. After baseline, Interventions I and II were applied for 3 months respectively. The results show a 58.9% quality improvement, 140% productivity increase and 113 times ROI. There was also a reduction in poor work postures e.g. in the raising of the arms and shoulders; bending, twisting and extending of the neck; and bending of left and right wrists, and the back. This research can be replicated in other factories that share similar processes.
Interactive learning is a pedagogical model that encourages students to be part of the lesson instead of passive observers, quietly sitting at a desk taking notes or memorizing information. Students interact with the material, each other and the teacher in an active way. The new emerging technologies that can overcome some of the potential difficulties in this area includes computer graphics, augmented reality, computational dynamics, and virtual worlds. Therefore, the manufacturing industry relies on new design concepts and methods undertake the challenges in integrating technologies to expedite the march towards industrial revolution 4.0.This paper reviews and investigates the current context of the use of interactive learning such as Virtual Reality(VR),Augmented Reality(AR),Computer aided design and manufacturing(CADCAM), computer graphics, computational dynamics and new emerging technologies that effect on students and lectures in learning and teaching environments for Manufacturing Engineering. Interactive learning is part of the factors that could influence the self-learning and regulations environments.
Previous research has emphasized the need to further investigate the impact of ISO 9001 on company performance in the manufacturing sector of developing countries. Indonesia is one of those developing countries where the implementation of ISO 9001 is yet to be adequately researched. The Indonesian automotive manufacturing industry is still unable to compete with Malaysia and Thailand even though many companies have implemented ISO 9001. This study aimed to examine the relationship between ISO 9001 and operational (productivity, customer satisfaction, and product quality) and business (sales growth, profit rate, and market share) performance of Indonesian automotive component manufacturing industries. It also aimed to identify major obstacles in the effective implementation of ISO 9001. Multiple linear regression analyses about operational and business performance were employed for this purpose. The sample size comprised 50 automotive component manufacturing industries located in the Jakarta, Bogor, Tangerang, and Bekasi region of Indonesia. The study demonstrates that the implementation of the ISO 9001:2015 quality management system has a significant positive impact on the operational performance as well as the business performance. Additionally, the operational performance has a significant positive impact on the business performance. This study also reveals the major obstacles in the effective implementation of ISO 9001 in the manufacturing industry, which include a lack of qualified personnel, inadequate training, employee resistance, and lack of commitment among top-level management executives. It offers clear implications for managers who focus on elements that will enhance the effectiveness of ISO 9001 implementation by choosing the correct strategies, allocating sufficient resources, and improving their firm's performance. The novelty of this study lies in filling the existing research gap, which involves a detailed examination of the relationship between the implementation of ISO 9001 and the company's performance, particularly in manufacturing industries of developing countries.