Machine failures during production period may impose thousands to millions of dollars to a manufacturing system. In this paper, the impact of machine failures on production lines in a closed-loop supply chain systems is examined. For this purpose, a new method is proposed for scheduling manufacturing workshops in a supply chain systems. The aim is to determine the best production plans in a manufacturing system by considering alternative preventive maintenance programs while machine failures can affect system performance. To solve the model, a hybrid Ant Colony and Simulated Annealing algorithms is developed and the results are compared with branch and bound method. Our findings show that the condition of emerging machine failure affects machines’ capacity which yields to lost sale. The impacts of using appropriate preventive maintenance on reducing lost sale is also examined. The results indicate that the proposed method can significantly reduce the level of sale variation in supply chain systems.

Since scheduling literature has a wide range of uncertainties, it is crucial to take these into account when solving performance measure problems. Otherwise, the performance may severely be affected in a negative way. In this paper, an algorithm is proposed to minimize the total completion time (TCT) of a two-machine no-wait flowshop with uncertain setup times within lower and upper bounds. The results are compared to the best existing algorithm in scheduling literature: the programming language Python is used to generate random samples with respect to various distributions, and the TCT of the proposed algorithm is compared to that of the best existing one. Results reveal that the proposed one significantly outperforms the best one given in literature for all considered distributions. Specifically, the average percentage improvement of the proposed algorithm over the best existing one is over 90%. A test of hypothesis is conducted to further confirm the results.

A new method of amidation of Carboxy Multi Walled Carbon Nanotubes (MWCNT-COOH) with diamine monomer such as ethylene diamine (EDA) and O-Phenylenediamine (OPDA) was applied by using a solution blending technique. The structure and properties of these composites have been investigated by FTIR, SEM, TEM, XRD, UV, DSC and TGA. The formation of Poly [MWCNT/ Amide] composites was confirmed and the DC electrical conductivity of poly-composites was in the range 4.5×10^-6-5.3×10^-6 S/cm due to the interaction between the nanotubes.

This study sought to investigate the impacts of big data, artificial intelligence (AI), and business intelligence (BI) on Firms' e-learning and business performance at Jordanian telecommunications industry. After the samples were checked, a total of 269 were collected. All of the information gathered throughout the investigation was analyzed using the PLS software. The results show a network of interconnections can improve both e-learning and corporate effectiveness. This research concluded that the integration of big data, AI, and BI has a positive impact on e-learning infrastructure development and organizational efficiency. The findings indicate that big data has a positive and direct impact on business performance, including Big Data External and Internal, Innovative Usage, Indexing, and Sources Accuracy. In addition, Artificial intelligence positively affects business performance, including Data Accuracy, Data Transparency, Data Speed, and Creative Thinking and Learning. Moreover, business intelligence has a direct and positive impact on business performance, including Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. In addition, the findings indicate that e-learning which represents system quality, information quality, and self-efficacy has a positive relationship on enhancing business performance. Interestingly, the present findings are inconsistent with those of previous studies showing the variables of interest which have no effect on e-learning and business performance. Taken together, the findings of this study suggest that firms should begin to apply processes related with applying e-learning and developing business performance. The novelty of the present study lies in highlighting the key dimensions of big data, artificial intelligence, and business intelligence when it comes to enhancing e-learning and business performance at Jordanian telecommunications industry.

Risk monitoring and control is often poorly implemented in construction projects because of a failure to monitor and manage identified risks. Construction companies experience significant losses due to project managers' lack of project risk monitoring and control in construction projects. Most studies have concentrated on risk identification, risk assessment, and risk analysis processes while neglecting crucial risk management processes of risk control, risk monitoring, and risk response. The lack of research on these three crucial processes highlights a gap in the literature concerning how these processes can increase the delivery of successful projects. The purpose of this study was to examine whether the utilization of project risk monitoring and control practices was related to project success in construction projects in the United States. An electronic survey instrument was used to collect data from a sample of 50 construction project managers in the Dallas-Fort Worth area in the state of Texas, in the United States. Spearman rho correlation analysis was used to examine the relationship between project risk monitoring and control practices and project success. The results of this study indicated that all project risk monitoring and control practices, including risk reassessment, risk audits, contingency reserves analysis, and risk status meetings, were significantly and positively related to project success in construction projects. One of the recommendations presented in this study was that future research should conduct the same study in developing countries to see if the study’s findings remain the same and generalizable. The study concluded that construction organizations should regularly consider the importance and usage of project risk monitoring and control practices and apply them to improve the success rate of a project.

A simple multi-attribute decision-making method based on ranking of alternatives and attributes is proposed in this paper. The method ranks the alternatives with respect to each of the attributes based on the corresponding performance measures. Similarly, the ranks are assigned to the attributes based on their importance as perceived by the decision maker. The ranks assigned to the alternatives with respect to each of the attributes and the ranks assigned to the attributes are converted to appropriate weights and the final composite scores of the alternatives are computed using these weights. An interesting feature of the proposed method is that the qualitative attributes (i.e. the attributes expressed in linguistic terms) need not require the use of fuzzy logic. The proposed method is very simple and useful in situations of limited time availability, presence of qualitative attributes, imprecise/incomplete/partial data, and decision maker’s limited attention and capability to process the information. The proposed method is proved easier and better compared to the other widely used decision-making methods. The proposed method will be tested further on more realistic problems of the industrial environment and the results will be reported soon.

This study explores and analyzes students’ difficulties in learning an ERP system to help design more appropriate teaching methods and materials. Global enterprises have widely used ERP systems to manage their operations effectively and efficiently. Hence, many business schools have offered courses on ERP systems to sharpen ERP skills for their students. To help design more appropriate teaching methods and materials for ERP learning, one must know students’ difficulties in understanding. This study analyzes students’ difficulties in learning the Oracle E-Business Suite ERP system through interviews and qualitative analysis. As a result, this study identifies five categories of problems in the various areas of the Revised Bloom’s Taxonomy. Their relevant educational objectives can guide the redesign of ERP teaching methods and materials. One of the difficulties belongs to the area of Remember Factual Knowledge. The rest of them are in Understand, Remember, Apply, and Analysis of Procedural Knowledge. Lastly, this study provides some implications for teaching ERP.

Digital startups are growing fast around the world, but new digital startups that are less than three years old or did not materialize when it came to funding while the business model is quite promising. Various literature shows that digital transformational leadership can encourage knowledge sharing and an organization's absorptive capacity to improve performance. This research investigates whether digital transformational leadership and empowering knowledge-based interaction, as well as the power of absorptive capacity, will be able to maintain digital startup sustainability through the improvement of performance. 144 digital startups that are established in a limited corporation and registered in the Baparekraf (Indonesia Tourism and Creative Economy Agencies) were used as the purposive sampling. Data collection was done through online questionnaires from each startup leader and processed with SmartPLS 3.0. The results of the research findings showed that the higher the degree of digital transformational leadership, the higher the degree of the digital performance of startups that include traction and financial performance, and also increased empowering knowledge-based interaction in the form of encouraging goal-oriented participative involvement, intra-team knowledge exchange, and continuous interactive engagement. Empowering knowledge-based interaction develops an absorption capacity process to produce the performance of digital startup organizations.

This study proceeds from a central interest in the importance of assessing the IS-Support provided to key-user groups. This study conceptualized a new multidimensional IS-Support construct with four dimensions: training, documentation, assistance and authorisation, which form the overarching construct – IS-Support. We argue that a holistic measure for assessing IS-Support should consist of dimensions, and measures, that together assess the support provided to IS key-user groups. The proposed IS-Support construct is defined as the support that IS key-user groups receive to increase their capabilities in utilizing information systems within the organization. Using a partial least square structural equation modeling (PLS-SEM) approach on data collected from 221 participants, this study rigorously hypothesized and validated the IS-Support model. Implications for research and practitioners as well as insights for future research is discussed.

This study aims at investigating the factors that determine the continuous intention to use smartwatches, one of the most prevalent types of wearable devices. The study extends the expectation confirmation model (ECM) by incorporating other variables (i.e., construct) which capture the unique context of smartwatches continuous usage, namely healthology, perceived aesthetics, habit, and social influence. Hypotheses were assessed using partial least square structural equation modeling (PLS-SEM) approach on data collected from 287 actual smartwatch users. The results reveal that performance expectancy, satisfaction, healthtology, perceived aesthetics and habit significantly influence the continuous usage of smartwatches, while social influence is non-significant. The research model of this study explains 65.7% of the variance in the continuous usage intention of smartwatches. The insights provided by this study suggest fruitful opportunities for future research. They can also help smartwatches companies, developers and marketers with strategies and directions for further development and growth by ensuring users’ continuous usage of smartwatches.