For over three decades, production firms have extensively espoused lean manufacturing (LM) approach for constantly enhancing their operations. Of late, due to the fusion of physical and digital systems within the Industry 4.0 evolution, production systems can upgrade by applying both notions and lift operational excellence to a new high. This is primarily the reason why digital business transformation has gained significance. Moreover, Industry 4.0 that is led by data assures huge strides in output. The sheer volume of pertinent data from the production systems employing servers, sensors, and cloud computing have made the data exchange procedure more gigantic and intricate. However, conventional systems do not extensively support LM in the context of Industry 4.0. Moreover, the previous studies by researchers in the same field, shown that there was no standard platform to manage the new technologies in LM. This study presents a discussion on the interrelated framework about the way Industry 4.0 has transformed production into an industry focusing on connective mechanisms and platforms which utilize data analytics from the real world. The theoretical framework proposed in this paper integrates LM, data analytics, and Internet of Things (IoT) to enhance decision support systems in process improvement. Data analytics in simulation is employed through Internet of Things to improve bottleneck problems by maintaining the principle of LM. The main information flow route within LM decision support system is demonstrated in detail to show how the decision-making process is done. The decision support mechanism has undergone up-gradation and the suggested framework has shown that the assimilated components could function together to augment the output.

Today, Industry 4.0 concerns a rapid advancement in manufacturing technologies which help industries increase their productivity. To adopt Industry 4.0 concept is still visionary by certain lean manufacturers when the communication technologies interfaces are not fully equipped at the production system. Most of the facilities towards digitalization are also expensive and require many specialists in different fields to manage the technologies. Therefore, most data analytics (DA) engineering is cannot be employed broadly for process enhancement by Industry 4.0 environment. However, starting with Internet of Things (IOT) concepts, Andon system with simulation was enhanced to support decision making in lean manufacturing. The aims of this research paper is to develop a decision support system (DSS) framework which intersects between Andon and simulation through IOT concept. A better decision-making information flow are demonstrated in detail. To illustrate the applicability of the DSS, it has been implemented in lean manufacturing for automotive part assembly. The results indicate that the DSS can easily be adopted in digital factories to support in planned and operational activities.

Nowadays, the digitalization of the production-based industries is driven by emerging technologies tools. The concept of lean manufacturing (LM) towards Industry 4.0 was developed where data analytics of engineering processes are analyzed and connected to reduce wastes. Many authors discuss about the benefits of extending data analytics as a method to support decision. However, the absence of comprehensive framework on how to embed LM and IT tools has existed as a new challenge. The aim of the research is to initiate a framework of model driven decision support system (MD-DSS) where data simulation and communication technologies are accompanied for manufacturing process improvement. In this research, Overall Equipment Effectiveness (OEE) data was captured through internet networking system and simulate to predict the improvement output. The main information flow route within MD-DSS are demonstrated in detail to show how decision-making process. To illustrate the applicability of the MD-DSS, it has been applied at food industry in Malaysia. The results show that the MD-DSS can easily be adopted in factories facilited with internet network to support decision-making of improvement plan activities.

The advent of the COVID-19 pandemic has increased the amount of plastic waste. The demand for food and plastic-packaged foodstuffs has increased, and the use of disposable utensils has increased. The large usage of plastic packaging was the main factor which contributed to the plastic waste disposal. Since these plastic wastes are not easily degraded, it has led into massive environmental pollution. Transitioning towards eco-friendly materials fornewsustainabletechnologieswillbecriticaltocombatingfuturepandemicsandreducetheplasticwaste.Hence, recycling of plastic waste and its re-utilization as green and eco-friendly construction material could decrease the tendency of environmental pollution. In this review paper, we have presented an overview of the published articles which recycled plastic waste as an eco-friendly aggregate material for different construction-based applications before COVID 19 and during COVID 19. Furthermore, the processing methods which were suggested in the published articles for recycling plastic waste have been discussed. A few construction materials could be developed by utilizing the plastic wastes, including the polymer-modified asphalt mixes, eco-bricks, and construction and demolitionmaterials.Theseproductscouldbeusedasnovelproductsfor othersustainableapplicationsandbenefits of circular economy. According to several reports, polymeric based plastic wastes can be effectively used as a recycledmaterial,decrease the demand for traditional material sandal so tremendously reduce the construction costs.

Abu Dhabi’s government has taken several initiatives to improve the safety of the city’s highways, such as reducing traffic accident occurrence, red light violations, and property damage associated with traffic accidents. However, the intersections are still associated with many severe accidents, property damage associated with accidents, and red light violations. To help authorities pinpoint the major contributors to the deterioration of the safety performance of the signalized junctions, this study employed a negative binomial regression model to investigate the effect of geometric road conditions (in terms of the number of lanes of streets found in four-leg, three-leg, and different types of intersection) on property damage, red light violations, and severe accidents as safety performance indicators. The research found that, in both three-leg and four-leg intersections, most accidents and traffic light violations occur when minor streets pass through the intersection. This can be solved by converting these minor streets into major streets by increasing the number of lanes. This way, the capability of the minor street’s handling of increased traffic from the major streets significantly increases. The results also show that traffic speed is a major contributor to safety performance deterioration in these intersections. This can be solved by posting speed limits at these intersections. Results have also shown that, in Direction 1, when the main street passes through the intersection, the property damage type of accident is significantly high. This may be due to the presence of a potentially hazardous property or road design flaws on that side of the road. This effect needs further investigation to determine the hazardous property or the road design flaws causing these accidents.