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.search engine platform where they provide various Internet services. Virtualization technology provides cloud computing with flexible extensions, dynamic allocation, resource sharing, and other features. The cloud computing model provides services to the user including software, hardware, platforms, and other IT infrastructure resources as required. The user simply uses resources depending on application needs, relying on on-demand access to computers and storage systems. In manufacturing, the cloud is used as a platform where various production resources and capabilities can be intelligently sensed and connected into the cloud. Then IoT technologies such as sensors or actuators can be used to automatically manage and control these resources so that they can be digitalized for sharing.One of the main goals to set-up a company is to obtain a generous profit. Therefore, to achieve a good profit is by increasing productivity. To manage high productivity, the computing simulation is become more widely to use in the lean production process. Through the simulation, decision-makers are allowing to study the optimal condition for the coming production line in the virtual world before making the change of production line. Simulation models entails oversimplified assumptions and rough approximations to overcome the complexity of manufacturing systems which widen the gap from reality . This has become a new challenge since modern information and communication technologies are a rather complex set of hardware, software and organizational solutions processed with the data (Koscielniak and Puto, 2015). Development of Methodology of Decision Support SystemIn the development stage, firstly, IoT ecosystem involves web-enabled smart devices that use sensors, embedded processors and communication hardware to collect, act and send the data acquired. Through...
Aluminium products are becoming popular and the demand is increasing because of its special properties such as excellent corrosion resistance with good strength, recyclable and low density compared to stecl. CurrentlM two methods used to recycle aluminium chips are conventional and direct conversion methods. Recycling ofaluminium chips by the direct conversion methods is relatively simple, censumed less energy and do not harm thc cnvironment.The cost of recycled materials is much lewer than the cost of primary alurniniurn production by conventional technique. This paper reviews the recycling technologies ofaluminium chips using powder meta11urgy technique. Kewerds: aruminium chip, aluminium recycling, solid state recycling, conventional recycling, direct recycling, powdermetallurgy
<span>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.</span>
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