The quality management ecosystem for predictive maintenance in the Industry 4.0 era

The Industry 4.0 era requires new quality management systems due to the ever increasing complexity of the global business environment and the advent of advanced digital technologies. This study presents new ideas for predictive quality management based on an extensive review of the literature on quality management and five realworld cases of predictive quality management based on new technologies. The results of the study indicate that advanced technology enabled predictive maintenance can be applied in various industries by leveraging big data analytics, smart sensors, artificial intelligence (AI), and platform construction. Such predictive quality management systems can become living ecosystems that can perform cause-effect analysis, big data monitoring and analytics, and effective decision-making in real time. This study proposes several practical implications for actual design and implementation of effective predictive quality management systems in the Industry 4.0 era. However, the living predictive quality management ecosystem should be the product of the organizational culture that nurtures collaborative efforts of all stakeholders, sharing of information, and co-creation of shared goals

Integration of blockchains with management information systems

In the era of the fourth industrial revolution (Industry 4.0), many Management Information Systems (MIS) integrate real-time data collection and use technologies such as big data, machine learning, and cloud computing, to foster a wide range of creative innovations, business improvements, and new business models and processes. However, the integration of blockchain with MIS offers the blockchain trilemma of security, decentralisation and scalability. MIS are usually Web 2.0 clientserver applications that include the front end web systems and back end databases; while blockchain systems are Web 3.0 decentralised applications. MIS are usually private systems that a single party controls and manages; while blockchain systems are usually public, and any party can join and participate. This paper clarifies the key concepts and illustrates with figures, the implementation of public, private and consortium blockchains on the Ethereum platform. Ultimately, the paper presents a framework for building a private blockchain system on the public Ethereum blockchain. Then,integrating the Web 2.0 client-server applications that are commonly used in MIS with Web 3.0 decentralised blockchain applications

HOW BIG DATA WILL BE AN ADDED VALUE TO SCM?

It is the era of digital information technology where almost everything is going smart. Thus, organizations move towards digitalization that cause the emergent of Big Data. Analyzing big data is the big challenge today. Being smart puts the world under a big challenge to adapt, change, and upgrade systems to analyze the Big Data using the high technology. Challenges are growing with the market and appears in different forms. Many of these challenges can be hard or difficult to handle on your own if you are a small to medium size business without the help of supply chain management system. The main objective of this paper is to contribute and examine these research questions: What are the Big Data Analytical tools used in SCM? In addition to the Impact of Analyzing Big Data on Supply Chain Management? The methodology used was a systematic review over the existing literature including Big Data, supply chain, SCM, and the impact of BD analytical tools on SCM. Data collected and unsystematically interpreted and the findings summarized in a subjective way that describes and discusses the literature from a contextual or theoretical point of view. Big data can tremendously affect the supply chain units and can add values to the overall supply chain operations by improving the processes to be more effective and efficient based on the analysis results. Big data analytics become a core differentiation factor for any organization that acquire it in the last few years

Artificial Intelligence for Managerial Information Processing and Decision-Making in the Era of Information Overload

In the big data era, managers are exposed to an increasing amount of structured and unstructured information that they must process daily to make decisions. In this context, artificial intelligence (AI) functionalities can support managerial information processing (IP), which forms the basis of managers’ decision-making. To date, little is known about the themes that managers face when integrating AI into their IP and decision-making. The present paper identifies these through three focus group interviews with managers from the financial industry, validates them through a survey and derives organizational implications. The results imply that organizations should (1) evaluate managerial IP tasks and matching AI systems, (2) (re)define roles for managers and AI systems, and (3) redesign management processes for sustainable human-AI interaction

Ketangkasan Kepemimpinan dan Kasus Akuntansi dalam Kemampuan Kepemimpinan

In the industrial revolution of 4.0, it focused on digital economic patterns, big data, robotic, and automation, which affected the fields of management, finance, and business as well as accounting, accountants were required to prepare for that era by self - sustaining the latest information technology and the ability to analyze financial reports. This research paper aims to examine leadership agility and accounting cases in leadership abilities.Based on the results of this research paper, it is hoped that the infommatics (it) technology can use big cloud-based data as storage and management of artificial information systems (sia) that can help reduce human error, improve effectiveness and efficiency and accuracy of the information information accumulated accumulated data. Thus, the need for human resources (human resources) such as accounting services (kja) and the public accounting offices (kap) would get fewer and fewer jobs. In this context, especially those running the accounting profession must continue to update their knowledge and knowledge primarily on the standardized accounting (psak), itenational accounting standard board (iasb) and intersections financial inequality that become global bank for Creating financial statements for accounting for government small medium micro business (umkm); Sharia, etap (an entity without public accountability) anda standard-based financial accounting testimony (psak)

Distributed storage optimization using multi-agent systems in Hadoop

Understanding data and extracting information from it are the main objectives of data science, especially when it comes to big data. To achieve these goals, it is necessary to collect and process massive data sets, arriving at the system in different formats at great velocity. The Big Data era has brought us new challenges in data storage and management, and existing state-ofthe-art data storage and processing tools are poised to meet the challenges while posing challenges to the next generation of data. Big Data storage optimization is essential for improving the overall efficiency of Big Data systems by maximizing the use of storage resources. It also reduces the energy consumption of Big Data systems, resulting in financial savings, environmental protection, and improved system performance. Hadoop provides a solution for storing and analysing large quantities of data. However, Hadoop can encounter storage management problems due to its distributed nature and the management of large volumes of data. In order to meet future challenges, the system needs to intelligently manage its storage system. The use of a multi-agent system presents a promising approach for efficiently managing hot and cold data in HDFS. These systems offer a flexible, distributed solution for solving complex problems. This work proposes an approach based on a multi-agent system capable of gathering information on data access activity in the HDFS cluster. Using this information, it classifies data according to its temperature (hot or cold) and makes decisions about data replication based on its classification. In addition, it compresses unused data to manage resources efficiently and reduce storage space usage

Guest Editorial: Special issue on data analytics and machine learning for network and service management-Part II

Network and Service analytics can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, analytics and machine learning have found ways to improve reliability, configuration, performance, fault and security management. In particular, we see a growing trend towards using machine learning, artificial intelligence and data analytics to improve operations and management of information technology services, systems and networks