The value of a decision can be increased through analyzing the decision logic, and the outcomes. The more often a decision is taken, the more data becomes available about the results. More available data results into smarter decisions and increases the value the decision has for an organization. The research field addressing this problem is Decision mining. By conducting a literature study on the current state of Decision mining, we aim to discover the research gaps and where Decision mining can be improved upon. Our findings show that the concepts used in the Decision mining field and related fields are ambiguous and show overlap. Future research directions are discovered to increase the quality and maturity of Decision mining research. This could be achieved by focusing more on Decision mining research, a change is needed from a business process Decision mining approach to a decision focused approach.
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The European Arctic has been recently experiencing an upsurge in mining activities. This is reflected in an on-going interest from the industry, regulators and the public. However, current and future prospects are highly sensitive to mineral price fluctuations. The EU is a major consumer and importer of Arctic raw materials. As the EU is concerned about the security of supply, it attempts to encourage domestic mineral extraction.Both Arctic communities and industry call for enhanced information flows, as well as improved and more inclusive decision-making frameworks. The EU should clearly articulate its interests related to mining in the European Arctic. The EU could further enhance its support for the collection and sharing of mining data and knowledge.The EU regulatory framework could better contribute to harmonising environmental, economic and social assessments, paying special attention to local social issues and indigenous rights. The EU, as a major global actor, can also influence international governance, standard-setting and co-operation to facilitate increased responsibility in mining activities, including through dialogue with mining industry.
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While there has been a growing body of research focused on corporate social responsibility (CSR) practices in developing economies, few studies have examined the factors shaping the CSR agenda in sub-Saharan countries. Using qualitative data obtained through semi-structured interviews with management and stakeholders, this paper examines the drivers of the CSR agenda pursued by Paladin (Africa), a subsidiary of an Australian multinational mining company (MNC) operating the first uranium mine in Malawi. The findings suggest that the CSR agenda in the mining industry in Malawi is strongly influenced by externally generated pressures such as civil society organisation activism and community expectations; although it is clear that other drivers such as public and private regulations and pressure from financial markets also played a role in pressurising Paladin to adopt a CSR agenda.
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In recent years, stakeholder engagement has increasingly become a catchphrase in response to calls for corporate accountability to their stakeholders in the developing countries. However, the processes and practices companies pursue to engage stakeholders tend to conspicuously be variable depending on whether one draws on the instrumental and descriptive perspectives of the stakeholder theory. The purpose of this paper is therefore to test these perspectives, which we do through considering the case of a subsidiary of a multinational firm fictitiously known as Ashford (Africa) Limited, which operates in Malawi, as a member of the global mining industry. Using qualitative data obtained from interviews with Ashford (Malawi)'s managers and stakeholders, this study highlights the significance of paying more attention to firm specific factors, community dynamics and the civil society (NGO) related factors, as they are fundamental to the effectiveness of stakeholder engagement agenda pursued by mining companies in the developing countries.
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Corporate Social Responsibility (CSR) has become an important concern in the mining sector in recent years but has been overlooked heavily in the context of developing countries. This article helps to bridge this gap by exploring management and stakeholders' perceptions of a Malawian-based Australian multinational mining company's CSR strategy. The findings suggest that management's views of CSR differ significantly from those of stakeholders. While managers have a classical and limited view of the firm's role in mining communities and wider society, stakeholders generally have a broader idea of what social responsibilities companies can assume within wider society.
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In recent years, a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This article discusses the current state of the art in the adoption of Industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of Industry 4.0 technologies. This article discusses the relevance of the following key Industry 4.0 technologies to construction: data analytics and artificial intelligence, robotics and automation, building information management, sensors and wearables, digital twin, and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This article also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector, a three-phase use of intelligent assets from the point of manufacture up to after build, and a four-staged R&D process for the implementation of smart wearables in a digital enhanced construction site.
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Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementations
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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From the article: Globalization and technological innovation has led to an increasing competition between telecommunication service providers and has eroded traditional product- and service-based differentiation. One way to gain a competitive advantage is to create distinctiveness by improving customer experience in such a manner that it leads to higher customer satisfaction and loyalty. One of the drivers to improve the customer experience is the service interface. To improve this service interface, organizations must get insight into their customer interaction process. The amount of data about customers and the service provider processes is increasing and becoming more readily available for analysis. Process mining is a technique to provide insight into these processes. In this paper, a framework is presented to improve the customer satisfaction by alignment of the business service delivery process and the customer experience by applying process mining.
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Data analytics seems a promising approach to address the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences in cooperation with the aviation industry has initiated a two-year applied research project to explore the possibilities of data mining. More than 25 cases have been studied at eight different MRO enterprises. The CRISP-DM methodology is applied to have a structural guideline throughout the project. The data within MROs were explored and prepared. Individual case studies conducted with statistical and machine learning methods, were successfully to predict among others, the duration of planned maintenance tasks as well as the optimal maintenance intervals, the probability of the occurrence of findings during maintenance tasks.
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