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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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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Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Kwaliteitscontroles in productieprocessen in de maakindustrie zijn vaak destructief en daarmee niet duurzaam. In dit project onderzoeken we hoe door toepassing van process mining op real time sensor data de kwaliteitscontrole al tijdens het productieproces kan worden uitgevoerd en potentiële problemen vroegtijdig ontdekt.
Kwaliteitscontroles in productieprocessen in de maakindustrie zijn vaak destructief en daarmee niet duurzaam. In dit project onderzoeken we hoe door toepassing van process mining op real time sensor data de kwaliteitscontrole al tijdens het productieproces kan worden uitgevoerd en potentiële problemen vroegtijdig ontdekt.Doel Het doel van het project is om op basis van realtime data de kwaliteit van het eindproduct van het productieproces te kunnen voorspellen en waar nodig het productieproces bij te sturen. Hiermee kan de industrie duurzamer werken. Resultaten Het project levert een AI software toolkit op met methoden en algoritmen voor toepassing in de productieprocessen in verschillende industrieën. Looptijd 15 januari 2021 - 15 november 2024 Aanpak Nieuwe process mining algoritmes worden ontwikkeld en getoetst in case studies bij verschillende industriële bedrijven. Op basis van de uitkomsten wordt een software toolkit ontwikkeld voor toepassing in de praktijk. Impact op onderwijs Studenten van instituut voor ICT gaan, samen met studenten van TU Eindhoven, cases studies uitvoeren bij verschillende industrieën. Cofinanciering Het project wordt gefinancierd door NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek).
