Innovation plays a critical role in the mining industry as a tool to improve the efficiency of its processes, to reduce costs, but also to meet the increasing social and environmental concerns among communities and authorities. Technological progress has also been crucial to allow the exploitation of new deposits in more complex scenarios: lower ore grades, extreme weather conditions, deeper deposits, harder rock mass, and high-stress environments. This paper discusses the importance of innovation for the mining industry and describes the mechanisms by which it is carried out. It includes a review of the drivers and actors involved and current trends. The digital transformation process that the industry is going through is analyzed, along with other relevant trends that are likely to shape the mining of the future. Additionally, a case study is presented to illustrate the technical and economic implications of developing a disruptive innovation project.
Global efforts to decarbonize heavy industry remain insufficiently aligned. While relatively new forms of international collaboration between and among states and companies are emerging, there is still considerable room to embark on more structured knowledge-sharing activities and coherent action among nations. In order to assess the concrete needs of an industry transition at scale, this paper analyzes 29 industry transition roadmaps across 13 countries, spanning the value chain of extractive, processing, and end-use heavy industry sectors. We compare and contrast these roadmaps according to the degree of ambition in decarbonization targets, the financial costs of implementing the roadmaps, and the key mitigation measures to achieve decarbonization targets. Importantly, this paper synthesizes and categorizes key policy, finance, and technology requirements called for to enable roadmap implementation. We demonstrate that the implementation of roadmaps across different industries and countries encounters common and comparable barriers and challenges, highlighting the need for international cooperation to facilitate global industry transitions.
Since the beginning of the industrial revolution, manufacturing has gone through different stages: the 1st technological islands, 2nd the mass production, 3rd the lean manufacturing and the 4th IIoT (for the year 2025); we must keep in mind the leadership in the production of goods today what the Eastern countries have (and are using stage 3), so that the current guidelines have the necessary meaning, which establishes a new way of producing more as the potential of technologies that are in the process of maturation such as: Artificial Intelligence, Big Data, 3D Printing and Robotics; find original solutions to the problems of productivity, customization, just in time and services.Artificial Intelligence is taking a leading role in solving manufacturing problems, with the purpose of eliminating all those areas that are blindly worked, and that therefore it is not possible to improve by suffering from data to: analyze them, obtain information, establish controls and improve. The above is achieved by establishing disruptive technologies that take control in real time.
Companies are currently facing substantial challenges with regard to Industry 4.0. In order to adapt to this changing environment, companies are moving from operations-centered business to project-driven business. This change requires an evolution in project management. Researchers and practitioners, inspired by the PMBOK (Project Management Body of Knowledge), have created maturity models to compare and evaluate organizations, but they did not specify any methodology to create adapted models to face this technological change. Therefore, this paper proposes an approach to understand under on principles existing project management maturity models were based, and how it is possible to create a new project management maturity model applicable in the emerging framework of industry 4.0. Then, we illustrate the new approach with the construction of a project maturity model used to measure the planning capability. Finally, we define limitations of the model and future research directions.
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