improvement in the resource efficiency of a product

improvement in the resource efficiency of a product

OShea, K.; Nash, R. An Introduction to Convolutional Neural Networks. However, they're only a few traces of any valuable improvements among them. Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks. Liang, Y.; Li, W.; Lu, X.; Wang, S. Fog computing and convolutional neural network enabled prognosis for machining process optimization. Improvements in both entire factories and individual components, such as ball bearings, were considered. Isolation forest, Local Outlier Factor and Q-Learning were only used once, while SARSA, DQN, DDQN, and Transformer were not applied at all. Practical Markov Chain Monte Carlo. Socioeconomic drivers of China's resource efficiency improvement: A This, in turn, is bound to increase the profit your MRF generates on a daily basis. permission is required to reuse all or part of the article published by MDPI, including figures and tables. This means that if a source deals with resource efficiency, it usually takes several aspects directly into account. A Theoretical Analysis of Deep Q-Learning. To evaluate the influence on resource efficiency within manufacturing companies, this paper focuses on operational input which directly affects natural resources, including material (including operating and auxiliary materials), energy, and water. But companies in every sector have found thousands of improvement opportunities. 2018. Hundreds of staff across the organization developed the skills to understand the root causes of losses and process inefficiencies. / Quality Resources / Continuous Improvement Continuous Improvement Quality Glossary Definition: Continuous improvement Continuous improvement, sometimes called continual improvement, is the ongoing improvement of products, services or processes through incremental and breakthrough improvements. Evaluate current business results. Future research should, therefore, address more AI applications with this explicit objective. Deng, F.; Guo, S.; Zhou, R.; Chen, J. Sensor Multifault Diagnosis with Improved Support Vector Machines. In recent years, there has been significant growth in the number of companies applying these approaches as they develop sustainability-driven production systems in their own operations. Ressourceneffizienz Durch Industrie 4.0Potenziale fr KMU des verarbeitenden Gewerbes. Available online: Saiga, K.; Ullah, A.S.; Kubo, A.; Tashi, A. In Proceedings of the 2013 27th International Conference on Advanced Information Networking and Applications Workshops, Barcelona, Spain, 2528 March 2013; pp. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency. In Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS), Malaxxa, Malaysia, 58 December 2011; pp. This paper presents an extensive literature review, which aims to provide an overview of current AI applications to increase resource efficiency in manufacturing companies. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive Hatiboglu, B.; Schuler, S.; Bildstein, A.; Hmmerle, M. Einsatzfelder von Knstlicher Intelligenz im Produktionsumfeld. Furthermore, this literature review highlights the fact that only few researchers have taken the direct environmental impact caused by training and implementing AI into account, although this may result in multiple possible savings of energy and resources. State of AI Report 2020. Resource efficiency Improving resource efficiency is among the top priorities in today's world, as governments, businesses and civil society are increasingly concerned about natural resource use, environmental impacts, material prices and supply security. Over a three-year period, the company rolled out the approach across its production network, applying anything from ten to 30 initiatives at each site to cut energy consumption by between 7 and 13percent. For many organizations, RPO offers a way to reduce carbon emissions by up to one-third in three to five years, with only limited investments in new equipment or technologies. ; Jau, L.W. After a decade of real-world experience across multiple companies and industries, the five principles described above have proved to be a robust, practical framework for any organization seeking to maximize the value generated from the resources it consumes. Lane, S.; Martin, E.B. A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm. ; Duran, B.S. This can be used for the automated quality control of components and products. In particular, AI methods such as agent-based modeling, expert systems, e.g., with fuzzy systems or evolutionary algorithms should be investigated for this purpose, since these are common AI methods, but not machine learning methods, and therefore were not assessed in this paper. PDF Efficiency, Production, and Resource Consumption - MIT Freitag, M.; Kck, M.; Alla, A.A. Potenziale von Data Science in Produktion und Logistik: Teil 2: Vorgehensweise zur Datenanalyse und Anwendungsbeispiele. Phys. SCP is about promoting resource and energy efficiency while providing access to basic services, green and decent jobs and a better quality of life for all. Conceptualization, L.W. Some high-level tools have been developed to improve industrial resource efficiency (Paola and Marco, 2010). These are intended to map and monitor the condition of the infrastructure being analyzed. Satisfactory Essays. This has been an area of significant progress in recent years. Each of those steps may only make a tiny difference to an organizations carbon footprint or resource consumption. Frontline actions can be especially powerful, often taking an organization a third of the way along the path to net-zero carbon emissionswith a 10 percent reduction coming from cost-free changes to operating procedures and the rest from further localized investments in technologies with a payback of less than two years. Yan, H.-S.; Li, W.-C. A scheduling procedure for a flow shop-like knowledgeable manufacturing cell with self-evolutionary features. 12: 6689. AI thus provides support for the evaluation, identification and implementation of improvement measures in manufacturing companies. You are accessing a machine-readable page. taking good care of the water we use. A prognostic algorithm for machine performance assessment and its application. They also learn as they work, continually improving their own performance. improvement in resource-use. 6468 Words. Xu, W.; Cao, L. Energy efficiency analysis of machine tools with periodic maintenance. ; Morris, A.J. By reducing attrition and production waste through improved simulation and planning, the material efficiency of the manufacturing system is improved. methods, instructions or products referred to in the content. future research directions and describes possible research applications. . Through at least 2025, the Inflation Reduction Act extends the Investment Tax Credit (ITC) of 30% and Production Tax Credit (PTC) of $0.0275/kWh (2023 value), as long as projects meet prevailing wage & apprenticeship requirements for projects over 1 MW AC.. For systems placed in service on or after January 1, 2025, the Clean Electricity Production Tax Credit and the Clean Electricity . Open Document. The limits here are determined by the theoretical maximum efficiency of a process. Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for To address climate change and the pressing environmental challenges such as biodiversity loss, the integration of sustainability into business operations is becoming increasingly important for companies and a key competitive advantage. In. One chemicals company found that more than 90 percent of its process variability could be explained by external factors, such as differences in temperature, humidity, or feedstock. Additionally, the two terms of resource efficiency, as well as efficiency itself, were identified as superordinate terms and included in the search string. positive feedback from the reviewers. As in other sectors, these companies are realizing that the journey to sustainability requires a holistic approach, combining actions both in the boardroom and on the shop floor(Exhibit 2). In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. ENERGY STAR can help you find energy-efficient products and homes. Keywords: efficiency, production, resource consumption, IPAT identity, rebound effect * Corresponding Author, phone (617) 253-2034, fax (617) 253-1556 . [. for Sustainable Development (Sustainable Development Goal 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic . BlackRock, the worlds largest asset manager, has told companies in its portfolio that it will vote against the reelection of directors at companies that fail to step up their efforts to protect natural resources and cut carbon emissions. Energy efficiency and conservation - U.S. Energy Information Available online: VDI Zentrum Ressourceneffizienz GmbH. All these additions serve as checks and balances that can significantly increase the amount of recyclables your MRF can handle. In Proceedings of the 2015 5th International Electric Drives Production Conference (EDPC 2015), Nuremberg, Germany, 1516 September 2015; IEEE: Piscataway, NJ, USA, 2015; pp. Production planning can additionally be supported by analyzing and classifying different production methods, e.g., with regard to efficiency. The first was the identification of a single metric that could account for the impact of multiple variables on overall process performance. In total, only 23 papers address resource efficiency improvements as an explicit goal, although only papers related to resource efficiency were included. Improvement of Efficiency - an overview | ScienceDirect Topics Improving crop productivity and resource use efficiency to ensure food Artificial Intelligence Applications for Increasing Resource Efficiency 110. Despite the stated limitations, it is our strong conviction that this paper adds significant value to the sustainability research field and AI, and lays the foundation for the further analysis of AI applications for increasing resource efficiency in manufacturing companies. In. Another typical use case of AI in the field of process optimization is fault detection in production. In Proceedings of the Eleventh International IBPSA Conference, Glasgow, Scotland, 2730 July 2009. The think limits principle is about setting ambitious goals that foster creative thinking and deliver significant leaps in performance. Please note that many of the page functionalities won't work as expected without javascript enabled. Editors Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Method to increase resource efficiency in production with the use of Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study. By defining AI and resource efficiency, this section is the basis for the following analysis. Liu, F.T. DQN and DDQN are variants of Q-Learning [, Since this paper focusses on AI applications within manufacturing companies, the definition of business divisions was based on the value chain according to [. Design and assessment of energetic agility measures in factories based on multivariate linear regression. While not solely focusing on sustainability in the context of industrial production, While the correlation between general sustainability and AI is discussed extensively in the literature (. ; Gower, P. Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process. Flick, D.; Ji, L.; Dehning, P.; Thiede, S.; Herrmann, C. Energy Efficiency Evaluation of Manufacturing Systems by Considering Relevant Influencing Factors. Yiakopoulos, C.; Gryllias, K.; Antoniadis, I. A Novel Cluster Head Selection Method based on K-Means Algorithm for Energy Efficient Wireless Sensor Network. Advanced-analytics approaches and AI technologies, such as neural networks, are now being applied to the control of industrial assets. However, it does not lead to greater sustainability on its own, but needs to be embedded in a sustainable framework with specific objectives. Please let us know what you think of our products and services. Additionally, this paper focuses on resource efficiency aspects, which have a direct impact on the natural resources comprising energy, material and water efficiency. To objectify the evaluation of the influences on resource efficiency, a scale was developed. Today, the transfer of benefits between lean principles and digital is a two-way flow. Iriondo, R. Machine Learning (ML) vs. You seem to have javascript disabled. Optimizing the performance of industrial processes is a complex endeavor. The project also identified longer-term opportunities to save even more through improvements to furnace condition and design. Cui, W.; Zhang, Y.; Zhang, X.; Li, L.; Liou, F. Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network. Kanungo, T.; Mount, D.; Netanyahu, N.; Piatko, C.; Silverman, R.; Wu, A. That step becomes a catalyst for innovation. In Proceedings of the 2004 Winter Simulation Conference, Washington, DC, USA, 58 December 2004; Volume 2, pp. A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. [. Ph.D. Thesis, Technische Universitt Mnchen, Mnchen, Germany, 2017. However, the potential influence . What Is Continuous Improvement? The Complete Guide - Kanbanize Quick Guide: Machine Learning im Maschinen- und Anlagenbau. Here, the AI applications itself, their aim of improvement, the addressed resource efficiency aspects, and the improvement object (product or process) were analyzed and, thus, suitable clusters were determined. ; Kim, H.; Jeong, H.W. Dobrev, D.A. Energy- and resource - efficiency can be aschieved, if.. Enterprises are paying attention to : Technology development / R&D 13 Own commitment /engage business And automakers are looking at ways to boost the use of recycled steel and aluminum in structural applications too. And more recent management innovations, such as agile, incorporate many of lean managements core concepts. After analyzing 70 research papers, it was found that only a minority of papers had resource efficiency as an explicit objective. Disclaimer/Publishers Note: The statements, opinions and data contained in all publications are solely Green AI. This con-cept of resource efciency is, in many ways, analogous to Leading companies are taking advantage of detailed databases containing hundreds of energy-efficiency levers, for example. Other AI methods are not used as frequently, as only five papers apply Naive Bayes classification, decision trees, LSTM, pattern recognition, Markov chain, k-means, and PCA. Bechtsis, D.; Tsolakis, N.; Vlachos, D.; Iakovou, E. Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles. How To Overcome the Biggest Obstacles to Kaizen Implementation - Autodesk In. Kok, J.N. In addition, the distribution of the identified sources can be analyzed regarding the AI method used, and, thus, the AI tasks. Tel. PDF Policy Guidance on Resource Efficiency - OECD Pai, P.-F.; Wu, T.-M.; Lin, K.-P.; Yang, S. Diagnosing Faulty Products in TFT-LCD Manufacturing Processes by Support Vector Machines with Principal Components Analysis. Jns, T.; Kall, N.; Tth, Z.E. 2019. To increase resource efficiency and, consequently, sustainability, AI is a promising technology, which helps to identify and improve the products and processes of manufacturing companies. They were aided by new analytical tools that helped them identify and evaluate the impact of detailed process changes. That step becomes a catalyst for innovation, encouraging the development of novel approaches that deliver big efficiency improvements. Therefore, the chemical industry focuses on alternative designs and continuous improvement leading to improved resource efficiency in the production process, the use-phase, as well as the end-of-life. A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems. Operational trend prediction and classification for chemical processes: A novel convolutional neural network method based on symbolic hierarchical clustering. In. In fact, the MRF can be made even more efficient using OCC screens, steel conveyor belts, and elevated inspection platforms. For more information, please refer to By efficiently maintaining machines, increases in material and energy efficiency can be achieved. The focus of some AI applications is very narrow, and they only address two out of four predefined aspects, for example, Trend analysis with linear and non-linear regression or clustering with Hierarchical Clustering. DeMartini, M.; Evans, S.; Tonelli, F. Digitalization Technologies for Industrial Sustainability. For example, training an AI algorithm may consume five times as much energy as a passenger car over its entire life cycle [. Thus, by taking the introduced definition into account, all impact categories are potentially affected. See further details. [. It needs to be taken into consideration that energy efficiency and GHG emissions are closely related and, thus, only a few papers address these aspects separately. Prasetiyo, B.; Alamsyah; Muslim, M.A. Choi, I.; Kim, H. An On-Device Deep Learning Approach to Battery Saving on Industrial Mobile Terminals. Selver, M.A. ; ODonnell, G. Characterising the Energy Consumption of Machine Tool Actuator Components Using Pattern Recognition. For 910915. Moll, P.; Schfer, A.; Coutandin, S.; Fleischer, J. Feature papers represent the most advanced research with significant potential for high impact in the field. Performance Evaluation of Predictive Classifiers for Knowledge Discovery from Engineering Materials Data Sets. Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. management of the waste we generate. Elsewhere, companies have transformed the performance management of their plants using the profit-per-hour approach. 2020. Schuster, M.; Paliwal, K. Bidirectional recurrent neural networks. The Time is Now: Decarbonizing America's Fuels and Products for a At one cement company, for example, the introduction of AI control improved process throughput by 11.6percent over a period of eight months. We use cookies on our website to ensure you get the best experience. Jagadish; Ray, A. Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA). Improvement of resource use efficiency versus mitigation of The theoretical-limit approach has helped one glassmaker understand the gap between actual and theoretical performancewhich turned out to be a delta of 20to 40 percent in most cases. ; IEEE: Piscataway, NJ, USA; pp. Rolling element bearing fault detection in industrial environments based on a K-means clustering approach. ; writingreview and editing, L.W., S.K. Wang, Y.; Zhang, Y.; Wu, Z.; Li, H.; Christofides, P.D. Here are top four tools to improve your process efficiency: 1. The resulting handbook, Unlocking Industrial Resource Productivity: Five Core Beliefs to Increase Profits through Energy, Material, and Water Efficiency (McKinsey Publishing, February 2016), set out five core beliefs for resource-productive operations, or RPO (Exhibit 1). RPO is a fast, powerful, and profitable way for companies to make significant progress on the journey to industrial decarbonization and sustainable production. Li, L.; Ota, K.; Dong, M. Deep Learning for Smart Industry: Efficient Manufacture Inspection System with Fog Computing. Susto, G.A. ; Youn, H.Y. The use cases illustrate promising approaches to increasing resource efficiency with AI applications and serve as a starting point for practitioners and applied research, contributing to the diffusion of AI applications, with resource efficiency in mind. ; Ibrahim, Z.; Khalid, M. A Hybrid Artificial Neural Network-Naive Bayes for solving imbalanced dataset problems in semiconductor manufacturing test process. 2020. most exciting work published in the various research areas of the journal. The importance of this holistic approach has only been reinforcedduring the more recent waves of industrial digitization. ENERGY STAR Home Upgrade Service Provider Partnership An optimized manufacturing system utilizes its infrastructure and inputs to a perfect extent. Hence, material efficiency, energy efficiency, and water efficiency are of interest in this paper. Doreswamy, H.K.S. Necessary improvement Definition & Meaning - Merriam-Webster Research indicates that consumers across all markets trust ENERGY STAR as a resource on clean energy and electrification. [. From: Advanced Oxidation Processes for Waste Water Treatment, 2018. As industrial companiesespecially in process industriesstrive for a zero-carbon future, a time-tested approach shows renewed value in helping reduce carbon by up to one-third in three to five years. Providing an overview of current AI applications, typical use cases were identified, including predictive maintenance, production planning, fault detection and prediction/predictive quality, as well as increasing energy efficiency in production and facility management. A 3-year experiment was conducted with . For example, one European chemicals company has been running a shop-floor-driven energy-efficiency program for a decade, and its teams are still finding new improvement opportunities every year. VDI Zentrum Ressourceneffizienz GmbH (Ed.) The main types of waste in Lean are Muda, Mura and Muri. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.; Kaiser, L.; Polosukhin, I. Wanner, J.; Herm, L.-V.; Hartel, D.; Janiesch, C. Verwendung binrer Datenwerte fr eine KI-gesttzte Instandhaltung 4.0. Additionally, the link between AI and resource efficiency is explored, clarifying the possible impact of AI application on resource efficiency. The market value of resources is well-documented and responds automatically to the locality and time at which resources are used. And it illustrates the role a resource-productivity approach can play as part of a broader set of solutions to help industries grow sustainably. ; Zhou, Z.-H. The same is true for water usage, which often serves as the auxiliary and operating supply. Regarding business units, in most papers, the (potential) influence on resource efficiency occurs within production planning and optimization. Ghaedi, M.; Hossainpour, M.; Ansari, A.; Habibi, M.; Asghari, A. Volume 469. The last of our five principles might have the most significant impact on the way industrial companies run their businesses in the coming years. New business models, such as bike- and car-sharing schemes and fashion-rental shops, are challenging traditional concepts of product ownership. ; Kosters, W.A. Renewable sourcing Solved Identify an example of an innovation from within the - Chegg In some markets, such as consumer electronics, trade-in programs and markets for second-life and refurbished goods are already enjoying considerable success. Our first core belief is that resource-productivity strategy should be based on lean principles, making use of existing lean expertise within the company. Gould, O.; Simeone, A.; Colwill, J.; Woolley, E.; Willey, R.; Rahimifard, S. Optimized Assembly Design for Resource Efficient Production in a Multiproduct Manufacturing System. Youssef, S.; Zimmer, C.; Szielasko, K.; Suri, Z.K. ; formal analysis, L.W. The resulting handbook, Unlocking Industrial Resource Productivity: Five Core Beliefs to Increase Profits through Energy, Material, and Water Efficiency (McKinsey Publishing, February 2016), set out five core beliefsfor resource-productive operations, or RPO (Exhibit1). In particular, classification and trend analysis methods are used for this purpose. . However, further work could add to the selected methods, and thus provide a more complete picture of AI applications for resource efficiency. The typical steps involved in performing a fit gap analysis are as follows: Assess existing business process. permission provided that the original article is clearly cited. Lighthouses unlock sustainability through 4IR technologies, Making supply-chain decarbonization happen, Delivering sustainable infrastructure: Insights from industry leaders, Visit our Manufacturing & Supply Chain page, Unlocking Industrial Resource Productivity: Five Core Beliefs to Increase Profits through Energy, Material, and Water Efficiency, sustainability issuesparticularly those driven by climate changehave become far more urgent, agile, incorporate many of lean managements core concepts, enforcing the correct settings on machines, real-time optimization of profit per hour, importance of this holistic approach has only been reinforced, transition from linear to circular economic models.

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