14 Synthesis of Resource Optimal Chemical Processes 347 Minbo Yang, Jian Gong, and Fengqi You. Through changes that included an improved circuit-board layout to make the product smaller, the use of recycled material, and optimization of logistics processes, it was able to reduce the carbon footprint of the device by more than one-quarter. Resource efficiency - in the context of using human resources in an agency or consulting firm - is about using your people to deliver the best ROI for your business. Liu, F.T. For example, training an AI algorithm may consume five times as much energy as a passenger car over its entire life cycle [. Improvements in both entire factories and individual components, such as ball bearings, were considered. At one cement company, for example, the introduction of AI control improved process throughput by 11.6percent over a period of eight months. articles published under an open access Creative Common CC BY license, any part of the article may be reused without most exciting work published in the various research areas of the journal. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. The included papers were then analyzed regarding the identified AI tasks, AI methods, business divisions, and their (potential) influence on resource efficiency aspects. In addition, some international institutions have developed handbooks and tools to help manufacturers identify, implement, and benefit from resource efficiency improvements for SMEs in a global context (Balczar, 2010; UNEP, 2011). Resource efficiency is the amount of value that you realize from a unit of a resource such as power, water, materials, land and labor. Performance Evaluation of Predictive Classifiers for Knowledge Discovery from Engineering Materials Data Sets. These typical AI use cases include: Fault detection and prediction/predictive quality [, Increasing energy efficiency in production [. Quick Guide: Machine Learning im Maschinen- und Anlagenbau. 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. Additionally, the identification of typical use cases helps practitioners and researchers to determine possible use cases for increasing resource efficiency within production. Define expected business results. They also learn as they work, continually improving their own performance. methods, instructions or products referred to in the content. 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. Wang, Y.; Velswamy, K.; Huang, B. An improvement in resource efficiency might involve, for example, a reduction in the amount of material used to manufacture or package the product, a reduction in the energy consumption of the product in use, extending the product's lifetime, or making it more recyclable or reusable at the end of its life. Available online: Saiga, K.; Ullah, A.S.; Kubo, A.; Tashi, A. Crucially, the profit-per-hour calculation incorporates a comprehensive set of revenue and cost drivers relevant to sustainability, such as potential revenue from selling excess energy, as well as costs from emissions, waste disposal, water usage, or carbon offsets. The application of Artificial Intelligence (AI) also plays an increasingly important role. In some markets, such as consumer electronics, trade-in programs and markets for second-life and refurbished goods are already enjoying considerable success. By providing a comprehensive meta-analysis of existing applications described in the scientific literature and identifying current research deficits, this article contributes to the research field of AI and resource efficiency. DeMartini, M.; Evans, S.; Tonelli, F. Digitalization Technologies for Industrial Sustainability. Prasetiyo, B.; Alamsyah; Muslim, M.A. Potenziale der Schwachen Knstlichen Intelligenz fr die Betriebliche Ressourceneffizienz. those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). Ghaedi, M.; Hossainpour, M.; Ansari, A.; Habibi, M.; Asghari, A. In Proceedings of the 2013 27th International Conference on Advanced Information Networking and Applications Workshops, Barcelona, Spain, 2528 March 2013; pp. Today, the transfer of benefits between lean principles and digital is a two-way flow. By clustering the identified papers according to their application, typical use cases for AI improving resource efficiency within manufacturing companies can be defined. Economists have several ways of measuring economic efficiency, based on the allocation of. On average, 60% of the resource efficiency aspects are potentially influenced by the identified papers (, Although most of a products environmental impact is determined at the development phase and although there is a high potential for AI applications in materials science (particularly due to the high variability and data availability), this review did not find an AI application which highly impacts resource efficiency during development. It is shown that AI methods have already been applied to increase resource efficiency in manufacturing companies, but only to a limited extent. Scime, L.; Beuth, J. A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems. Part B J. Eng. Lean thinking encourages companies to develop robust, streamlined processes, an important prerequisite for further improving efficiency by digitization or the application of advanced analytical tools. The main types of waste in Lean are Muda, Mura and Muri. [. A lot has happened in the intervening years as sustainability issuesparticularly those driven by climate changehave become far more urgent. In the future, detailed analyses could be prepared for such differing levels. Kanyama, M.; Nyirenda, C.; Temaneh Nyah, C. Anomaly Detection in Smart Water Metering Networks. ENERGY STAR is the simple choice for energy efficiency, making it easy to find products that will save you money and protect the environment. ; Tegmark, M.; Nerini, F.F. In other words, the rebound effect means that improving the efficiency of resource-use per unit is outstripped by the absolute increase in demand for the goods and the deterioration of resource efficiency in consumption. Due to the high urgency of tackling climate change and reducing Greenhouse Gas (GHG) emissions, a potential reduction in the latter was analyzed alongside the (potential) improvement in energy, material and water efficiency. How to measure resource efficiency How to improve resource efficiency What is resource efficiency? [, Breunig, M.M. However, getting the most from the approach will mean extending the lean tool kit to incorporate sustainability-specific concepts, such as energy recovery or waste-material reuse. Predictive maintenance, in this context, is made possible by recording relevant time series data over the use phase of a machine. We are happy to say that the fundamentals still hold. In framing the relationship between efficiency improvements and resource consumption, the familiar IPAT identity can be used. In Proceedings of the 2017 28th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), Saratoga Springs, NY, USA, 1518 May 2017; pp. It also offered readers a guide to analytical approaches and improvement measures that could unlock more business value while reducing the impact on the planet. Sustainable Reverse Engineering Process. The improved coordination of various production processes and steps bears a large potential to increase energy efficiency. Comparing actual energy consumption with ideal energy consumption over a full year of production showed that the improvements could be achieved through a combination of measures, including selecting the right assets for each task, fine-tuning equipment speed, rethinking storage capacity, and minimizing pressure losses. Chin, R.T. Hochreiter, S.; Schmidhuber, J. Technical improvements to production systems need to go hand in hand with changes to management systems and to mindsets and behaviors across the organization. The first was the identification of a single metric that could account for the impact of multiple variables on overall process performance. taking good care of the water we use. Incorporating these concepts allows an existing lean production system to evolve into a true sustainability-driven production system. In the context of the Lean methodology, continuous improvement seeks to improve every process in your company by focusing on enhancing the activities that generate the most value for your customer while removing as many waste activities as possible. Therefore, hotspots for a specific AI application should be identified, e.g., by an evaluation of the environmental impact via Life Cycle Assessment. Identify requirements to fulfill the gap. Sustainable consumption and production (SCP) is a key focus of our work. ; IEEE: Piscataway, NJ, USA; pp. Another typical use case of AI in the field of process optimization is fault detection in production. A crop variety must possess several desirable characters like, high yield, superior quality, early maturity . The objective is to find patterns, in order to predict failures and prevent them through early maintenance measures. ; Kosters, W.A. Attention Is All You Need. Kanungo, T.; Mount, D.; Netanyahu, N.; Piatko, C.; Silverman, R.; Wu, A. 2019. The typical steps involved in performing a fit gap analysis are as follows: Assess existing business process. Design and assessment of energetic agility measures in factories based on multivariate linear regression. Rather than merely comparing their performance with peers or checking off a list of established efficiency-improvement measures, loss thinking encourages companies to understand the fundamentals of their processes and the issues that drive inefficiency. This paper contributes to the necessity of integrating sustainability with AI applications by explicitly examining the aspect of resource efficiency increases caused by AI applications in manufacturing companies. Resource efciency also focuses on the decoupling of economic performance from resource use, thus allowing the rate of economic growth to outpace the rate of re-source consumption (UNEP 2011; SCU 2012). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. ; Etzioni, O. Application of Markov Chains for Modeling and Managing Industrial Electronic Repair Processes. Cortes, C.; Vapnik, V. Support-Vector Networks. After analyzing 70 research papers, it was found that only a minority of papers had resource efficiency as an explicit objective. 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. and A.S. All authors have read and agreed to the published version of the manuscript. Feature papers represent the most advanced research with significant potential for high impact in the field. Wehle, H.-D.; Dietel, M. Industrie 4.0Lsung zur Optimierung von Instandhaltungsprozessen. Consequently, patterns are identified and deviations from the regular conditions of the monitored system are detected rapidly and, in some cases, proactively [, Consequently, various operational inputs such as, for example, capital, human resource and know-how-related aspects are not considered in particular [. The use of recycled materials has become a marketing point for sportswear brands and car companies. Available online: Wang, P. On Defining Artificial Intelligence. In. Traditional lean improvements often result in sustainability benefits as a byproduct. Among those, energy efficiency was the most commonly addressed resource efficiency aspect. With the help of a decision tree, Evans et al. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. 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. Economic efficiency refers to how effectively a society's scarce resources are used to produce goods. Part III Improving Resource Efficiency by Process Improvement 345. 2 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. ENERGY STAR can help you find energy-efficient products and homes. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. Decker, M. Entwicklung Eines Ganzheitlichen Prognosemodells zur Kompensation von Varianzen in Prozessfolgen Mittels Support Vektor Maschinen. A large-scale ten-year operational energy-efficiency program at another chemicals player focused on capability building among frontline process engineers. Therefore, possible contributions by AI applications to sustainability should be included from the beginning. https://doi.org/10.3390/su13126689, Waltersmann L, Kiemel S, Stuhlsatz J, Sauer A, Miehe R. Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing CompaniesA Comprehensive Review. Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age. Zendehboudi, A.; Baseer, M.; Saidur, R. Application of support vector machine models for forecasting solar and wind energy resources: A review. Thats putting extra pressure on thousands of suppliers to reduce their own environmental footprints as well. OShea, K.; Nash, R. An Introduction to Convolutional Neural Networks. Flick, D.; Ji, L.; Dehning, P.; Thiede, S.; Herrmann, C. Energy Efficiency Evaluation of Manufacturing Systems by Considering Relevant Influencing Factors. By defining AI and resource efficiency, this section is the basis for the following analysis. The project has accrued almost $50million in annual savings so far. Eng. In Proceedings of the 2015 5th International Electric Drives Production Conference (EDPC 2015), Nuremberg, Germany, 1516 September 2015; IEEE: Piscataway, NJ, USA, 2015; pp. The technology term from (1) is represented in (2) as the product of Resources over Quantity - which represents the resource intensity of the goods and . There were no time restrictions, and it was searched up to the latest issue available. In addition, AI applications are becoming more relevant for practice and attractive for companies, due to current developments in the IT field. 8994. Dobrev, D.A. Zhang, J.; Wang, P.; Yan, R.; Gao, R.X. Ullah, A.S. What is knowledge in Industry 4.0? (This article belongs to the Special Issue. Yiakopoulos, C.; Gryllias, K.; Antoniadis, I. Increasing resource and energy efficiency through circularity In a world facing resource scarcity, any successful strategy for sustainable development needs to find solutions for decoupling economic growth and the use of resources. Di Vaio, A.; Palladino, R.; Hassan, R.; Escobar, O. New business models, such as bike- and car-sharing schemes and fashion-rental shops, are challenging traditional concepts of product ownership. Doreswamy, H.K.S. [. Johnson, S.C. Hierarchical clustering schemes. https://www.mdpi.com/openaccess. In. In. Rolling element bearing fault detection in industrial environments based on a K-means clustering approach. Keywords: efficiency, production, resource consumption, IPAT identity, rebound effect * Corresponding Author, phone (617) 253-2034, fax (617) 253-1556 . 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. ; Moon, S.J. 6468 Words. positive feedback from the reviewers. Miehe, R.; Waltersmann, L.; Sauer, A.; Bauernhansl, T. Sustainable production and the role of digital twinsBasic reflections and perspectives. In the years since we published our handbook, the circular economy has moved from the radical to the mainstream. / 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. Corea, F. AI Knowledge Map: How to Classify AI Technologies. Glossar zum Ressourcenschutz. Wanner, J.; Herm, L.-V.; Hartel, D.; Janiesch, C. Verwendung binrer Datenwerte fr eine KI-gesttzte Instandhaltung 4.0. A circular economy is one of those potential solutions. A North American midstream oil and gas company used the theoretical-limits concept to identify energy savings of 15 to 25 percent across its operations. 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. Lane, S.; Martin, E.B. Philos. Waltersmann, L.; Kiemel, S.; Stuhlsatz, J.; Sauer, A.; Miehe, R. Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing CompaniesA Comprehensive Review. No special Moreover, developments in other areasnotably the rapid evolution of digital manufacturing approaches and Industry 4.0have supercharged some RPO tools, making them more powerful, more flexible, and easier to use. This is a significant challenge that requires innovative . 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. E-mail address: a.weyand@ptw.tu-darmstadt.de Abstract In order to decrease carbon emissions and to address the problem of resource scarcity, the production industry as major consumer of energy and resources is forced to act and increase the resource efficiency of their production lines. See further details. Conversion Efficiency The amount of available sunlight that a solar panel converts to electricity is known as conversion efficiency. The observed improvement of efficiency of decomposition was interpreted by a joint effect of a higher production of OH radicals in the presence of TiO2, partial adsorption of pollutants onto TiO2 surface, and possible certain catalytic effects. They were aided by new analytical tools that helped them identify and evaluate the impact of detailed process changes. and J.S. However, the potential influence of AI applications on resource efficiency has not been investigated. According to the U.S. Energy Information Administration's (EIA's) annual survey of electric power sales, revenue, and energy efficiency (Form EIA-861), in 2020, 502 electric utilities had EE programs that resulted in an estimated 28,167,459 megawatthours (MWh) (or about 28.2 billion kilowatthours [kWh]) reductions (savings) in total annual . ; Jau, L.W. Too often, sustainability transformations run aground for the same reason other major change programs fail: too much focus on creating the perfect technical tools, such as cost curves, and too little on the humans who will use them. Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for Optimizing the performance of industrial processes is a complex endeavor. Workflow Software. ; Lee, S. Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map. Industrial-resource productivity and the road to sustainability. [. Operations teams must manage trade-offs among throughput, yield, energy consumption, and environmental impact. Improvements in resource efficiency can result in lower pressures on the environment by reducing the overall consumption of materials in the economy. You seem to have javascript disabled. A prognostic algorithm for machine performance assessment and its application. Nishant, R.; Kennedy, M.; Corbett, J. Ph.D. Thesis, Technical University of Darmstadt, Darmstadt, Germany, 2008. the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, ODriscoll, E.; Kelly, K.; Cusack, D.O. Bechtsis, D.; Tsolakis, N.; Vlachos, D.; Iakovou, E. Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles. Method for the Investigation of Mold Filling in the Fiber Injection Molding Process Based on Image Processing. Further research could focus on these aspects. 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. In fact, the MRF can be made even more efficient using OCC screens, steel conveyor belts, and elevated inspection platforms. Multiple requests from the same IP address are counted as one view. Contribution of Working Groups I, II, and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Windenergie: Zuverlssige Integration in die Energieversorgung, Energieeffizienz in DeutschlandEine Metastudie: Analyse und Empfehlungen, The Quest for Artificial Intelligence: A History of Ideas and Achievements, Encyclopedia of Life Support Systems (EOLSS), Business Intelligence for Enterprise Internet of Things, Startup mit System: In 24 Schritten zum Erfolgreichen Entrepreneur, Environmental and Natural Resource Economics, An Introduction to Data: Everything You Need to Know about AI, Big Data and Data Science, Applied Regression Analysis: Includes Disk, Statistical Methods for Engineers and Scientists, Pattern Recognition and Machine Learning, Corrected at 8th printing 2009. BASF has taken an important step by updating its sustainability assessment method TripleS (Sustainable Solution Steering) in order to further develop its product portfolio even more strongly in the direction of climate protection, resource efficiency and circular economy in the future and to meet the growing sustainability requirements in its markets with innovative solutions. Long short-term memory for machine remaining life prediction. In the context of industrial manufacturing, the implementation of AI and the associated possibilities for data analysis can, for example, result in optimized production planning. Wang, Y.; Zhang, Y.; Wu, Z.; Li, H.; Christofides, P.D. Deep Learning vs. Neural Networks: Whats the Difference? If lean thinking doesnt receive the public attention it once did, thats because lean principles have long since become part of the operational DNA of many organizations. The increase in energy efficiency was identified as being the most commonly addressed resource efficiency aspect, and the use cases for the increase in energy efficiency in production and facility management are defined accordingly. The following are illustrative examples. This has been an area of significant progress in recent years. Find support for a specific problem in the support section of our website. Resource efficiency is the ratio of added . Compared with traditional manual control, these systems can respond faster and make better decisions. Introduction Food security ( Liu et al., 2021 ), high use-efficiency of resources ( Liu et al., 2020 ), and mitigation of environmental impacts ( Tian et al., 2020) are the central concerns of sustainable agricultural development. An efficient k-means clustering algorithm: Analysis and implementation. Wang, Y.; Li, H.; Qi, C. An adaptive mode convolutional neural network based on bar-shaped structures and its operation modeling to complex industrial processes. However, further work could add to the selected methods, and thus provide a more complete picture of AI applications for resource efficiency. Resource Efficiency is a very important, but relatively underutilized, concept that is essential for sustainable development. ; Morris, A.J. ; visualization, L.W. This section is divided into two subsections. A Theoretical Analysis of Deep Q-Learning. Energy- and resource - efficiency can be aschieved, if.. Enterprises are paying attention to : Technology development / R&D 13 Own commitment /engage business Ph.D. Thesis, University of Stuttgart, Stuttgart, Germany, 2018. A differentiation and analysis of the suitability of AI applications within a specific industry sector or production process was, therefore, not possible. AI thus provides support for the evaluation, identification and implementation of improvement measures in manufacturing companies. It will also require changes to performance indicators and management systems so that teams are provided with incentives to meet resource-productivity goals. Climate Change 2014: Synthesis Report. Loss thinking encourages companies to understand the fundamentals of their processes and the issues that drive inefficiency. A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. In. Yang, Y.; Juntao, L.; Lingling, P. Multi-robot path planning based on a deep reinforcement learning DQN algorithm. Tsiliyannis, C.A. Willenbacher, M.; Wohlgemuth, V. Einsatzmglichkeiten von Methoden der Knstlichen Intelligenz zur Optimierung von Stoff- und Energiestrmen und prototypische Umsetzung auf der Basis von Stoffstromnetzen.