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. Those results give leaders room to make boardroom-level moon shot investments in entirely different products, processes, or geographies. Thus, by taking the introduced definition into account, all impact categories are potentially affected. And the various levers at the disposal of these teams may reinforce or undermine one another. [. 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. All these additions serve as checks and balances that can significantly increase the amount of recyclables your MRF can handle. From: Advanced Oxidation Processes for Waste Water Treatment, 2018. 5.5 Batch Production 112. 1. ; Akay, O.; Alim, F.; Bardak, S.; lmez, M. An automated industrial conveyor belt system using image processing and hierarchical clustering for classifying marble slabs. Another typical use case of AI in the field of process optimization is fault detection in production. Hundreds of staff across the organization developed the skills to understand the root causes of losses and process inefficiencies. Please note that many of the page functionalities won't work as expected without javascript enabled. Only one paper addressed learning tasks and, thus, reinforcement learning. 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. Shehabi, A.; Smith, S.; Sartor, D.; Brown, R.; Herrlin, M.; Koomey, J.; Masanet, E.; Horner, N.; Azevedo, I.; Lintner, W. Murugesan, S. Harnessing Green IT: Principles and Practices. Bechtsis, D.; Tsolakis, N.; Vlachos, D.; Iakovou, E. Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles. The improved coordination of various production processes and steps bears a large potential to increase energy efficiency.
Energy Efficiency | ENERGY STAR Yiakopoulos, C.; Gryllias, K.; Antoniadis, I. Ten activities are analyzed, including pig iron production, aluminum . DeMartini, M.; Evans, S.; Tonelli, F. Digitalization Technologies for Industrial Sustainability. Schuster, M.; Paliwal, K. Bidirectional recurrent neural networks. Philos. Wanner, J.; Herm, L.-V.; Hartel, D.; Janiesch, C. Verwendung binrer Datenwerte fr eine KI-gesttzte Instandhaltung 4.0. Kanyama, M.; Nyirenda, C.; Temaneh Nyah, C. Anomaly Detection in Smart Water Metering Networks. Consumers have become more vocal about sustainability issues and more likely to act on their views. Sustainable Reverse Engineering Process. 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. ; Jau, L.W. For more information, please refer to Evans, L.; Lohse, N.; Summers, M. A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information. The Use of Artificial Intelligence Methods to Assess the Effectiveness of Lean Maintenance Concept Implementation in Manufacturing Enterprises. Workflow Software. Operations teams must manage trade-offs among throughput, yield, energy consumption, and environmental impact. articles published under an open access Creative Common CC BY license, any part of the article may be reused without Available online: Kavlakoglu, E. AI vs. Machine Learning vs. Yan, J.; Lee, J. Degradation Assessment and Fault Modes Classification Using Logistic Regression. This, in turn, is bound to increase the profit your MRF generates on a daily basis. Improvements in resource efficiency can result in lower pressures on the environment by reducing the overall consumption of materials in the economy. The application of Artificial Intelligence (AI) also plays an increasingly important role. / 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. Editors Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. 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. 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. Weimer, D.; Scholz-Reiter, B.; Shpitalni, M. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. Moreover, the link between AI and resource efficiency could be displayed in more detail. In Proceedings of the 2017 28th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), Saratoga Springs, NY, USA, 1518 May 2017; pp. Define expected business results.
Sustainability innovations for resource and energy efficiency The transition from linear to circular economic modelsrequires companies to consider the products they make today as resources for the future and to find ways to optimize the value generated by those resources through multiple life cycles. Hence, material efficiency, energy efficiency, and water efficiency are of interest in this paper. Eng. It established an energy-efficiency center of excellence, tasked with helping sites identify new improvement opportunities and apply measures found effective by colleagues at other plants. However, they're only a few traces of any valuable improvements among them. Economists have several ways of measuring economic efficiency, based on the allocation of. Design and assessment of energetic agility measures in factories based on multivariate linear regression. All articles published by MDPI are made immediately available worldwide under an open access license.
Improving the Resource Efficiency and Resource Circulation of Products ; Boullion, T.L. Deep Learning-based Human Motion Prediction Considering Context Awareness for Human-Robot Collaboration in Manufacturing. Additionally, this paper focuses on resource efficiency aspects, which have a direct impact on the natural resources comprising energy, material and water efficiency. Through the definition of the search string, methods involving machine learning were selected as the focus of this research. ENERGY STAR is the simple choice for energy efficiency, making it easy to find products that will save you money and protect the environment. 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. Scime, L.; Beuth, J. Dobrev, D.A. Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks. 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. Editors select a small number of articles recently published in the journal that they believe will be particularly Therefore, possible contributions by AI applications to sustainability should be included from the beginning.
PDF Efficiency, Production, and Resource Consumption - MIT [. Among those, energy efficiency was the most commonly addressed resource efficiency aspect. Kuhlmann, T.; Sauer, A. Our first core belief is that resource-productivity strategy should be based on lean principles, making use of existing lean expertise within the company. Youssef, S.; Zimmer, C.; Szielasko, K.; Suri, Z.K. In total, 70 research papers published between 1982 and September 2020 were identified during the literature research process. This has been an area of significant progress in recent years. Fan, J.; Wang, Z.; Xie, Y.; Yang, Z. Cheng, Y.; Zhu, H.; Wu, J.; Shao, X. 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. Applying this unit, circularity is defined as the percentage of the value of stressed resources incorporated in a service or product that is returned after its end-of-life. Moll, P.; Schfer, A.; Coutandin, S.; Fleischer, J. Despite the use of such a variety of AI methods, methods of the AI task Modelling and Language Processing are the most frequently applied. There is still much to be done, however. The 70 research papers identified were analyzed and classified according to their influence on resource efficiency. Log In Sign Up Username . However, no quantification of the improvement is given, or the improvement is below 1%; None, if the AI application does not influence any of the resource efficiency aspects; N/A, if not enough information is available to evaluate the potential influence.
Improving crop productivity and resource use efficiency to ensure food Furthermore, the optimization of building infrastructure, such as heating, cooling and lighting, is identified as typical use case. Prasetiyo, B.; Alamsyah; Muslim, M.A. The market value of resources is well-documented and responds automatically to the locality and time at which resources are used. Schmid, M. Kognitive Prozesssteuerung zur Steigerung der Ressourceneffizienz in der Druckindustrie.
Resource efficiency - OECD The Time is Now: Decarbonizing America's Fuels and Products for a In addition, some methods enable fault prediction, which allows for predictive intervention in the production process (process control) to avoid faults. GMA/ITG-Fachtagung Sensoren und Messsysteme 2019, Nachhaltige Betriebliche Umweltinformationssysteme, Charakterisierung von Mittels 3D-Druck Verarbeiteten Biogenen Holz-Filamenten unter Anwendung der Raman-Spektroskopie, Proceedings of the 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, 2017, Advances and New Trends in Environmental Informatics. and J.S. Volume 469. Adam, A.; Chew, L.C. Wang, Y.; Zhang, Y.; Wu, Z.; Li, H.; Christofides, P.D. Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. 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 S.K. 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. You are accessing a machine-readable page. And it illustrates the role a resource-productivity approach can play as part of a broader set of solutions to help industries grow sustainably. Practical Markov Chain Monte Carlo. Today, the transfer of benefits between lean principles and digital is a two-way flow. Quick Guide: Machine Learning im Maschinen- und Anlagenbau. In another similar survey, 33% of the 360 firms from Spain and Mexico adopted the Kaizen methodology. Yu, C.-M.; Chien, C.-F.; Kuo, C.-J. Although AI is seen as a technology with disruptive potential in companies and research, it lacks a universally valid definition and is not clearly distinguished from general IT [, While often falsely equated with AI, machine learning is a subset of AI, comprised of various sub-branches such as deep learning [, Generally, individual or combinations of various AI methods in manufacturing environments are able to analyze unstructured data such as audio or image signals. Potenziale der Schwachen Knstlichen Intelligenz fr die Betriebliche Ressourceneffizienz. ODriscoll, E.; Kelly, K.; Cusack, D.O. Therefore, Resource Efficiency is one of THE key . In. 93104. Further research could focus on these aspects. New right to repair legislation in Europe requires manufacturers of home appliances to offer spare parts for at least ten years after the date of original sale. That step becomes a catalyst for innovation, encouraging the development of novel approaches that deliver big efficiency improvements. By reducing attrition and production waste through improved simulation and planning, the material efficiency of the manufacturing system is improved. 867871. Reinforcement Learning: A Survey. Elsewhere, companies have transformed the performance management of their plants using the profit-per-hour approach. and R.M. This section is divided into two subsections. When analyzing the identified papers, it became clear that many addressed similar use cases, especially within the business division production. 2012. ; Boers, E.J. [, Breunig, M.M. R. Soc. Chin, R.T. Johansson, M.; Thollander, P. A review of barriers to and driving forces for improved energy efficiency in Swedish industryRecommendations for successful in-house energy management. The second essential innovation was the development of tools that could enable the real-time optimization of profit per hourin an industrial system. Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. These typical AI use cases include: Fault detection and prediction/predictive quality [, Increasing energy efficiency in production [. 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. The changes required only limited capital investment, with a payback of two to five years. . Shah, H.; Gopal, M. A fuzzy decision tree-based robust Markov game controller for robot manipulators. And lean thinkings focus on equipping frontline teams with the skills and tools to improve their own workplaces is a perfect model for the detailed, granular work required to improve resource productivity. Against this background, this article provides an overview of the current AI applications and how they affect resource efficiency. 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 . Incorporating these concepts allows an existing lean production system to evolve into a true sustainability-driven production system. Part III Improving Resource Efficiency by Process Improvement 345. How have our five principles of RPO stood the test of time? Conceptualization, L.W. 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.
Improvement of resource use efficiency versus mitigation of 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 . Flick, D.; Ji, L.; Dehning, P.; Thiede, S.; Herrmann, C. Energy Efficiency Evaluation of Manufacturing Systems by Considering Relevant Influencing Factors. Among other things, they promised to take back from customers products at the end-of-life stage and to develop second-life markets and remanufacturing programs. Wu, X.; Kumar, V.; Quinlan, J.R.; Ghosh, J.; Yang, Q.; Motoda, H.; McLachlan, G.J. To achieve their full potential, circular-economy concepts require collaboration across the full value chain, from the design of safe, easily recycled materials and of products that support repairs, upgrades, disassembly, and remanufacturing to the development of an ecosystem of providers to deliver those services. 413417. ; Kim, H.; Jeong, H.W. 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. This paper contributes to research in this field by systematically examining papers and revealing research deficits. In Proceedings of the 2013 27th International Conference on Advanced Information Networking and Applications Workshops, Barcelona, Spain, 2528 March 2013; pp. With the help of a decision tree, Evans et al. ; Tegmark, M.; Nerini, F.F. ; Gower, P. Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process. Sustainable consumption and production (SCP) is a key focus of our work. There were no time restrictions, and it was searched up to the latest issue available. The theoretical-limits approach helps companies move beyond the benchmarking trap. 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). 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 literature review, following PRISMA guidelines, was conducted at the end of September 2020. 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. 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. Using decision tree-based data mining to establish a sizing system for the manufacture of garments. 2 permission is required to reuse all or part of the article published by MDPI, including figures and tables. 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 [. Resource efficiency is closely related to various environmental impacts caused by resource utilization, and improving resource efficiency is of great significance to a country's economic growth and environmental improvement (Lu and Schandl, 2021; Pauliuk et al., 2021; Sun et al., 2020 ). Unwanted material and energy losses due to wear and tear on tools and machines are prevented, and components are only exchanged if needed. Artificial Intelligence (AI)Crucial Differences. ; Beghi, A.; McLoone, S. Anomaly detection through on-line isolation Forest: An application to plasma etching. In Proceedings of the 2004 Winter Simulation Conference, Washington, DC, USA, 58 December 2004; Volume 2, pp. 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. Liang, Y.; Li, W.; Lu, X.; Wang, S. Fog computing and convolutional neural network enabled prognosis for machining process optimization. We would like to thank VDI Zentrum fr Ressourceneffizienz GmbH for the professional management and Deloitte Consulting GmbH for the realization of the entire study Potenziale der schwachen knstlichen Intelligenz fr die betriebliche Ressourceneffizienz. Long short-term memory for machine remaining life prediction. VDI Zentrum Ressourceneffizienz GmbH (Ed.) 2017. On average, one research paper addresses roughly 2.5 of the predefined resource efficiency aspects. 2020.
Frontiers | A Theory on the Future of the Rebound Effect in a Resource In general, a high variety of AI applications was found during the literature review; for example, papers dealing with humanmachine interaction, material science, or route optimization in logistics. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 1519 December 2008; IEEE: New York, NY, USA, 2008; pp. ; visualization, L.W.
Improving Resource Efficiency: The Ultimate Guide for 2023 Within this research, the majority of papers were identified as (potentially) improving resource efficiency in the business unit of production. 6468 Words. Sustainability. Optimized production planning reduces energy consumption, for example, through schedule and capacity planning. One chemicals company put lean principles at the heart of a worldwide effort to reduce greenhouse-gas emissions. Resource efficiency is the amount of value that you realize from a unit of a resource such as power, water, materials, land and labor. Advanced-analytics approaches and AI technologies, such as neural networks, are now being applied to the control of industrial assets. Jones, N. How to stop data centres from gobbling up the worlds electricity. 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. Sustainability 2021, 13, 6689. Lean principles and sustainability are highly synergistic: both require organizations to eliminate waste and losses relentlessly wherever they find them and to strive to incrementally improve the performance and efficiency of their operations. In these studies, the biggest obstacles to Kaizen implementation included: Lack of concerted efforts by executives. 2020. Li, L.; Ota, K.; Dong, M. Deep Learning for Smart Industry: Efficient Manufacture Inspection System with Fog Computing. Method for the Investigation of Mold Filling in the Fiber Injection Molding Process Based on Image Processing. Energy- and resource - efficiency can be aschieved, if.. Enterprises are paying attention to : Technology development / R&D 13 Own commitment /engage business ; Lee, S. Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map.
Resource efficiency European Environment Agency Isolation Forest. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive Deng, F.; Guo, S.; Zhou, R.; Chen, J. Sensor Multifault Diagnosis with Improved Support Vector Machines. ; supervision, R.M. 133138. interesting to readers, or important in the respective research area. Miehe, R.; Waltersmann, L.; Sauer, A.; Bauernhansl, T. Sustainable production and the role of digital twinsBasic reflections and perspectives. ; Moore, A.W.
How to Increase the Efficiency of Your Material Recycling - Crigler IPA-IAO-Forschung und Praxis. Find support for a specific problem in the support section of our website.
PDF Policy Guidance on Resource Efficiency - OECD 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 [. Within 47 papers, this was just seen as a positive side effect. A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems. ; Shapiai, M.I. Over the past year, an extraordinary number of companies have made public commitments to ongoing reductions in carbon emissions and resource consumption, for example to achieve 100percent renewable-energy use or major carbon-footprint reductions per employee. Sci. Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification.
Improvement of Efficiency - an overview | ScienceDirect Topics Meyes, R.; Donauer, J.; Schmeing, A.; Meisen, T. A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data. Additionally, the identification of typical use cases helps practitioners and researchers to determine possible use cases for increasing resource efficiency within production. MDPI and/or
How to Improve Process Efficiency (+Benefits, KPIs, Tools) Solved WOOCOOMADUSE . Identify an example of an innovation - Chegg Manuf. 1. 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. 5.5.1.2 Material Efficiency 115. .
Energy efficiency and conservation - U.S. Energy Information Deep Learning vs. Neural Networks: Whats the Difference? Mech. Building these factors into the profit-per-hour model allowed the plants management team to focus exclusively on the remaining efficiency gaps. [. As well as this, a variety of industry sectors and, thus, production processes, was included in the literature review, as the focus was narrowed down to manufacturing companies only. Conversion Efficiency The amount of available sunlight that a solar panel converts to electricity is known as conversion efficiency. Thus, the concept of eco-efficiency Top 10 algorithms in data mining.
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