Sustainability

42 pages, 4632 KiB

Open AccessReview

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

by Zeki Murat Çınar, Abubakar Abdussalam Nuhu, Qasim Zeeshan, Orhan Korhan, Mohammed Asmael and Babak Safaei

Sustainability 2020, 12(19), 8211; https://doi.org/10.3390/su12198211 - 5 Oct 2020

Cited by 400 | Viewed by 55806

Abstract

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, [...] Read more.

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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11 pages, 3851 KiB

Open AccessArticle

An LSTM Based Generative Adversarial Architecture for Robotic Calligraphy Learning System

by Fei Chao, Gan Lin, Ling Zheng, Xiang Chang, Chih-Min Lin, Longzhi Yang and Changjing Shang

Sustainability 2020, 12(21), 9092; https://doi.org/10.3390/su12219092 - 31 Oct 2020

Cited by 7 | Viewed by 4637

Abstract

Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data [...] Read more.

Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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13 pages, 1517 KiB

Open AccessArticle

Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments

by Farha Pulukool, Longzhuang Li and Chuntao Liu

Sustainability 2020, 12(24), 10499; https://doi.org/10.3390/su122410499 - 15 Dec 2020

Cited by 5 | Viewed by 2699

Abstract

Hailstorms have caused damages in billions of dollars to industrial, electronic, and mechanical properties such as automobiles, buildings, roads, and aircrafts, as well as life threats to crop and cattle populations, due to their hazardous nature. Hence, the relevance of predicting hailstorms in [...] Read more.

Hailstorms have caused damages in billions of dollars to industrial, electronic, and mechanical properties such as automobiles, buildings, roads, and aircrafts, as well as life threats to crop and cattle populations, due to their hazardous nature. Hence, the relevance of predicting hailstorms in the future has significant scientific, economic, and societal benefits. However, climate models do not have adequate resolutions to explicitly resolve these subscale phenomena. One solution is to estimate the probability of these storms by using large-scale atmospheric thermodynamic environment variables from climate model outputs, but the existing methods only carried out experiments on small datasets limited to a region, country, or location and a large number of input features. Using one year of Tropical Rainfall Measuring Mission (TRMM) observations and European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim) reanalysis on a global scale, this paper develops two deep-learning-based models (an autoencoder and convolutional neural network (CNN)) as well as a machine learning approach (random forest) for hailstorm prediction by using only four attributes—convective potential energy, convective inhibition, 1–3 km wind shear, and warm cloud depth. In the experiments, the random forest approach produces the best hailstorm prediction performance compared to the other two methods. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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19 pages, 7109 KiB

Open AccessArticle

Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data

by Ahmadhon Akbarkhonovich Kamolov and Suhyun Park

Sustainability 2021, 13(11), 5823; https://doi.org/10.3390/su13115823 - 21 May 2021

Cited by 8 | Viewed by 3584

Abstract

Implementing AI in all fields is a solution to the complications that can be troublesome to solve for human beings and will be the key point of the advancement of those spheres. In the marine world, specialists also encounter some problems that can [...] Read more.

Implementing AI in all fields is a solution to the complications that can be troublesome to solve for human beings and will be the key point of the advancement of those spheres. In the marine world, specialists also encounter some problems that can be revealed through addressing AI and machine learning algorithms. One of these challenges is determining the depth of the seabed with high precision. The depth of the seabed is utterly significant in the procedure of ships at sea occupying a safe route. Thus, it is considerably crucial that the ships do not sit in shallow water. In this article, we have addressed the fuzzy c-means (FCM) clustering algorithm, which is one of the vigorous unsupervised learning methods under machine learning to solve the mentioned problems. In the case study, crowdsourced data have been trained, which are gathered from vessels that have installed sound navigation and ranging (SONAR) sensors. The data for the training were collected from ships sailing in the south part of South Korea. In the training section, we segregated the training zone into the diminutive size areas (blocks). The data assembled in blocks had been trained in FCM. As a result, we have received data separated into clusters that can be supportive to differentiate data. The results of the effort show that FCM can be implemented and obtain accurate results on crowdsourced bathymetry. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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31 pages, 3688 KiB

Open AccessArticle

Moving Ad Hoc Networks—A Comparative Study

by Mohammed Abdulhakim Al-Absi, Ahmed Abdulhakim Al-Absi, Mangal Sain and Hoonjae Lee

Sustainability 2021, 13(11), 6187; https://doi.org/10.3390/su13116187 - 31 May 2021

Cited by 42 | Viewed by 8544

Abstract

An ad hoc network is a wireless mobile communication network composed of a group of mobile nodes with wireless transceivers. It does not rely on preset infrastructure and is established temporarily. The mobile nodes of the network use their own wireless transceivers to [...] Read more.

An ad hoc network is a wireless mobile communication network composed of a group of mobile nodes with wireless transceivers. It does not rely on preset infrastructure and is established temporarily. The mobile nodes of the network use their own wireless transceivers to exchange information; when the information is not within the communication range, other intermediate nodes can be used to relay to achieve communication. They can be widely used in environments that cannot be supported by wired networks or which require communication temporarily, such as military applications, sensor networks, rescue and disaster relief, and emergency response. In MANET, each node acts as a host and as a router, and the nodes are linked through wireless channels in the network. One of the scenarios of MANET is VANET; VANET is supported by several types of fixed infrastructure. Due to its limitations, this infrastructure can support some VANET services and provide fixed network access. FANET is a subset of VANET. SANET is one of the common types of ad hoc networks. This paper could serve as a guide and reference so that readers have a comprehensive and general understanding of wireless ad hoc networks and their routing protocols at a macro level with a lot of good, related papers for reference. However, this is the first paper that discusses the popular types of ad hoc networks along with comparisons and simulation tools for Ad Hoc Networks. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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18 pages, 3401 KiB

Open AccessEditor’s ChoiceArticle

Candidate Digital Tasks Selection Methodology for Automation with Robotic Process Automation

by Daehyoun Choi, Hind R’bigui and Chiwoon Cho

Sustainability 2021, 13(16), 8980; https://doi.org/10.3390/su13168980 - 11 Aug 2021

Cited by 25 | Viewed by 5506

Abstract

Today’s business environments face rapid digital transformation, engendering the continuous emerging of new technologies. Robotic Process Automation (RPA) is one of the new technologies rapidly and increasingly grabbing the attention of businesses. RPA tools allow mimicking human tasks by providing a virtual workforce, [...] Read more.

Today’s business environments face rapid digital transformation, engendering the continuous emerging of new technologies. Robotic Process Automation (RPA) is one of the new technologies rapidly and increasingly grabbing the attention of businesses. RPA tools allow mimicking human tasks by providing a virtual workforce, or digital workers in the form of software bots, for automating manual, high-volume, repetitive, and routine tasks. The goal is to allow human workers to delegate their tedious routine tasks to a software bot, thus allowing them to focus on more difficult tasks. RPA tools are simple and very powerful, according to cost-saving and other performance metrics. However, the main challenge of RPA implementation is to effectively determine the business tasks suitable for automation. This paper provides a methodology for selecting candidate tasks for robotic process automation based on user interface logs and process mining techniques. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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28 pages, 4482 KiB

Open AccessArticle

Engaging with Artificial Intelligence (AI) with a Bottom-Up Approach for the Purpose of Sustainability: Victorian Farmers Market Association, Melbourne Australia

by Stéphanie Camaréna

Sustainability 2021, 13(16), 9314; https://doi.org/10.3390/su13169314 - 19 Aug 2021

Cited by 11 | Viewed by 5185

Abstract

Artificial intelligence (AI) is impacting all aspects of food systems, including production, food processing, distribution, and consumption. AI, if implemented ethically for sustainability, can enhance biodiversity, conserve water and energy resources, provide land-related services, power smart cities, and help mitigate climate change. However, [...] Read more.

Artificial intelligence (AI) is impacting all aspects of food systems, including production, food processing, distribution, and consumption. AI, if implemented ethically for sustainability, can enhance biodiversity, conserve water and energy resources, provide land-related services, power smart cities, and help mitigate climate change. However, there are significant issues in using AI to transition to sustainable food systems. AI’s own carbon footprint could cancel out any sustainability benefits that it creates. Additionally, the technology could further entrench inequalities between and within countries, and bias against minorities or less powerful groups. This paper draws on findings from a study of the Victorian Farmers’ Markets Association (VFMA) that investigated the complexity of designing AI tools to enhance sustainability and resilience for the benefit of the organisation and its members. Codesign workshops, both synchronous and asynchronous, semi-structured interviews, and design innovation methods led the VFMA to experiment with an AI tool to link sustainable soil practices, nutrient rich produce, and human health. The analysis shows that the codesign process and an agile approach created a co-learning environment where sustainability and ethical questions could be considered iteratively within transdisciplinary engagement. The bottom-up approach developed through this study supports organisations who want to engage with AI while reinforcing fairness, transparency, and sustainability. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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16 pages, 3905 KiB

Open AccessArticle

by Proshikshya Mukherjee, Prasant Kumar Pattnaik, Ahmed Abdulhakim Al-Absi and Dae-Ki Kang

Sustainability 2021, 13(19), 10579; https://doi.org/10.3390/su131910579 - 24 Sep 2021

Cited by 14 | Viewed by 2384

Abstract

Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from [...] Read more.

Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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18 pages, 6421 KiB

Open AccessArticle

An Efficient Method for Capturing the High Peak Concentrations of PM_2.5 Using Gaussian-Filtered Deep Learning

by Inchoon Yeo and Yunsoo Choi

Sustainability 2021, 13(21), 11889; https://doi.org/10.3390/su132111889 - 27 Oct 2021

Cited by 1 | Viewed by 1961

Abstract

This paper proposes a deep learning model that integrates a convolutional neural network with a gate circulation unit that captures patterns of high-peak PM_2.5 concentrations. The purpose is to accurately predict high-peak PM_2.5 concentration data that cannot be trained in general [...] Read more.

This paper proposes a deep learning model that integrates a convolutional neural network with a gate circulation unit that captures patterns of high-peak PM_2.5 concentrations. The purpose is to accurately predict high-peak PM_2.5 concentration data that cannot be trained in general deep learning models. For the training of the proposed model, we used all available weather and air quality data for three years from 2015 to 2017 from 25 stations of the National Institute of Environmental Research (NIER) and the Korea Meteorological Administration (KMA) observatory in Seoul, South Korea. Our model trained three years of data and predicted high-peak PM_2.5 concentrations for the year 2018. In addition, we propose a Gaussian filter algorithm as a preprocessing method for capturing high concentrations of PM_2.5 in the Seoul area and predicting them more accurately. This model overcomes the limitations of conventional deep learning approaches that are unable to predict high peak PM_2.5 concentrations. Comparing model measurements at each of the 25 monitoring sites in 2018, we found that the deep learning model with a Gaussian filter achieved an index of agreement of 0.73–0.89 and a proportion of correctness of 0.89–0.96, and compared to the conventional deep learning method (average POC = 0.85), the Gaussian filter algorithm (average POC = 0.94) improved the accuracy of high-concentration PM_2.5 prediction by an average of about 9%. Applying this algorithm in the preprocessing stage could be updated to predict the risk of high PM_2.5 concentrations in real time. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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26 pages, 906 KiB

Open AccessArticle

A Supervised Machine Learning Classification Framework for Clothing Products’ Sustainability

by Chloe Satinet and François Fouss

Sustainability 2022, 14(3), 1334; https://doi.org/10.3390/su14031334 - 25 Jan 2022

Cited by 13 | Viewed by 5328

Abstract

These days, many sustainability-minded consumers face a major problem when trying to identify environmentally sustainable products. Indeed, there are a variety of confusing sustainability certifications and few labels capturing the overall environmental impact of products, as the existing procedures for assessing the environmental [...] Read more.

These days, many sustainability-minded consumers face a major problem when trying to identify environmentally sustainable products. Indeed, there are a variety of confusing sustainability certifications and few labels capturing the overall environmental impact of products, as the existing procedures for assessing the environmental impact of products throughout their life cycle are time consuming, costly, and require a lot of data and input from domain experts. This paper explores the use of supervised machine learning tools to extrapolate the results of existing life cycle assessment studies (LCAs) and to develop a model—applied to the clothing product category—that could easily and quickly assess the products’ environmental sustainability throughout their life cycle. More precisely, we assemble a dataset of clothing products with their life cycle characteristics and corresponding known total environmental impact and test, on a 5-fold cross-validation basis, nine state-of-the-art supervised machine learning algorithms. Among them, the random forest algorithm has the best performance with an average accuracy of 91% over the five folds. The resulting model provides rapid environmental feedback on a variety of clothing products with the limited data available to online retailers. It could be used to quickly provide interested consumers with product-level sustainability information, or even to develop a unique and all-inclusive environmental label. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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14 pages, 1048 KiB

Open AccessArticle

Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

by Gary Storey, Qinggang Meng and Baihua Li

Sustainability 2022, 14(3), 1458; https://doi.org/10.3390/su14031458 - 27 Jan 2022

Cited by 59 | Viewed by 5244

Abstract

Reduction in chemical usage for crop management due to the environmental and health issues is a key area in achieving sustainable agricultural practices. One area in which this can be achieved is through the development of intelligent spraying systems which can identify the [...] Read more.

Reduction in chemical usage for crop management due to the environmental and health issues is a key area in achieving sustainable agricultural practices. One area in which this can be achieved is through the development of intelligent spraying systems which can identify the target for example crop disease or weeds allowing for precise spraying reducing chemical usage. Artificial intelligence and computer vision has the potential to be applied for the precise detection and classification of crops. In this paper, a study is presented that uses instance segmentation for the task of leaf and rust disease detection in apple orchards using Mask R-CNN. Three different Mask R-CNN network backbones (ResNet-50, MobileNetV3-Large and MobileNetV3-Large-Mobile) are trained and evaluated for the tasks of object detection, segmentation and disease detection. Segmentation masks on a subset of the Plant Pathology Challenge 2020 database are annotated by the authors, and these are used for the training and evaluation of the proposed Mask R-CNN based models. The study highlights that a Mask R-CNN model with a ResNet-50 backbone provides good accuracy for the task, particularly in the detection of very small rust disease objects on the leaves. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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16 pages, 1298 KiB

Open AccessArticle

Intersection Signal Timing Optimization: A Multi-Objective Evolutionary Algorithm

by Xinghui Zhang, Xiumei Fan, Shunyuan Yu, Axida Shan, Shujia Fan, Yan Xiao and Fanyu Dang

Sustainability 2022, 14(3), 1506; https://doi.org/10.3390/su14031506 - 27 Jan 2022

Cited by 22 | Viewed by 4752

Abstract

The rapid motorization of cities has led to the increasingly serious contradiction between supply and demand of road resources, and intersections have become the main bottleneck of traffic congestion. In general, capacity and delay are often used as indicators to improve intersection efficiency, [...] Read more.

The rapid motorization of cities has led to the increasingly serious contradiction between supply and demand of road resources, and intersections have become the main bottleneck of traffic congestion. In general, capacity and delay are often used as indicators to improve intersection efficiency, but auxiliary indicators such as vehicle emissions that contribute to sustainable traffic development also need to be considered. It is necessary to evaluate intersection traffic efficiency through multiple measures to reflect different aspects of traffic, and these measures may conflict with each other. Therefore, this paper studies a multi-objective urban traffic signal timing problem, which requires a reasonable signal timing parameter under a given traffic flow condition, to better take into account the traffic capacity, delay and exhaust emission index of the intersection. Firstly, based on the traffic flow as the basic data, combined with the traffic flow description theory and exhaust emission estimation rules, a multi-objective model of signal timing problem is established. Secondly, the target model is solved and tested by the genetic algorithm of non-dominated sorting framework. It is found that the Pareto solution set of traffic indicators obtained by NSGA-III has a larger domain. Finally, the search mechanism of evolutionary algorithm is essentially unconstrained, while the actual traffic signal timing problem is constrained by traffic environment. In order to obtain a better signal timing scheme, this paper introduces the method of combining hybrid constraint strategy and NSGA-III framework, abbreviated as HCNSGA-III. The simulation experiment was carried out based on the same target model. The simulated results were compared with the actual scheme and the timing scheme obtained in recent research. The results show that the indices of traffic capacity, delay and exhaust emission obtained by the proposed method have more obvious advantages. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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18 pages, 3576 KiB

Open AccessArticle

Using Hybrid Artificial Intelligence and Machine Learning Technologies for Sustainability in Going-Concern Prediction

by Der-Jang Chi and Zong-De Shen

Sustainability 2022, 14(3), 1810; https://doi.org/10.3390/su14031810 - 5 Feb 2022

Cited by 11 | Viewed by 3648

Abstract

The going-concern opinions of certified public accountants (CPAs) and auditors are very critical, and due to misjudgments, the failure to discover the possibility of bankruptcy can cause great losses to financial statement users and corporate stakeholders. Traditional statistical models have disadvantages in giving [...] Read more.

The going-concern opinions of certified public accountants (CPAs) and auditors are very critical, and due to misjudgments, the failure to discover the possibility of bankruptcy can cause great losses to financial statement users and corporate stakeholders. Traditional statistical models have disadvantages in giving going-concern opinions and are likely to cause misjudgments, which can have significant adverse effects on the sustainable survival and development of enterprises and investors’ judgments. In order to embrace the era of big data, artificial intelligence (AI) and machine learning technologies have been used in recent studies to judge going concern doubts and reduce judgment errors. The Big Four accounting firms (Deloitte, KPMG, PwC, and EY) are paying greater attention to auditing via big data and artificial intelligence (AI). Thus, this study integrates AI and machine learning technologies: in the first stage, important variables are selected by two decision tree algorithms, classification and regression trees (CART), and a chi-squared automatic interaction detector (CHAID); in the second stage, classification models are respectively constructed by extreme gradient boosting (XGB), artificial neural network (ANN), support vector machine (SVM), and C5.0 for comparison, and then, financial and non-financial variables are adopted to construct effective going-concern opinion decision models (which are more accurate in prediction). The subjects of this study are listed companies and OTC (over-the-counter) companies in Taiwan with and without going-concern doubts from 2000 to 2019. According to the empirical results, among the eight models constructed in this study, the prediction accuracy of the CHAID–C5.0 model is the highest (95.65%), followed by the CART–C5.0 model (92.77%). Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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31 pages, 11426 KiB

Open AccessEditor’s ChoiceReview

Promoting Sustainability through Next-Generation Biologics Drug Development

by Katharina Paulick, Simon Seidel, Christoph Lange, Annina Kemmer, Mariano Nicolas Cruz-Bournazou, André Baier and Daniel Haehn

Sustainability 2022, 14(8), 4401; https://doi.org/10.3390/su14084401 - 7 Apr 2022

Cited by 6 | Viewed by 4739

Abstract

The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using [...] Read more.

The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using “digital twins” can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organization’s 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future. Full article

(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)

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