Hasil untuk "Industrial engineering. Management engineering"

David James Wagg, K. Worden, R. Barthorpe et al.

This paper presents a review of the state of the art for digital twins in the application domain of engineering dynamics. The focus on applications in dynamics is because: (i) they offer some of the most challenging aspects of creating an effective digital twin, and (ii) they are relevant to important industrial applications such as energy generation and transport systems. The history of the digital twin is discussed first, along with a review of the associated literature; the process of synthesizing a digital twin is then considered, including definition of the aims and objectives of the digital twin. An example of the asset management phase for a wind turbine is included in order to demonstrate how the synthesis process might be applied in practice. In order to illustrate modeling issues arising in the construction of a digital twin, a detailed case study is presented, based on a physical twin, which is a small-scale three-story structure. This case study shows the progression toward a digital twin highlighting key processes including system identification, data-augmented modeling, and verification and validation. Finally, a discussion of some open research problems and technological challenges is given, including workflow, joints, uncertainty management, and the quantification of trust. In a companion paper, as part of this special issue, a mathematical framework for digital twin applications is developed, and together the authors believe this represents a firm framework for developing digital twin applications in the area of engineering dynamics.

A. Zonnenshain, Ron S. Kenett

Abstract Quality is a crucial dimension of products and processes. It is considered a competitive advantage for companies and organizations in the global market. Quality models and practices went through several evolutionary steps during modern history—from inspection to control, to quality assurance, to quality management and quality by design. These quality models follow the evolutions and revolutions in industry. It seems however, that in the last few years the quality discipline went into stagnation—very few innovative models for quality are being proposed and quality professionals in companies and organization have apparently lost their leadership positions. Also, the research for new and innovative quality models is scarce. The fourth industrial revolution is an opportunity for the quality movement to become a leading force. This poses significant challenges to the quality profession by emphasizing the need to adapt to technology innovations, to modern data analytics and to the entrepreneurships ecosystem that characterize an era of the fourth industrial revolution. In this paper, we present a framework for a quality discipline supporting the fourth industrial revolution. We propose to call it Quality 4.0. The paper also offers future directions for quality and reliability engineering that leverage opportunities derived from the fourth industrial revolution. Specifically, we discuss: (1) Quality as a data driven discipline, (2) the application of modeling and simulation for evidence-based quality engineering, (3) health monitoring and prognostics for quality, (4) integrated quality management, (5) maturity levels with respect to the fourth industrial revolution, (6) integrating innovation with quality and managing for innovation, (7) Quality 4.0 and data science, (8) integrating reliability engineering with quality engineering, and finally, (9) information quality. We are aware that these directions are still not a comprehensive picture of Quality 4.0. We claim however, that they constitute a substantial basis to update the body of knowledge and practices of the quality profession.

Mayuranath SureshKumar, Hanumanthrao Kannan

Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context-dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem-space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue-based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.

Jinxin Yang, E. Lim, Lina Tan et al.

To date, organic–inorganic hybrid perovskite solar cells (PSCs) have reached a certified efficiency of 25.7%, showing great potential in upscale industrial commercialization. However, a huge obstacle facing the industrialization of PSCs is the decreased efficiency and long‐term stability when upscaling the device area. To overcome these issues, blade‐coating methods have been developed to fabricate large‐area PSCs due to their capability to deposit uniform large‐area perovskite films. Ink engineering plays an important role in the blade‐coating, especially for crystallinity and defect control. In this review, the blade‐coating method to fabricate large‐area perovskite films is first introduced. Then, the perovskite ink engineering for blade‐coating PSCs is systematically summarized. Specifically, the effects of perovskite composition management and solvent engineering on perovskite film quality are discussed, and recent efforts in additive strategy to passivate perovskite defects are also summarized. Subsequently, recent advances in functional layer ink engineering and fully blade‐coated PSCs are summarized. Moreover, the applications of blade‐coating method in hole transporting material‐free carbon‐based PSCs are discussed. Finally, some suggestions and an outlook on this field are provided to help facilitate highly efficient and stable blade‐coated PSCs.

Д. Луцак, М. Ткач

У статті розглянуто актуальність проблеми впровадження віртуальних голосових асистентів зі штучним інтелектом у сучасне життя. Аналізуються особливості їх застосування у різних сферах, від персонального використання до інтеграції в бізнес- процеси, з метою підвищення ефективності роботи та покращення якості обслугову- вання. Віртуальні асистенти допомагають оптимізувати взаємодію з цифровими пристроями, виконують різноманітні завдання на основі голосових або текстових команд, що робить їх універсальними інструментами для автоматизації повсякденних функцій. Окремо досліджується здатність цих систем до навчання та адаптації до індивідуальних потреб користувачів завдяки застосуванню технологій штучного інтелекту. Проаналізовано останні дослідження науковців у цій сфері та наведено приклади ефективного застосування віртуальних асистентів у різних галузях. Бібл. 14, іл. 2, табл. 2

Angga Yesaya, Vivi Arisandhy, David Try Liputra

Penelitian mengenai pemilihan atau penentuan supplier dengan menggunakan metode AHP maupun kombinasinya dengan metode TOPSIS dalam penentuan supplier di industri penghasil kasur pegas belum banyak dilakukan. Penelitian ini akan membahas penerapan gabungan metode AHP dan TOPSIS dalam penentuan supplier di industri penghasil kasur pegas sehingga diharapkan hasil yang diperoleh lebih akurat dibandingkan jika hanya menggunakan salah satu metode. PT XYZ melakukan pemesanan bahan baku kawat kepada 4 supplier dan mengalokasikan pesanan dengan jumlah yang sama rata. Langkah pertama yang dilakukan adalah penentuan kriteria dan subkriteria untuk pemilihan supplier dan penyusunan hierarki. Selanjutnya dilakukan penyusunan kuesioner perbandingan berpasangan untuk menentukan tingkat kepentingan kriteria dan subkriteria. Setelah dilakukan perhitungan dengan metode AHP, diperoleh bobot kriteria dan subkriteria yang menjadi input untuk metode TOPSIS. TOPSIS digunakan untuk mengetahui supplier yang memiliki kinerja terbaik. Hasil yang diperoleh adalah kriteria Price dan subkriteria Harga Bahan Baku mempunyai tingkat kepentingan tertinggi dalam penilaian kinerja supplier kawat karena memiliki bobot tertinggi. Perusahaan dapat tetap menggunakan kebijakan multi-supplier dengan memprioritaskan supplier 1, namun ada beberapa kriteria dari kinerja supplier 1 yang perlu ditingkatkan jika dibandingkan dengan supplier lainnya. Kata Kunci: AHP, Bahan Baku Utama, Kasur Pegas, Supplier, TOPSIS.

Chen Xinyu, Hui Tianfang, Li Yanlin et al.

As artificial intelligence (AI) is increasingly integrated into society, people are relying on it more and more, and higher requirements are put forward for the safety and ethical standards of AI. This article explores the development of artificial intelligence technology and its potential safety and ethical challenges in various fields. In terms of security, the risk of adversarial attacks is analyzed in depth, and the robustness of the model is enhanced through adversarial training and data enhancement techniques. In addition, it is recommended to adopt measures such as data encryption and differential privacy to address data privacy and security issues. Regarding ethical considerations, this paper identifies the origins of algorithmic bias and argues for mitigating it through rigorous testing, validation, and regulatory frameworks. It also highlights the importance of increasing the transparency and explainability of AI to enhance public trust. Finally, the paper emphasizes the importance of defining accountability for AI behavior and suggests establishing laws and regulations that effectively govern AI applications. In conclusion, the study argues that the development of AI should emphasize safety and ethical considerations. Through the combination of technical intervention, legal supervision and social responsibility, the sustainable development of artificial intelligence is effectively promoted.

Daniel Mendez, Paris Avgeriou, Marcos Kalinowski et al.

Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.

Christian Arnold, Daniel Kiel, K. Voigt

The Industrial Internet of Things (IIoT) poses large impacts on business models (BM) of established manufacturing companies within several industries. Thus, this paper aims at analyzing the influence of the IIoT on these BMs with particular respect to differences and similarities dependent on varying industry sectors. For this purpose, we employ an exploratory multiple case study approach based on semi-structured expert interviews in 69 manufacturing companies from the five most important German industries. Owing the lack of previous research, our study contributes to the current state of management literature by revealing the following valuable insights with regard to industry-specific BM changes: The machine and plant engineering companies are mainly facing changing workforce qualifications, the electrical engineering and information and communication technology companies are particularly concerned with the importance of novel key partner networks, and automotive suppliers predominantly exploit IIoT-inherent benefits in terms of an increasing cost efficiency.

Jianxin Xie

Materials genome engineering represents the new frontier of materials research, and is disrupting the conventional “trial and error” paradigm for materials innovation. In the present perspective, the author reflects on the major achievements already made in five sub‐domains, including high‐efficiency materials computation and design, revolutionary experimental technologies, materials big data technologies, research and development of advanced materials, and industrial applications. Furthermore, the author lays out five crucial directions of future efforts for maturing the relevant technologies. These directions include cross‐scale modeling and computational design, artificial intelligence for materials science, automatic and intelligent experimentation, digital twin, and data resource management and sharing.

A. McEwan, E. Marchi, R. Spinelli et al.

Plantation forests are established, and expanding, to satisfy increasing global demand for timber products. Shifting societal values, such as safety, productivity, environmental, quality and social are influencing the plantation forestry sector. This is primarily driven through an ever increasing world population, which in turn influences the way nations view the value systems by which they live. More people require more resources—also forest products. Also, the availability of information is influencing the pace of technological development. These changes could result in a difference in the management of plantations that could affect the forest engineering systems of the future. This review aimed to summarize the current status of plantation forests; summarize future developments and possible scenarios in forest plantation management for the various products; and assess whether these developments in a plantation environment could affect the harvesting systems used. Factors influencing the form of plantations include the type and nature of the plantation owner; the change in demand for different and new forest products; climate change factors, including the use of biomass for energy, carbon sequestration and trading; ecosystem services and other products and services; and sustainability certification of forest management. The impact and influence of these factors were summarised into a series of key drivers that will influence the technology used in harvesting machines, as well as the choice of harvesting machines, systems and methods. These drivers were the effect of variations in tree size, the expansion of plantation areas onto more difficult terrain, diversity in plantation design, increased attention towards site impacts and the increased use of biomass for energy. Specific information is provided regarding how the harvesting systems could be affected.

N. Mohamed, J. Al-Jaroodi, Imad Jawhar et al.

Healthcare systems are complex systems that need effective and efficient operations, optimizations, management, and control to offer reliable, high-quality, and cost-effective healthcare services. There are different approaches to improve the management of healthcare systems including utilizing the healthcare systems engineering principles. Healthcare systems engineering views a healthcare organization as a system and applies the engineering analysis and design principles to improve different aspects of healthcare services provided in that system. While this approach can provide many advantages for healthcare organizations, there are also many challenges hindering the ability of healthcare systems engineers from effectively accomplishing their mission. The initiation of the digital twin technology formed several potential methods for various industrial sectors to enhance their operations. Accordingly, they can help improve productivity, cost-effectiveness, reliability, quality, and flexibility. This paper studies how digital twins can be utilized for improving healthcare systems engineering processes and outcomes to enhance different aspects of healthcare systems. The paper discusses some of the challenges of healthcare systems engineering and how these challenges can be relaxed by utilizing digital twins. The paper also develops a conceptual framework to utilize digital twins for improving healthcare systems engineering processes and outcomes and discusses the prospects of such utilization on achieving the goals of healthcare systems engineering. In addition, the paper provides some discussions on the impact of this utilization and the future research and development projections of the employment of digital twins for healthcare systems engineering.

Gang Qiao, Xizhu Qiang, Lei Wan et al.

In this paper, in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) systems, we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting theory. Then, we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method, Orthogonal Matching Pursuit (OMP). The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval, which prevents OMP-based methods from achieving better performance. To overcome this drawback, the image deblurring-based channel estimation method, in which the channel estimation problem is analogized to one-dimensional image deblurring, was proposed to improve the large compensation distance of traditional OMP. The advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.

E. Shahri, P. Pedreiras, L. Almeida

The increasing prominence of concepts such as Smart Production and Industrial Internet of Things (IIoT) within the context of Industry 4.0 has introduced a new set of requirements for the engineering of industrial systems, including support for dynamic environments, timeliness guarantees, support for heterogeneity, interoperability and reliability. These requirements are further exacerbated at the network level by the notable rise in the number and variety of devices involved. To stay competitive in this ever-changing industrial landscape while boosting productivity, it is vital to meet those requirements, combining established protocols with emerging technologies. Software-Defined Networking (SDN) is the forefront traffic management paradigm that offers flexibility for complex industrial networks, enabling efficient resource allocation and dynamic reconfiguration. Message Queuing Telemetry Transport (MQTT) is a low-overhead protocol of the application layer that is gaining popularity in the scope of the IoT and IIoT. However, its Quality-of-Service (QoS) policies do not support timeliness requirements. This article presents a framework that seamlessly integrates SDN and MQTT, enhancing network management flexibility while satisfying real-time requirements found in industrial environments. It leverages the User Properties of MQTTv5 to allow specifying real-time requirements. MQTT traffic is intercepted by a Network Manager that extracts real-time information and instructs an SDN controller to deploy corresponding network reservations. MQTT traffic across multiple edge networks is propagated by selected brokers using multicasting. Extensive experiments validate the proposed approach, demonstrating its superiority over MQTT and Direct Multicast-MQTT (DM-MQTT) DM-MQTT in latency reduction. A response time analysis, validated experimentally, emphasizes robust performance across metrics.

Jukka Ruohonen, Kalle Hjerppe

Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.

Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh

Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.

Ranim Khojah, Mazen Mohamad, Philipp Leitner et al.

Large Language Models (LLMs) are frequently discussed in academia and the general public as support tools for virtually any use case that relies on the production of text, including software engineering. Currently there is much debate, but little empirical evidence, regarding the practical usefulness of LLM-based tools such as ChatGPT for engineers in industry. We conduct an observational study of 24 professional software engineers who have been using ChatGPT over a period of one week in their jobs, and qualitatively analyse their dialogues with the chatbot as well as their overall experience (as captured by an exit survey). We find that, rather than expecting ChatGPT to generate ready-to-use software artifacts (e.g., code), practitioners more often use ChatGPT to receive guidance on how to solve their tasks or learn about a topic in more abstract terms. We also propose a theoretical framework for how (i) purpose of the interaction, (ii) internal factors (e.g., the user's personality), and (iii) external factors (e.g., company policy) together shape the experience (in terms of perceived usefulness and trust). We envision that our framework can be used by future research to further the academic discussion on LLM usage by software engineering practitioners, and to serve as a reference point for the design of future empirical LLM research in this domain.

Nadia Alshahwan, Mark Harman, Inna Harper et al.

In this paper we address the following question: How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE applies a series of semantic filters that discard code that fails to meet these twin guarantees. This overcomes the potential problem of LLM's propensity to hallucinate. It allows us to generate code using LLMs, independently of any human. The human plays the role only of final code reviewer, as they would do with code generated by other human engineers. This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal.

Sergio Rico

Case studies are a popular and noteworthy type of research study in software engineering, offering significant potential to impact industry practices by investigating phenomena in their natural contexts. This potential to reach a broad audience beyond the academic community is often undermined by deficiencies in reporting, particularly in the context description, study classification, generalizability, and the handling of validity threats. This paper presents a reflective analysis aiming to share insights that can enhance the quality and impact of case study reporting. We emphasize the need to follow established guidelines, accurate classification, and detailed context descriptions in case studies. Additionally, particular focus is placed on articulating generalizable findings and thoroughly discussing generalizability threats. We aim to encourage researchers to adopt more rigorous and communicative strategies, ensuring that case studies are methodologically sound, resonate with, and apply to software engineering practitioners and the broader academic community. The reflections and recommendations offered in this paper aim to ensure that insights from case studies are transparent, understandable, and tailored to meet the needs of both academic researchers and industry practitioners. In doing so, we seek to enhance the real-world applicability of academic research, bridging the gap between theoretical research and practical implementation in industry.

Chao Huang, Siqi Bu, H. Lee et al.