scholarly journals Assessment Framework of Smart Shipyard Maturity Level via Data Envelopment Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 1964
Author(s):  
Jong Hun Woo ◽  
Haoyu Zhu ◽  
Dong Kun Lee ◽  
Hyun Chung ◽  
Yongkuk Jeong
Keyword(s):  
Data Envelopment Analysis ◽  
Industrial Revolution ◽  
New Technologies ◽  
Smart Manufacturing ◽  
Data Envelopment ◽  
Maturity Level ◽  
Productivity Improvement ◽  
Shipbuilding Industry ◽  
Wide Range ◽  
Smart Factories

The fourth industrial revolution (“Industry 4.0”) has caused an escalating need for smart technologies in manufacturing industries. Companies are examining various cutting-edge technologies to realize smart manufacturing and construct smart factories and are devoting efforts to improve their maturity level. However, productivity improvement is rarely achieved because of the large variety of new technologies and their wide range of applications; thus, elaborately setting improvement goals and plans are seldom accomplished. Fortunately, many researchers have presented guidelines for diagnosing the smartness maturity level and systematic directions to improve it, for the eventual improvement of productivity. However, most research has focused on mass production industries wherein the overall smartness maturity level is already high (e.g., high-level automation). These studies thus have limited applicability to the shipbuilding industry, which is basically a built-to-order industry. In this study, through a technical demand survey of the shipbuilding industry and an investigation of existing smart manufacturing and smart factories, the keywords of connectivity, automation, and intelligence were derived and based on these keywords, we developed a new diagnostic framework for smart shipyard maturity level assessment. The framework was applied to eight shipyards in South Korea to diagnose their smartness maturity level, and a data envelopment analysis (DEA) was performed to confirm the usefulness of the diagnosis results. By comparing the DEA models, the results with the smart level as an input represents the actual efficiency of shipyards better than the results of conventional models.

scholarly journals A Revised Inverse Data Envelopment Analysis Model Based on Radial Models

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 803
Author(s):  
Xiaoyin Hu ◽  
Jianshu Li ◽  
Xiaoya Li ◽  
Jinchuan Cui
Keyword(s):  
Data Envelopment Analysis ◽  
Efficiency Index ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Numerical Examples ◽  
Wide Range ◽  
Dea Models ◽  
Overall Efficiency ◽  
Decision Making Units

In recent years, there has been an increasing interest in applying inverse data envelopment analysis (DEA) to a wide range of disciplines, and most applications have adopted radial-based inverse DEA models. However, results given by existing radial based inverse DEA models can be unreliable as they neglect slacks while evaluating decision-making units’ (DMUs) overall efficiency level, whereas classic radial DEA models measure the efficiency level through not only radial efficiency index but also slacks. This paper points out these disadvantages with a counterexample, where current inverse DEA models give results that outputs shall increase when inputs decrease. We show that these unreasonable results are the consequence of existing inverse DEA models’ failure in preserving DMU’s efficiency level. To rectify this problem, we propose a revised model for the situation where the investigated DMU has no slacks. Compared to existing radial inverse DEA models, our revised model can preserve radial efficiency index as well as eliminating all slacks, thus fulfilling the requirement of efficiency level invariant. Numerical examples are provided to illustrate the validity and limitations of the revised model.

scholarly journals Eco-efficiency Assessment in Agriculture: A Literature Review Focused on Methods and Indicators

2020 ◽  
Vol 12 (7) ◽  
pp. 118
Author(s):  
Luis H. Suzigan ◽  
Carlos Rosano Peña ◽  
Patricia Guarnieri
Keyword(s):  
Data Envelopment Analysis ◽  
Literature Review ◽  
Social Impact ◽  
Efficiency Analysis ◽  
Environmental Indicators ◽  
Social Dimension ◽  
Data Envelopment ◽  
Agricultural Systems ◽  
Efficiency Assessment ◽  
Wide Range

Combining economic performance with environmental and social concern has been a developing topic in recent decades. Eco-efficiency analysis is a widely applied tool to assess the efficiency of agricultural systems, while increasingly considering their environmental and social impact. The main objective of this article is to accomplish a literature review on the application of eco-efficiency analysis in agricultural systems, focusing on methods and indicators that are most regarded for the quantitative assessment of agricultural eco-efficiency. The literature review found two main methods most widely applied to assess eco-efficiency: Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA), which are often combined. LCA is generally focused on the assessment of the environmental impacts of products and practices. DEA is mostly used to measure the eco-efficiency of decision-making units, such as farms, regions, or countries, and has no subjective focus on neither technical nor environmental performance. Both methods share a wide range of economic and environmental indicators but fail to incorporate the social dimension of sustainability into the eco-efficiency analysis. A simple framework, based on Data Envelopment Analysis, is offered to assess the eco-efficiency of the Brazilian agriculture, aiming at identifying the benefits and limitations of the analysis.

scholarly journals Demand and Supply of Cut Flowers Production in Krishnagiri District of Tamil Nadu - An Approach by Data Envelopment Analysis

2021 ◽  
Author(s):  
R. Thulasiram ◽  
S. Usha Nandhini ◽  
S. Amarnath
Keyword(s):  
Data Envelopment Analysis ◽  
Tamil Nadu ◽  
Sampling Method ◽  
Total Sample ◽  
Data Envelopment ◽  
Cut Flowers ◽  
Demand And Supply ◽  
Horticultural Crops ◽  
Efficiency Measures ◽  
Wide Range

Background: India has been bestowed with wide range of climate and physio-geographical conditions making it suitable for growing various kinds of horticultural crops. Out of which the awareness on usage of cut flowers for various occasions has raised the demand for cut flowers in the market, especially Tamil Nadu. The overall objective of the study is to estimate the demand and supply of cut flowers in Tamil Nadu. Methods: Hosur block in Krishnagiri district of Tamil Nadu was purposively selected as it is the leader in area and production of rose flowers. A two-stage random sampling method was adopted to select the sample farms with a total sample size of 120. Simple percentage analysis and Data Envelopment Analysis were used to discuss the results. Result: The important period of demand for cut flowers in Hosur block are events like Navratri, Christmas, New Year, Valentine Day, Therthiruvizha, Ramjan and Bakrith. There were 164 days in a year which would be auspicious. On an average 450 bunches of rose, 320 bunches of gerberas and 150 bunches of carnations are used in an event in addition to some other flowers. The technical efficiency measures for Roses indicated that most farmers belonged to the least efficient category ( less than 90 per cent) with a proportion of 62.50 per cent to total.

scholarly journals Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

2018 ◽  
Vol 10 (12) ◽  
pp. 4779 ◽  
Author(s):  
Yuquan Meng ◽  
Yuhang Yang ◽  
Haseung Chung ◽  
Pil-Ho Lee ◽  
Chenhui Shao
Keyword(s):  
Energy Efficiency ◽  
Industrial Revolution ◽  
Rapid Development ◽  
Sustainable Manufacturing ◽  
Business Owners ◽  
Smart Manufacturing ◽  
Future Studies ◽  
Manufacturing Technologies ◽  
New Generation ◽  
Smart Factories

With the rapid development of sensing, communication, computing technologies, and analytics techniques, today’s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as governments, and substantial efforts are devoted to both areas, the intersection of the two has not been fully exploited. Conventionally, studies in sustainable manufacturing and smart manufacturing have different objectives and employ different tools. Nevertheless, in the design and implementation of smart factories, sustainability, and energy efficiency are supposed to be important goals. Moreover, big data based decision-making techniques that are developed and applied for smart manufacturing have great potential in promoting the sustainability of manufacturing. In this paper, the state-of-the-art of sustainable and smart manufacturing is first reviewed based on the PRISMA framework, with a focus on how they interact and benefit each other. Key problems in both fields are then identified and discussed. Specially, different technologies emerging in the 4th industrial revolution and their dedications on sustainability are discussed. In addition, the impacts of smart manufacturing technologies on sustainable energy industry are analyzed. Finally, opportunities and challenges in the intersection of the two are identified for future investigation. The scope examined in this paper will be interesting to researchers, engineers, business owners, and policymakers in the manufacturing community, and could serve as a fundamental guideline for future studies in these areas.

Returns to Scale as an Established Scaling Indicator: Always a Good Advisor?

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Andreas Dellnitz ◽  
Wilhelm Rödder
Keyword(s):  
Data Envelopment Analysis ◽  
Real World ◽  
Returns To Scale ◽  
Increasing Returns ◽  
Data Envelopment ◽  
Productivity Improvement ◽  
Increasing Returns To Scale ◽  
Numerical Examples ◽  
Real World Application ◽  
A Company

AbstractIn data envelopment analysis (DEA), returns to scale (RTS) are a widely accepted instrument for a company to reveal its activity scaling potentials. In the case of increasing returns to scale (IRS), a company learns that upsizing activities improves its productivity. For decreasing returns to scale (DRS), the instrument likewise should depict a downsizing force, again for improving productivity. Unfortunately, here the classical RTS concept shows misbehavior. Under certain circumstances, it is the wrong indicator for scaling activities and even hides respective productivity improvement potentials. In this paper, we study this phenomenon, using the DEA concept, and illustrate it via little numerical examples and a real-world application consisting of 37 Brazilian banks.

scholarly journals Solving fuzzy multi-objective shortest path problem based on data envelopment analysis approach

2021 ◽  
Author(s):  
M. Bagheri ◽  
Ali Ebrahimnejad ◽  
S. Razavyan ◽  
F. Hosseinzadeh Lotfi ◽  
N. Malekmohammadi
Keyword(s):  
Data Envelopment Analysis ◽  
Shortest Path ◽  
Shortest Path Problem ◽  
Data Envelopment ◽  
Practical Applications ◽  
Multi Objective ◽  
Decision Making Unit ◽  
Wide Range ◽  
Time Distance

AbstractThe shortest path problem (SPP) is a special network structured linear programming problem that appears in a wide range of applications. Classical SPPs consider only one objective in the networks while some or all of the multiple, conflicting and incommensurate objectives such as optimization of cost, profit, time, distance, risk, and quality of service may arise together in real-world applications. These types of SPPs are known as the multi-objective shortest path problem (MOSPP) and can be solved with the existing various approaches. This paper develops a Data Envelopment Analysis (DEA)-based approach to solve the MOSPP with fuzzy parameters (FMOSPP) to account for real situations where input–output data include uncertainty of triangular membership form. This approach to make a connection between the MOSPP and DEA is more flexible to deal with real practical applications. To this end, each arc in a FMOSPP is considered as a decision-making unit with multiple fuzzy inputs and outputs. Then two fuzzy efficiency scores are obtained corresponding to each arc. These fuzzy efficiency scores are combined to define a unique fuzzy relative efficiency. Hence, the FMOSPP is converted into a single objective Fuzzy Shortest Path Problem (FSPP) that can be solved using existing FSPP algorithms.

scholarly journals Domain Specific Assets: A Software Platform for Use Case Driven Human Friendly Factory Interaction

2020 ◽  
Author(s):  
Felix Brandt ◽  
Eric Brandt ◽  
Javad Ghofrani ◽  
David Heik ◽  
Dirk Reichelt
Keyword(s):  
Industrial Revolution ◽  
New Technologies ◽  
Use Case ◽  
Human Being ◽  
Software Platform ◽  
Domain Specific ◽  
Self Organized ◽  
Production Factors ◽  
Wide Range ◽  
Current Production

In current efforts to digitize manufacturing and move it into the fourth stage of the industrial revolution, a wide range of integration solutions is being considered to enable manufacturing to adapt to change. In transforming a factory into a self-organized, autonomous factory, companies are currently struggling with rapidly changing requirements and production factors, among other things. This is a particular problem for the human being as an actor within the factory, as the amount of new technologies and protocols increases the training effort. Proprietary interfaces of the control providers, a wide range of different communication protocols, complicate the understanding of the production processes, the evaluation and testability of new use cases and increase the danger of creating silos of knowledge as well as building collaboration barriers. As a solution to these problems, we propose an open software platform and define a way to model use case driven domain specific asset representation (DSA) that focuses on the human being and his needs for representing the factory in a way that it meets his requirements for the current production needs. We therefore conducted research on google scholar on human factors in industry 4.0 and used technologies as well as already existing platforms and their architecture.

scholarly journals Will our next revolution support or obstruct the achievement of the Sustainable Development Goals?

Bionatura ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 832-835
Author(s):  
Nicolas Serrano-Palacio ◽  
Jorge Gómez-Paredes
Keyword(s):  
Sustainable Development ◽  
Production Efficiency ◽  
Industrial Revolution ◽  
New Technologies ◽  
The Sustainable Development ◽  
Ripple Effects ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Development Goals ◽  
The Internet Of Things

The so called “Fourth Industrial Revolution” (4IR) 1, is an emerging phenomenon which will likely transform our lives and affect multiple sectors of society. This new revolution encompasses and combines a wide range of new technologies, such as quantum computing, nano and bio-technology, artificial intelligence (AI), the internet of things (IoT), and advance automation. Foreseeing all the impacts and ripple effects that these technologies will have in our societies, in the next years, is a sizeable and difficult task. Much of the debate has usually been focused on automation, which the Cambridge Dictionary defines as “the use of machines or computers instead of people to do a job, especially in a factory or office” 2. The ongoing debate focuses, on the potential of automation to generate production efficiency benefits vs. the threat to increase unemployment lines. But the actual effects (positive and negative) of this revolution may be much wider and deeper, including social and environmental impacts closely related to sustainable development. Following, we present a brief non-exhaustive commentary on some of the potential advantages and disadvantages of the 4IR from the perspective of the 17 goals adopted by all parties to the United Nations on September 2015, as part of an agenda to tackle global problems and reach sustainable development3.

scholarly journals A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

2021 ◽  
Vol 13 (10) ◽  
pp. 264
Author(s):  
Tuuli Katarina Lepasepp ◽  
William Hurst
Keyword(s):  
Literature Review ◽  
Medical Device ◽  
Systematic Literature Review ◽  
Digital Libraries ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
New Technologies ◽  
Quality Analysis ◽  
Smart Manufacturing ◽  
Synonymous Term

Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing.

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