scholarly journals Productivity Improvement in the Pride's Spare Parts Manufacturing using Computer Simulation and Data Envelopment Analysis

2014 ◽  
Vol 95 (7) ◽  
pp. 12-18
Author(s):  
Bahareh Vaisi ◽  
Sadigh Raissi
Keyword(s):  
Computer Simulation ◽  
Data Envelopment Analysis ◽  
Spare Parts ◽  
Data Envelopment ◽  
Productivity Improvement ◽  
Parts Manufacturing

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 The Performance Analysis of Beibu Gulf airlines Based on DEA Model during the Pre-COVID-19 Pandemic Period

CONVERTER ◽  
2021 ◽  
pp. 230-235
Author(s):  
Yichia Lin, Wenlung Chang, Wongchai Anupong, Bowen Long
Keyword(s):  
Data Envelopment Analysis ◽  
Low Cost ◽  
Spare Parts ◽  
Aviation Fuel ◽  
Operational Performance ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Beibu Gulf ◽  
Data Envelopment ◽  
Dea Model

During the COVID-19 pandemic period, all airlines experienced a severe impact and the route operating cost is very susceptible to the impact of flight duration and aviation fuel prices. This paper analyzes the operation performance of Beibu Gulf airlines (low cost airline company) with data envelopment analysis CCR model in pre-COVID-19 pandemic period. Under the domestic vigorous promotion of tourism development and people's huge demand for travel, the airlines in mainland China continue rapid development, which accelerates the emergence of local airlines other than the four major airlines and leads to increasingly fierce operation competition in the civil aviation industry. Behind the competition among airlines, the operational performance of airlines can best reflect the company's development status. In this context, airlines should choose appropriate operational strategies to strengthen its competitiveness and operational capabilities. The DEA model is a mature input-output research tool, and there have been many studies related to operational performance of the aviation industry. By using Data Envelopment Analysis (DEA) solver software, the input and output indicators from 2017 to 2019 are analyzed. Preliminary results show that routes and oil price factors have not reached effective status. Beibu Gulf Airlines gradually shifts to low-cost mode, the faced challenges are as follows: 1. The competition among domestic low-cost airlines; 2. The current poor overall service quality of low-cost airlines as evaluated by customers; 3. How to arrange routes, flight, service strategy, etc. Airbus uses enhanced aviation systems for this series of aircraft to improve the overall reliability of the aircraft, reduce maintenance and spare parts costs, thus helping airlines greatly reduce maintenance costs, which is very beneficial to low-cost airlines. Based on this, this paper puts forward some suggestions, such as optimizing routes, developing feeder flights in second tier cities of popular destinations, controlling fuel costs, making low-cost aviation fuel reserves, reducing the weight of passengers' carry-on luggage or charging additional baggage charges.

scholarly journals Factors Promoting Innovation and Efficiency in the Construction Industry: A Comparative Study of New Zealand and Australia

2015 ◽  
Vol 15 (2) ◽  
pp. 63-80 ◽  
Author(s):  
Will Chancellor ◽  
Malcolm Abbott ◽  
Chris Carson
Keyword(s):  
New Zealand ◽  
Data Envelopment Analysis ◽  
Research And Development ◽  
Comparative Study ◽  
Construction Industry ◽  
Skills Training ◽  
Analysis Approach ◽  
Data Envelopment ◽  
Productivity Improvement ◽  
Policy Makers

There have been numerous concerns about the lack of productivity improvement in the New Zealand construction industry.  The aim of this paper, therefore, is to determine the main drivers of productivity in the industry. The research used is a two-staged data envelopment analysis approach to achieve the aim. In terms of improvements to the productivity of construction in New Zealand, the study found that although there is a potential for gains through the greater use of research and development, apprentice training and degree education, as well as the consolidation of some building companies, there will be some limits to the gains that might be made. One main implication of the findings of the study, therefore, is that a renewed focus on education and skills training should be a priority of companies and policy makers in New Zealand. 

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.

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