Using a Novel Grey DANP Model to Identify Interactions between Manufacturing and Logistics Industries in China

As a crucial part of producer services, the logistics industry is highly dependent on the manufacturing industry. In general, the interactive development of the logistics and manufacturing industries is essential. Due to the existence of a certain degree of interdependence between any two factors, interaction between the two industries has produced a basis for measurement; identifying the key factors affecting the interaction between the manufacturing and logistics industries is a kind of decision problem in the field of multiple criteria decision making (MCDM). A hybrid MCDM method, DEMATEL-based ANP (DANP) is appropriate to solve this problem. However, DANP uses a direct influence matrix, which involves pairwise comparisons that may be more or less influenced by the respondents. Therefore, we propose a decision model, Grey DANP, which can automatically generate the direct influence matrix. Statistical data for the logistics and manufacturing industries in the China Statistical Yearbook (2006–2015) were used to identify the key factors for interaction between these two industries. The results showed that the key logistics criteria for interaction development are the total number of employees in the transport business, the volume of goods, and the total length of routes. The key manufacturing criteria for interaction development are the gross domestic product and the value added. Therefore, stakeholders should increase the number of employees in the transport industry and freight volumes. Also, the investment in infrastructure should be increased.

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Identifying Comprehensive Key Criteria of Sustainable Development for Traditional Manufacturing in Taiwan

Sustainability ◽

10.3390/su10093275 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3275 ◽

Cited By ~ 2

Author(s):

Zon-Yau Lee ◽

Mei-Tai Chu ◽

Shiuann-Shuoh Chen ◽

Chih-Hung Tsai

Keyword(s):

Sustainable Development ◽

Manufacturing Industry ◽

Value Added ◽

Manufacturing Industries ◽

Key Factors ◽

Analytic Hierarchy ◽

The Sustainable Development ◽

Conceptual Innovation ◽

Traditional Manufacturing ◽

New Framework

The traditional manufacturing industry has suffered from changing global demands and rapid technology upgrades. It is critical to incorporate innovation into contemporary manufacturing for sustainable development. A comprehensive interpretation of the determinants and directions of technical change is greatly needed. Therefore, this study aims to explore innovative applications that can enhance the value of manufacturing and examine the key factors associated with these innovations. In this paper, three traditional manufacturing industries are analyzed using the research methodologies analytic hierarchy processing (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL), resulting in a set of six key criteria for innovative manufacturing. The causal relationships considering the six criteria in three industries are analyzed. The main contribution of this study is to establish a new framework for the sustainable development of traditional manufacturing industries. This could help to support conceptual innovation in these industries and establish a pragmatic approach to increase sustainable development. From the research results, the following suggestions can be made. The traditional manufacturing industries in Taiwan should focus on design and innovation. This can enhance the value-added and international competitiveness of industrial chains, enterprises, and products. Ultimately, it can lead to the sustainable development of Taiwan’s traditional industries.

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Obtaining the Factors Affecting Bioeconomy

Environmental and Climate Technologies ◽

10.2478/rtuect-2019-0018 ◽

2019 ◽

Vol 23 (1) ◽

pp. 277-291 ◽

Cited By ~ 1

Author(s):

Indra Muizniece ◽

Lauma Zihare ◽

Dagnija Blumberga

Keyword(s):

Scientific Literature ◽

Value Added ◽

Decisive Role ◽

Analysis Method ◽

System Dynamics Model ◽

Key Factors ◽

Dynamics Model ◽

Factors Affecting ◽

Development Scenarios ◽

Link Strength

Abstract At the scientific level it is being increasingly recognised that the concept of bioeconomy has transdisciplinary nature, but there is still no consensus on key factors that would accelerate the development of sustainable bioeconomy. Therefore, within the framework of this study, certain factors, their interactions and link strength with bioeconomy from a scientific perspective are identified. A bibliometric analysis method is used to achieve this aim in such a way that the keyword information on the published scientific literature in relation to the bioeconomy is translated into quantifiable data. This way the relevance of the bioeconomy factors and the strength of their direct and mutual interaction with the bioeconomy will be determined. From this study it can be concluded that the strongest links with bioeconomy are for biomass, bioenergy, biotechnology and innovations. No significant link was found for such factors as: behaviour, production, pollution and infrastructure. It becomes clear that, in the view of scientists, the decisive role in the development of the bioeconomy resulting from the use of bio resources in the higher value-added production is for technologies. These results are used to build a framework for a system dynamics model that can be used for modelling bioeconomy development scenarios in the future.

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Improving the methods for labor productivity calculation

Economic Analysis Theory and Practice ◽

10.24891/ea.19.5.920 ◽

2020 ◽

Vol 16 (5) ◽

pp. 920-934

Author(s):

V.V. Lymar'

Keyword(s):

Labor Productivity ◽

Systems Approach ◽

Value Added ◽

Key Factors ◽

Productivity Improvement ◽

Cost Component ◽

Factors Affecting ◽

The Social ◽

Social And Economic Development ◽

The Cost

Subject. The article addresses the methods for calculating the labor productivity, as their efficient use enables to improve the social and economic development of the society, ensure stable economic growth and competitiveness of national economy. Objectives. The study focuses on identifying and structuring the factors that have an impact on labor productivity growth, analyzing the current methods for labor productivity calculation applied by Russian companies, and developing practical recommendations. Methods. I employ various methods of information gathering and processing, including the analysis of appropriate laws and regulations, official statistical data, public reporting of domestic enterprises, etc. The study also draws on the systems approach, and comparative and statistical analysis. Results. I offer a classification of the key factors affecting the labor productivity, reveal the value added as the most effective indicator for labor productivity evaluation. The cost component in value added most correctly reflects the dynamics of production as compared with the revenue-based methodology. The classification helps understand the most effective directions and methods of labor productivity improvement. Conclusions. The offered methodology is the most effective. It enables to present an adjusted algorithm for labor productivity calculation, using the value added.

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Study on Productivity Improvement in Medium Scale Manufacturing Industry by Execution of Lean Tools

International Journal of Engineering Research in Africa ◽

10.4028/www.scientific.net/jera.48.193 ◽

2020 ◽

Vol 48 ◽

pp. 193-207

Author(s):

Gunji Venkata Punna Rao ◽

S. Nallusamy ◽

P.S. Chakraborty ◽

S Muralikrishna

Keyword(s):

Lean Manufacturing ◽

Manufacturing Industry ◽

Value Added ◽

Manufacturing Industries ◽

Total Quality ◽

Productivity Improvement ◽

Medium Scale ◽

Regular Production ◽

Current Scenario ◽

Tools And Techniques

In current scenario, all the manufacturing industries are placing constant efforts for their endurance in the current global volatile economy. Manufacturing industries are annoying to implement new and professional techniques in their regular production processes. Some of the recognized tools are applied and their awareness has been growing among the industries, especially in production sector. Last two decades have witnessed an explosion in the area of quality and productivity improvement initiatives in the manufacturing industries by different tools and techniques such as lean manufacturing, total quality management, total productive maintenance, six sigma implementation etc. The objective of this study is to enlighten the importance of lean techniques implementation in a medium scale belt manufacturing industry. This research study helps to exhibit the existing hidden potential in the selected industry as well as a selection of appropriate techniques for productivity improvements. Also, it aims to eradicate wastes and non-value added activities at every stage in order to enhance the overall productivity. From the results after implementation of appropriate lean techniques it was found that, the lead time was reduced about 1256 minutes and the overall production was increased by about 9%.

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Industry 4.0 adoption key factors: an empirical study on manufacturing industry

Journal of Advances in Management Research ◽

10.1108/jamr-03-2020-0039 ◽

2020 ◽

Vol 17 (5) ◽

pp. 697-725

Author(s):

Sanjiv Narula ◽

Surya Prakash ◽

Maheshwar Dwivedy ◽

Vishal Talwar ◽

Surendra Prasad Tiwari

Keyword(s):

Industry 4.0 ◽

Manufacturing Industry ◽

Principal Component ◽

Manufacturing Industries ◽

Maturity Model ◽

Critical Factors ◽

Key Factors ◽

Content Type ◽

Starting Point ◽

Shed Light

PurposeThis research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.Design/methodology/approachThis article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.FindingsThis study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.Research limitations/implicationsThe proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.Originality/valueThe existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.

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Factors Affecting the Evolution of Advanced Manufacturing Innovation Networks Based on Cloud Computing and Multiagent Simulation

Mathematical Problems in Engineering ◽

10.1155/2021/5557606 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Wang Jianbo ◽

Xing Cao

Keyword(s):

Cloud Computing ◽

Manufacturing Industry ◽

Value Added ◽

Weak Links ◽

High Tech ◽

Advanced Manufacturing ◽

Evolution System ◽

Factors Affecting ◽

Multiagent Simulation ◽

Manufacturing Innovation

Facing the pressure of low-cost competition brought by the homogenization of commodities, the manufacturing industry seeks to survive by providing services. By providing outsourcing of value-added services to date, we are focusing on innovation in our business model. With the advancement of science and technology, manufacturing innovation is facing higher challenges, especially the popularization of the Internet, which makes the manufacturing industry have to move closer to new industries. Based on cloud computing, this paper conducts a multiagent simulation on the evolution factors of the innovation network of advanced manufacturing. This article takes three types of simulation subjects: evolutionary network, manufacturing (cluster), and innovation evolution system as the research objects. The factors affecting the evolution of the research are innovation resources, innovation opportunities, innovation desire, innovation pressure, relationship strength, network scale, and network scope. Network differences carry over variable indicators and analyze quantitative regression indicators and then build a research model. The research results show that the average conversion efficiency of the manufacturing industry (0.523) is significantly lower than the average R&D innovation efficiency (0.725), which to a certain extent indicates that the manufacturing industry still has weak links in the export conversion stage at the back end of the innovation value chain. Some of the companies may have problems such as low ability to transform scientific and technological achievements and insufficient export competitiveness of high-tech products, which to a large extent affects and restricts the improvement of manufacturing export transformation efficiency.

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Factors Affecting Eating Out in Restaurants: A Study on Customers of Dhaka City

Global Journal of Management and Business Research ◽

10.34257/gjmbrevol20is3pg17 ◽

2020 ◽

pp. 17-27

Author(s):

Dr. Rezwanul Huque Khan ◽

Farah Naz Aditi

Keyword(s):

Value Added ◽

Demographic Variables ◽

Food Presentation ◽

Dhaka City ◽

Factors Affecting ◽

Gender And Age ◽

Two Factors ◽

Quality Of Food ◽

Eating Out

Eating out in restaurants is becoming a part of lifestyle among Bangladeshi citizens. While the popularity of eating out is increasing, parallelly, the number of restaurants has been rising at an astounding speed. Given the potential of the restaurant business in the country, this study investigates the reasons for which customers choose a particular restaurant to eat out and whether those reasons vary across demographic variables. Our study shows ambiance and quality of food are the top two factors that customers consider to choose a restaurant for eating out. It also shows that customers do not usually care much about menu variety, food presentation, and value-added deals. We found differences in preference exist among these factors across gender and age and showed the influence of gender and age on the frequency of visit and purpose of the visit. The findings of this study provide major implications for the marketers and entrepreneurs which we articulate at the end.

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A Hybrid, Data-Driven Causality Exploration Method for Exploring the Key Factors Affecting Mobile Payment Usage Intention

Mathematics ◽

10.3390/math9111185 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1185

Author(s):

Ching Ching Fang ◽

James J. H. Liou ◽

Sun-Weng Huang ◽

Ying-Chuan Wang ◽

Hui-Hua Huang ◽

...

Keyword(s):

Decision Making ◽

Structural Equation ◽

Multiple Criteria Decision Making ◽

Data Driven ◽

Equation Modeling ◽

Mobile Payment ◽

Research Approach ◽

Validity And Reliability ◽

Key Factors ◽

Factors Affecting

Several methodologies for academically exploring causality have been addressed in recent years. The decision-making trial and evaluation laboratory (DEMATEL), one of the multiple criteria decision-making (MCDM) techniques, relies on expert judgements to construct an influential network relation map (INRM), revealing the mutual causes and effects of the criteria and dimensions for presentation of the results in a visual manner. The interactional impacts may be evaluated without considering the presumed hypotheses. The DEMATEL has been successfully utilized to assist in complex decision-making problems in various contexts. However, there is controversy about the reliance upon expert judgements, which could be subjective. Thus, this study seeks to overcome this dispute by developing a data-driven, concept-based novel hybrid model which the authors call SEM-DEMATEL. The model first constructs the direct effects between indicators based on structural equation modeling (SEM) and then utilizes DEMATEL to confirm the interdependence among the variables and identify their causes and effects. Finally, an empirical study exploring the key factors affecting mobile payment usage intention is further conducted to demonstrate the feasibility, validity, and reliability of the novel SEM-DEMATEL research approach. The results identify that the perceived value is the key influencing indicator of m-payment usage intention, and the objectivity and efficiency of the research results are compared.

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Intellectual Capital and the Performance of Manufacturing Companies in Indonesia

JMM UNRAM - MASTER OF MANAGEMENT JOURNAL ◽

10.29303/jmm.v7i3.293 ◽

2018 ◽

Vol 7 (3) ◽

Author(s):

Rahmat Setiawan ◽

Budi Yuda Prawira

Keyword(s):

Firm Performance ◽

Intellectual Capital ◽

Manufacturing Industry ◽

Stock Exchange ◽

Value Added ◽

Manufacturing Industries ◽

Manufacturing Companies ◽

Asset Return ◽

Return On Equity ◽

Structural Capital

This study aimed at examining the effect of intellectual capital and its components covering value added capital employed, value added human capital, and structural capital value added on the firm performance. Intellectual capital was measured by using Pulic’s model, while the firm performance was measured by return on asset, return on equity, and market-to- book ratio. The samples used in this study were 103 manufacturing industries and we also investigated every subsector of the manufacturing industries including 51 basic and chemical industries, 30 miscellaneous industries, and 22 consumer goods industry listed on Indonesia Stock Exchange during the period of 2012 up to 2016. Multiple regression analysis was used to test the hypothesis. The result of the study showed that intellectual capital had a significant positive effect on return on asset, return on equity, and market-to-book intellectual capital on the firm performance in each subsector of the manufacturing industry. Value added capital employed as a component of intellectual capital was the most influential component on the firm performance. This findings indicated that a firm with great and well managed of capital employed, allowing a firm to improve their performance.

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Ecological Efficiency Evaluation and Spatiotemporal Characteristics Analysis of the Coupling Coordination of the Logistics Industry and Manufacturing Industry

10.21203/rs.3.rs-1109799/v1 ◽

2021 ◽

Author(s):

Juan Su ◽

Tong Shen ◽

shuxin jin

Keyword(s):

Carbon Emissions ◽

Spatial Data ◽

Manufacturing Industry ◽

Spatial Differentiation ◽

Manufacturing Industries ◽

Spatial Data Analysis ◽

Sustainable Supply Chain ◽

Ecological Efficiency ◽

Logistics Industry ◽

Positive Effects

Abstract The coupling coordination of the logistics industry and manufacturing industry conducive to the sustainable development of logistics and manufacturing and the stability of sustainable supply chain. The logistics and manufacturing industries are not only the basic industries that support social development, but also the industries with high carbon emissions. Firstly, this paper classifies the carbon emissions from the logistics industry and manufacturing industry as undesirable outputs, evaluates the ecological efficiency of the logistics industry (LEE) and manufacturing industry (MEE) in the Yangtze River Delta from 2006 to 2019 by using the unexpected slacks-based measure (SBM) model. Secondly, the coupling coordination method is used to analyze the coupling coordination scheduling of industrial ecological efficiency. Thirdly, the paper analyzes the spatial differences of the coupling coordination ecological efficiency between logistics industry and manufacturing industry (MLCC) by using the exploratory spatial data analysis method. Finally, the spatial econometric model is used to analyze the driving factors of the MLCC. The results show: The ecological efficiency of the manufacturing industry has steadily improved. The ecological efficiency of the logistics industry presents the rising trend in fluctuation. The level of the coupling coordination development between the logistics and manufacturing industries is high. The results of the spatial heterogeneity analysis show that the spatial differentiation of high-high agglomeration and low-low agglomeration is obvious. The spatial agglomeration characteristics are relatively stable, and the spatial diffusion effect is strong; In space, the MLCC shows a trend of developing from multiple agglomeration areas to one agglomeration area. The results of driving factor analysis show that foreign direct investment(FDI), government intervention(GI) and human capital(HP) have positive effects on the MLCC, while industrial structure(IS), environmental regulation(ER) and energy intensity(EI) have negative effects on the MLCC.

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