Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yasanur Kayikci
Keyword(s):  
Decision Making ◽  
Transport System ◽  
Environmental Performance ◽  
Stream Processing ◽  
Data Driven ◽  
Content Type ◽  
Sustainable Logistics ◽  
Processing Data ◽  
Decision Making Model ◽  
Resilience Capacity

PurposeAs the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains.Design/methodology/approachA causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted.FindingsThe result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure.Originality/valueIn this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.

The framework of data-driven and multi-criteria decision-making for detecting unbalanced bidding

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huimin Li ◽  
Limin Su ◽  
Jian Zuo ◽  
Xiaowei An ◽  
Guanghua Dong ◽  
...  
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Public Procurement ◽  
Unit Price ◽  
Data Driven ◽  
Relative Distance ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Content Type ◽  
The Government

PurposeUnbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry.Design/methodology/approachThe identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance.FindingsThe proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage.Originality/valueThe data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.

scholarly journals Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

Grey assessment and prediction of the financial agglomeration degree in central five cities

2014 ◽  
Vol 4 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Li Li ◽  
Guo-hui Hu
Keyword(s):  
Decision Making ◽  
Scientific Development ◽  
Fuzzy Decision Making ◽  
Grey Prediction ◽  
Fuzzy Decision ◽  
Content Type ◽  
Multiple Dimension ◽  
The Government ◽  
Development Tendency ◽  
Decision Making Model

Purpose – At present, financial agglomeration tendency in domestic and foreign countries is increasingly evident. Therefore, from a comparative perspective, this paper aims to assess and predict the financial agglomeration degree in central five cities. Design/methodology/approach – According to the diversity of evaluating indexes and the uncertainty of financial agglomeration, this paper constructs a set of indexes of evaluating the financial agglomeration degree, comprehensively evaluates the financial agglomeration degree of the five cities – Wuhan, Changsha, Zhengzhou, Nanchang and Hefei – in China's middle region from 2001 to 2010 by using the multiple dimension grey fuzzy decision-making model, and predicts their development tendency by using the GM (1, 1, β) model. Findings – The results show that the multiple dimension grey fuzzy decision-making pattern cannot only be used to determine the weights of evaluating indexes, but also get the fuzzy partition and ranking order of the financial agglomeration in central five cities. The grey prediction results can objectively reflect the development tendency of the financial agglomeration in central five cities. Practical implications – From the results, it is necessary for any competitive city to clarify their relative strengths and weaknesses in order for the accurate location and scientific development, and it also provides a reference for the government decision-making. Originality/value – The paper succeeds in using the multiple dimension grey fuzzy decision-making model to measure the financial agglomeration degree of the five central cities and the grey prediction model to predict future trends.

scholarly journals Construction of capital procurement decision making model to optimize supplier selection using Fuzzy Delphi and AHP-DEMATEL

2018 ◽  
Vol 25 (5) ◽  
pp. 1528-1547 ◽  
Author(s):  
Anil Kumar ◽  
Amit Pal ◽  
Ashwani Vohra ◽  
Sachin Gupta ◽  
Suryakant Manchanda ◽  
...  
Keyword(s):  
Decision Making ◽  
Automobile Industry ◽  
Supplier Selection ◽  
Selection Process ◽  
Strategic Decision ◽  
Extensive Literature ◽  
Content Type ◽  
Fuzzy Delphi ◽  
Selection For ◽  
Decision Making Model

Purpose Supplier selection for capital procurement is a major strategic decision for any automobile company. The decision determines the success of the company and must be taken systematically with the utmost transparency. The purpose of this paper is to construct capital procurement decision-making model to optimize supplier selection in the Indian automobile industry. Design/methodology/approach To achieve the stated objective, a combined approach of fuzzy theory and AHP-DEMATEL is applied. Evaluation parameters are identified through an extensive literature review and criteria validation has been introduced through a Fuzzy Delphi method by using fuzzy linguistic scales to handle the vagueness of information. AHP is employed to find the priority weight of criteria, although an inter-relationship map among criteria is not possible through AHP alone since it considers all criteria as independent. To overcome this, DEMATEL is used to establish cause-effect relationships among criteria. Findings The results show that the total cost of ownership (TOC) is the first weighted criterion in supplier selection for capital procurement, followed by manufacturing flexibility and maintainability, then conformity with requirement. The cause-effect model shows that supplier profile, TOC, service support and conformity with requirement are in the cause group and are considered to be the most critical factors in selecting the supplier. Originality/value The study’s outcome can help the automobile industry to optimize their selection process in selecting their suppliers for capital procurement; the proposed model can provide guidelines and direction in this regard.

A risk-based integrated decision-making model for green supplier selection

Kybernetes ◽  
2019 ◽  
Vol 49 (4) ◽  
pp. 1229-1252 ◽  
Author(s):  
Morteza Yazdani ◽  
Prasenjit Chatterjee ◽  
Dragan Pamucar ◽  
Manuel Doval Abad
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Supplier Selection ◽  
Potential Contribution ◽  
Effect Analysis ◽  
Content Type ◽  
Green Supplier Selection ◽  
Construction Company ◽  
Green Suppliers ◽  
Decision Making Model

Purpose Supply chain (SC) environment is surrounded by risk variables. This issue is regarded as an emerging and strategic problem which must be resolved by SC executives. The ability to measuring green supplier’s performance and affecting risk variables to demonstrating effective suppliers list has a potential contribution to be investigated. This paper aims to develop a decision-making model to assess green suppliers under legislation and risk factors. This leads to fewer disruptions in managing the SC and its impact to further improvement. It also presents research concepts forming a new approach for identification, prediction and understating relationship of supply risk. Design/methodology/approach At primal stage, different risk factors that influence green suppliers’ performance are indicated and their relationship is analyzed using decision-making trial and evaluation laboratory (DEMATEL) method. At the same time, failure mode and effect analysis is used to determine risk rating of each supplier. Finally, the evaluation based on distance from average solution (EDAS) method ranks suppliers and several comparisons and analysis are performed to test the stability of the results. The approaches include comparison to technique for order performance by similarity to ideal solution, multi-attributive border approximation area comparison, Vlse Kriterijumska Optimizacija I Kompromisno Resenje and complex proportional assessment methods, followed by analysis of rank reversal, weight sensitivity analysis and effect of dynamic metrics. Findings A real-time case study on green supplier selection (GSS) problem of a reputed construction company of Spain has been presented to demonstrate the practical aspects of the proposed method. In practice, though organizations are aware of various risks from local and global suppliers, it is difficult to incorporate these risk factors for ranking the suppliers. This real-case application shows the evaluation and incorporation of risk factors into the supplier selection model. Practical implications The proposed multi-criteria decision model quantitatively aids managers in selecting green suppliers considering risk factors. Originality/value A new model has been developed to present a sound mathematical model for solving GSS problems which considers the interaction between the supplier selection risk factors by proposing an integrated analytical approach for selecting green suppliers strategically consisting of DEMATEL, FMEA and EDAS methods.

Cross-border to Taiwan but not China

2019 ◽  
Vol 15 (1) ◽  
pp. 21-36
Author(s):  
Wai Ching Choy ◽  
Pui Yan Flora Lau
Keyword(s):  
Decision Making ◽  
Undergraduate Students ◽  
Dynamic Decision Making ◽  
International Mobility ◽  
Snowball Sampling ◽  
Content Type ◽  
Cross Border ◽  
Depth Interview ◽  
Drastic Increase ◽  
Decision Making Model

Purpose This study aims to find out why some students from Hong Kong (HK) consider higher education in Taiwan, rather than in China or elsewhere. It also attempts to build a decision-making model to advance the conventional push-pull logic associated with this particular issue. Design/methodology/approach The authors interviewed 11 undergraduate students from HK via an in-depth interview. Interviewees were recruited by snowball sampling. To protect the privacy of the interviewees, all names of the informants in this paper are pseudonyms. Findings A dynamic decision-making mechanism, which includes three major layers, namely, the macro, meso and micro levels, has been developed to demonstrate that HK students made their decision based on a recursive fashion with bounded rationality, rather than on a linear fashion with complete rationality. Research limitations/implications Although the relatively small number of interviewees has limited the representativeness of the research, the authors suggest that rather than claiming representativeness, the study attempts to tease out the diversity of the decision-making process and mechanisms. Originality/value The drastic increase in the number of HK students in Taiwan proves the current research study, which is the first qualitative research on the phenomenon, as a timely one. In addition, the present study is one of the few examples of studying students’ international mobility from a more economically advanced region (HK) to a less economically advanced one (Taiwan).

Identification and prioritization of factors influencing organization risk tolerance level

2019 ◽  
Vol 16 (4) ◽  
pp. 417-435
Author(s):  
Mohammad Khalilzadeh ◽  
Shiba Masoumi ◽  
Isa Masoumi
Keyword(s):  
Decision Making ◽  
Risk Tolerance ◽  
Project Manager ◽  
Validity And Reliability ◽  
Statistical Population ◽  
Content Type ◽  
Tolerance Level ◽  
Factors Influencing ◽  
Critical Issues ◽  
Decision Making Model

Purpose Identifying and prioritizing the risks are considered as critical issues in risk management; otherwise, non-considering the risks will lead to the problems such as delays in project implementation, increased costs, loss of reputation, loss of clients, reduced revenue and liquidity and even bankruptcy. The paper aims to discuss these issues. Design/methodology/approach In this paper, the factors influencing the organization risk tolerance level were identified. Then, the factors increasing and decreasing the risk tolerance level were determined by a decision-making model. Finally, a comprehensive model was considered for risk measuring and preparing a risk failure structure chart, in order to determine the factors influencing it as well as the measurement criteria and then they were ranked using the taxonomy method. In this study, the size of the statistical population was 130 (six small and medium manufacturer and service provider companies). Based on Cochran’s sample size formula, 97 questionnaires containing 30 questions were randomly distributed among the population. Validity and reliability of the questionnaire were confirmed. The data were analyzed by SPSS 22. Findings Given the hypotheses of this study, the first hypothesis was rejected and the other hypotheses were accepted. The final ranking was done using the taxonomy method; the personality of the project manager was ranked at first; income, credit and capital were ranked second and the number of personnel was ranked third. Moreover, the TOPSIS method was used for ranking to compare the results. Originality/value In this research, the identification and ranking of these factors have taken place in several small- and medium-sized organizations; in addition, the rankings are conducted using the taxonomy decision-making method.

An application of grey based decision making approach for the selection of manufacturing system

2014 ◽  
Vol 4 (3) ◽  
pp. 447-462 ◽  
Author(s):  
Om Ji Shukla ◽  
Gunjan Soni ◽  
G. Anand
Keyword(s):  
Decision Making ◽  
Lean Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Strategic Decision ◽  
Decision Matrix ◽  
Content Type ◽  
Manufacturing Organization ◽  
Decision Making Model ◽  
Selection Of

Purpose – In the current customer-driven market, the manufacturers have to be highly responsive and flexible to deliver a variety of products. Hence, to meet this dynamic and uncertain market changes, the production system, which enables the manufacturing of such variety of products should be able to meet such diverse, dynamic changes. Hence, selecting a suitable manufacturing system is a key strategic decision for today's manufacturing organization, which needs to survive in these uncertain market conditions. Hence, the purpose of this paper is to present a decision-making model for selecting the best manufacturing system and also discuss the criteria on the basis of which the management can select the same. Design/methodology/approach – A case of small- and medium-sized company is presented, in which the management is deciding to establish a most suitable manufacturing system. To supplement this, a suitable multi-criteria decision-making model (MCDM), the grey approach is used to analyze manufacturing system alternatives based on various decision criteria to arrive a comparative ranking. Findings – An extensive analysis of grey-based decision-making model described grey decision matrix, grey normalized decision matrix, grey weighted normalized decision matrix and grey possibility degrees for three alternatives revealed that lean manufacturing systems was found to be the most suitable manufacturing system among three alternatives for a given case. Research limitations/implications – The same study can be extended by including sub-criteria with main criteria for selection of manufacturing system by utilizing two MCDM techniques such as AHP or ANP with Grey approach. Practical implications – The Grey approach has been discussed in a detailed way and it will be useful for the managers to use this approach as a tool for solving similar type of decision-making problems in their organizations in the future. Originality/value – Although, the problem of selecting a suitable manufacturing system is often addressed both in practice and research, very few reports are available in the literature of Grey-based decision models that demonstrated its application for selecting a suitable manufacturing systems.

A decision-making model to support the design of a strategic supply chain configuration

2018 ◽  
Vol 29 (3) ◽  
pp. 515-532 ◽  
Author(s):  
Guang Song ◽  
Luoyi Sun ◽  
Yixiao Wang
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Support Network ◽  
Supply Chain Network ◽  
Affecting Factors ◽  
Content Type ◽  
Supply Chain Configuration ◽  
Decision Making Model ◽  
Industry Sectors ◽  
Chain Configuration

Purpose The purpose of this paper is to apply an empirically based approach to develop a decision-making model that comprehensively incorporates the potential affecting factors and the related significant drivers that support network designers in selecting the appropriate strategic supply chain configuration or checking the coherence of an existing supply chain structure in four industry sectors. Design/methodology/approach The decision-making model is developed based on an empirical study that integrates multiple case studies and statistical analyses. In total, 113 best-in-class manufacturing firms in four sectors are studied to investigate their strategic supply chain configurations and the information of identified affecting drivers. The factor analysis and regression analysis are conducted to classify the drivers into five factor groups, and to identify the significant drivers used to develop the decision-making model. Findings The findings of this research are three-pronged. First, 12 significant drivers related to 5 factor groups affecting strategic supply chain network design (SCND) are identified. Second, a decision-making model is developed to support users in strategic SCND. Last, the main characteristics of various strategic supply chain configurations are summarized in four industry sectors. Research limitations/implications The authors identified valuable insights for both academics and practitioners based on the identified significant affecting drivers and the developed decision-making model. In addition, this study also proposes two potential research lines on the study of additional contextual affecting factors and decision issues in strategic SCND. Originality/value This study could be the first attempt to use an empirically based method to develop a decision-making model aimed at supporting the preliminary design of a supply chain network. Therefore, the drawbacks of a pure qualitative conceptual model and optimization model are eliminated.

Grey relational decision making model of three-parameter interval grey number based on AHP and DEA

2019 ◽  
Vol 10 (1) ◽  
pp. 25-37
Author(s):  
Bingjun Li ◽  
Xiaoxiao Zhu
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Agriculture Industry ◽  
Parameter Interval ◽  
Grey Number ◽  
Index Weight ◽  
Decision Making Model ◽  
Grey Relational ◽  
Grey Relation

Purpose The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA), based on the previous study of grey relational decision-making model, and it considers the advantages of the decision-making schemes and the subjective preferences of decision makers. Design/methodology/approach First of all, through AHP, the preference of each index is analyzed and the index weight is determined. Second, the DEA model is adopted to obtain the index weight from the perspective of the most beneficial to each scheme and objectively reflect the advantages of different schemes. Then, assign the comprehensive weights to each index of the grey relational decision-making model of three-parameter interval grey number, and calculate the grey relation degree of each scheme to rank the schemes. Findings The effectiveness of the model is proved by an example of carrier aircraft selection. Practical implications The applicability of this model is analyzed by taking carrier aircraft selection as an example. In fact, this model can also be widely used in agriculture, industry, economy, society and other fields. Originality/value In this paper, the combination of AHP and DEA is used to determine the index weight. Based on which, the grey relation degree under the three-parameter interval grey number is calculated. It intended the application space of the grey relational decision-making model.

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