Environmentally responsible freight transport service providers' assessment under data-driven information uncertainty

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aalok Kumar ◽  
Ramesh Anbanandam
Keyword(s):  
Decision Making ◽  
Emerging Economies ◽  
Service Providers ◽  
Freight Transportation ◽  
Freight Transport ◽  
Data Driven ◽  
Transport Service ◽  
Content Type ◽  
Environmentally Responsible ◽  
Transport Industry

PurposeFreight transportation practices accounted for a significant share of environmental degradation and climate change over the years. Therefore, environmentally responsible transport practices (ERTPs) become a serious concern of freight shippers and transport service providers. Past studies generally ignored the assessment of ERTPs of freight transport companies during a transport service contract. To bridge the above literature gap, this paper proposed a hierarchical framework for evaluating freight transport companies based on ERTPs.Design/methodology/approachIn a data-driven decision-making environment, transport firm selection is affected by multiple expert inputs, lack of information availability, decision-making ambiguity and background of experts. The evaluation of such decisions requires a multi-criteria decision-making method under a group decision-making approach. This paper used a data-driven method based on the intuitionistic fuzzy-set-based analytic hierarchy process (IF-AHP) and VIseKriterijumska Kompromisno Rangiranje (IF-VIKOR) method. The applicability of the proposed framework is validated with the Indian freight transport industry.FindingsThe result analysis shows that environmental knowledge sharing among freight transport actors, quality of organizations human resource, collaborative green awareness training programs, promoting environmental awareness program for employees and compliance of government transport emission law and practice have been ranked top five ERTPs which significantly contribute to the environmental sustainability of freight transport industry. The proposed framework also ranked freight transport companies based on ERTPs.Research limitations/implicationsThis research is expected to provide a reference to develop ERTPs in the emerging economies freight transport industry and contribute to the development of a sustainable freight transport system.Originality/valueThis study assesses the environmental responsibility of the freight transportation industry. The emerging economies logistics planners can use proposed framework for assessing the performance of freight transportation companies based on ERTPs.

scholarly journals Decision-Making in Planning International Freight Transport

2020 ◽  
pp. 1-14
Author(s):  
Dariusz Masłowski ◽  
Małgorzata Dendera-Gruszka ◽  
Ewa Kulińska ◽  
Joanna Rut
Keyword(s):  
Decision Making ◽  
Research Methods ◽  
Service Providers ◽  
Freight Transport ◽  
Transport Service ◽  
Generalized Model ◽  
International Transport ◽  
Decision Making Model ◽  
Existing Data

The planning of international freight transport is one of the most important tasks carried out in transport companies. The aim of this publication is to improve the process of planning international transport by creating a generalized model presented on an example in Europe and a decision-making model developed for it. In the field of research methods, the methods of observation of transport service providers and analysis of existing data were used.

Reducing transportation emissions

2016 ◽  
Vol 23 (3) ◽  
pp. 674-703 ◽  
Author(s):  
Henrik Pålsson ◽  
Ola Johansson
Keyword(s):  
Service Providers ◽  
Freight Transportation ◽  
Industrial Sector ◽  
Content Type ◽  
Logistics Service Providers ◽  
Minor Influence ◽  
Transportation Emissions ◽  
Reduce Emissions ◽  
The Impact ◽  
Practical Implications

Purpose – The purpose of this paper is to examine the intention of companies to reduce transportation emissions by 2020 and the barriers and the discriminating factors that affect the reduction. Design/methodology/approach – A literature review identified potential logistical and technical actions and their barriers, and discriminating factors for reducing transportation emissions. A survey of freight transport-intensive industries in Sweden examined the effects of, intention for implementation of and barriers to 12 actions to reduce CO2 emissions from freight transportation. In total, 172 logistics managers responded, representing a response rate of 40.3 per cent. Findings – Logistics service providers (LSPs) and freight owners are likely to reduce a considerable amount of CO2 emissions from freight transportation by 2020 using a combination of actions. The lowest level of confidence was for reducing CO2 emissions by changing logistics structures, while there was greater confidence by means of operational changes. The actions have few barriers, but there is often a combination of barriers to overcome. Three discriminating factors influence the intention of a firm to reduce transportation emissions: perceived potential, company size and LSP/freight owner. The industrial sector of a freight owner has minor influence. Companies that are particularly likely to reduce emissions are LSPs, large companies, and those that perceive a large reduction potential. Research limitations/implications – Logistical and technical barriers appear to hinder companies from implementing actions, while organisational barriers and external prerequisites do not. Barriers cannot be used to predict companies’ intentions to reduce transportation emissions. The authors examined the impact of three discriminating factors on reduction of transportation emissions. The research is based on perceptions of well-informed managers and on companies in Sweden. Practical implications – The findings can be used by managers to identify firms for benchmarking initiatives and emissions-reducing strategies. Originality/value – The study provides insights into intended CO2 reductions in transportation by 2020. It presents new knowledge regarding barriers and discriminating factors for implementing actions to reduce transportation emissions.

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.

scholarly journals A comparison of high-speed internet service in Ghana: an analytical hierarchy approach

2018 ◽  
Vol 31 (1) ◽  
pp. 181-198 ◽  
Author(s):  
Michael F. Frimpon ◽  
Ebenezer Adaku
Keyword(s):  
Decision Making ◽  
High Speed ◽  
Service Providers ◽  
Analytical Techniques ◽  
Analytical Framework ◽  
Sub Saharan Africa ◽  
Performance Criteria ◽  
Internet Service ◽  
Content Type ◽  
Sub Saharan

Purpose The rising proportion of internet users in Sub-Saharan Africa and the lack of analytical techniques, as decision support systems, in choosing among alternative internet service providers (ISPs) by consumers underpin this study. The purpose of this paper is to propose an approach for evaluating high-speed internet service offered by ISPs in a sub-Saharan African country. Design/methodology/approach Using a sample size of 150, pairwise comparisons of two ISPs along five criteria of cost, usability, support, reliability and speed were performed by ten person groups of university students working in various organizations in Ghana and undertaking an online Six Sigma Course. Geometric means were employed to aggregate the scores in 15 groups, and these scores were then normalized and used as input into an analytical hierarchy process grid. Findings The results show that consumers of internet services highly emphasize the cost attribute of internet provision in their decision making. On the other hand, it was realized that consumers least emphasize the support provided by ISPs in their decision making among alternative ISPs. Originality/value This study has sought to provide an analytical framework for assessing the quality of service provided by alternative ISPs in a developing economy’s context. The evaluating criteria in this framework also reveal the key consumer requirements in internet service provision in a developing economy’s environment. This, to a large extent, will inform the marketing strategies of existing ISPs in Ghana as well as prospective ones intending to enter the Ghanaian market. Besides, the National Communication Authority, a regulator of communication services provision in Ghana, will be informed about the performances of the ISPs along five performance criteria. This is expected to aid in their regulatory functions.

Selection of resilient suppliers in manufacturing industries post-COVID-19: implications for economic and social sustainability in emerging economies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhijit Majumdar ◽  
Jeevaraj S ◽  
Mathiyazhagan Kaliyan ◽  
Rohit Agrawal
Keyword(s):  
Decision Making ◽  
Incomplete Information ◽  
Emerging Economies ◽  
Supplier Selection ◽  
Fuzzy Decision Making ◽  
Ideal Solution ◽  
Fuzzy Decision ◽  
Content Type ◽  
Intuitionistic Fuzzy ◽  
Selection Of

PurposeSelection of resilient suppliers has attracted the attention of researchers in the past one decade. The devastating effect of COVID-19 in emerging economies has provided great impetus to the selection of resilient suppliers. Under volatile and uncertain business scenarios, supplier selection is often done under imprecise and incomplete information, making the traditional decision-making methods ineffective. The purpose of this paper is to demonstrate the application of a fuzzy decision-making method for resilient supplier selection.Design/methodology/approachA group of three decision makers was considered for evaluating various alternatives (suppliers) based on their performance under different primary, sustainability and resilience criteria. Experts' opinion about each criterion and alternative was captured in linguistic terms and was modelled using fuzzy numbers. Then, an algorithm for solving resilient supplier selection problem based on the trapezoidal intuitionistic fuzzy technique for order preference by similarity to ideal solution (TrIFTOPSIS) was introduced and demonstrated through a case study.FindingsA closeness coefficient was used to rank the suppliers based on their distances from intuitionistic fuzzy positive-ideal solution and intuitionistic fuzzy negative-ideal solution. Finally, the proposed fuzzy decision making model was applied to a real problem of supplier selection in the clothing industry.Originality/valueThe presented TrIFTOPSIS model provides an effective route to prioritise and select resilient suppliers under imprecise and incomplete information. This is the first application of intuitionistic fuzzy multi-criteria decision-making for resilient supplier selection.

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.

Consumer empowerment in insurance

2018 ◽  
Vol 36 (6) ◽  
pp. 1073-1097 ◽  
Author(s):  
Pascal Buehler ◽  
Peter Maas
Keyword(s):  
Decision Making ◽  
Self Efficacy ◽  
Service Providers ◽  
Time Lag ◽  
Decision Making Process ◽  
Consumer Empowerment ◽  
Content Type ◽  
Perceived Performance ◽  
Performance Risk ◽  
The Impact

Purpose The purpose of this paper is to enhance the understanding of consumer empowerment in the relationship between consumers and service providers. It draws on self-efficacy theory to conceptualize consumer empowerment and explain the impact on perceived performance risk in insurance decision making. Design/methodology/approach This study employs data collected from an online survey involving 487 consumers in Switzerland, who recently decided on an insurance service. A structural equation model quantifies both the psychological effects on consumers’ perception of insurance services and behavioral effects on their decision-making process. Findings Perceived consumer empowerment is conceptualized by perceived self-efficacy and perceived controllability. Both have a significant impact on perceived performance risk, while the former is partially mediated by the preference to delegate the decision to a surrogate. Moreover, customers’ involvement in the purchase process moderates both the direct and indirect effect of perceived self-efficacy on perceived performance risk. Research limitations/implications The results are based on consumers’ perceptions from a single country. Furthermore, consumers’ perceptions were surveyed with a time lag after the decision-making process. To increase rigor, perceptions should be collected during decision making. Practical implications Results show that consumer empowerment can be employed as a risk reduction strategy. Consumers with self-efficacy and controllability beliefs perceive significantly less performance risk; however, practitioners should consider that consumers are also motivated to make decisions independently rather than delegating their decisions. Furthermore, consumer empowerment depends on consumer will. For largely indifferent consumers, empowerment does not affect risk or decision delegation preference. Originality/value The study is among the few empirical works to examine the effects of consumer empowerment on the consumer-service provider relationship on an individual level. Furthermore, applying consumer empowerment in relationship marketing implies a shift in research focus to the question of how consumers construe decision-making situations rather than objectively measuring the state of consumer relationship.

Implementation of 3D printing and the effect on decision making in logistics management

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stefan Hecker
Keyword(s):  
Decision Making ◽  
3D Printing ◽  
Service Providers ◽  
Logistics Management ◽  
Service Development ◽  
Content Type ◽  
Disruptive Technologies ◽  
Logistics Service ◽  
Service Portfolio ◽  
The Impact

PurposeFrom a synthesis of literature, the purpose of this paper is to present a conceptual service development methodology showing the impact of 3D printing as a disruptive technology to the service portfolio. The methodology is designed to support practitioners and academics in better understanding the impact of disruptive technologies may have to the service portfolio and participate in the technology.Design/methodology/approachA literature review is conducted and based on these findings a conceptual framework has been developed.FindingsThe design of a methodology for the development of 3D printing services is used to evaluate the disruption potential of 3D printing and to implement the technology in the service portfolio of a logistics service provider. The disruption potential of 3D printing influences a logistics manager by make to order decisions. In addition, it could be proven the service portfolio was diversified.Research limitations/implicationsLiterature directly dealing with technology-based service development for decision making in logistics management is rare and thus the methodology is built on insights, compiled from the distinct research areas. Further research should be performed on this nascent topic.Practical implicationsLogistics service providers may use the developed methodology to revise their service portfolio by the consideration of disruptive technologies, in order to reduce strategic misdecisions regarding the range of services.Originality/valueThis paper looks specifically at decision making for implementing disruptive technologies to the service portfolio.

Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology

2020 ◽  
Vol 120 (6) ◽  
pp. 1149-1174 ◽  
Author(s):  
K.H. Leung ◽  
Daniel Y. Mo ◽  
G.T.S. Ho ◽  
C.H. Wu ◽  
G.Q. Huang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Service Providers ◽  
Series Data ◽  
Illustrative Case ◽  
Implementation Framework ◽  
Inference System ◽  
Content Type

PurposeAccurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.Design/methodology/approachThe paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.FindingsA structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.Research limitations/implicationsResults from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.Originality/valueEarlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.

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