Abstract

This research project developed a framework for assessing digital transformation (DT) maturity in large government-owned corporations (GOCs), focusing on integrating building information modelling (BIM), digital engineering (DE) practices, value of information, data integration aspects, and critical information management competencies, relating to asset management, asset delivery, and asset performance. Recognising DT’s strategic importance in asset lifecycle management, researchers created a practical DT maturity assessment tool applied to an Australian GOC. The framework and Excel assessment tool outline DT maturity across seven categories and 56 indicators, using the analytical hierarchy process (AHP) to rank indicators based on expert input. The tool enables straightforward data input and DT maturity evaluation, helping organisations understand their current DT status and set improvement targets. Workshops with DT professionals validated the tool’s effectiveness through a case study, offering insights into DT maturity levels and a roadmap for technological integration and data-driven decision-making. The research highlights DT’s role in enhancing efficiency, innovation, and safety, emphasising the need for strategic alignment with organisational objectives.

Introduction

The building and infrastructure industries are gradually transitioning towards building information modelling (BIM) as an object-based digital representation of an asset’s physical and functional characteristics [1], [2] and digital engineering (DE) practices to enable more productive methods of planning, designing, constructing, operating, and maintaining assets, using digital processes [3]. As detailed later in the paper, “digital” encompasses approaches, tools and technologies such as BIM and DE together with data science, the Internet of Things (IoT), and AI solutions integrated to digitise assets throughout the lifecycle. Asset owners recognise the importance of digital transformation (DT) in delivering and managing assets [4], as it offers many benefits throughout the asset lifecycle and can enhance efficiency, value for money, productivity, innovation, and safety.

1.1. DT of large infrastructure asset ownersGovernments and government-owned corporations (GOCs) are typically owners and operators of large assets, such as buildings, infrastructure, and other asset holdings across the spectrum of services. Governments and industries must transition from ‘digital by exception’ towards ‘digital by default’ [5], [6]. The infrastructure sector should adopt an evidence-based approach to infrastructure planning, delivery, and operations, including BIM, DE, embedded sensors, and digital asset management [7]. DT offers significant potential value and benefits to government agencies that manage assets across the whole-of-asset lifecycle. DT can assist in strategic investing and partnering with local governments and industry to deliver more benefits, including [8]:

Conclusion

The dynamic evolution of the building and infrastructure sectors towards digitalised practices underscores the growing importance of asset owners engaging in comprehensive DT. This research project has developed a multifaceted framework for assessing DT maturity tailored to the needs of large GOCs.

The existing assessment tools and methods have been reviewed for suitability in assessing the DT in whole-of-life asset owner organisations. The reviewed tools offer varying assessment scopes, including a capability assessment, maturity assessment, and benchmarking. The need and potential benefit of a new maturity assessment tool that would balance assessment accuracy and completion efficiency were established based on the shortcomings and limitations of existing DT maturity tools and methods. The key contributions of the new DT maturity assessment framework and tool developed by the research team include (i) a holistic view on the assessment of how well organisations use data and digital solutions, focusing on BIM capabilities, the value of information, data integration aspects, components of the smart city concept and critical information management competencies; (ii) an evidence-based scoring approach including assigning weight to various assessment indicators based on their importance to ensure the accuracy of the assessment; (iii) a multiperspective assessment allowing for more accurate organisation-wide analysis; (iv) short-term and long-term recommendations based on the current and target levels gap to facilitate performance improvement while realising the benefits and value of getting to each maturity level.

Abstract

The Balanced Scorecard (BSC) is a performance management tool that analyzes the relationship between tangible and intangible assets. Its ability to render business strategy visible has earned it the distinction of being a strategic management model. Sustainability has become an important part of daily and business life today. With the increasing importance given by stakeholders to environmental and social issues, companies find it necessary to evaluate sustainability activities in their performance evaluations. In order to meet this need, the Sustainability Balanced Scorecard (SBSC) emerged with the addition of a sustainability perspective or parameters to BSC. The number of academic studies pertaining to the application of BSC, which numerous companies and subjects to research have utilized, is inadequate. Due to its hierarchical structure and the necessity to evaluate numerous criteria, BSC is suitable for using multi-criteria decision-making (MCDM) methods. In this paper, an integrated approach consisting of SBSC, the Picture Fuzzy Analytic Hierarchy Process (PF-AHP), and the Objective Matrix (OMAX) method is proposed for the performance measurement of companies. The PF-AHP method has been reorganized for ease of application in determining the perspectives, strategic objectives, and Key Performance Indicators (KPIs) weights within SBSC. Additionally, the OMAX method has been employed for calculating the performance scores. This proposed approach was implemented in a factory operating in the glass industry. The SBSC created for the company includes five perspectives, 16 strategic objectives, and 34 KPIs. PF-AHP method was used to determine the weights of perspectives, strategic objectives, and KPIs to be used in the performance score calculation with OMAX. The weight of financial perspective is 0.267, the weight of the customer perspective is 0.247, the weight of the internal process perspective is 0.191, the weight of the learning and development perspective is 0.161, and the weight of the sustainability perspective is 0.134. The evaluations made by the decision-makers indicate that the company’s performance measurement still adheres to the traditional framework. The performance score of the company is 6.14 out of 10.

Introduction

The concept of sustainability, which includes economic, social, and environmental perspectives, has become an important issue in today’s world in order to cope with global challenges such as increasing poverty and climate change. This situation has brought pressure from stakeholders on terms of sustainability. At this point, the concept of corporate sustainability has emerged. Corporate sustainability is defined as meeting the needs of its direct and indirect stakeholders without compromising its ability to meet their future needs, taking into account both the administrative and operational activities of the organization (Nicoletti Junior et al., 2018). In addition, corporate sustainability also stands out as systematic management efforts for companies to voluntarily integrate environmental and social issues into management (Hansen and Schalteggar, 2016).

National and international legislation on sustainability perspectives is being developed (Kaplan and Norton, 2004), but reporting of social and environmental activities is still voluntary for companies (Huang et al., 2014). However, the importance given to the concept of sustainability will create a difference among competitors (Nicoletti Junior et al., 2018).

For a company to be successful in terms of corporate sustainability, it is not enough to be successful in a single perspective of sustainability. Corporate sustainability aims for companies to make progress in all three perspectives of sustainability (economic, environmental, and social) (Eifert and Julmi, 2022). Companies adopt environmental management systems practices to achieve the goals they set in order to meet the social and environmental expectations of stakeholders. However, these applied environmental management systems cannot fully reveal the contribution of sustainability performance to economic performance (Tsalis et al., 2013). As technological developments towards sustainability are increasing, companies need an effective tool to achieve their sustainability goals (Al-Mawali, 2023).

Conclusion

In order to succeed in their competitive environment, companies should embrace strategic management and measure the effectiveness of their management systems. The BSC has emerged as a performance measurement method to assess this success. The traditional BSC consists of four perspectives: financial, customer, internal processes, and learning and growth. With the growing importance of the concept of sustainability, which defines the measurement of environmental and social performance, the sustainability perspective has been added as the fifth perspective to the BSC, giving rise to the concept of SBSC. Since its introduction to the literature, the BSC has maintained its popularity and applied in numerous companies and investigated in various research studies. The multi-criteria nature of the BSC has led researchers to integrate it with MCDM.

In this paper, the modified PF-AHP method was used to weight the criteria in the SBSC. Additionally, the SBSC was combined with the OMAX method to express the performance numerically. Firstly, a team was selected within the company to create the SBSC, and strategic objectives, KPIs and indicators were identified through references from the literature and the company’s knowledge base. Existing data was collected for calculating the performance score, and based on this data, targets were established. The weights for SBSC perspectives, objectives, and KPIs were calculated using the PF-AHP method with the input of opinions from five DMs. The obtained weight values were then utilized in the OMAX method to determine the company’s performance score.

Abstract

     Internal audit quality is the foundation for a company’s survival and development across the world. As a result, global efforts have been made to develop a scientific and accurate evaluation index for internal audit quality. However, literature shows that existing internal audit quality evaluation indices have many flaws, such as a lack of systematic internal audit evaluation indicators, poor execution, and inability to identify priority areas. To address gaps in the literature, this study intended to construct an internal audit quality evaluation index utilizing the joint approaches of the Balanced Scorecard, Delphi Process, and Analytical Hierarchy Process. A systematic literature review, examination of companies’ internal audit guidelines, Balanced Scorecard, and Delphi approaches resulted in a multilevel internal audit quality evaluation index with five dimensions (stakeholder satisfaction, stakeholder contribution, financial results, internal audit process, and learning and growth) and 36 indicators. The Analytical Hierarchy Process revealed that the most prioritized dimension is the internal audit process. The consistency ratio and evaluation feedback from the listed companies’ internal auditors, management, and audit committee members revealed that the results are valid and reliable.

Introduction

     Internal audit quality is the foundation for a organization’s survival and development across the world. According to Boskou et al., 2019, internal audit quality (IAQ) is a critical component of an organization’s sustainability since it assists the business in maintaining effective controls and avoiding fraud. Several publications (Ashbaugh-Skaife et al., 2008; Hoai et al., 2022) have shown that effective internal control may improve the quality of financial reporting. It is commonly acknowledged that IAQ is a critical component of successful internal control, on which financial reporting quality is also dependent. It helps the organization’s operations since it is a separate activity that assists a corporation in achieving its goals by employing a well-organized approach to risk management, internal control, and governance efficiency and effectiveness (Stewart and Subramaniam, 2010; El Gharbaoui and Chraibi, 2021). Scholars from all around the world have discovered a link between IAQ and organizational performance (Hazaea et al., 2020; Kaawaase et al., 2021). Hence, IAQ is of interest to managers, shareholders, investors, auditors, and consequently international organizations and nations.

     In line with the usefulness of the IAQ, there have been global efforts in establishing a scientific evaluation index for IAQ. International organizations and regulatory bodies such as the Financial Reporting Council (FRC) and the largest six audit firms in the United Kingdom; the Public Company Accounting Oversight Board (PCAOB) in the United States; the Chartered Accountants of Australia and New Zealand (CAANZ); the Accounting and Corporate Regulatory Authority (ACRA) in Singapore; and the International Organization of Securities Commissions (IOSCO) put forward indicators for evaluating IAQ. For instance, PCAOB published 28 indicators for evaluating audit quality while CAANZ published only nine indicators. The Committee of Sponsoring Organizations of the Treadway Commission (COSO) also published five components of a firm’s internal control system with 17 principles (COSO, 2013).

Conclusion and recommendations

     The need for a scientific and reliable IAQE index to guide companies to improve internal auditing influenced this study. This study constructed the IAQE index by combining literature review, preliminary analysis, and review of companies’ internal audit guidelines, BSC, Delphi Process, and AHP methodologies. A systematic literature review, review of companies’ internal audit guidelines, BSC, and Delphi approaches resulted in a multilevel IAQE index comprising five dimensions and 36 indicators. Eight indicators were selected stakeholder dimension, nine indicators for stakeholder contribution dimension, seven indicators for financial results dimension, six indicators for internal audit process dimension, and six indicators for learning and growth dimension. The AHP results through experts’ judgment of the dimensions and indicators revealed that all the five dimensions are important in the IAQE index system. Overall, “achieving internal audit objectives” had the highest weight while “the number of complaints received by the internal audit department” had the lowest weight in the IAQE index system. The CR and feedback evaluation from the internal auditors and audit committee members of the selected enterprises revealed the acceptability of the results. In line with internal auditing standards, the developed IAQE index fits well with internal audit components since the index offers an independent, objective assurance and consulting among key stakeholders internal auditing for evaluating and improving the efficacy of risk management, control, and governance systems.

1- Introduction

2- Literature review

3- Methodologies

4- Analyzing E-commerce distribution mode in a big-data context: a case analysis of JD.Com

5- Conclusion

References

Abstract

This paper analyzes the existing distribution modes adopted by China’s e-commerce enterprises. Based on the empirical analysis of the electronic mall at JD.com (Jing-Dong), this paper compares and investigates the different logistics distribution modes faced by e-commerce enterprises embracing the new features, new challenges, and new advantages of big data. The Analytic Hierarchy Process (AHP) method and entropy value are applied to investigate the e-commerce enterprise distribution choice mode and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to verify the model. Our research analysis and results bear strong managerial insights for e-commerce logistics distribution practitioners.

Introduction

Business consumers in the digital age have become increasingly informed, challenging industrial marketing and sales teams to adapt their traditional marketing strategies to fully embrace buyers’ preferences and expectations. Forrester, in its latest research report, predicts that > 20% of companies will begin to apply modern technologies in their industrial marketing platforms in order to optimize the engagement between business buyers and sellers (Robertson et al., 2018). Technologies such as artificial intelligence (AI) and big data analytics have created unprecedented opportunities for companies to exploit their data assets for business-to-business (B2B) market initiatives. For instance, incorporating Lattice Engine’s predictive analytics into its marketing initiatives, the industrial marketing team at Akamai was able to better segment its customers and to send personalized messages, sextupling its lead-to-opportunity conversion rate (Anderson 2018). In addition, companies including Google, Amazon, Facebook, and Apple have all made great efforts in the field of industrial marketing through collecting and utilizing big data. All of this underlines the importance of big data as a crucial factor in global marketing operations (Miguel & Casado 2016). Data also plays a key role in making different decisions about business supply chains and logistics operations that are closely related to the industrial marketing field. Supply chain management deals with creating and maintaining linkages between different entities with specific responsibilities, ranging from raw material procurement to enduser product interactions. Logistics management ensures that relevant work support methods, such as traffic management, warehouse management, inventory management, packaging, and order tracking, are in place. Employing a large and diverse range of data in logistics and supply chain management, companies can understand the needs and preferences of their customers. Electronic commerce (e-commerce) giants such as Amazon, Flipkart, and Snapdeal have been collecting and exploring data from customers, orders, inventory, and other information (Meena, 2017). The success of e-commerce companies now depends largely upon how efficiently they capture, store, and use data. The advent of the big-data era has further strengthened the relationship between logistics distribution and e-commerce, and this presents new opportunities, such as the expansion of enterprise information, the sharing of distribution channels, and the integration of data resources. Particularly, e-commerce enterprises can accurately predict the future needs of customers and can fulfill personalized services to customers. In addition, they can organize and coordinate the distribution activities beforehand in a well-planned way, allowing for better selection and innovation of distribution modes.

1- Introduction

2- The risk early warning method

3- Case study

4- Discussion

5- Conclusion

References

Abstract

Food safety risks has received great attention in all world. And the reasonable effectiveness of security warnings can reduce public panic and risk losses. Therefore, this paper proposes an improved risk early warning method for food safety detection data based on the analytic hierarchy process (AHP) integrating the quality control analysis method. The AHP based on the entropy weight can obtain risk values for food safety component data. And the risk matrix of the risk component is obtained by the risk probabilities of the components. Then the corresponding risk levels are calculated using the quality control analysis method to release the risk warning information. Finally, a case study of dairy product safety data from the GuiZhou province in China is conducted to verify the feasibility and reliability of the proposed method. Moreover, the proposed method can scientifically and reasonably determine the risk level information. Furthermore, the risk management is provided to effectively reduce risk losses of the country though relevant quality inspection departments.

Introduction

With the rapid development of the economy, the food safety and quality have raised a higher requirement. If making correct and timely warning of food safety, people’s fears will be alleviated, and the harm caused by the food security crisis will be reduced. Nowadays, there are more panic and unintended consequences bring by false warnings. And the food safety risk is serious. Meanwhile, more and more food safety problems involving complex food safety data are occurred (Ma, Hou, Liu, & Xue, 2016). Because the food safety risk monitoring foundation of China is weak, it is very important to customize a risk monitoring model based on the basic national conditions (Tang, 2013). Due to the superior processing characteristics of complex food safety data technology, many dig data analysis and artificial intelligence methods of food safety risk assessment and early warning were proposed (Liu, Li, Yang, & Guo, 2018a; Wang, Yang, Luo, He, & Tan, 2015). Samuel et al. (Samuel, Asogbon, Sangaiah, Fang, & Li, 2017) used the fuzzy analysis hierarchical process (AHP) technique to calculate the global weight of attributes based on their individual contributions and predicted the high frequency risk of patients by training the artificial neural network (ANN) classifier. Wang et al. (Wang & Yue, 2017) formulated an early warning strategy for the safety risks arising from food transportation in the real-time monitoring of food safety to reduce the risk of food supply chain.

1- Introduction

2- Methodology

3- Application of the model to design of RAF

4- Results and discussion

5- Conclusion

References

Abstract

In this article, a novel hybridized Multi-Attribute Decision Model (MADM) is developed to identify an optimal design of a Reconfigurable Assembly Fixture (RAF) from a set of alternative design concepts. The model combines the comparative advantage of Fuzzy Analytic Hierarchy Process (FAHP) and the computational strength of the Fuzzy Weighted Average (FWA) based on left and right scores in order to obtain aggregates for the design alternatives considering the relative importance of the design criteria as needed in the optimal design. The model was applied to evaluate four design concepts of a RAF with six design features having numerous sub-features. Results obtained from the evaluation process shows that there are differences in final values of the design alternatives. However, a close variation exists between these values. These differences can be accrued to the interrelationships between the design features and sub-features obtained from the Fuzzy Synthetic Extent (FSE) of the FAHP and an unambiguity judgment of the FWA when aggregating availability of the design features and sub-features in the design alternatives.

Introduction

Robust design of Products and industrial machineries is important from conceptualization to manufacturing and up till usage in order for manufacturers to obtain a share of the competitive market flooded with changeable designs (Olabanji, 2018). The need for these robust designs calls for development of different design concepts of a particular product or machine before a detail design analysis can be done (Song et al., 2013). The availability of alternative design concepts necessitates the need for selection of optimal design concept (Wei and Chang, 2008). Concept selection in engineering design has attracted importance in recent times because it has a direct implication on the quality of the final design. Problems that arises when it is not properly done includes; repetitive alterations and modifications of drafted designs, prolonged developmental time and amplified cost of actualization (Tiwari et al., 2017). In order to arrive at robust design of a new product or engineering component, identification of design attributes and sub features viz a viz the numerous functional requirements from the customers or intended end users becomes the first task (Ayag and Ozdem, 2007; Brackea et al., 2017). The engineering design process attempts to give a holistic approach to identification of the design attributes, sub features and functional requirements. It follows an established design standard by proposing four phases (product planning and clarification of task, conceptual design, embodiment design and detail design). These phases are usually applied to arrive at a detail design of the new product (Yeo et al., 2004; Olabanji and Mpofu, 2014). Also, the engineering design process can be imagined to have a set of eleven steps as described by (Ayag and Ozdem, 2007). The relationship between these two analogies is described in Figure 1 where these four phases are disintegrated into the eleven steps. It may be assumed that the information needed in each step and phase will also follow the same manner. However, since the concept selection step is a decision-making process, adequate information is needed for successful selection process. Concept selection in engineering design can be modelled as a multicriteria decision-making (MCDM) problem since it involves multiple design attributes that are having different sub features. Considering the steps in the engineering design process as an all-inclusive approach, it is possible to develop a relationship between the constraints, design attributes and sub features for determining optimal design concept using the multi-criteria analysis as presented in Figure 2.

1. Introduction

2. Methods

3. Results

4. Discussion

5. Conclusions

References

Abstract

The improper disposal of agriculture residues (ARs) (such as open burning of straw) in China leads to waste of energy potential and atmospheric environmental problems. Converting ARs to energy is of importance for regional energy and environmental sustainability. In order to help decision-makers select optimal technologies among multiple alternatives and promote the development of ARs-to-energy industries, this study conducts integrated assessment and prioritization of seven bioenergy technologies (BETs). A criteria system consisting of four aspects (environmental, technological, economic and social aspects, in total 15 criteria) is constructed. Life cycle environmental and techno-economic assessments are conducted within the boundary ranging from ARs collection and transportation, energy conversion to final use of bioenergy products. Combined with the results of the life cycle assessments and the advices from two groups of experts, the fuzzy Analytic Hierarchy Process (AHP) is adopted to determine the weights of the criteria and quantify the performances of the BETs. Based on the results of the fuzzy AHP, the VIKOR method is finally employed to determine the sustainability sequence of the BETs. From single-dimensional performance, direct-combustion power generation has the best environmental benefit; briquette fuel has the best economic benefit. From performances of integrated-dimensions, direct-combustion power generation, gasification power generation and briquette fuel are recognized as the most sustainable technologies under both the environmental priority situation and economic priority situation. The methods and results presented are expected to provide reference to development planning of ARs as well as other types of bioenergy.

Introduction

China is a large agricultural country with abundant agricultural bioresources. It could produce more than 0.7 billion tons of agricultural residues (ARs) every year (Qiu et al., 2014). Most of the ARs are directly burned in fields causing serious environmental issues, such as smog and haze (Sun et al., 2017). In addition, China is the largest energy-consuming country, surpassing even the USA (Yang et al., 2018). It prompts to exploit bioenergy as alternative renewable energy to enhance energy security, reduce Greenhouse Gas (GHG) emissions, increase business opportunities, and accelerate rural economic development, especially in the developing countries (Liu et al., 2018; Wang et al., 2017a). In 2016, the Chinese national energy administration formulated the development goal in the “medium and long-term development plan for bioenergy”, where the target of replacing 58 million tons of standard coal (tce) by using bioenergy annually by 2020 was proposed (National Energy Administration, 2016). In this context, converting ARs to energy has become a promising pathway to regional energy and environmental sustainability. ARs can be converted through numerous bioenergy technologies (BETs) to divergent forms of energy products including heat, power, biofuels or a combination of them (such as converting straw to power, bioethanol, briquette fuel, biogas etc.) (Lo, 2014; Song et al., 2015a). It is usually difficult to select suitable and sustainable energy conversion technologies for ARs (Ren et al., 2014) as different BETs have different performances in terms of economic benefits, environmental impacts, technological concerns and social-political aspects (Sharma et al., 2013). Furthermore, bioenergy systems often have high level of uncertainties that are difficult to quantify because the data available are often vague, incomplete or inconsistent.

Introduction

Literature review

Methodology

Case study: information security RA for corrugated cardboard sector

Conclusion

References

Abstract

Risk analysis (RA) contains several methodologies that object to ensure the protection and safety of occupational stakeholders. Multi attribute decision-making (MADM) is one of the most important RA methodologies that is applied to several areas from manufacturing to information technology.With the widespread use of computer networks and the Internet, information security has become very important. Information security is vital as institutions are mostly dependent on information, technology, and systems. This requires a comprehensive and effective implementation of information security RA. Analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) are commonly used MADM methods and recently used for RA. In this study, a new RA methodology is proposed based on AHP–TOPSIS integration extended with Pythagorean fuzzy sets. AHP strengthened by interval-valued Pythagorean fuzzy numbers is used to weigh risk parameters with expert judgment. Then, TOPSIS with Pythagorean fuzzy numbers is used to prioritize previously identified risks. A comparison of the proposed approach with three approaches (classical RA method, Pythagorean fuzzy VIKOR and Pythagorean fuzzy MOORA) is also provided. To illustrate the feasibility and practicality of the proposed approach, a case study for information security RA in corrugated cardboard sector is executed.

Introduction

Information is a tool that people use to communicate among themselves from the moment they start living together. The nature and type of information technology have changed dramatically over the past decade. Simple and single batch applications are transformed into distributed computing environments including multitasking real-time control, and distributed processing. It is at least as important as the information itself to determine that information is valuable or worthless, or to measure the value carried by it. The most general definition of information security is that our own information is not passed on to anyone else. It is a combination of three main elements called “privacy”, “integrity”, and “accessibility”. Information is protected from unauthorized access which is called privacy. Integrity defined as information that is not altered by unauthorized persons. Information is available when authorized people are needed. Information is reachable and available when authorized people are needed which is called accessibility. If any of these three basic security elements are damaged, a security weakness occurs. Information security RA is essential for any corporate organizational system. It is essential to ensure that controls and expenditures are in full compliance with the risks that the organization is experiencing or experienced before. Organizations’ heavy dependence on information systems necessitates managing risks related to them [1]. One of the most important aspects of information security is technical measures. Given better access control policy models, better tools for system assessment and assurance should be resolved, including better ways to detect cryptographic formal evidence, protocols, approved firewalls, intrusions and malicious codes [2].

Abstract

Hierarchical classification is an effective approach to categorization of large-scale text data. We introduce a relaxed strategy into the traditional hierarchical classification method to improve the system performance. During the process of hierarchy structure construction, our method delays node judgment of the uncertain category until it can be classified clearly. This approach effectively alleviates the ‘block’ problem which transfers the classification error from the higher level to the lower level in the hierarchy structure. A new term weighting approach based on the Least Information Theory (LIT) is adopted for the hierarchy classification. It quantifies information in probability distribution changes and offers a new document representation model where the contribution of each term can be properly weighted. The experimental results show that the relaxation approach builds a more reasonable hierarchy and further improves classification performance. It also outperforms other classification methods such as SVM (Support Vector Machine) in terms of efficiency and the approach is more efficient for large-scale text classification tasks. Compared to the classic term weighting method TF*IDF, LIT-based methods achieves significant improvement on the classification performance.

Introduction

The task of text classification is to assign a predefined category to a free text document. With more and more textual information available online, hierarchical organization of text documents is becoming increasingly important to manage the data. The research on automatic classification of documents to the categories in the hierarchy is needed.

Most of the classifiers make the decision in the same flat space. Classification performance degrades quickly with larger scale data sets and more categories, especially in terms of the classification time. On the other hand, a hierarchical classification method organizes all of the categories into a tree like structure and trains a classifier on each node in the hierarchy. The classification process begins from the root of the tree until it reaches the leaf node which denotes the final category for the document.

1. Introduction

Health technology assessment (HTA) is a multi-disciplinary evaluation process that allows the analysis and assessment of health technologies by considering the direct or indirect medical-clinical, organizational, economic, social, legal and ethical implications in the short and long term using the same technologies [4,16,17,19,35]. Through this methodology, each alternative is assigned a weight based on the opinion provided by a decision-maker, and then, weight vectors are placed in a final vector that will determine the priority of each alternative. Key application areas in the literature include personal [51] social [34] and industrial criterion [21]; management; manufacturing; engineering [54,55]; education; government [33] planning, selecting a best alternative; and resource allocation [37,45,47,57,60]. Among the various applications of the HTA, this paper takes advantage of the analytic hierarchy process (AHP) technique and focuses on a specific application of this methodology to a dynamic system in clinical. However, the AHP uses a hierarchical structure in which linear dependencies between items of different decision-making levels are one way down the hierarchy and there are no dependencies between elements of the same cluster of items belonging to different clusters. For this reason, it cannot be considered suitable for the modelling of complex problems, which are characterized by dependencies, interactions and feedback and especially by the dynamic nature of the decision taken [58]. With these premises, in this paper the AHP and HTA methodologies have been implemented through a dynamic simulation model and overcome the main limitations of the single methodologies. After this introduction, Section 2 reports an overview of AHP and HTA-related works. Section 3 discusses the materials and methods of this research. In Section 4, the results of the implemented methodology are presented. Finally, Section 5 reports conclusions, implications and limitations of this paper.

2. Related work

The bibliographic review of multiple criteria decision-making (MCDM) tools realized by Steuer ([50]) provides an important overview of the decision-making topic. The main research papers on this topic identified by this bibliographic review have been confirmed and updated adopting the well-known systematic literature review approach defined by Centobelli et al. [6,7]. The AHP is a multi-criteria decision analysis methodology that was developed in the 1970s by Thomas L. Saaty. AHP evaluates a set of alternatives and creates a final problem by splitting decision making into many sub-problems that are equal and can be solved by summarizing sub-problems in which results of the initial problem are evaluated [48,59]. Among MCDM techniques, the AHP still suffers from some theoretical disputes. One major criticism is that the assumption of independence among the criteria can be considered a limitation of the AHP in certain cases. In fact, one of the main aspects of the AHP is the assumption of independence between the various levels of the hierarchical structure in terms of both the criteria and sub-criteria [46]. An initial solution to this critique was offered by Saaty, introducing the analytic process network (ANP), a generalization of the AHP with feedback to adjust the weights. However, the decision-maker must answer a considerable number of questions, which can be complex and affect the linear hierarchical structure typical of the AHP. For this purpose, a simplified version of the ANP would be useful for the simplified wider adoption of the method.

1- Introduction

2- Literature Review

3- Criteria for Ship Management Firm Selection

4- Advanced Evaluation Method for SMF Selection

5- Application of Fuzzy-TOPSIS on Ship Management Firm Selection

6- Concluding Remarks

References

Abstract

Although extant literature has stressed importance of the use of ship management and identified the selection factors, relative evaluation of comprehensive selection factors is found to be lacking. This study aims to evaluate the overall performance and rank of the ship management firms by adopting an integrated model of Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) in order to support the critical decision making on SMF selection based on multiple criteria. This study contributes to enhancing the selection criteria of ship management firms by the ship-owners, and at the same time identifies the areas that the ship management firms require to improve their service standards. The result indicates that competency is the most important criterion, followed by cost, courtesy, organisation characteristics and image.

Introduction

Shipping is the backbone of global logistics and supply chain. Its nature is complex, so conducting shipping business requires expertise in a wide range of ship operations. Ship management industry is a well-established sector within and as an integral part of the value chain in the maritime industry has been operating in its own right for around half a century. Today, third party Ship Management Firms (hereafter SMF) account for 35% of the management of global shipping with SMF such as V-ships, Columbia Ship Management, Barber International, ASP Ship Management and Orient Ship Management as some of the leaders (Branch and Robarts, 2014). With the growing prevalence of SMF, there is a need for studying their competitive position which currently lacks in academic research (Mitroussi, 2004a). The growth of a SMF represents a revolutionary change in the concept and practice of shipping business (Mitroussi, 2003). Over the last few decades a large number of ship owners have separated their ownership and control of vessels. Originally, management of ships was considered integral with its ownership, but, increasingly this activity has been outsourced.

1- Introduction

2- Background and literature review

3- Proposed two-step hybrid approach

4- A case study and results

5- Conclusion and discussion

References

Abstract

Sustainability of agro-food supply chains has recently become the subject of greater interest from consumers, firms, governmental organizations and academia as the environment continues to deteriorate. One of the most critical factors influencing the sustainability of an agro-food supply chain is its network design. A particularly challenging aspect in this context is the broad range of influencing indicators associated with the Triple Bottom Line (TBL) of sustainability that need to be considered. However, many of these indicators could not be fully integrated or measured by single-step optimization problems. This paper presents a critical literature review of operational research methods for the design of sustainable supply chains. A novel two-stage hybrid solution methodology is proposed. In the first stage, a partner selection is performed using a hybrid multi criteria decision making based on Analytic Hierarchy Process (AHP) method and the Ordered Weighted Averaging (OWA) aggregation method. The result obtained in the first stage is used in the second stage to develop a multi-objective mathematical model to optimize the design of the supply chain network. This approach allows the simultaneous consideration of all three dimensions of sustainability including carbon footprint, water footprint, number of jobs created and the total cost of the supply chain design. The proposed approach generates a Pareto frontier to aid users in making decisions. Numerical experiments are completed utilizing data from an agro-food company to demonstrate the efficiency and effectiveness of the proposed solution methodology. The analyzes of the numerical results provide important organizational, practical and policy insights on (1) the impact of financial and environmental sustainability on supply chain network design (2) the tradeoff analysis between environmental emission, water footprint, societal implications and associated cost for making informed decision on supply chain investment.

Introduction

Growing environmental, social, and ethical concerns and increased awareness of the effects of food production and consumption on the natural environment have led to increased pressure from consumer organizations, environmental advocacy groups and policy makers on agro-food companies to deal with the sustainability of their supply chains. Interest in sustainability of supply chains has grown over the last decade. Achieving sustainability entails reaching a balance between economic growth, environmental protection and social conditions. A sustainable supply chain refers to the ways in which organizational innovations and policies in supply chain management are considered in the context of sustainable development [1,49]. It is imperative to consider sustainability in agro-food supply chain, since it relates to marked environmental and social impacts. In 2010, the International Resource Panel of the United Nations Environment Programme found that agriculture and food consumption are two of the most important drivers of environmental pressures including habitat change, climate change, water use and toxic emissions [34]. Most of the current research focuses on the improvement of individual firms or processes rather than the design of an entire supply chain. Although considerable effort has been put into researching efficiency and economic performance measures, there is very little research available concerning the influence of all three dimensions of sustainability and decisions on agro-food supply, which could, in turn, offer managers the prescriptive models required to create a sustainable agro-food supply chain.