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The purpose of the paper is to develop a methodological framework for supply chain risk management using the hierarchical holographic modeling approach. It analyses supply chain risks in a systematic manner and develops a hierarchical methodology for identifying, prioritizing and managing the potential supply chain risks.

This research reviews supply chain risk management literature and develops a conceptual framework, which outlines general principles and guidelines for managing risks in a systematic manner. Through decomposition, the complexity of supply chain risk can be identified by analyzing smaller subsystems.

The paper provides a conceptual framework to identify supply chain risks from multiple overlapping perspectives. The structured filtering and ranking procedure enables decision-makers to focus on the most critical risks. The research shows that the supply chain risks associated with the sub-systems within the hierarchical structure contribute to and ultimately determine the risks of the overall supply chain system.

The risks associated with each sub system within the hierarchical structure can contribute to and determine the risks of the overall supply chain system. Further applications in various companies and industry sectors would benefit supply chain managers on a case-by-case basis.

The hierarchical risk identification framework can serve as guidance for applications to specific supply chain systems and processes. The framework from a holistic overlapping perspective can efficiently and effectively help supply chain managers identify supply chain risks and facilitate the evaluation of the subsystem risks.

The paper applies system thinking in supply chain management and presents an efficient and practical framework for supply chain risk identification and evaluation.

The purpose of this paper is to propose a three-tier assessment model (TAM), aiming to identify and evaluate the competitiveness level of companies. The existing problem is that companies find it difficult to choose a proper model which can be deployed to benchmark with competitors in terms of their competiveness level in the marketplace. Most of the available models are not appropriate or easy to use. The proposed assessment model is able to provide an insight for better planning and preparation so as to gain a better chance of success comparing with their competitors. Most importantly, the proposal model adopts a pragmatic approach and can be implemented without going through tedious mathematical calculations and analysis.

TAM embraces three different approaches deployed in various stages of the application process. The first stage is to identify the relevant criteria using hierarchical holographic modeling and the second stage is to assess the associated weightings of these criteria used to rate the potential competitiveness of related companies. The technique used in stage two is known as fuzzy analytic hierarchy process (FAHP) which is a combination of two well-established methods including fuzzy logic and analytical hierarchical programming. In stage three, a technique known as technique for order preference by similarity to the ideal solution (TOPSIS) is adopted to benchmark the level of competitiveness covering several companies in the same industry.

In this paper, a case study is conducted in order to validate the feasibility and practicality of the proposed model. Results indicate that TAM can be easily applied in various industrial settings by practitioners in the field for supporting operations management practices.

Significant amount of work is needed to ensure that the proposed model can be practically deployed in real industrial settings.

This proposed model is able to capitalize on the benefits of the HMM, FAHP and TOPSIS methods and offset their deficiencies. Most importantly, it can be applied to various industries without complex modification.

This paper suggests a hybrid model to assess competitiveness level embracing three different techniques with the unique feature which is able to provide an insight for better planning and preparation in order to excel competitors. Companies may be able to follow the procedures and steps suggested in the paper to implement the model which is proven to be pragmatic and can be applied in real situations.

The purpose of this paper is to propose a hybrid risk management model, focusing on identification and evaluation of potential risk scenarios in industry/enterprise level, which assists in preventing negative impacts from adverse risks.

The proposed hybrid risk management model embraces the concept of hierarchical holographic modelling (HHM), enterprise‐wise risk management (ERM) and risk filtering, ranking, and management (RFRM) that could be applied in real commercial settings. A case study is conducted in order to validate this comprehensive theoretical model.

This study shows the potential risks that may be faced by the textile industry in Hong Kong. Corresponding responses are suggested for the risks in different levels, which provide a systematic approach in managing the risks.

The use of a single case study may limit the generalizability of the findings.

The risks suffered by the textile industry are identified through the case study, which provide an insight for better planning and preparation, so as to gain a better chance of success than that of competitors.

The proposed model does not only provide theoretical merits to the literature but can also be applied to different industries for risk management practices.

The purpose of this paper is to provide a systematic classification for frameworks, methods, and models of in-project quantitative risk analysis (IQRA) for the last 30 years.

An extensive literature review is conducted to identify pertinent IQRA works. Identified IQRA frameworks/methods/models are then classified on the basis of commonalities in key attributes and assumptions. Linkages between each category of IQRAs and dimensions of complexity are also observed.

Around 70 key publications on IQRAs are identified. Major attributes for each work are described. Five distinct categories of IQRAs emerge with unique linkages to complexity dimensions. An analytical framework in the form of a matrix is presented to illuminate evolution on modeling characteristics and to indicate a relationship between respective category and dimensions of project complexity.

The research coverage is intended to be comprehensive but it is by no means exhaustive. This study highlights research opportunities in IQRAs and the possible extension toward in-project quantitative complexity analysis (IQCA).

The proposed matrix provides guidance to practitioners to select the appropriate category of IQRAs for a specific project complexity type in a contingency fashion. The study highlights lessons from development and utilization of IQRAs. Outstanding issues from IQRAs are discussed to avoid similar drawbacks for IQCAs.

This study provides an original framework/matrix to classify extant works in IQRAs. It also establishes an association between IQRAs and the emerging conceptual works of complexity.

Given the competitive environment and complicated relationships in supply chains in the modern era, it is important to take into account internal and external risks. In addition, proper methods must be designed to evaluate these risks correctly. The purpose of this paper is to provide a suitable map based on the artificial neural network technique to assess and classify the risk levels of retailers who have interconnected rules in the downstream of the supply chain.

In this research, a model for risk assessment with a hexagonal grid and 2D self-organizing map was applied.

According to the results, the model used in the study can provide a basis for classification of retailers based on the specified risk levels defined by the experts and risk managers of the company. Also with the model’s visual output, managers can have a better understanding of the distribution of the risk level of retailers.

The proposed methodology can be adopted by managers to assess the risk of members involved in the supply chain, helping them to formulate the risk mitigation strategies based on the risk levels.

As a part of the risk management process, organizations can use this developed method to reduce the existing risks imposed by the members or customers on the company.

This paper investigates the overall system risk for a foodgrains supply chain capturing the interrelationship among the risk factors and the effect of risk mitigation strategies.

This paper first calculates the weight of risk factors using an integrated approach of failure mode, effects analysis and fuzzy VIKOR technique. Next, the weights are utilized as input for the weighted fuzzy Petri-net (WFPN) approach to calculate the system risk.

Two different WFPN models are developed based on the relationships among the risk factors, and both models demonstrate a higher risk value for the overall system.

The proposed methodology will help practitioners or managers understand the complexity involved in the system by capturing the interrelationship behaviour. This study also considers the concurrent effect of risk mitigation strategies for calculating the overall system risk, which helps to improve the system’s performance.

This paper investigates the risks involved in the Indian foodgrain supply chain (FSC) and proposes risk mitigation taxonomy to enable decision making.

This paper used failure mode and effect analysis (FMEA) for risk estimation. In the traditional FMEA, risk priority number (RPN) is evaluated by multiplying the probability of occurrence, severity and detection. Because of some drawbacks of the traditional FMEA, instead of calculating RPN, this paper prioritizes the FSC risk factors using fuzzy VIKOR. VIKOR is a multiple attribute decision-making technique which aims to rank FSC risk factors with respect to criteria.

The findings indicate that “technological risk” has a higher impact on the FSC, followed by natural disaster, communication failure, non-availability of procurement centers, malfunctioning in PDS and inadequate storage facility. Sensitivity analysis is performed to check the robustness of the results.

The outcomes of the study can help in deriving detailed risk mitigation strategy and risk mitigation taxonomy for the improved resilience of FSC.

Specifically, this research investigates the risks for foodgrains supply chain system for a developing country such as India, an area which has received limited attention in the present literature.

The threat environment countries face is a dynamic one, with many emerging technologies. This paper presents unique challenges as countries evaluate which technologies to pursue in support of national security. Rather than addressing a broad range of strategic options, this paper limits its scope to a single type of aircraft. Thus, the purpose of this paper is to demonstrate a methodology to counter the most likely future threats to a long‐range strike aircraft.

To address future threats, the paper examined the most likely course of technology development within the context of various alternative futures. To decompose general threat scenarios into specific risk scenarios, the risk filtering, ranking, and management (RFRM) framework was used. After identifying the most significant risk scenarios, decision tree analysis provided insight into whether or not to pursue a given technology (e.g. electromagnetic pulse hardening, redundant control structures, etc.).

It is found the RFRM and decision tree tools to be very complementary in developing a credible scenario‐based decision model that incorporates expected technology development and alternative futures.

The paper is not intended to be a technical report on advanced technologies or predict future technologies and the world geopolitical situation. However, the approach explored should serve as a foundation for more detailed analysis that incorporates formal studies, technology demonstrations, and additional research into a coherent decision structure that can be evaluated and adjusted over time.

The paper combines the RFRM and decision tree tools to examine concepts from both technology development and alternative futures.

The purpose of the paper is to present research trends in the food supply chain in the context of changes in food systems due to globalization, urbanization, environmental concerns, technological changes and changes in food consumption patterns in the world.

The present investigation was performed by bibliometric analysis using the VOSviewer software, visualization software developed by Nees and Waltman (2020). In this work we performed co-citation, bibliographic coupling and keyword evolution analyses.

The results show that research in the food supply chain is rapidly changing and growing. By applying co-citation analysis, The authors found that the intellectual structure of the food supply chain has evolved around six clusters, namely, (a) collaboration and integration in the supply chain (b) sustainable supply chain management, (c) food supply chain management (FSCM), (d) models for decision-making in the food supply chain, (e) risk management in the supply chain and (g) quality and food logistics in the supply chain. However, based on bibliographic coupling analysis, The authors find that new or emerging research niches are moving toward food supply market access, innovation and technology, food waste management and halal FSCM. Nevertheless, the authors found that the existing research in each of the thematic clusters is not exhaustive.

The limitation of the research is that the analysis mainly relates only to the bibliometric approach and only one database, namely, Scopus. Broader inclusion of databases and deeper application of content analysis could expand the results of this research.

There are limited studies that have examined research trends in food supply chains in both developed and developing countries using bibliometric analysis. The present investigation is novel in identifying the thematic research clusters in the food supply chain, emerging issues and likely future research directions. This is important given the dynamics, consumer demand for quality food, technological changes and environmental sustainability issues in food systems.

The aim of this paper is to provide an approach to analyze the performance of TV programs and to identify what can be done to improve them.

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Ng-model, Grey relational analysis (GRA), and principal component analysis (PCA) were applied to evaluate the programs, using audience, share, and duration as the performance criteria.

By comparing TOPSIS to the Ng-model, PCA, and GRA, we verified that SVD and bootstrap SVD TOPSIS provide a good balance between equal-weights TOPSIS and the other models. This is because SVD and bootstrap SVD TOPSIS break down the data to a higher degree, but are less impacted by outliers compared to the long tail models.

To determine which TV programs should be replaced or modified is a complex decision that has not been addressed in the literature. The advantage of using a multi-criteria decision-making (MCDM) approach is that analysts can choose as many criteria as they want to rank TV programs, rather than relying on a single criterion (e.g., audience, share, target rating point).

This work represents the first time that robust MCDM methodology is applied to an audience data set to analyze the performance of TV programs and to identify what can be done to improve them. This study shows the application of a detailed methodology that is useful for the improvement of TV programs and other entertainment industry content.