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This paper presents an alternative approach to evaluating the overall efficiency and performance of Taiwanese container ports. Specifically, a parallel activity with series structure concept in the form of data envelopment analysis (MNDEA) is used to construct a model that applies to three different activities: harbor management, stevedoring and warehousing operations. We will further divide each activity into two process types, production processes and services processes. We will also adopt a Delphi survey approach and use the Analytic Network Process (ANP) to identify these processes’influence dependence and their degree of importance for the MNDEA model setting. An empirical application demonstrates the performance of Taiwanese container ports by using MNDEA with window analysis techniques via the directional distance functionThe results demonstrate that the application is effective in indicating and/or suggesting resource-adjustments, while considering which undesirable output levels and shared inputs were involved. The results also present directions for possible improvements in workplace efficiency.

This research sought to identify the best strategy for avoiding corporate amnesia in the context of the Industry 5.0 and an aging society.

To achieve this goal, a multi-phase methodology based on analytic network process was proposed and tested in one of the biggest companies in the bakery industry.

The results highlight that online communities of practice and storytelling are the best way to avoid corporate amnesia. The most important factors are commitment, work satisfaction and organizational culture. Commitment and work satisfaction also enhance the use of online communities of practice, while work satisfaction and organizational culture foster the use of storytelling.

This article proposes a nexus between knowledge management and operations management. This research also presents a decision-making tool that can help managers determine the most appropriate strategy for avoiding corporate amnesia.

Unforeseen events can disrupt the operational process and negatively impact emergency resources optimization and its supply chain. A limited number of studies have addressed risk management issues in the context of emergency supply chains, and this existing research lacks inbuilt and practical techniques that can significantly affect the reliability of risk management outcomes. Therefore, this paper aims to identify and practically analyze the specific risk factors that can most likely disrupt the normal functioning of the emergency supply chain in disaster relief operations.

This paper has used a three-step process to investigate and evaluate risk factors associated with the emergency supply chain. First, the study conducts a comprehensive literature review to identify the risk factors. Second, the research develops a questionnaire survey to validate and classify the identified risk factors. At the end of this step, the study develops a hierarchical structure. Finally, the research investigates the weighted priority of the validated risk factors using the fuzzy-analytical hierarchy process (FAHP) methodology. Experts were required to provide subjective judgments.

This paper identified and validated 28 specific risk factors prevalent in emergency supply chains. Based on their contextual meanings, the research classified these risk factors into two main categories: internal and external risk factors; four subcategories: demand, supply, infrastructural and environmental risk factors; and 11 risk types: forecast, inventory, procurement, supplier, quality, transportation, warehousing, systems, disruption, social and political risk factors. The most significant risk factors include war and terrorism, the absence of legislative rules that can influence and support disaster relief operations, the impact of cascading disasters, limited quality of relief supplies and sanctions and constraints that can hinder stakeholder collaboration. Therefore, emergency supply chain managers should adopt appropriate strategies to mitigate these risk factors.

This study will contribute to the general knowledge of risk management in emergency supply chains. The identified risk factors and structural hierarchy taxonomic diagram will provide a comprehensive risk database for emergency supply chains.

The research findings will provide comprehensive and systemic support for respective practitioners and policymakers to obtain a firm understanding of the different risk categories and specific risk factors that can impede the effective functioning of the emergency supply chain during immediate disaster relief operations. Therefore, this will inform the need for the improvement of practices in critical aspects of the emergency supply chain through the selection of logistics and supply chain strategies that can ensure the robustness and resilience of the system.

This research uses empirical data to identify, categorize and validate risk factors in emergency supply chains. This study contributes to the theory of supply chain risk management. The study also adopts the fuzzy-AHP technique to evaluate and prioritize these risk factors to inform practitioners and policymakers of the most significant risk factors. Furthermore, this study serves as the first phase of managing risk in emergency supply chains since it motivates future studies to empirically identify, evaluate and select effective strategies that can eliminate or minimize the effects of these risk factors.

Contrasted with some other industries, the construction industry has been linked with the most noteworthy accident occurrence rate, the majority of which has been related to poor health and safety practises. This paper therefore sets out to conduct a comprehensive review of the critical success factors that can aid sustainable health and safety practises on construction sites in Malaysia.

The review focussed heavily on published reports, drawn between the years 2000 and 2022. The Scopus database was used for gathering the articles reviewed for this study.

After reviewing various literature studies, a total of 106 critical success factors were identified. All these factors were then categorised under the three pillars of sustainability. A total of 48 factors were grouped under the economic factors, 37 factors were grouped under the social factors and the remaining 21 factors were grouped under the environmental factors.

This paper conducted a comprehensive review of the critical success factors for bridging sustainability and health and safety. This study will help in developing a sustainable health and safety model that can drastically reduce the accident rate on the construction site.

In this paper, we propose a scenario based global supply chain planning (GSCP) process considering demand uncertainty originated from various global supply chain risks. To generate the global supply chain plan, we first formulate a GSCP model. Then, we need to generate several scenarios which can represent various demand uncertainties. Lastly, a planning procedure for considering those defined scenarios is applied. Unlike the past related researches, we adopt the fuzzy set theory to represent the demand scenarios. Also, a scenario voting process is added to calculate a probability (possibility) of each scenario. An illustrative example based on a real world case is presented to show the feasibility of the proposed planning process.

This study aims to propose a method known as the fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) for complex project selection in organizations. To fulfill study objectives, the factors responsible for making a project complex are collected through literature review, which is then analyzed by fuzzy TOPSIS, based on three decision-makers’ opinions.

The selection of complex projects is a multi-criteria decision-making (MCDM) process for global organizations. Traditional procedures for selecting complex projects are not adequate due to the limitations of linguistic assessment. To crossover such limitation, this study proposes the fuzzy MCDM method to select complex projects in organizations.

A large-scale engine manufacturing company, engaged in the energy business, is studied to validate the suitability of the fuzzy TOPSIS method and rank eight projects of the case company based on project complexity. Out of these eight projects, the closeness coefficient of the most complex project is found to be 0.817 and that of the least complex project is found to be 0.274. Finally, study outcomes are concluded in the conclusion section, along with study limitations and future works.

The outcomes from this research may not be generalized sufficiently due to the subjectivity of the interviewers. The study outcomes support project managers to optimize their project selection processes, especially to select complex projects. The presented methodology can be used extensively used by the project planners/managers to find the driving factors related to project complexity.

The presented study deliberately explained how complex projects in an organization could be select efficiently. This selection methodology supports top management to maintain their proposed projects with optimum resource allocations and maximum productivity.

Nowadays businesses tend to compete with rivals by improving capability to meet customer demands. One of the key to improve logistics efficiency of a firm is to select appropriate supplier. In the past, to select the most suitable supplier, most people evaluated performance by using average performance or variance from historical data but did not mentioned skewness. In other words, skewness impact on supplier performance is ignored by researchers and buyers. In fact, supplier with greatest average performance does not confirm to be the most suitable one because of uncertainties which make its performance skew either to the left or right, i.e., lower or higher than expectation. Therefore, this empirical study aims to discover and determine the important role of skewness on supplier selection problem. After identifying influential criteria on supplier selection, we analyze skewness effect on suppliers’ performance in each criterion by surveying real data of suppliers’ performances. Skewness effect can be rated in 3 levels; no effect, moderately effect, and highly effect. The results show that, there is only one criterion with no skewness effect, which is price. Criteria which have high skewed performance, for both of medium-sized and large-sized buyers, are lead time, product quality and reliability, and on-time delivery. Also, skewness has higher effect on suppliers’ performance of medium-sized buyers than large-sized buyers. The conclusion surprisingly shows that, skewness is the best index to distinguish between good and bad suppliers, while mean is the worst index.

Governments around the world instituted guidelines for calculating energy efficiency of vehicles not only by models, but by the whole universe of new vehicles registered. This paper compiles Multi-criteria decision-making (MCDM) studies related to automotive industry. We applied a Systematic Literature Review on MCDM studies published until 2015 to identify patterns on MCDM applications to design vehicles more fuel efficient in order to achieve full compliance with energy efficiency guidelines (e.g., Inovar-Auto). From 339 papers, 45 papers have been identified as describing some MCDM technique and correlation to automotive industry. We classified the most common MCDM technique and application in the automotive industry. Integrated approaches were more usual than individual ones. Application of fuzzy methods to tackle uncertainties in the data was also observed. Despite the maturity in the use of MCDM in several areas of knowledge, and intensive use in the automotive industry, none of them are directly linked to car design for energy efficiency. Analytic Hierarchy Process was identified as the common technique applied in the automotive industry.