Showing 4 results for Delphi Method
Peyman Akhavan, Reza Hosnavi , Sanjaghi Mohammad ,
Volume 20, Issue 3 (9-2009)
Abstract
This paper is to develop a knowledge management (KM) model in some Iranian academic research centers (ARC) based on KM critical success factors. General KM critical success factors (CSF) were identified through literature review. Then the research procedure led to the identification of KM critical success factors in Iranian ARCs including 16 different factors. It was done through first stage survey by about 300 sample targets. Then, these 16 factors were surveyed separately again by experts through a Delphi panel. The experts suggested their practical solutions for exploiting the 16 factors in ARCs through a KM framework based on a KM cycle. This 2 years research has been done during 2006 to 2008.
Mariam Ameli, Somayeh Sadeghi,
Volume 33, Issue 2 (6-2022)
Abstract
To respond to the urgent call for preventive action against COVID-19 pandemic implications for societies, this research is carried out. The main aim of our research is providing a new insight for the effects of the newly emerged restrictions by COVID-19 on the SD Goals (SDGs). This research applied a qualitative approach for supporting the SDGs achievement post-COVID in Iran, as a developing country in the Middle East, in two phases. In the first phase, using a fuzzy Delphi method, the SDGs affected by COVID-19 were identified. In the next phase, a fuzzy cognitive map, as a qualitative system dynamics modeling, was conducted to specify the key interconnections among the SDGs post COVID-19. Finally, three strategies including focus on people in vulnerable situation, support for industrial units and small and medium-sized enterprises, and national aggregation to Fight COVID-19 were examined. As a result, different scenarios associated with the three proposed strategies were tested based on the identified interconnections among the SDGs to reduce the potential negative effects of COVID-19 crisis on the achievement of the SDGs. The results provide a decision support for stakeholders and policy makers involved in SD action plan.
Zahra Taherikhonakdar, Hamed Fazlollahtabar,
Volume 35, Issue 2 (6-2024)
Abstract
These days, industries, individuals and organizations are highly dependent on software. Software plays an important role in our daily life. They use in embedded systems, databases, computers, mobiles etc. Great demand for ICT cause environmental problem and endanger the future sustainability. In this case, sustainable development has become a hot research topic in software engineering community. Sustainability as a software quality is a general term. Therefore, there is a chance that software developers mislead about develops sustainable software. Therefore, there are some questions that should be answered to help practitioners to develop sustainable software: how developers could develop green and sustainable software? What requirements should be considered to reach green and sustainable software? Which non-functional requirement has an effect on each sustainability dimension? In this paper, we selected 20 non-functional requirements out of 60. It was identified the effective non-functional requirements in green and sustainable software development by using Delphi method then via interpretive structural modeling (ISM). The study aimed to pave the way for software eco-labeling and help users to choose the green and sustainable one. Also, provide software developers with guideline to develop green and sustainable software by identifying effective non-functional requirements. This would lead to the sustainable future and green environment.
Assia Bilad, Mounia Zaim, Faical Zaim,
Volume 37, Issue 2 (6-2026)
Abstract
The increasing adoption of artificial intelligence (AI) tools in manufacturing supply chains has intensified competition and highlighted the need for effective approaches to improve production quality. However, selecting the most appropriate AI tools remains challenging due to multiple evaluation criteria and uncertainty in expert judgments. This study proposes a hybrid fuzzy multi-criteria decision-making framework combining Fuzzy Delphi, Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to assess the impact of AI tools on production quality. The Fuzzy Delphi method is used to achieve expert consensus on relevant quality criteria, FAHP determines their relative importance, and Fuzzy TOPSIS ranks AI tools according to their performance. The results reveal that quality control and process performance criteria are the most influential in evaluating production quality. Predictive maintenance is identified as the most effective AI tool for enhancing production quality, followed by computer vision and machine learning applications. A case study conducted on Moroccan manufacturing firms further confirms the positive role of AI adoption in improving production quality across the supply chain. This research provides a practical decision-support framework for managers and contributes to the literature by offering a structured and robust approach for evaluating AI tools under uncertainty.
