Siamak Kheybari

Bio: Siamak Kheybari is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Multiple-criteria decision analysis & Computer science. The author has an hindex of 6, co-authored 14 publications receiving 194 citations. Previous affiliations of Siamak Kheybari include NEOMA Business School & Delft University of Technology.

Measuring the Importance of Decision-Making Criteria in Biofuel Production Technology Selection

Abstract: Environmental problems, combined with a finite supply of fossil fuels, have made the use of renewable energy sources necessary. Biomass is a renewable source of energy that has played a very important role in energy production in recent years. Because there are a number of technologies that can be used to convert biomass into energy, it is important to select the best option. The fact that multiple options are available that need to be evaluated based on a set of decision-making criteria makes this a multicriteria decision-making problem. This paper takes the first step in proposing an evaluation framework and identifying the importance of the relevant decision-making criteria in biofuel production technology selection. To determine the importance of the selection criteria, experts were asked to respond to an online questionnaire based on the best–worst method. The results indicate that air pollution, land use change, and human expertise are the three most important criteria for selecting the best biofuel production technology in our case country, Iran.

ABC classification according to Pareto’s principle: a hybrid methodology

Abstract: So far, many methods have been proposed to classify items based on ABC analysis, but the results of these methods have had relatively low compliance with the principles of ABC. More precisely, collective value and sometimes the number of items belonging to each category in the methods provided do not meet the basic requirements of ABC called Pareto’s principle. In this study, a number of hybrid methodologies including Shannon’s entropy, TOPSIS (the technique for order preference by similarity to ideal solution) and goal programming are respectively used for determining the weight of criteria which are effective in the inventory items classification, calculations of each item value and its classification based on Pareto’s principle. To this end, the value of each item as well as classification of inventory items is calculated based on Pareto’s principle. The performance of the proposed method is evaluated through (1) statistical analysis, (2) checking the percentage of similarity with other methods and (3) comparison with another method in terms of the number and value allocated to each class. The results confirm the capability of the listed method.

A supply chain transparency and sustainability technology appraisal model for blockchain technology

Abstract: Blockchain technology is a technology that can effectively support supply chain transparency. An important initial managerial activity is for organisations in supply chains to evaluate and select t...

Barriers and overcoming strategies to supply chain sustainability innovation

Abstract: This study identifies a list of barriers that hinders adoption, implementation and upscaling of sustainable supply chain innovation in the manufacturing industry. It further proposes overcoming strategies that seek to aid management decision to dealing with these barriers systematically. A multi-criteria decision analysis method, the Best-Worst Method (BWM), is adopted to aid in the evaluation and prioritization of the barriers and their overcoming strategies within the Indian manufacturing industry, an emerging economy. The results depict that, “lack of technical expertise and training”, “lack of R&D and innovation capabilities”, “popularity of traditional technology”, “high initial investment in latest technology” and “fear of extra workload and loss of flexibility” are the top five barriers that confronts the Indian manufacturing companies in their quest for adopting and implementing sustainable supply chain innovation practices. In addition, the overcoming strategic pathway for dealing with these barriers are provided. The findings provide managerial and policy insights for guiding the formation of strategic operations framework and resource allocation if these Indian manufacturing firms seeks to build sustainability into their supply chain innovations.

Critical success factors for a circular economy : Implications for business strategy and the environment

Abstract: Eco-efficiency and resource optimization for business strategy and the environment can be achieved by the circular economy (CE) practices in supply chains (SCs). The leather industry is a significant industrial contributor to the economic growth of some countries, but at the same time, it leads to tremendous environmental pollution. This research focuses on the identification and evaluation of critical success factors (CSFs) needed in the business strategy development of CE practices as well as to minimize environmental pollution in leather industry SCs. The CSFs are identified via a comprehensive literature review and are validated by experts' opinions. The validated CSFs are further analyzed using the best–worst method (BWM) and the decision-making trial and evaluation laboratory (DEMATEL). The BWM is used to identify the weights of the CSFs, and DEMATEL is used to determine the cause–effect relationship between the CSFs. The findings show that “leadership and top management commitment” is the most important CSF. Six CSFs are classified as causal towards CE practices: “leadership and top management commitment,” “strong legislation towards CE practices,” “ecological scarcity of resources,” “knowledge of CE practices,” “funding support for R&D from the government,” and “competitor pressure on CE practices.” The findings of this study can help managers in the leather industry implement CE practices in their existing SCs to minimize waste.