Investigating enablers to improve transparency in sustainable food supply chain using F-BWM

Food Supply Chains (FSC) are complex and dynamic in behavior and prone to increasing risks of unsustainability. Consumers increasingly demand food quality, safety, and sustainability, which are fast becoming issues of great importance in FSC. Lack of real-time information sharing and connectivity among stakeholders make these issues tougher to mitigate. Supply chain transparency (SCT) is thus an essential attribute to manage these supply chain complexities and enhance the sustainability of FSC. The paper identifies and analyses key enablers for SCT in FSC. Several technical, as well as sustainability-related enablers, contribute to the implementation of SCT. The identified enablers are analyzed using Fuzzy-best worst methodology (F-BWM), which determine the most critical factors using the decision maker’s opinion. Extending BWM with fuzzy logic incorporates the vagueness of human-behaviour into decision making approach. The results of this research provides decision makers with the priority of enablers to the decision maker. Enhancing these enablers in will help improve the transparency for better management of FSC. The article expands upon the practical as well as theoretical implications of SCT on sustainability in FSC. It addresses the requirement of including sustainability in the decision-making process. The results demonstrate the effectiveness of the F-BWM for the decision making process. The study is conducted by considering downstream supply chain activities in Indian context. It is one of the first studies that analyzes SCT enablers using F-BWM method in Indian context. The study contributes towards improving the environmental, economical, and social sustainability of FSC

Predicting changing pattern: building model for consumer decision making in digital market

YesConsumers have the multiple options to choose their products and services, which have a significant impact on the pattern of consumer decision making in digital market and further increases the challenges for the service providers to predict their buying pattern. In this sense, the purpose of this paper is to propose a structural hierarchy model for analyzing the changing pattern of consumer decision making in digital market by taking an Indian context. Design/methodology/approach: To accomplish the objectives, the research is conducted in two phases. An extensive literature review is performed in the first phase to list the factors related to the changing pattern of consumer decision making in digital market and then fuzzy Delphi method is applied to finalize the factors. In the second phase, fuzzy analytic hierarchy process (AHP) is employed to find the priority weights of finalized factors. The fuzzy set theory allows capturing the vagueness in the data. Findings: The findings obtained in this study shows that consumers are much conscious about innovative and trendy products as well as brand and quality; therefore, the service providers must think about these two most important factors so that they can able to retain their consumer in their online portal. Practical implications: The analysis shows that “innovative and trendy” is the first priority factor for the consumers followed by “brand and quality” and “fulfilment and time energy.” The proposed model can help the marketers and service providers in predicting customers’ preferences and their changing pattern efficiently under vague surroundings. The outcomes of this research work not only help the service provider to update their products and services according to consumers’ needs but can also help them to increase profit and minimize their risk. Originality/value: This work contributes to consumer research literature focusing on problem evaluation in the context of changing pattern of consumer decision making in digital era

Performance evaluation of reverse logistics in food supply chains in a circular economy using system dynamics

Supply chains are composed of multiple stakeholders who have complex interrelationships. In addition, the forward and reverse flow of materials, information, human resources, and finance occurs among different stakeholders in closing the loop of supply chains. Reverse logistics (RL) activities are gaining importance in terms of size and quantity due to both economic and environmental concerns. These flows in RL in supply chains are both dynamic and complex in nature. Further, the environmental impact of RL activities has barely been considered in holistic way in available literature. In this study, a system dynamics model has been developed to analyze and comprehend the green performance of RL activities by predicting the environmental impact of RL activities. The proposed model has been validated by a case study in the context of a food supply chain. In the company where the case study is carried out, the environmental effects of RL activities have been analyzed. These activities in a food supply chain in terms of CO2 (carbon dioxide), NOx (nitrogen oxide), SO2 (sulfur dioxide), and PM (particulate matter) emissions have been predicted through a system dynamics model for the years 2020 to 2024. The proposed methodology is applied in a food supply context, a major player in retail business, especially in emerging economies. According to our findings, the RL activities in a food supply chain can significantly contribute to green performance management by minimizing food waste and loss; hence, the environmental impacts of such activities should be closely examined from a managerial perspective

Smart circular supply chains to achieving SDGs for post-pandemic preparedness

Purpose: The coronavirus disease 2019 (COVID-19) pandemic created heavy pressure on firms, by increasing the challenges and disruptions that they have to deal with on being sustainable. For this purpose, it is aimed to reveal the role of the smart circular supply chain (SCSC) and its enablers towards achieving Sustainable Development Goals (SDGs) for post-pandemic preparedness. Design/methodology/approach: Total interpretive structural modelling and Matrice d'Impacts Croises Multipication Applique' a un Classement (MICMAC) have been applied to analyse the SCSC enablers which are supported by the natural-based resource view in Turkey's food industry. In this context, industry experts working in the food supply chain (meat sector) and academics came together to interpret the result and discuss the enablers that the supply chain experienced during the pandemic for creating a realistic framework for post-pandemic preparedness. Findings: The results of this study show that “governmental support” and “top management involvement” are the enablers that have the most driving power on other enablers, however, none of them depend on any other enablers. Originality/value: The identification of the impact and role of enablers in achieving SDGs by combining smart and circular capabilities in the supply chain for the post-pandemic

Further results on cerium fluoride crystals

A systematic investigation of the properties of cerium fluoride monocrystals has been performed by the ''Crystal Clear'' collaboration in view of a possible use of such crystals for the construction of high precision electromagnetic calorimeters for the future generation of high luminosity accelerators. A large sample of different crystals grown by several producers has been studied. The spectroscopic characteristics, the transmission, luminescence aid excitation spectra and the decay time curves are analysed. The light yield of the different crystals is measured with photomultipliers and Si photodiodes and compared to reference standards like BGO and NaI(Tl). The radiation damage behaviour is then presented for gamma and neutron irradiations, at different doses and dose rates, including thermal and optical bleaching