The aim of this study is to identify the benefits of digital supply chain and explore the effects of these benefits of the adoption of digital supply chain. The study was conducted using data collected by a questionnaire from a sample consisting of supply chain informant employees from companies in the food industry. Three benefits were selected for the current study, which are supply chain agility, organizational performance, and supply chain risk management. The results showed that digital supply chains have numerous benefits from which these three benefits have significant positive effects on digital supply chain adoption. Therefore, it was concluded that companies’ adoption of digital supply chains depends on a bundle of benefits not only related to the supply chain itself such as supply chain agility and risk management but also incorporates the company as a whole in terms of its organizational performance. It was recommended that companies should recognize the benefits of digital supply chains and make their decisions based on the desired outcomes of DSCs.

The aim of this study is to investigate the effect of supply chain risk management on supply chain resilience in the presence of Internet-of-Things as an intermediate variable. In other words, the study seeks to identify whether supply chain risk management completely affects supply chain resilience. Collecting data by a questionnaire from a sample composed of managers of Jordanian industrial firms, the results show that supply chain risk management has a direct and indirect effect on supply chain resilience through Internet-of-Things. These results do not support the hypothesis that supply chain risk management completely affects supply chain resilience and accepted the hypothesis that Internet-of-Things intervenes the effect of supply chain risk management on supply chain resilience. The study contributes to the literature through filling a research gap regarding the mediating role of Internet-of-Things in the relationship between supply chain risk management and supply chain resilience and contributes to the industry through instructing managers to adopt technologies such as Internet-of-Things to help their firms cope with supply chain risks.

This paper investigates the effect of artificial intelligence (product information, recommendation, and social media exposure) and payment flexibility on the operational performance of ecommerce retailers. The study is based on Transactional Cost Analysis, Material Flow, and Technology Integration theories. It considered a sample size of 270 members out of the population of 769 employees from five ecommerce companies operating in the region (Namshi.com, Noon, Joly Chic, Extra, and Styli). The analysis involved constructing a structural equation model to examine the trickle-down effect of the variables included in the study. The study concluded that artificial intelligence and payment flexibility are the core reasons that the retailers in the region are registering operational success in the retail market.

This study aims to identify and mitigate supply chain risks associated with organic rice in Thailand, based on the principle of supply chain risk management (SCRM). The risk measurement is performed using Best-Worst method (BWM) for ranking the criticality of different factors in order to find the appropriate ways for improving and developing new ideas for supply risk chain management. The study identifies 26 risk factors associated with the organic rice supply chain based on the literature and interviews with four experts. The order of risk priority in the organic rice supply chain in descending order (the top 5) is as follows: Lack of efficient equipment or machinery, Lack of organic rice mill, Lack of labor, Transportation cost, and Production cost. The SCRM guidelines of organic rice in Thailand include cost reduction and investment in infrastructure.

This exploratory paper will investigate the concept of supply chain risk management involving supplier monitoring within a cooperative supply chain. Inventory levels and stockouts are the key metrics. Key to this concept is the assumptions that (1) out-of-control supplier situations are causal triggers for downstream supply chain disruptions, (2) these triggers can potentially be predicted using statistical process monitoring tools, and (3) carrying excess inventory only when needed is preferable as opposed to carrying excess inventory on a continual basis. Simulation experimentation will be used to explore several supplier monitoring strategies based on statistical runs tests, specifically "runs up and down" and/or "runs above and below" tests. The sensitivity of these tests in detecting non-random supplier behavior will be explored and their performance will be investigated relative to stock-outs and inventory levels. Finally, the effects of production capacity and yield rate will be examined. Results indicate out-of-control supplier signals can be detected beforehand and stock-outs can be significantly reduced by dynamically adjusting inventory levels. The largest benefit occurs when both runs tests are used together and when the supplier has sufficient production capacity to respond to downstream demand (i.e., safety stock) increases. When supplier capacity is limited, the highest benefit is achieved when yield rates are high and, thus, yield loss does not increase supplier production requirements beyond its available capacity.

Mining is one of the most important sectors in most countries. It produces raw material for other sectors such as industry, agriculture, etc. Therefore, governments always seek the solutions to prevent or at least reduce the risk of mining industry to minimize the waste of time and resource. One of the most popular risk in mining industry that should be clearly assessed is supply chain. There is a variety of methods to evaluate and classify risks. Fuzzy set is one of the most appropriate methods to categorize and evaluate risks, because this method is able to take into account the uncertainty involved in the process of risk assessment. In this article, fuzzy inference system is applied to evaluate and assess the supply chain risk of the Iranian mining industry. This research shows that the proposed model had a high accuracy and efficiency for assessing the risk of mining industry.