Purpose Using the resource-based theoretical view of the firm, this paper aims to explore how firms’ efforts to integrate blockchain technology (BCT) into their supply chain systems and activities enable certain supply chain capabilities and, consequently, improve their supply chain performance. Design/methodology/approach Using an abductive research approach, a qualitative content analysis was conducted on 126 cases of firms attempting to implement a blockchain technology-enabled supply chain system (BCTeSCS). These firms spanning across multiple industries were identified using the Nexis Uni database. Findings Findings reveal that present BCTeSCS efforts are more-oriented toward improving operational-level capabilities (information sharing and coordination capabilities) than strategic-level capabilities (integration and collaboration capabilities). These operational and strategic-level capabilities alongside BCTeSCS deliver several supply chains performance outcomes such as quality compliance and improvement, process improvement, flexibility, reduced cost and reduced process time. However, outcomes may vary by industry type based on their uncertainties. Research limitations/implications Given the nascent state of BCT, accessibility to primary data about ongoing BCTeSCS efforts is limited. The presented framework is based on 126 cases of secondary information. Within this constraint, the paper finds scope to future empirical research by proposing a resource-based framework of BCTeSCS and related propositions. Practical implications The results and discussion of this study serve as useful guidance for practitioners involved in BCTeSCS integrations. Social implications The paper creates a BCTeSCS scenario for stakeholders to assume its potential socio-economic and socio-environmental pressures. Originality/value This paper is one of the initial attempts to examine BCTeSCS efforts across multiple industries, and thus, promises a broad future research scope.

Naphtha is an important distillation product of crude oil, and is used as a raw material for first-generation products such as ethylene, propylene, gasoline, xylene (BTX), and others. However, due to the different sources of crude oil, differences in naphtha composition impact the quality of conversion processes. Parameters such as pressure, charge flow, and temperature need to be adjusted for conversion efficiency. This work aims to compare naphtha samples from different origins, through the analysis of distillation curve (ASTM D86), density (ASTM D4052), total sulfur (ASTM D4294), and n-paraffins, iso-paraffins, olefins, naphthene, and aromatics (PIONA, ASTM D5134). Among these parameters evaluated in naphtha, the ones that showed the greatest correlation with the type of oil and its origin was the amount of total sulfur, number of aromatics, and paraffins. The three imported evaluated naphtha presented values greater than 200 mg/kg of total sulfur, aromatics above 9%w, and paraffins (P + I) below 76%w, while the national naphtha presented sulfur contents of at most 141 mg/kg, aromatics below 7%w, and paraffins (P + I) above 78%w. Finally, the study of this type of hydrocarbon enables the understanding of the needs of Latin American refineries and the world in relation to its treatment. National petrochemical companies have more difficulty in processing this product, causing an increase in naphtha importation by 108.51% from 2020/2021 in Brazil. Given this scenario, the Brazilian government should invest more in its petrochemical plants to reduce these imports, which, in the long term, would have a positive impact on the quality and value of naphtha byproducts.

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The mathematical study known as queueing theory has recently become a major point of interest for many government agencies and private companies for increasing efficiency. One such application is vehicle queueing at an international port-of-entry (POE). When queueing, fumes from idling vehicles negatively affect the overall health and well-being of the community, especially the U.S. Customs and Border Protection (CBP) agents that work at the POEs. As such, there is a need to analyze and optimize the border crossing queuing operations to minimize wait times and number of vehicles in the queue and, thus, reduce the vehicle emissions. For this research, the U.S.–Mexico POE located at The Gateway International Bridge in Brownsville, Texas, is used as a case study. Due to data privacy concerns, the hourly wait times for vehicles arriving at the border had to be extracted manually each day using a live wait time tracker online. The data extraction was performed for the month of March 2022. Using these wait times, a queueing simulation software, SIMIO, was used to develop an interactive simulation model and calibrate the service rates. The output from the SIMIO model was then used to develop an artificial neural network (ANN) to predict hourly particulate matter content with an R2 of 0.402. From the ANN, a predictive equation has been developed, which may be used by CBP to make operational decisions and improve the overall efficiency of this POE. Thus, lowering the average wait times and the emissions from idling vehicles in the queue.