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The conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and Russia is also impacted by the Russia-Ukraine crisis. This study aims to compile the most recent data on how the present global economic crisis is affecting it, with particular emphasis on the Indian economy.

This research develops a mathematical forecasting model to evaluate how the Russia-Ukraine crisis would affect the Indian economy when perturbations are applied to the major transport sectors. Input-output modeling (I-O model) and interval programing (IP) are the two precise methods used in the model. The inoperability I-O model developed by Wassily Leontief examines how disruption in one sector of the economy spreads to the other. To capture data uncertainties, IP has been added to IIM.

This study uses the forecasted inoperability value to analyze how the sectors are interconnected. Economic loss is used to determine the lowest and highest priority sectors due to the Russia-Ukraine crisis on the Indian economy. Furthermore, this study provides a decision-support conclusion for studying the sectors under various scenarios.

In future studies, other sectors could be added to study the Russian-Ukrainian crises’ effects on the Indian economy. Perturbation is only applied to transport sectors and could be applied to other sectors for studying the effects of the crisis. The availability of incomplete data is a significant concern in this study.

Russia-Ukraine conflict is a significant blow to the global economy and affects the global transportation network. This study discusses the application of the IIM-IP model to the Russia-Ukraine conflict. It also forecasts the values to examine how the crisis affected the Indian economy. This study uses a variety of scenarios to create a decision-support conclusion table that aids decision-makers in analyzing the Indian economy’s lowest and most affected sectors as a result of the crisis.

This study aims to analyse the effect of competition on retail fuel prices in a small European Union (EU) country with high market concentration.

The researchers use a panel data set to estimate a fuel price equation that includes supply and demand factors as well as time-fixed effects.

The study finds that more competitors in the local market decrease prices, whereas the high market share of oligopoly brands does not condition this effect. Additionally, independent brands set lower prices than wholesalers, and gas stations located near the borders of almost all neighbouring countries are associated with higher prices.

The study suggests that Slovenia’s retail fuel market maintains competitive pricing despite high oligopolistic shares because of historical regulatory influences that shaped firm behaviour and pricing strategies, along with geographical and economic factors such as Slovenia’s role as a transit country. External competitive pressures from neighbouring countries and high levels of traffic, combined with the remnants of regulatory structures, help prevent market abuses and keep fuel prices lower than in other EU countries.

It also indicates that policy should encourage fiercer competition in the local market by increasing the density of gas stations, especially from independent brands.

These findings may be associated with specific country characteristics. This paper introduces unique findings that shed light on the impact of a small market on competition, with a particular focus on highlighting the effect of oligopolistic brands.

The aim of this study was to evaluate the performance of fuel flow processes in a network of eight gas stations, located in the mesoregion of Alto Paranaíba and Triângulo Mineiro.

Two multi-criteria decision support methods were applied, respectively, of a statistical and mathematical nature, namely, Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA). The research method used was quantitative, with a brief complement of qualitative research, and descriptive in purpose, supported by the inductive method. The data collection stage took place with the support of interviews, with the application of a structured questionnaire, and non-probabilistic sampling, for convenience.

It was possible to verify that the gas station that stood out the most was station 2 (GS2), which achieved maximum efficiency, a fact that can be justified by the analysis resulting from the application of PCA, as for the product purchase variable (PP), the GS2 is the one that buys the most fuel, and is also the one with the largest storage capacity (C), and the highest volume of product sales (PS), which suggests signs of balance between supply and demand for this station, justifying its prominence.

The limitations of the study were related to the DEA technique, which requires a number of variables/indicators three times smaller than the number of DMUs considered, and the difficulty in obtaining financial data on the DMUs analyzed. Considering the security and anonymity of the gas station network, it was not possible to use this data.

The performance assessment of fuel flow processes carried out in this study promotes the efficient use of available resources as well as identifying efficient DMUs that represent benchmarks for improving management processes and performance of inefficient DMUs.

From a social perspective, this study promotes the improvement of the quality of flow processes and effective management of the fuel supply chain, ensuring the safe storage and transportation of fuels to customer supply. Performance management in this sector moves other sectors of the economy, since an efficient unit represents a balance between supply and demand, and consequently, boosts the regional economy, promoting economic growth of the population. Hiring qualified labor for this purpose also represents one of the implications of the study. From an environmental perspective, optimizing flow processes generates a reduction in greenhouse gas emissions and encourages the formulation of public policies aimed at consolidating sustainable practices.

Performance management applied to the context of the fuel supply chain is a relevant topic that has been little explored in scientific research, with a low level of information detail. This study using the inductive method allows the generalization and replication of this management pattern in other organizations in the sector in order to increase the efficiency of the fuel distribution system, with the perspective of maximizing outputs and reducing input consumption. In this aspect, the study introduces possibilities for advancement in social and environmental perspectives based on the effective management of fuel logistics.

Assembly line is a common production form and has been effectively used in many industries, but the imprecise processing time of each process makes production line balancing and capacity forecasting the most troublesome problems for production managers. In this paper, uncertain man-hours are represented as interval grey numbers, and the optimization problem of production line balance in the case of interval grey man-hours is studied to better evaluate the production line capacity.

First, this paper constructs the basic model of assembly line balance optimization for the single-product scenario, and on this basis constructs an assembly line balance optimization model under the multi-product scenario with the objective function of maximizing the weighted greyscale production line balance rate, second, this paper designs a simulated annealing algorithm to solve problem. A neighborhood search strategy is proposed, based on assembly line balance optimization, an assembly line capacity evaluation method with interval grey man-hour characteristics is designed.

This paper provides a production line balance optimization scheme with uncertain processing time for multi-product scenarios and designs a capacity evaluation method to provide managers with scientific management strategies so that decision-makers can scientifically solve the problems that the company's design production line is quite different from the actual production situation.

There are few literary studies on combining interval grey number with assembly line balance optimization. Therefore, this paper makes an important contribution in this regard.

This study aims to explore and segment consumer preferences for electric and hybrid vehicles in Germany, Sweden, the Netherlands and Turkey, focusing on understanding the various factors that influence consumer decisions in these markets.

Using latent class analysis (LCA) on data collected through online surveys and discrete choice experiments, this research categorizes consumers into distinct segments. The approach allows for a nuanced understanding of how various factors such as income level, fuel cost, age, CO₂ emissions, purchase price, vehicle range, policy policies and environmental concerns interact with shape consumer preferences.

The analysis uncovers significant heterogeneity in consumer preferences for electric and hybrid vehicles across Germany, Sweden, the Netherlands and Turkey, revealing four key segments: “Eco-Driven Innovators,” “Value-Focused Pragmatists,” “Tech-Savvy Early Adopters” and “Reluctant Traditionalists.” “Eco-Driven Innovators” prioritize environmental benefits and are less sensitive to price, demonstrating a strong inclination toward vehicle CO₂ emissions and policy policies. “Value-Focused Pragmatists” weigh economic factors heavily, showing a sharp interest in fuel costs and purchase prices but are open to considering electric and hybrid vehicles if they present clear long-term savings. Technology-savvy early adopters are attracted by the latest technological advancements in vehicles, regardless of the type, and are motivated by factors beyond just environmental concerns or cost savings. Lastly, “Reluctant Traditionalists” exhibit minimal interest in electric and hybrid vehicles due to concerns over charging infrastructure and upfront costs. This detailed segmentation illustrates the diverse motivations and barriers influencing consumer choices, from governmental policies and environmental concerns to individual financial considerations and technological appeal.

This study stands out for its pioneering application of LCA to dissect the complexity of consumer preferences for electric and hybrid vehicles, a methodological approach not widely used in this research domain. Using LCA, the authors are able to uncover nuanced consumer segments, each with distinct preferences and motivations, providing a depth of insight into market dynamics that traditional analysis methods may overlook. This approach enables a more granular understanding of how diverse factors – ranging from environmental concerns to economic considerations and technological attributes – interact to shape consumer choices in different countries. The findings not only fill a critical gap in the existing literature by mapping the intricate landscape of consumer preferences, but also offer a novel perspective on strategizing market interventions. Therefore, the application of LCA enriches the discourse on sustainable transportation, offering stakeholders, manufacturers, policymakers and researchers – a refined toolkit for navigating the evolving market dynamics and fostering the adoption of electric and hybrid vehicles.

Combustion of fuel with oxidizer inside a combustion chamber of an internal combustion engine forms inevitable oxides of nitrogen (NOx) due to high temperature at different locations of the combustion chamber. This study aims to quantify NOx formed inside the combustion chamber using two fuels, a conventional diesel (n-heptane) and a biodiesel (methyl oleate).

This research uses a computational fluid dynamics simulation of chemically reacting fluid flow to quantify and compare oxides of nitrogen (NOx) in a compression ignition (CI) engine. The study expends species transport model of ANSYS FLUENT. The simulation model has provided the temperature profile inside the combustion chamber, which is subsequently used to calculate NOx using the NOx model. The simulation uses a single component hydrocarbon and oxygenated hydrocarbon to represent fuels; for instance, it uses n-heptane (C₇H₁₆) for diesel and methyl-oleate (C₁₉H₃₆O₂) for biodiesel. A stoichiometric air–fuel mixture is used for both fuels. The simulation runs a single cylinder CI engine of 650 cm³ swept volume with inlet and exhaust valves closed.

The pattern for variation of velocity, an important flow parameter, which affects combustion and subsequently oxides of nitrogen (NOx) formation at different piston locations, is similar for the two fuels. The variations of in-cylinder temperature and NOx formation with crank angles have similar patterns for the fuels, diesel and biodiesel. However, the numerical values of in-cylinder temperature and mass fraction of NOx are different. The volume averaged static peak temperatures are 1,013 K in case of diesel and 1,121 K in case of biodiesel, while the mass averaged mass fractions of NOx are 15 ppm for diesel and 141 ppm for biodiesel. The temperature rise after combustion is more in case of biodiesel, which augments the oxides of nitrogen formation. A new parameter, relative mass fraction of NOx, yields 28% lower value for biodiesel than for diesel.

This work uses a new concept of simulating simple chemical reacting system model to quantify oxides of NOx using single component fuels. Simplification has captured required fluid flow data to analyse NOx emission from CI engine while reducing computational time and expensive experimental tests.

The purpose of this study is to use empirical data to examine the hierarchical impact of the Internet of things capability on supply chain integration (SCI), supply chain capability (SCC) and firm performance (FP) in the UK retail industry.

A deductive approach was employed to carry out this research. Structural equation modelling (SEM) was performed using the partial least square method (SmartPLS 3.3.3) to test theoretical predictions which underlie the relationships among Internet of things capability (IoTC), SCI, SCC and FP. Data are collected using an online survey completed by senior executives of 66 large, medium and small firms within the UK retail industry.

The empirical results of this research reveal that IoTC has a significant positive effect on the UK retail industry FP through the mediating role of SCI and SCC.

The research results from this study provide useful management insights for firms within the retail industry into the development of effective strategies for integrating their supply chain alongside the adoption of IoTC into SCI, consequently leading to improvements in FP.

Although previous studies have explored the impact of IoT on FP through the sequential mediating role of SCI and SCC, few have explored the impact of the IoT capability (IoTC) on FP through sequential mediators, i.e. SCI and SCC. This study examines the relationship between IoTC, SCI, SCC and FP in the UK retail industry supply chain to address this knowledge gap. Moreover, this study examines the effects of IoTC on FP by applying partial least square (PLS)-SEM techniques. Testing the sequential mediating role of SCI and SCI is undertaken, and the relationships among IoT-enabled SCI and SCC is analysed to improve FP. The robustness check's result through PLSpredict analysis also confirms the power of the model proposed in this study.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.

This study aims to optimize heat transfer efficiency and minimize friction factor and entropy generation in hybrid nanofluid flows through porous media. By incorporating factors such as melting effect, buoyancy, viscous dissipation and no-slip velocity on a stretchable surface, the aim is to enhance overall performance. Additionally, sensitivity analysis using response surface methodology is used to evaluate the influence of key parameters on response functions.

After deriving suitable Lie-group transformations, the modeled equations are solved numerically using the “spectral local linearization method.” This approach is validated through rigorous numerical comparisons and error estimations, demonstrating strong alignment with prior studies.

The findings reveal that higher Darcy numbers and melting parameters are associated with decreased entropy (35.86% and 35.93%, respectively) and shear stress, increased heat transmission (16.4% and 30.41%, respectively) in hybrid nanofluids. Moreover, response surface methodology uses key factors, concerning the Nusselt number and shear stress as response variables in a quadratic model. Notably, the model exhibits exceptional accuracy with $R^2$ values of 99.99% for the Nusselt number and 100.00% for skin friction. Additionally, optimization results demonstrate a notable sensitivity to the key parameters.

Lubrication is a vital method to minimize friction and wear in the automobile sector, contributing significantly to energy efficiency, environmental conservation and carbon reduction. The incorporation of nickel and manganese zinc ferrites into SAE 20 W-40 motor oil lubricants, as defined by the Society of Automotive Engineers, significantly improves their performance, particularly in terms of tribological attributes.

This work stands out for its focus on applications such as hybrid electromagnetic fuel cells and nano-magnetic material processing. While these applications are gaining interest, there is still a research gap regarding the effects of melting on heat transfer in a NiZnFe_2O_4-MnZnFe_2O_4/20W40 motor oil hybrid nanofluid over a stretchable surface, necessitating a thorough investigation that includes both numerical simulations and statistical analysis.

While, supply chain resilience (SCRES) continues to be a dominant topic in both academic and business literature and has gained more attention recently, there is limited knowledge on SCRES capabilities specific to business functions. The purpose of this paper is to identify and investigate capabilities shared between supply, operations and logistics that are most important for SCRES.

To address this gap, the authors followed a multi-method research approach. First, the authors used the grounded theory method to generate a theoretical framework based on interviews with 51 managers from five companies in automotive SCs. Next, the authors empirically validated the framework using a survey of 340 SC professionals from the manufacturing industry.

Five significant capabilities emerged from the qualitative study; all were significant in empirical validation. This research advances the knowledge of SCRES as it informs managerial decision-making by identifying capabilities common to supply, logistics and operations that impact SCRES.

This research advances the knowledge of SCRES as it informs managerial decision-making by identifying capabilities common to supply, logistics and operations that impact SCRES. In addition, the findings of this research help managers better allocate resources among significant capabilities.