Operational Efficiency

Supply chain integration represents the extent based on which a producer strategically cooperates with its supply chain partners and collaboratively manages inter-organizational and intra-organizational processes. The current research has been conducted to examine the effect of supply chain integration on operational efficiency and value creation. In order to do this, Annan’s et al. (2016) standard questionnaire was used to measure the research variables. The research method was descriptive correlational. In order to answer the research questions and investigate the research hypotheses, structural equations and path analysis by means of the partial least squares method (PLS) were used with Smart-PLS software. The obtained results indicate that there is a positive and significant relationship between the inter-firm networking resources and dysfunctional competitions on the supply-chain integration. The results also approve the positive and significant effect of supply chain integration on customer value creation and the company’s operational efficiency.

The research on the Markowitz model and optimization of its portfolio using a variety of evaluation indicators and meta-beta-algorithms has always been the focus of attention of accounting and finance researchers. The results of studies carried out by various types of optimization methods are different in the Markowitz modified models. The purpose of this study is to measure the optimal portfolio and its corresponding return, with respect to the portfolio in the traditional Markowitz model, as well as to compare the position of the refining and petrochemical companies versus stock market outperformers, through integrating the operational criteria and the new indicators of liquidity using the genetic algorithm in the Markowitz model. Therefore, financial data related to the research variables for 35 cases of TSE-listed refinery and petrochemical companies from 2012 to 2016 fiscal years were extracted from Rahavard Novin database software and simulated by the genetic algorithm. The results show that returns on the stock portfolio optimized using the genetic algorithm and without considering the liquidity limitations and filters have a significant and positive difference with the return on the stock portfolios optimized with regard to the liquidity limitations and filters. Furthermore, the application of liquidity limitations and filters in the formation of optimal stock portfolios leads to a conservative increase in the choice of stocks (portfolio formation), which leads to a reduction in the risk and return of investment in such portfolios.

The objective of this study is to explore the influence of predictive maintenance technologies on operational efficiency in manufacturing startups, focusing on implementation processes, operational impacts, and the challenges encountered. This qualitative study employed semi-structured interviews to gather data from key stakeholders in manufacturing startups, including founders, operations managers, and maintenance engineers. A total of 22 participants were interviewed, with the sample size determined by theoretical saturation. The interviews were transcribed verbatim and analyzed using NVivo software. Thematic analysis was conducted to identify and categorize key themes and subthemes related to the implementation and impact of predictive maintenance technologies. The analysis revealed three main themes: Implementation Process, Operational Impact, and Challenges and Barriers. Within these themes, several categories and concepts emerged. The Implementation Process theme highlighted the importance of planning, technology selection, system integration, employee involvement, pilot testing, change management, and post-implementation review. The Operational Impact theme identified efficiency gains, predictive analytics, maintenance scheduling, resource optimization, and quality improvement as significant outcomes. The Challenges and Barriers theme underscored technological challenges, financial constraints, organizational resistance, skill gaps, data management issues, and the necessity of vendor support. The findings indicate that predictive maintenance technologies significantly enhance operational efficiency in manufacturing startups by reducing downtime, increasing productivity, and optimizing resource utilization.

This study aims to investigate the impact of R&D intensity on operational efficiency and strategic alliances in medium-sized companies. Specifically, it seeks to understand how these variables interact to influence a firm's commitment to research and development activities, ultimately affecting their innovation and market performance. A cross-sectional design was employed, with a sample of 230 participants drawn from medium-sized companies. The sample size was determined using the Morgan and Krejcie table. Data were collected through structured questionnaires assessing R&D intensity, operational efficiency, and strategic alliances. Pearson correlation analysis was conducted to examine the relationships between the dependent variable (R&D intensity) and each independent variable (operational efficiency and strategic alliances). Linear regression analysis was performed to explore the combined effect of the independent variables on R&D intensity. All analyses were conducted using SPSS version 27. Pearson correlation coefficients indicated significant positive relationships between R&D intensity and operational efficiency (r = 0.53, p = 0.001), and between R&D intensity and strategic alliances (r = 0.47, p = 0.002). The regression analysis showed that operational efficiency and strategic alliances together explain 40% of the variance in R&D intensity (R² = 0.40, F(2, 227) = 19.25, p = 0.000). Multivariate regression results confirmed that both operational efficiency (B = 0.07, β = 0.42, p = 0.001) and strategic alliances (B = 1.10, β = 0.35, p = 0.000) are significant predictors of R&D intensity. The study concludes that operational efficiency and strategic alliances significantly enhance R&D intensity in medium-sized companies. These findings suggest that improving operational processes and fostering strategic partnerships are critical for increasing a firm's investment in research and development. The results are consistent with previous research and provide valuable insights for both academia and industry practitioners. Future research should consider longitudinal designs and explore additional variables to further understand these relationships.

This study aims to explore the adoption and implementation of emerging technologies in Small and Medium-sized Enterprises (SMEs). By understanding the motivations, challenges, benefits, and future plans of SME founders, this research provides insights into how these businesses leverage technology to enhance their competitiveness and operational efficiency. The study employed a qualitative research design, utilizing semi-structured interviews with 25 founders of SMEs across various industries. Participants were selected using purposive sampling to ensure diverse representation. Data collection continued until theoretical saturation was achieved. The interview data were transcribed and analyzed using NVivo software, following a thematic approach to identify key themes and insights related to technology adoption and implementation in SMEs. The study identified several motivations for technology adoption, including competitive advantage, cost efficiency, customer demand, innovation drive, and growth opportunities. Key challenges faced by SMEs included financial constraints, technical difficulties, resistance to change, regulatory and compliance issues, knowledge gaps, vendor dependence, and time constraints. The benefits realized from technology adoption encompassed improved operational efficiency, enhanced customer satisfaction, revenue growth, better data analytics, increased employee productivity, and greater market adaptability. Future plans of SMEs included continued investment in technology, scaling up implementation, focusing on employee training and development, forming strategic partnerships, enhancing cybersecurity measures, and improving customer engagement. The adoption and implementation of emerging technologies present significant opportunities for SMEs to enhance their competitiveness and operational efficiency. However, SMEs must navigate various challenges to realize these benefits. By adopting a strategic approach and leveraging external partnerships, SMEs can successfully implement new technologies and drive sustainable growth. This study provides valuable insights into the experiences of SME founders, informing both practice and policy in the context of technological advancement in small and medium-sized enterprises.

Objectives: Market indices are essential indicators of overall market performance, yet the geographic distribution of firms may significantly influence market behavior. This study investigates the relationship between firm location and stock market indices to uncover how regional dispersion impacts overall market performance. Methodology/Design/Approach: The study adopts a descriptive-survey design complemented by analytical methods. Geographic Information Systems (GIS) and relevant APIs were utilized to gather detailed data on company locations. Hypotheses were tested using regression analysis and fixed effects models, with additional correlation assessments to examine the interplay between firm locations and stock market performance. Findings: The empirical results demonstrate a negative regression coefficient between firm geographic location and the stock market index. Specifically, each positive unit change in geographic location is associated with a decrease of approximately 93.297 units in the overall index. Moreover, if all firms were hypothetically concentrated in a single geographic region, the index would be projected at 12,235. These findings suggest that relocating firms from higher to lower geographic regions could reduce the overall stock market index. Innovation: This study offers original insights into the spatial dynamics of stock market behavior by highlighting the significant influence of geographic distribution on overall market indices. It contributes to a nuanced understanding of how regional clustering or dispersion of firms may shape financial market outcomes.

Background: The cloud-native architectures have reinvented the original strategies of the companies’ IT infrastructure approach and became popular due to the concepts of modularity, scalability, and resilience. These architectures respond to the shortcomings of the monolithic architectures to meet the new business challenges and workloads, including embracing innovation technologies like Artificial Intelligence and big data processing solutions. Objective: This study was designed with the objective of assessing the performance and business viability of cloud-native systems, based on critical indicators such as availability, resilience to failure, resource use, and compatibility with innovative technologies. The objective was to define the barriers and possibilities for improving cloud native architectures in various enterprises. Methods: A cross-sectional research, consideration, experiment test and case study and performance analysis. Response time, CPU and memory consumption and recovery time were compared across the range of throughput from 1000 to 12000 requests per second. To enhance the interpretational framework, key usage scenarios in the three sectors of healthcare, retail and finance were collected and compared with the results. Results: Cloud-native systems proved to provide high availability rates (> 99.9%), resource scalability, and component resource efficiency. With the use of AI in combination with big data analytics, improvement in performance was realized. But some of the problems that were seen include vendor lock, integration issues, and fluctuating peak load issues. Conclusion: All identified improvements signify the potential of cloud-native architectures for improving enterprise IT functioning. It is thus possible to continue perfecting the identified challenges to enhance their effectiveness, optimal for the current dynamic digital environment.

Background: Telecommunications networks are exposed to numerous issues concerning equipment and that causes network outage, which proves very expensive. Basic maintenance methodologies like reactive or even scheduled preventive maintenance cannot cope up with the increasing trends in the facilities of telecom companies. Objective: The article examines how AI is applied to support predictive maintenance so that telecommunication networks can perform as intended with reduced downtime. Methods: The review of existing AI algorithms is presented, focusing on the ML models and deep learning methods. Network operations and maintenance logs are analyzed for data to assess the capabilities of the AI models in terms of prediction. It identifies and analyses such quantifiable parameters as the failure rate prediction accuracy and the response time cut. Results: Computerisation of the forecast maintenance revealed a corresponding decrease in equipment failure incidences and generally reduced time lost due to unscheduled stops. Through the improved network performance, the response to potential threats was quicker than before and services became more reliable and inexpensive to offer. Conclusion: To reduce network outages, reduce network vulnerability, and maximize the efficiency of telecommunications operations, the use of AI-based predictive maintenance can be viewed as a prospect. As technology advances, newer versions of AI algorithms will provide improved predictive strength and incorporation into the telecommunications system.