The focus of this research is to analyze how supply chains’ management affects production costs in the cardboard and Packaging sector in Peru, specifically through the creation of artificial neural networks (ANN) to improve the logistical activities. Non-experimental quantitative design was applied, collected the data from the Year 2020 to the Year 2024 and sought to assess variables such as supplier capacities, stocks held, bottom line costs incurred and stock out ratios. The study revealed that there exists a proportionate inverse relationship between the logistical costs and production costs, proving that as the cost of acquiring goods needed for production as well as the cost of keeping and managing stock decreases, the overall production cost also decreases significantly. The ANN model was able to perform cost predictions with a high degree of accuracy which points out the relevance of sophisticated instruments in the shift of the supply chain. Also, it is important to note the core contribution of the research – effective logistics management is emphasized as a way of increasing competition in industries where supply chains are of critical importance. This research reinforces the effectiveness of designing ANN in minimizing costs, while adding knowledge to the reporting practice of the companies aimed at bettering their costs. The results are a good contribution in terms of technological change in logistics aimed at helping the organizations remain flexible in a changing economy.

This document addresses the Nurse Scheduling Problem (NSP) and presents a dynamic multi-criteria optimization model for its solution considering a predefined time horizon. The purpose is to maximize the level of "work well-being" of nurses formulated as the minimization of "aversion" which translates into costs or penalties for certain undesirable work shifts. For this, a series of criteria are defined to estimate the preference structure of nurses according to the hospital center specifications by assigning costs for undesirable shift assignments. The proposed methodology involves developing a heuristic to decompose the global problem into daily subproblems for which a dynamic algorithm is implemented that considers a cost accumulation process for all criteria and all nurses. Daily models are dynamically solved by modifying the coefficients of the well-being function to achieve equity throughout the planning period by updating and accumulating different averages. This methodology has shown satisfactory results for scheduling work shifts for doctors, paramedics, security guards, and drivers in numerous hospital centers in Colombia.

This study aims to select accountants for a business in Vietnam. The study engaged in focused discussions with experts to establish the criteria for an accountant. Next, structured interviews were conducted with experts to collect data comparing each pair of criteria and expert scoring data for each candidate according to each criterion. Then, the Analytical Hierarchy Process (AHP) was applied to determine the weight Wj of each criterion of an accountant, and finally the TOPSIS method was applied to find the similarity coefficient with the ideal solution Ci* for each candidate selection option. The result was that candidate A1 was selected because he had the highest Ci* coefficient of 0.81479; at the same time, through the weighted results Wj of the criteria, it showed that experts highly appreciated the candidate for the following outstanding characteristics: communication skills (W1 = 0.4108), professional skills (W4 = 0.2527), and personal skills (W2 = 0.1613).

The study explored the influence of the main dimensions of accounting information (AI) relevance and reliability on supply chain performance (supply chain exchange information, supply chain collaboration, supply chain integration) at Noon e-commerce companies in Saudi Arabia. The researcher followed the descriptive analytical approach to describe the study variables based on previous studies and explore the study gap. The study adopted a questionnaire, of which 170 were collected. The data was analyzed using partial least squares (PLS) through structural equation modeling (SEM). The results indicated a positive effect of the relevance and reliability of AI on the dimensions of SC performance (SC exchange information, SC collaboration, SC integration), there is a positive effect of the reliability of AI on the dimensions of supply chain performance (SC exchange information, SC integration) and a negative effect of the reliability of AI on the SC collaboration parties. These results clarified the value and benefit of accounting information in improving supply chain performance.

Using technology, such as human-machine interfaces, can enhance operational performance processes and increase the flexibility of the supply chain. Human-machine interfaces can produce operational control systems quickly and accurately. The research aims to explore the impact of human-machine interface on operational performance through collaborative decision making and supply chain agility. The sample criteria are the manufacturing companies with over 20 employees in Indonesia. The questionnaires were distributed offline (76 respondents) and online through Google Forms (427 respondents), so 503 questionnaires were valid—data processing using SmartPLS software version 4.0. The study results showed that the human-machine interface technology positively affects collaborative decision-making, supply chain flexibility, and operational performance with coefficients of 0,559, 0,490, and 0,340, respectively. Collaborative decision-making involving customer partners in planning decisions and communicating decisions with external partners influences supply chain flexibility by a coefficient of 0.375 and operational performance by 0.149. Moreover, supply chain flexibility with flexible planning and production processes and flexible labor placement influences operational performance by a coefficient of 0.381. The practical contribution of research enlightens company managers to build integrated systems and automation. It encourages top management and owners to think about investing in machines with high automation in the economy. Besides, these findings enrich the theoretical background in supply chain management and the resource-based view.

The shift in learning communication patterns in higher education emerged along with the end of the pandemic, accelerating the transformation of digital communication. This study aims to explain the digital communication variable of the CMC model, the digital technology variable of the technology and determinism model, the SIKA LMS variable of the technology acceptance model (TAM), the discipline variable of the Attitude and Behavior theory model, and the graduation variable of the Media Dependence model as part of the communication using the computer. Quantitative research method with SEM PLS analysis. Furthermore, the data collection technique was used with a proportionate stratified random sampling population of 3416 students and a sample of 302 active students working on the final project. The analysis in this study uses Structural Equation Modeling, with 5 variables, namely digital communication (X1), digital technology (X2), SIKA LMS (X3), student discipline (X4) and timely graduation (X5), conducting outer model tests, goodness of fit models, and model testing inner. Results show that the discipline variable obtained the highest average of 4.22. In contrast, the digital communication transformation obtained a very significant direct influence on the shift in learning patterns for timely graduation.

In recent years, fintech has received much attention due to the introduction of new technologies in banking and electronic payment. For financial service providers to compete in the industries, they should apply the business model as a conceptual framework to improve performance. The current research is exploratory and tries to identify the factors influencing fintech design in electronic payment using the Osterwalder business model. This study aims to integrate three methods named DEMATEL, ISM, and ANP from MCDM techniques. To analyze the identified factors affecting the design of fintech in electronic payment, the indicators were examined in terms of influence and effectiveness by the DEMATEL method, then the levels of influence and effectiveness of the factors were investigated using the interpretive structural modeling method. Finally, the network analysis method was used to prioritize the factors. The findings showed that recognizing and identifying electronic payment customers is the most effective among the factors, and determining the type of relationship with customers is the most impressionable factor. In addition, after ranking the factors, the type of relationship with customers was the first rank, and the criteria of the company's cost structure and revenue streams were determined as the second and third, respectively.

This study uses the Resource-Based View (RBV) and technology, organization, and environment (TOE) theories to examine how smart supply chain (SSC) practices affect financial performance (FP) in enterprises of various sizes. Our results show that SSC benefits larger enterprises more financially than smaller firms. SSC has a statistically significant effect on green supply chain management (GSCM) and sustainable supply chain performance (SSCP), and the strength of the relationship declines with a decline in firm size. Smaller enterprises are more receptive to competitive pressure and implement GSCM alongside SSC. Our findings show that SSCP improves financial performance, while GSCM does not, even in large enterprises. Further, mediation effects show that GSCM mediates the relationship between SSC and SSCP, whereas it does not mediate between SSC and FP across all sizes. The impact of SSC on FP is sequentially mediated via GSCM and SSCP. Using a non-linear approach (ANN), we also rank independent variables for small, medium, and large firms. Our research provides important implications.

This research is aimed at analyzing perception and identifying determinants of the decision to convert to organic mango farming in Mekong Delta (MD) Vietnam. The research was conducted by using a direct survey data set from 109 household heads in this region collected by stratified random sampling method. The research method used was descriptive statistics and the binary Logit model. The research results revealed some interesting points. In the total observations gathered, only about half of households decided to convert, mainly due to local implementation and awareness of safety for consumers and environmental protection. Still, the most important reason for farmers to convert was to get a higher selling price. The binary Logit model analyzing the determinants found that the older the farmer, the more difficult it is to decide to convert. At the same time, training and enhancing awareness about organic farming will increase the probability of conversion decisions. Based on the research results, several relevant solutions on investment, production linkage, and propaganda to raise people's awareness were recommended, thereby increasing the probability of deciding to convert.

Annual increases in power use need improved energy management. Several universities and research organizations have focused on pursuing energy efficiency and renewable energy to satisfy this stipulation. This study provides a thorough and organized systematic review of over 2000 operational research studies conducted between 2019 and 2023. In summary, this study explores potential innovations to enhance existing literature utilizing mathematical tools in energy management. Our systematic literature review indicates that geometric planning is a novel mathematical technique in energy management. Based on the specific problems, this paper discusses geometric planning and proposes its integration with other mathematical techniques.