Cost Management Research Articles

Yield Yield loss and economic threshold estimates for pod borer (Helicoverpa armigera) in late maturing pigeonpea (Cajanus cajan)

Pod borer [Helicoverpa armigera (Hubner)] is a major biotic constraint to global pigeonpea production. The farmers largely apply synthetic insecticides to control this pest in pigeonpea. The study was conducted to quantify yield losses and estimate economic thresholds for H. armigera. The late maturing cultivar, IPA-203 was infested with different larval densities to determine the insect density-yield loss relationships. The yield response was variable and grain yield declined with increasing larval density from 1–5/plant. Yield loss of 4.56 kg/ha was evident for each unit increase in larval population. Economic threshold estimates indicated that the control measures for pod borer in IPA 203 cultivar should be initiated at an insect density of ≈1 larva/2 plants or at 3% pod damage to avoid the economic crop losses. The damage potential and economic threshold estimates are prerequisite for integrated pest management and reducing production costs (Alves et al. 2017). Present results indicated that relatively low infestations of H. armigera can cause significant yield reduction in pigeonpea hence intensive monitoring and timely control measures are necessary. Based on present economic threshold estimates, the farmers could decide the timing and kind of control measures to be initiated against pod borer in late pigeonpea cultivars to avert the economic losses.

Electric vehicle charging station demand prediction model deploying data slotting

Accurate prediction of energy requirement at charging station is essential for optimizing infrastructure usage, ensuring grid stability, and minimizing operational cost. Literatures suggest deployment of machine learning techniques to forecast the station demand. One major challenge associated with development of machine learning models is the inherent uncertainty in electric vehicle charging behaviour that includes variations in charging patterns, user preferences, and vehicle types. The conventional pre-processing techniques fail to dislodge nonlinearities and highly random patterns that include very low or zero-charging. Employing such techniques affects the model's forecast accuracy. This article performs data-slotting during pre-processing stage and then selects the best among 1-h, 2-h, 3-h and 4-h slots, to frame the feature vectors. The 4-h data with minimum variance is suggested to frame the dataset. Four distinct datasets, comprising different combination of average and total demands as predictor and response respectively are considered. The created dataset is deployed in Random Forest, Categorical Boosting, Extreme Gradient Boosting and Light Gradient Boosting models. The article recommends Categorical Boosting Regression model with least mean absolute error, mean square error and root mean square error of 0.0726, 0.0112, and 0.1059 respectively. Furthermore, the use of feature vector comprising of aggregated load for prescribed slots and the response representing the aggregated demand is observed to provide the least prediction error by the suggested model. The suggested model fed by the proposed feature vector offers significant advantage to charging station operator by enhancing the operational efficiency while performing resource and cost management with strategic planning.

A deep reinforcement learning approach towards distributed Function as a Service (FaaS) based edge application orchestration in cloud-edge continuum

Serverless computing has emerged as a new cloud computing model which in contrast to IoT offers unlimited and scalable access to resources. This paradigm improves resource utilization, cost, scalability and resource management specifically in terms of irregular incoming traffic. While cloud computing has been known as a reliable computing and storage solution to host IoT applications, it is not suitable for bandwidth limited, real time and secure applications. Therefore, shifting the resources of the cloud-edge continuum towards the edge can mitigate these limitations. In serverless architecture, applications implemented as Function as a Service (FaaS), include a set of chained event-driven microservices which have to be assigned to available instances. IoT microservices orchestration is still a challenging issue in serverless computing architecture due to IoT dynamic, heterogeneous and large-scale environment with limited resources. The integration of FaaS and distributed Deep Reinforcement Learning (DRL) can transform serverless computing by improving microservice execution effectiveness and optimizing real-time application orchestration. This combination improves scalability and adaptability across the edge-cloud continuum. In this paper, we present a novel Deep Reinforcement Learning (DRL) based microservice orchestration approach for the serverless edge-cloud continuum to minimize resource utilization and delay. This approach, unlike existing methods, is distributed and requires a minimum subset of realistic data in each interval to find optimal compositions in the proposed edge serverless architecture and is thus suitable for IoT environment. Experiments conducted using a number of real-world scenarios demonstrate improvement of the number of successfully composed applications by 18%, respectively, compared to state-of-the art methods including Load Balance, Shortest Path algorithms.

A socio-economic assessment of an emerging technology in the mining industry

Abstract Purpose This article provides methodological insights to evaluate the socio-economic risk of an emerging froth flotation technology for the mining sector with the goal of guiding the design and development process. This technology is used to separate valuable particles based on surface properties among minerals and, if properly developed, could be used to valorize fine particles that currently existing technology cannot separate and thus become waste material. Methods The Social Hotspot assessment utilized the Product Social Impact Life Cycle Assessment (PSILCA) database to analyze social hotspots in the relevant industrial sector. In addition, a survey captured the viewpoints of technology developers regarding additional potential social risk and opportunities. The final results were defined by combining these two analyses, conducted according to the 2020 UNEP guidelines for Social Life Cycle Assessment of Products and Organizations. For the economic assessment, the Material Flow Cost Accounting (MFCA) methodology (ISO14051) was applied, considering material costs, system costs, energy costs, and waste management costs for both the current situation and a future industrial-scale scenario. Results and discussion The study emphasizes the importance of tailoring methodological approaches for case studies involving low Technology Readiness Level (TRL) technologies based on available data. The state of technology development has led to different results for the economic and social analyses, primarily due to the difficulty in accurately predicting potential social impacts at this stage. The social analysis identified potential risks and 28 subcategories of impacts across different stakeholder categories. The economic assessment found that energy costs (49%) were the highest contributor to the MFCA cost of the future scenario, followed by system costs (29%). Conclusions and recommendations The study concludes that conducting a socio-economic analysis during the developmental stage of a technology is valuable for identifying critical hotspots that require monitoring, effectively guiding the research and development phase. This application represents a unique case in the mining sector and could be a first step in defining a methodological approach suitable for low TRL technologies. Analyzing both social and economic risks provides a more comprehensive perspective on sustainability, complementing environmental risk assessments.

Better safe than sorry? The effect of asymmetric cost management on firm resilience in manufacturing firms

PurposeGlobal business practices show that during periods of demand volatility, manufacturing firms often engage in asymmetric cost management (ACM), a behavior in which costs increase faster than they decrease when demand decreases by the same amount. However, whether managing asymmetric costs will enhance or impede firm resilience remains an open question. We aim to investigate the impact of ACM on firm resilience and its boundary conditions.Design/methodology/approachUsing unbalanced panel data of 2,273 Chinese manufacturing listed companies from 2002 to 2021, we conduct an empirical analysis using a double fixed effects model.FindingsOur findings reveal that ACM has a negative effect on firm resilience. This suggests that in coping with external environmental fluctuations, ACM fails to fulfill its expected role effectively. Instead, it manifests as a severe agency problem affecting firm resilience. Further, we find that managerial myopia and digitalization diminish the negative effect, while customer instability exacerbates it.Originality/valueThis study contributes to the literature on the organizational resilience of manufacturing firms by providing an in-depth understanding of cost management and emphasizing the need to consider agency issues carefully when managing asymmetric costs.

Green Quality Management and Green Target Costing and Their Role in Achieving Competitive Advantage

This study mainly aims at the extent of the possibility of achieving integration between green cost management techniques, including green quality management and green target costing, and the extent of the possibility of achieving many benefits from this integration, including environmental benefits as well as economic benefits for the company and achieving competitive advantage. We have concluded that companies can achieve competitive advantage by adopting the proposed model of integration between green quality management and green target costing, with the use of green value engineering in excluding non-value-adding activities and enhancing value-adding activities, which contributes to enhancing customer value and reducing costs, as well as providing environmentally friendly and differentiated green products.

Environmental Cost Disclosure and Financial Performance of Oil and Gas Firms

General Background: The relationship between environmental cost disclosure and financial performance is essential in environmentally impactful sectors like oil and gas. Specific Background: This study focuses on listed oil and gas firms in Nigeria, examining health and safety costs and waste management costs as predictors of return on assets (ROA). Knowledge Gap: Existing research shows inconsistent findings on the effects of environmental disclosures on financial performance, necessitating further investigation. Aims: This study aims to clarify the impact of environmental cost disclosures on financial performance, particularly through health and safety and waste management costs. Results: Using ex-post-facto research design and secondary data analysis, the findings reveal a significant positive relationship between environmental cost disclosures and financial performance, highlighting the importance of these costs on ROA. Novelty: By applying legitimacy theory and focusing on specific environmental costs, this research fills a gap in Nigerian literature. Implications: The study recommends enhanced environmental policies for oil and gas firms, promoting accountability and aligning financial performance with ecological responsibilities. Highlights: Significant positive relationship exists between environmental cost disclosures and financial performance (ROA) in Nigerian oil and gas firms. Health and safety costs and waste management costs are critical predictors of financial performance. The study emphasizes the need for enhanced environmental policies and corporate accountability in the oil and gas sector. Keywords: Health, Safety Cost, Waste Management Cost, ROA

Application Analysis of BIM Technology in Construction Engineering Cost Management

With the rapid development of information technology, Building Information Modeling (BIM) technology has gradually occupied an important position in the construction industry. With the introduction of BIM technology, the cost control of construction projects can be greatly improved [1]. The use of this technology can not only reduce the project approval time but also improve the quality of the project. In addition, it can also help the construction personnel to solve the project changes, maintenance costs, insurance rates, and other related issues, so that the operation of the project is more efficient and economical. This paper will discuss the role of BIM technology in depth, and explore the advantages and disadvantages of each link through the research of the project, to provide a reference for the promotion of this new project. By constantly adjusting the application strategy, the goal is to meet the current market demand.

Economic Substantiation of Innovative Solutions for Direct Cooperation between Manufacturers and Restaurants

The traditional supply chain management between manufacturers and restaurants is not effective enough, considering its high costs, long turnaround times and reliance on third parties, which creates challenges to profitability and agility. This study aims to find out how this system helps optimise costs and supply chain management within the restaurant industry. Data was obtained through manager interviews for some middling and high-end restaurants presenting some operational issues confronted before and after the system was installed. A sample of 50 restaurants was selected using a stratified sampling technique, targeting various restaurant categories. A structured questionnaire was designed to interview restaurant managers on operational efficiency, cost reduction, and supply chain performance. The logistics costs, transaction costs and overall efficiency improvements, among other quantitative metrics, were monitored for the first and second intervals. To achieve this, the analysis used a difference-in-difference methodology, where restaurants before and after the implementation of the system were compared concerning specific performance indicators.Data was processed using SPSS Statistics 27, ensuring accuracy and reliability in the analysis. The findings show that wastage management cost efficiency has improved to 15%, while significant decreases have also been observed regarding transaction time and logistics costs. This also resulted in a substantial decline in time-to-order delivery which positively influences operational speed. Based on these findings, it is suggested that direct trade models would benefit the restaurant business in terms of cost savings and enhanced performance. The policy recommendations include promoting digital supply chain platforms using tax credits, assisting SMEs with government-sponsored projects, and facilitating direct interaction between producers and consumers.

Internal Audit of an Oral Pathology Laboratory: Perspectives on Finances and Operational Management.

Background Internal audits are essential tools for enhancing the operational efficiency, quality, and effectiveness of healthcare departments. Audits enable the departments and laboratories to meet the changing needs of the healthcare environment by giving a detailed picture of the department's operations and highlighting areas for possible growth and development. Aims and objectives This study focuses on the biopsies received in the Oral Pathology Department at Saveetha Dental College, aiming to evaluate biopsy trends, financial performance, and resource utilization over one year. Materials and methods The oral pathology department audit covered the period from 1st April 2023 to 31st March 2024. The institutional human ethical committee and scientific review board approved the retrospective audit. It involved a comprehensive analysis of biopsy data, financial records, and material usage. Data on different biopsy types (excisional, incisional, frozen sections), immunohistochemistry, cytology, and special stains were collected and analyzed across four quarters. Financial performance was assessed by comparing total income and expenses, while resource utilization was examined through the use of histopathological blocks and other consumables. Statistical analysis (chi-square) was performed using IBM SPSS Statistics for Windows, Version 23 (Released 2015; IBM Corp., Armonk, New York, United States). A P-value less than 0.05 was considered statistically significant. Results We received 1100 cases during the study period. Excisional biopsies were the most common, with 474 (43.09%) cases, followed by incisional biopsies with a total of 432 (39.27%). Out of total cases of 1100, the second quarter (July-September 2023) had the highest case volume of 305 (27.72%), while the third quarter (October-December 2023) recorded the lowest of 250 (22.72%) cases. A financial audit revealed an annual deficit of ₹1,03,321 primarily due to higher expenses towards laboratory reagents. The overall expense incurred per case was ₹448.5. Tissue blocks cost ₹85.23 per case (19.00%) of the average cost per case. The chi-square test analysis was insignificant among the different types of biopsies and the reagent consumption across the four quarters. Conclusion The audit identified critical areas for improvement in both clinical workload and financial management. High volumes of biopsies, but net financial deficits highlight the need for better cost management and resource utilization strategies to maintain sustainability without compromising diagnostic quality.

ПРОЦЕСНО-ОРІЄНТОВАНИЙ ПІДХІД ДО ОБЛІКУ ВИТРАТ Й КАЛЬКУЛЮВАННЯ СОБІВАРТОСТІ ПРОДУКЦІЇ ПІДПРИЄМСТВ З ВИРОБНИЦТВА ЗБІРНИХ ЗАЛІЗОБЕТОННИХ КОНСТРУКЦІЙ

Costs are one of the most important economic categories, and production costs are the main indicator of construction materials industry enterprises activity. In today's difficult business conditions for Ukrainian enterprises, taking into account the specifics of the production process of companies producing prefabricated reinforced concrete structures, constant monitoring of production costs helps to maintain the profitability and competitiveness of these business entities. Therefore, the development of management tools and cost accounting using a process-oriented approach for enterprises producing building materials allows taking into account all the features of production processes influencing the size and behavior of costs. The purpose of the article is to reveal and improve the features of cost accounting and costing of products according to production processes at enterprises producing precast reinforced concrete structures. The article analyzes the main directions of classification of production processes at enterprises of the production of prefabricated reinforced concrete structures and its influence on the organization of accounting. The directions for grouping costs in production processes are detailed and attention is focused on its impact on accounting and control of the costs. This will affect the quality of management decisions and the main performance indicators of the enterprises under study. In the process of research, the main foreign methods of accounting for costs and calculating the cost of products in production processes, which are possible for use at enterprises of the production of precast reinforced concrete structures, were determined. It was found that the ABC-costing method is the most acceptable from the point of view of the technological process of manufacturing products of the studied enterprises. Its combination with the regulatory method of cost accounting and product costing will provide significant opportunities for strategic planning of production costs, will contribute to the accuracy of product costing, and will improve the process of management decision-making.

Environmental Management Strategies for Cost-Effective Waste Management in Hospitals

General background: Waste management in hospitals is crucial due to its environmental impact. Specific background: RSU Al Islam HM Mawardi Krian struggles with waste management while adhering to PSAP No. 1 of 2010 and maintaining efficiency. Knowledge gap: Limited research exists on applying environmental management accounting and operational strategies for hospital waste management in line with accounting standards. Aims: This study analyzes the impact of environmental management accounting and strategies on waste management costs at RSU Al Islam HM Mawardi Krian, assessing compliance with PSAP standards. Results: The hospital has implemented processes of identification, recognition, measurement, recording, presentation, and disclosure per PSAP No. 1 of 2010, managing waste effectively and incurring environmental costs. Novelty: Offers insights into aligning hospital environmental cost management with government standards. Implications: Structured environmental accounting and strategies can enhance waste management, improve hospital reputation, ensure regulatory compliance, and potentially increase revenue. Highlights: RSU Al Islam HM Mawardi complies with PSAP No. 1 of 2010 in managing waste costs. Proper environmental accounting improves transparency and operational efficiency. Structured strategies can boost hospital reputation and revenue. Keywords: Environmental Management Accounting, Operational Strategies, Waste Management Costs, Hospital Compliance

Распределение затрат на закупки в условиях использования стратегии отсрочки в цепях поставок

The article discusses the method of distributing procurement costs in the context of using the deferral strategy in the CP, which allows participants to obtain additional competitive advantages by reducing the time period from product development and raw material procurement to actual sales. The proposed approach is based on the application of graph theory to procurement cost management, namely, the use of graph coloring algorithms for distributing or combining such costs over different time periods. The examples discussed in the article are especially relevant in the context of turbulent modern markets, which have a direct impact on the diversity and specificity of economic relations between CP participants.

AI for Cloud Cost Management: Predictive and Prescriptive Analytics

With reference to the previous information and literature review, the purpose of this research paper is to evaluate the impact of multithreading concerning computer resource utilization and performance by undertaking and comparing various crucial multithreading techniques, including work stealing, fork join, and the use of the thread pool control. In an experimental context, benchmark applications that can be characteristic of several domains were evaluated in a controlled multicore computing platform. Time, CPU occupation, memory usage and throughputs were determined systematically and compared according to the various workloads. The results highlight the superiority of the work-steing algorithm in keeping the execution time and CPU usage low compared to the rest as a sign of its efficiency in managing work loads. Further, the memory usage and the throughput statistics showed the degree of inefficiency of each algorithm and performance penalty. Interviews with developers elaborated on more practical issues of multithreading as the generally rigid nature of the problems suggested that optimal solutions called for adaptive measures in practice. Based on the results achieved in this research, developers and researchers can increase their knowledge about available multithreading algorithms and make suggestions to choose and apply them with high efficiency and low resource consumption.

Sustainable digital fashion in a metaverse ecosystem

The fashion industry ranks among the top polluters globally, yet the emergence of virtual worlds offers a chance for brands to switch to digital clothing as a greener option. This research explores whether the metaverse can enhance sustainability in fashion or worsen environmental issues, especially those linked to non-fungible tokens (NFTs). We introduce a game theory model to analyze the strategic dynamics between brand manufacturers and digital fashion platforms, factoring in platforms' abilities to reduce emissions. The model uncovers how these uncertainties influence pricing, investment, and performance outcomes. Our analysis is supported by a case study on DressX, a prominent digital fashion platform, showing that digital fashion brings technological innovation and environmental advantages. Platforms with greater emission reduction capacities and their collaborators witness increased revenues. However, information asymmetry complicates strategic decision-making, and the environmental implications of digital technologies underpinning the metaverse remain a critical concern. Platforms with advanced capabilities gain a competitive edge by managing costs effectively, whereas manufacturers profit at the platforms' expense. However, there is a strategic equilibrium that benefits both sides. Our study indicates that maximizing the environmental benefits of digital fashion demands responsible actions from all stakeholders, including careful technology selection, clear communication of capabilities, cost management, and strategic cooperation. Our game theory approach, combined with a real-world case study, provides a unique perspective on the strategic and environmental dynamics of digital fashion in the metaverse, contributing to the growing body of literature on sustainable fashion and digital ecosystems.

Use of benchmarking as a tool of lean production in the mechanism of cost management in an agricultural organization

The mechanism of cost management, reflecting the peculiarities of its organization at the microand macro-level, with the application of lean production principles (focus on creating value for the customer, continuous improvement, strategic orientation, loss reduction, visualization and transparency, etc.) is presented. Methodological approaches to cost rationing in the production of agricultural products, taking into account the conditions of its production (resource availability of organizations) and the level of efficiency of resource expenditure are substantiated. The novelty of the considered approach consists in the use of the tool of lean production – cost benchmarking. It allowed to calculate the reserves of growth of productive indicators of agricultural producers’ activity taking into account their provision with long-term and short-term assets, labor resources.

Analisis Penerapan Akuntansi Lingkungan Terhadap Pengendalian Biaya Operasional Pengelolaan Limbah pada Rumah Sakit Karya Husada

This research aims to find out how environmental accounting is applied to control operational costs for waste management at Karya Husada Hospital. This research was conducted using qualitative methods. Data sources use primary data and secondary data. The data analysis technique used is descriptive qualitative using the Miles and Huberman model. The results of this research show that Karya Husada Hospital has managed its waste well, this is proven by the absence of problems or complaints from the surrounding community regarding waste at the hospital. Karya Husada Hospital has implemented environmental accounting and carried out the process of Recognizing, Measuring, Recording, Presenting, and also Disclosure of environmental related costs. Karya Husada Hospital does not have a specific environmental cost report. Controlling operational costs for waste management at Karya Husada Hospital has not been effective, where there has been a deviation from realized costs exceeding previously budgeted costs. This deviation occurred because the amount of medical and domestic waste increased along with the increase in patient and family visits at Karya Husada Hospital.

Heterogeneous uncertainty matters! Evidence based on firms' cost management decisions

The literature traditionally treats the uncertainty firms encounter as a whole, neglecting the inherent heterogeneity of uncertainty that prevails in the real world. To address this critical issue, this study employs natural language processing methods to decompose uncertainty into three distinct levels: firm-level, industry-level, and macro-level uncertainty. We then examine the impact of heterogeneous uncertainty on corporate decision-making from a cost management perspective. Our findings indicate that while uncertainty as a whole increases cost stickiness, this effect mainly stems from firm-level uncertainty rather than industry-level and macro-level uncertainty. These findings suggest that firms only respond to uncertainty when they perceive uncertainty at the firm level. Cross-sectional tests indicate that the impact of uncertainty on cost stickiness is more pronounced when firms face higher adjustment costs, lower financing constraints, and lower competitive pressures than otherwise. Additionally, uncertainty diminishes firms' cost elasticity, curtailing their production flexibility. Collectively, our results provide insights into comprehending the internal mechanisms of firms' decision-making in the face of uncertainty.

A Survey of Data-Driven Construction Materials Price Forecasting

The construction industry is heavily influenced by the volatility of material prices, which can significantly impact project costs and budgeting accuracy. Traditional econometric methods have been challenged by their inability to capture the frequent fluctuations in construction material prices. This paper reviews the application of data-driven techniques, particularly machine learning, in forecasting construction material prices. The models are categorized into causal modeling and time-series analysis, and characteristics, adaptability, and insights derived from large datasets are discussed. Causal models, such as multiple linear regression (MLR), artificial neural networks (ANN), and the least square support vector machine (LSSVM), generally utilize economic indicators to predict prices. The commonly used economic indicators include but are not limited to the consumer price index (CPI), producer price index (PPI), and gross domestic product (GDP). On the other hand, time-series models rely on historical price data to identify patterns for future forecasting, and their main advantage is demanding minimal data inputs for model calibration. Other techniques are also explored, such as Monte Carlo simulation, for both price forecasting and uncertainty quantification. The paper recommends hybrid models, which combine various forecasting techniques and deep learning-advanced time-series analysis and have the potential to offer more accurate and reliable price predictions with appropriate modeling processes, enabling better decision-making and cost management in construction projects.

The Moroccan leather sector between modernity and authenticity: what problems? What solutions?

The leather sector remains one of the sectors that encounters a multitude of problems on an international scale, particularly in Morocco. Therefore, we carried out a literature review through which we were able to identify the major problems found within the leather sector. After that, we conducted a case study on the Chouara tannery in the Fes Meknes region where we studied the problems found within the sector by using the 5M and SWOT methods as well as the proposal of certain improvement actions with a view to revitalizing the sector. The novelty of our work lies in the proposal of certain functionalities that could play the role of product lifecycle management (PLM) within the sector, such as life management, complaint management and cost management since the notion of PLM is absent within traditional tanneries. In addition, we propose integrating ecological PLM to limit the amount of garbage generated during the execution of tasks.