Cost Control Model Research Articles

Reliable Cost Prediction and Control for Intelligent Manufacture: A Key Performance Indicator Perspective

Article Reliable Cost Prediction and Control for Intelligent Manufacture: A Key Performance Indicator Perspective Hang Geng 1,*, Alireza Mousavi 2, Nikolaos Grigorios Markatos 2, Kai Chen 1, and Xuan Gou 1 1 School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 2 Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom * Correspondence: Hang.Geng@uestc.edu.cn Received: 10 July 2023 Accepted: 11 October 2023 Published: 26 March 2024 Abstract: Intelligent manufacturing is facing significant challenges in adapting to the ever-changing equipment, instrumentation, process and economics. Such a trend together with the pressure to reliably control and contain production costs means that frequent adjusting decisions are required to adapt to incessant volatility imposed on manufacturing systems. Under this circumstance, cost-effective and quality-guaranteed manufacturing strategies would be the most logical route to reducing production costs. In this paper, a novel dynamical cost prediction and control (CPC) model is proposed to support collective decision-making in intelligent manufacturing, where the model output is the real-time prediction of possible manufacturing costs, while the inputs are generic manufacturing key performance indicators covering inventory, product quality, production efficiency, resource utilisation and environmental impact. This proposed CPC model distinguishes itself from existing ones for its capability to translate manufacturing data (at both the physical level and operation management level) into financial metrics that contribute to forming a common language between engineering, financial and administrative departments of an enterprise. The case study about the assembly line of optoelectronic devices demonstrates that, although different enterprise departments have different priorities, our CPC model helps them to achieve certain consensus on intended production that finally creates satisfactory profitability for the company at controlled manufacturing costs.

Cost and Quality Control Model for The Clinical Pathway of Cesarean Section

Cost and Quality Control Model for The Clinical Pathway of Cesarean Section

A model for the application of big data analytics in cost prediction and control in construction projects

Abstract Construction project cost control is an important aspect of project management. This paper aims to provide an effective cost control management mode for the construction industry by studying the application of big data technology in cost prediction and control management of construction projects. The standard particle swarm algorithm is optimized by establishing a particle object element model, performing iterative and hierarchical updating of particles, and introducing the Tent mapping strategy. Then it is used in the optimization of the Extreme Learning Machine algorithm to design a construction project cost prediction and control model based on APSO-LLM. The index system of the construction project cost prediction and control model is constructed, and the convergence effect, prediction effect, and case application of the model are analyzed. The APSO-LLM model enhances the convergence speed and search accuracy, boosts the optimal fitness by 0.037, and maintains a construction cost prediction error below 3%. The study proves the APSO-LLM model’s applicability, accuracy, and good prediction effect in cost prediction and construction project control.

Simulation study of inventory cost control based on system dynamics

Abstract In this study, we employ system dynamics to analyze the interplay between various factors influencing inventory cost control, resulting in a comprehensive subsystem model. The inventory strategy subsystem, based on the pipeline delay function, and the inventory cost subsystems, considering the actual situation of the enterprise, are combined to construct the inventory cost control system dynamics model. According to the validity test of the inventory cost control model, there are less than 10% simulation errors in the total transportation cost and total inventory cost data of the enterprise, and the model’s reliability is high. The simulation experiment of the model on the inventory cost control of the enterprise shows that the total inventory cost decreases to 4,398,330 yuan on the 100th day after shortening the order delay time, and the total inventory cost decreases by 1,160,450 yuan on 32 days after controlling the arrival delay time. This dynamic model proves highly effective and offers a dependable approach to managing inventory costs across various enterprises.

Research on Fine Cost Control of Civil Engineering Works Supported by Building Information Modeling

Abstract The problems of cost control in the development process of the construction industry are endless, and cost control, as a core task in the process of project management, has a decisive role in the realization of project economic benefits. This paper combines the DEMATEL method and WBS work breakdown structure to screen and decompose the factors affecting cost under the cost control theory. Then, BIM5D technology is introduced to construct a refined cost control model based on the combination of the earned value method and BIM technology, in which the BIM-5D model can become its grassroots database and combined with the earned value method to provide the basic data for cost monitoring, cost early warning, cost correction and cost prediction, to realize an effective and refined control of costs. The case study shows that enterprise infrastructure, human resource management, internal logistics, procurement and bidding, cooperative and competitive relationships, technology development, project construction, completion and acceptance, and subsequent maintenance services all affect civil engineering costs. The control method can effectively control the construction progress and cost status, can play a good effect on the schedule-cost synergistic control, and provide support for the project management decisions of the constructor in the construction stage.

Reliable Cost Prediction and Control for Intelligent Manufacture: A Key Performance Indicator Perspective

Article Reliable Cost Prediction and Control for Intelligent Manufacture: A Key Performance Indicator Perspective Hang Geng 1,*, Alireza Mousavi 2, Nikolaos Grigorios Markatos 2, Kai Chen 1, and Xuan Gou 1 1 School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 2 Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom * Correspondence: Hang.Geng@uestc.edu.cn Received: 10 July 2023 Accepted: 11 October 2023 Abstract: Intelligent manufacturing is facing significant challenges in adapting to the ever-changing equipment, instrumentation, process and economics. Such a trend together with the pressure to reliably control and contain production costs means that frequent adjusting decisions are required to adapt to incessant volatility imposed on manufacturing systems. Under this circumstance, cost-effective and quality-guaranteed manufacturing strategies would be the most logical route to reducing production costs. In this paper, a novel dynamical cost prediction and control (CPC) model is proposed to support collective decision-making in intelligent manufacturing, where the model output is the real-time prediction of possible manufacturing costs, while the inputs are generic manufacturing key performance indicators covering inventory, product quality, production efficiency, resource utilisation and environmental impact. This proposed CPC model distinguishes itself from existing ones for its capability to translate manufacturing data (at both the physical level and operation management level) into financial metrics that contribute to forming a common language between engineering, financial and administrative departments of an enterprise. The case study about the assembly line of optoelectronic devices demonstrates that, although different enterprise departments have different priorities, our CPC model helps them to achieve certain consensus on intended production that finally creates satisfactory profitability for the company at controlled manufacturing costs.

Mathematical Model on Minimality of Vaccination Costs of Covid-19 Using Fractional Order

An extremely efficient method of halting the horrible spread of pandemics is vaccination. This research investigates the cost-effectiveness of vaccine doses using a fractional-order commensurate model. The ABC type fract-integrals of COVID-19 multi-compartmental model is used to investigate the efficacy of vaccination dosages, vaccine shortages, and dose distribution. In order to assess the costs of vaccination and its use to combat COVID-19 in India, we created a fractional order model with 4 compartments of vaccinated and people waiting for booster shots have beed framed. Vaccination cost control model for this study is designed and analysed through the existence, stability and threshold measures. Through numerical convergence and simulations, the increase in disease severity brought on by microbial pathogens is investigated. Using fixed point theorems, the existence and singularity of the solution space for the framed model are assessed. Inventory control entropy on fractional derivative is used to assess the minimisation of vaccine costs

Engineering Consulting Project Budget Allocation and Cost Control Model Based on Baseline Productivity

This study developed an engineering consulting project budget allocation and control model based on baseline productivity of staff of various positions for each work item. The research model included two modules, namely the staff budget and time budget. The staff budget module determined the working hours and budgets of staff of various positions for each work item of the new project. The time budget module is to plan an implementation period and budget allocation for each work item of the new project. Four findings were inferred from the obtained results. First, the control chart method was an appropriate method for determining the baseline productivity of engineering consulting project. Second, the root mean square error (RMSE) of the staff budget module was 1.99 – 2.81 NT$/m, and the budget allocation results obtained with this module were similar to those produced by experienced project managers. Third, the RMSE of the time budget module was 2.23%, which is more accurate than are existing practical methods. Fourth, the accuracy of the budget stress index proposed by this study was 81.48%–84.00%. This index can assist project managers in determining projects, work items, and cost control benchmarks for staff working at various positions.

Improving the cost control system in the housing and utilities sector

Subject. We consider expenses of enterprises operating in the sphere of housing and utilities. Objectives. The purpose is to improve the methodology for cost control of enterprises for the provision of housing and communal services. Methods. The study employs matrix modeling methods. Results. We substantiate the need to apply ‘reference tariffs’ based on ‘reference costs’ in the housing and utilities sector, propose a matrix model of cost control, which helps identify deviations from the specified reference values and make necessary management decisions. Conclusions. The resulting model enables to control costs in the housing and utilities sector, to increase the effectiveness of tariff regulation. The offered matrix modeling adapts to the specifics of activities of any economic entity.

Application of Mathematical Modeling in Cost Control of Medical Equipment Procurement in Public Hospitals.

The purpose of this study is to strengthen the procurement management of public hospitals and ensure the quality of medical equipment. In this paper, combined with the procurement process of medical equipment in public hospitals, on the basis of RFID technology, a JRP model of random demand for medical equipment procurement in public hospitals is established. At the same time, according to the type and quantity of decision variables, the DE solution algorithm is redesigned, and based on the support of the algorithm, a mathematical model for cost control of medical equipment procurement in public hospitals is established. It can be seen from the analysis results that the cost control system for medical equipment procurement in public hospitals based on the mathematical model proposed in this paper can play an important role in public hospital equipment procurement and promote the standardized operation of public hospital procurement management.

Invested Costs and Risk Control Model of Social Governance Based on Fuzzy Algorithm

In view of the fact that the current methods cannot effectively and correctly control the invested costs and risk of social governance, resulting in a higher risk rate in the invested costs, a fuzzy algorithm-based invested costs and risk control model for social governance is proposed. This paper analyzes the classification and causes of risk, expounds the methods of risk identification and risk assessment, constructs and studies the invested costs and risk control model of social governance, establishes the fuzzy judgment matrix of risk control, calculates the single-layer ranking weight vector of risk fuzzy judgment matrix, and determines the fuzzy judgment matrix. The membership degree of risk influencing factors to the risk level is input into the social governance invested cost and risk control model to obtain the corresponding risk assessment results and predict the invested cost. According to the comparison of experimental results, through the test of risk degree and risk rate, it is verified that the maximum risk level value of the model is 20, which can minimize the risk degree; the risk control coefficient of the model is between 0.6 and 1.0, which can effectively reduce the probability of risk and achieve the purpose of design.

Application of Big Data Analysis in Cost Control of Marine Fishery Breeding

In order to improve the stability of the cost control of marine fishery culture, a method of controlling the cost of marine fishery culture based on big data analysis algorithm was proposed. We establish the cost analysis model of marine fishery, use the big data correlation analysis method to conduct distributed mining on the cost characteristics of marine fishery, deeply grasp the relevance of data characteristics, use the information fusion method to build the constraint parameter analysis model of aquaculture cost operation, limit the amount of calculation, and use the adaptive neural network weighted training method to adaptively optimize the cost control to avoid falling into local optimization. The objective model of aquaculture cost control is established, and the cost constraint is carried out by the parameter optimization method. The statistical feature quantity of marine fishery cost control is obtained, and the cost control of marine fishery is realized by the feature recombination method. The simulation results show that this method is more stable in the cost control of marine fishery culture and improves the adaptability of the cost control of marine fishery culture.

Study on Pollution Cost Control Model under Asymmetric Information Based on Principal Agent

Abstract Pollution cost control is key to solve pollution problem. The paper takes pollution control cost of pollution control contract between management authority and pollutant discharge enterprise as research object, considers pollution control quality level, pollution control quality inspection and pollution control cost model, and establishes pollution control cost model of management authority and pollutant discharge enterprise, including rational constraints of pollutant discharge enterprise. And it analyzes principal-agent relationship between the two under condition of asymmetric information, and un-observability of pollution control level is shown as hiding information of sewage enterprises. In essence, it is problem of adverse selection in principal-agent. Pollution control cost of management is objective function. The first order condition of pollution control cost of sewage enterprise is transformed into state space equation, and optimal control of problem is solved by using maximum principle. In particular, management authority, as principal, uses pollution control provisions to reward, punish and encourage pollutant discharge enterprises as agents.

Retraction Note: Cost control model of marine crude oil pollutant degradation based on game analysis

Retraction Note: Cost control model of marine crude oil pollutant degradation based on game analysis

Investigation of Vehicle Logistics Cost Control Model Based on Internet of Things Mode Based on E-commerce Platform

Objectives: The rise of the Internet of Things and e-commerce platform has enabled logistics companies to embark on a fast-growing road. How to effectively control the cost of vehicle transportation under the condition of continuous increase in the total volume of logistics business is the key to influencing the sustainable development of logistics enterprises. Methods: In order to reduce the cost of vehicle transportation, the use of artificial intelligence ant colony algorithm to build intelligent deployment model is explored. Results: After analyzing the principle and implementation flow of the traditional ant colony algorithm, the ant colony algorithm is updated and optimized from the perspective of many dynamic factors and large changes in the logistics vehicle. Conclusion: A combination of optimal algorithm parameters is constructed to help logistics companies find the most cost-effective vehicle scheduling plan. Simulation tests show that the optimized ant colony algorithm can quickly find the optimal cost-effective route and effectively control the vehicle logistics costs.

Cost Control and Project Cost Analysis of Intelligent Building under Internet of Things

The conventional urban system has a very disorganized and unmanageable mode of operation, and the information between the systems has not been efficiently shared and interconnected. Smart cities and intelligent building will be the future trend of urban development. With the advent of new concepts and techniques brought by the Internet of Things (IoT) across the world, all fields of life are progressively shifting towards smart societies. Based on the prediction algorithm of the IoT, this paper constructs the cost control and cost prediction model of intelligent building. Combined with the characteristics of the current smart city construction, three engineering cost schemes (S0, S1, and S2) are constructed, and the cost simulation of these three examples is carried out to verify the cost control and cost prediction model of the intelligent building. Experimental results show that the total cost of each cost level of the three schemes is lower than the conventional total cost, and the total cost change rate ranges are −121.6%∼−27.6%, −210.3%∼−47.2%, and −150.3%∼−22.3%, respectively. The proposed smart city cost prediction model can reduce project cost, which has great economic significance for the construction of smart cities.

The Construction and Research of Control System and Accounting Informatization by Visualization Mode

This article raises the issue of enterprise cost and project management control, analyzes the cost and expense project management and control model, and proposes the definition of daily business documents and cost types involved in the cost and expense project management and control model. In the cost and expense project management and control model, the following concepts are proposed: target cost, budgeted cost, actual cost, contract split cost, non-contract split cost, pending split cost, dynamic cost, design and cost expense project management Control related daily business processing documents, that is, contract documents, non-contract documents, cost documents to be incurred, and payment documents, and decompose to generate corresponding contract split costs, non-contract split costs, pending split costs, and corresponding The payment plan can be compared with the real-time monitoring of the target cost and budget cost through the dynamic cost (the sum of contract splitting cost, non-contract splitting cost, and pending splitting cost) and the real-time monitoring of the payment time, payment amount and payment plan Realize the management and control of cost items. At the same time, the paper puts forward the basic idea of the design of the cost and expense project management and control system, analyzes the flow of the cost and expense project management and control system, and demonstrates the operating effect of the cost and expense project management and control system.

RETRACTED ARTICLE: Cost control model of marine crude oil pollutant degradation based on game analysis

RETRACTED ARTICLE: Cost control model of marine crude oil pollutant degradation based on game analysis

E-Commerce Enterprise Supply Chain Cost Control under the Background of Big Data

Since the twentieth century, it has been an era of rapid development of information technology; the scale of data is almost the growth rate of the blowout type; no matter what it is, a large number of enterprises or departments are increasing a large number of cost data. However, the current cost management model still remains in the traditional management method and lacks a smarter big data analysis method. In addition, there is a lot of research on big data applications, and there are few e-commerce supply chains. Therefore, the research purpose of this study is to use big data technology to explore a series of practical operation methods for supply chain Cultural Communication Enterprises and summarize the operation mode of building SCC control by using big data technology. In terms of research methods, this study combined bibliographic review and empirical analysis, explored cost-based mobile e-commerce (EU) cost control related to big data information, used smart and digital analysis methods to thoroughly analyze CCE business issues from internal and external supply chains, established an e-commerce business supply chain cost control model based on big data technology and elaborated cost control procedures and measures. Finally, it summarized the research results and drew conclusions to provide a theoretical basis for promoting enterprises products products to reduce supply chain costs. The research in this study has achieved a breakthrough in the cost management and control of EE; it provides empirical guidance and theoretical reference for EE to adopt big data technology for cost command of supply chain (CCSC), could help EE to reduce cost of supply chain management to gain higher profit margins, and promote e-commerce industry as a whole to the next level eventually. This study concluded that the use of big data technology for cost command can solve a series of problems effectively, such as the lack of systematic analysis of cost, the lack of contractual partners, the serious waste of sales links, and the policy errors of logistics links, and continuously improve the enterprise management level and the decline of comprehensive cost. The application mode of supply chain CCE enterprises using big data technology constructed in this study has universal applicability.

Enhancing software development cost control by forecasting the cost of rework: preliminary study

<p><span>Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product functional complexity and the team competency. Finally, we evaluated the proposed model using a dataset drawn from five actual projects. The results showed a significant enhancement in forecasting accuracy.</span></p>