Managerial Decision Modeling Business Analytics With Spreadsheets Fourth Edition

Managerial Decision Modeling Business Analytics With Spreadsheets Fourth Edition – Open access policy open access program Open Program Practices for Special Issues editorial process Research and Publishing Ethics Article Processing Fee View Awards

All published articles are available worldwide under an open access license. No special permission is required to use fully or partially republished articles from figures and tables. For articles published under the Creative Commons CC open license, any part of the article may be reused without permission. As long as the original article is clearly credited. For more information, please visit https://openaccess.

Managerial Decision Modeling Business Analytics With Spreadsheets Fourth Edition

The featured paper represents cutting-edge research with significant potential for high impact in this field. The featured article should be an original article that summarizes many methods and approaches, outlines the scope of future research. and identify possible research applications.

Business Analytics 2321

Special reports submitted by scientific editors by invitation or personal recommendation. And must receive positive feedback from researchers.

Editor-selected articles based on recommendations from scientific journal editors from around the world. Editors select articles that have been published recently. This is a small number of journals that they believe will be of interest to readers or important to the field of research involved. Its aim is to provide an overview of the most interesting works published in the journal’s various research areas.

Received: February 1, 2022 / Revised: March 2, 2022 / Approved: March 7, 2022 / Published: March 14, 2022

(This article is about the application of special problems of machine learning and big data analytics in environmental sustainability.)

Identifying Maturity Dimensions For Smart Maintenance Management Of Constructed Assets: A Multiple Case Study

With the advent of the fourth industrial revolution The application of artificial intelligence in the manufacturing sector is expanding. Maintenance is one of the most important activities in the manufacturing process and requires attention. Predictive maintenance (PdM) has become a priority in the industry to reduce maintenance costs and achieve sustainable operational management. The principle of PdM is to predict the next failure. Consequently, relevant maintenance is planned before the expected failure. in building maintenance management Building managers often use reactive or preventive maintenance mechanisms. However, preventive maintenance cannot prevent failures. And preventive maintenance cannot predict the future state of mechanical, electrical, or plumbing components, so such components are repaired in advance to improve the service life of the facility. This paper develops a PdM planning model using smart methods. The developed method consists of five main steps: (a) data cleaning, (b) normalization of the data, (c) selection of optimal features, (d) predictive decision making of the network, and (e) Prediction First, the data is cleaned and normalized to organize PdM-related data within defined boundaries. Selection of the best features will be done later to reduce redundant information. Optimal feature selection is performed using hybrid Jaya and Sea Lion Optimization (SLnO) algorithms. Machine learning or deep learning is difficult to produce accurate results. Because the prediction values ​​vary. Therefore, a support vector machine (SVM) is used to make decisions about the prediction network. The SVM defines the prediction network for the relevant domains. Finally, the predictions are done using a recurring neural network (RNN) in RNN weights were optimized using the J-SLnO hybrid. A comparative analysis showed that the proposed model can effectively predict the future state of components for maintenance planning using the kit. Airplane engine and lithium-ion battery data

Preventive care planning Industry 4.0; sustainable production machine learning support vector machine recurrent neural network Sea lion optimization according to Jaya

With the advent of Industry 4.0, every industrial sector is using computers and digitization. One of which is to protect the environment [1, 2]. Maintenance is required to extend the life of equipment. The service life of the system can be extended with maintenance. to reduce the risk of accidents financial loss and loss of life Machine downtime must be carefully calculated and maintenance planned in advance. Predictive maintenance (PdM) is widely used in industries such as manufacturing [3], automotive [4], and aerospace [5]. and the statistical inference method [6] can detect the expected failure of the device and predict the failure time. Furthermore, by using PdM, the next failure time can be accurately predicted [7, 8]. ]

Preventive maintenance is used as a calendar-based approach to periodically checking the building portion of maintenance management. This allows facility management (FM) personnel to plan accordingly. However, preventive maintenance cannot predict the parts requiring repair or the future state of the material before improving the service life of the material. and reactive maintenance cannot prevent failures. [9] The primary purpose of PdM is to identify failures early. and subsequent deterioration by improving the condition of the component using previously acquired information. Therefore, previous actions are taken into account when formulating future maintenance procedures [10]. This type of maintenance is known as conditional maintenance.

Introduction To Management Science A Modeling And Case Studies Approach With Spreadsheets 5th Editio By Xezz

In addition, PdM overcomes the above limitations by predicting potential failures and replacing original components when engineered materials are in good condition to extend service life [11]. Primarily by sensors [12, 13], there are two major ways to collect data using continuous position monitoring: sensor monitoring and structural monitoring. PdM decisions require the collection of different types of data. including maintenance records verification information cause identification Impact of failures and work orders

In addition to these management methods Computer-based applications are also used to improve Facility Maintenance Management (FMM) service efficiency [14]. Machine learning techniques are gaining popularity in manufacturing [15, 16] today. Computer-assisted facility management systems (CAFM) [17] and computer-aided maintenance management systems (CMMS) [18] are popular forms of building maintenance. Both CAFM and CMMS are based on it. However, Excel spreadsheets and paper documents are often used to transmit FM data. However, their use causes delays in responding to service requests. which caused abnormal maintenance [19]

Building information modeling (BIM) [20] is used in the architecture, engineering and construction (AEC)/FM industry to streamline maintenance activities and maintain maintenance records. including problem types and failure locations [21]. Therefore, BIM has the potential to enhance the performance of FMMs. In addition to BIM, systems such as sensor networks such as the Internet of Things (IoT) or Radio Frequency Identification (RFID) are also available. [ 22 ] [22] Data can be gathered about the condition of the facility. Analysts investigating PdM decision support system problems have been introduced in the previous article [23] for conditional maintenance, remediation and protection. However, the data integration process was not considered in decision support models and models. Conditional forecasting Techniques based on machine learning have been widely used in various applications [24 , 25 ]; Therefore, these techniques can be used effectively in PdM.

The remainder of this article is organized as follows. Tasks related to general PdM prediction algorithms are discussed in Part 2. The PdM planning procedure is described in Chapter 3. Objective models and optimization properties applied to the proposed PdM scheme are discussed. In Section 4, select and arrange the best features. Conclusions and recommendations for further work are presented in Section 7.

Managerial Decision Modeling With Spreadsheets (3rd Edition)

[26] Chen et al. [26] introduced a Cox Hazard Deep Learning (CoxPHDL) methodology to address data censorship and data diffusion issues related to functional maintenance data analysis. The primary goal of this model is to consider the benefits of deep learning and optimize reliability to ensure efficient results. safe Then, a Cox proportional hazard model (CoxPHM) was used to measure the censored data between time intervals (TBF). A short-term memory (LSTM) network was developed to train a method for predicting TBF based on from pre-processing data to analyze the performance of the proposed model The experiment was conducted in The results showed that the proposed LSTM network maximized the root mean squared tolerance (RMSE) and the mercy correlation coefficient (MCC).

Chen et al. [27] proposed a model based on BIM and IoT techniques for FMM. This model has an application and data layer to achieve the best maintenance efficiency. in the application layer There are four modules for implementing PdM: maintenance planning module; condition forecasting module Condition Assessment Module Health Monitoring and Warning Modules and the collection and integration of data from FM systems, IoT networks, and BIM models was performed at the data layer. Both Artificial Neural Network (ANN) and SVM machine learning algorithms are used to predict the behavior of mechanical, electrical and plumbing modules.

A machine learning method has been developed to apply the PdM of the nuclear infrastructure [28]. Here, logistic regression (LR) and SVM are used and compared to predict performance.

Business analytics data analysis & decision making 6th edition, managerial decision modeling with spreadsheets 3rd edition, managerial decision modeling with spreadsheets 3rd edition solutions, managerial decision modeling with spreadsheets solutions, judgment in managerial decision making 8th edition, modeling with spreadsheets, managerial decision modeling with spreadsheets pdf, managerial decision modeling with spreadsheets, managerial decision modeling, spreadsheet modeling and decision analysis 8th edition, managerial decision modeling with spreadsheets 3rd edition pdf, optimization modeling with spreadsheets