Search results

The purpose of the food traceability is used to retain the good quality of raw material supply, diminish the loss and reduced system complexity.

The proposed hybrid algorithm is for food traceability to make accurate predictions and enhanced period data. The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers.

In order to survive with the existing financial circumstances and the development of global food supply chain, the authors propose efficient food traceability techniques using the internet of things and obtain a solution for data prediction.

The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers. The experimental analysis depicts that proposed algorithm has high accuracy rate, less execution time and error rate.

This study aims to comprehensively identify supply chain risks and their causes, the factors influencing supply chain management and techniques to successfully mitigate and control supply chain risks in construction projects. This study developed a comprehensive framework showing various supply chain risks and how these risks that influence project execution are systematically identified and managed for the overall construction project success.

The research conducted was characterised by its descriptive, exploratory and quantitative nature. The collection of quantitative data was conducted by means of structured online questionnaires. The sample consisted of 205 construction project professionals who were selected randomly. This group included individuals with various roles in the construction industry, such as project managers, civil/structural engineers mechanical engineers, risk managers, architects, quantity surveyors, electrical engineers, construction managers, health, safety and environment managers, estate managers and other professionals. All participants were actively involved in construction projects located in the Gauteng province of South Africa. The data was analysed, using descriptive statistical methods, including factor analysis, reliability assessment and calculations of frequencies and percentages.

The result showed that predictable delivery, funding schedule, inventories, balanced demands, production capabilities, timely procurement, construction supply chain management coordination, delivery reliability, the proximity of suppliers, identification of supply chain risks in the conceptualisation stage of a project, identification of supply chain risks in the planning stage of a project, identification of supply chain risks in the execution stage and the reconciliation of material flows of the subcontractors with the contractors were identified as the key factors that influenced the construction supply chain management the most. The result also showed that subcontractor’s negative attitudes towards supply chain management, procurement delays, imbalanced demands, clients’ negative attitudes towards other project stakeholders, unpredictable delivery reliability, disorganised construction supply chain management approach, delayed funding, low delivery reliability, poor inventories, poor construction supply chain co-ordination, suppliers’ negative attitudes towards supply chain management and when the material flows of the subcontractors with the contractors are not reconciled were identified as the factors that have the greatest impacts on construction supply chain risks management.

For future research, it is recommended to incorporate fourth industrial revolution) such as machine learning prediction models and algorithms, Artificial intelligence and blockchain to identify and manage supply chain, supply chain risks and project stakeholders involved in supply chain in construction projects. Green construction or sustainable construction was not fully covered in this study. The findings will be beneficial for sustainable construction projects in developing countries for sustainability, although it did not extensively cover green buildings and related risks.

Supply chain risk is one of the major challenges facing the construction industry because construction projects are complex by nature involving a lot of activities and participants with different responsibilities and tasks therefore it is highly recommended to implement the proposed frameworks in this paper from the conceptualisation stage to the execution stage, carefully identifying parties involved in supply chain, supply chain management, stakeholders, tasks, activities, responsibilities and supply chain risks generated as a result of the interactions between stakeholders involved in supply chain management and coordination to realise project objectives. The findings will be a foundation for identifying and managing supply risks in sustainable buildings in developing countries.

Supply chain management is crucial in every enterprise. Managing supply chain risks is a major aspect of risk and disaster management and this implies that supply chain excellence is achievable by building communication, trust and mutual objectives, no blame culture, performance measurement, constant improvement and partnering.

The implementation of construction supply chain risk management framework involves assessing the impacts of these supply chain risks on the objectives of construction projects with respect to time, cost, safety, health, environment, stakeholders, financial performance, client satisfaction and quality.

Determining the relevant information using financial measures is of great interest for various stakeholders to analyze the performance of the firm. This paper aims at identifying these financial measures (ratios) which critically affect the firm performance. The authors specifically focus on discovering the most prominent ratios using a two-step process. First, the authors use an exploratory factor analysis to identify the underlying dimensions of these ratios, followed by predictive modeling techniques to identify the potential relationship between measures and performance.

The study uses data of 25 financial variables for a sample of 1923 Indian manufacturing firms which exist continuously between 2011 and 2018. For prediction models, four popular decision tree algorithms [Chi-squared automatic interaction detector (CHAID), classification and regression trees (C&RT), C5.0 and quick, unbiased, efficient statistical tree (QUEST)] were investigated, and the information fusion-based sensitivity analyses were performed to identify the relative importance of these input measures.

Results show that C5.0 and CHAID algorithms produced the best predictive results. The fusion sensitivity results find that net profit margin and total assets turnover rate are the most critical factors determining the firm performance in an Indian manufacturing context. These findings may enable managers in their decision-making process and also have vital implications for investors in assessing the performance of the firm.

To the best of the authors’ knowledge, the current paper is the first to address the application of decision tree algorithms to predict the performance of manufacturing firms in an emerging economy such as India, with the latest data. This practical perspective helps the organizations in managing the critical parameters for the firm’s growth.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

This is the second in a series of papers focused on alcohol and substance abuse rehabilitation centers. Centers face the ongoing challenge of validating outcomes to meet the burden of evidence for insurance companies. In the first paper, data mining was used to establish baseline patterns in treatment success rates, for the Futures: Palm Beach Rehabilitation Center, that have a direct impact on a client’s ability to receive insurance coverage for treatment programs. In this paper, we examine 2016 outcomes and report on facility efficacy, alumni progression and sobriety, and forecast treatment success rates (short and long term) in support of client insurability. Data collection has been standardized and includes admissions data, electronic medical records data, satisfaction survey data, post-discharge survey data, Centers for Disease Control (CDC) data, and demographic data. Clustering, partitioning, ANOVA, stepwise regression and stepwise Logistic regression are applied to the data to determine statistically significant drivers of treatment success.

The changes in the industry have gone through many stages until today. These changes can be called a transition phase of industries that lasts for years. The process that started with the emergence of the Industry 1.0 concept first came to the concept of Industry 4.0. Recently, Industry 5.0 concepts are spoken around the world. Germany, where the concept of Industry 4.0 first emerged, is leading Industry 4.0 revolution. With the emergence of this concept, different topics such as smart factories, cybersecurity, simulation, 3D printers, and autonomous robots have come to the fore. These subheadings are undoubtedly very important topics for a factory. To fully implement the Industry 4.0 system, these subheadings must be applied and their sustainability must be ensured. In this study, the 3D printer selection process of a factory that wants to fully integrate its factory into a new revolution is discussed. This problem is called a multicriteria decision problem in terms of being very different criteria and alternatives. Decision-making trial and evaluation laboratory (DEMATEL) method was used to solve the problem, and the results were interpreted.

In this study, a multi-criteria decision-making (MCDM) approach for evaluating airworthiness factors were presented. The purpose of this study is to develop an acceptable rationale for operational activities in civil and military aviation and for design, production and maintenance activities in the aviation industry that can be used in-flight safety programs and evaluations.

In aviation, while the initial and continuing airworthiness of aircraft is related to technical airworthiness, identifying and minimizing risks for avoiding losses and damages are related to operational airworthiness. Thus, the airworthiness factors in civil and military aviation were evaluated under these two categories as the technical and operational airworthiness factors by the analytic hierarchy process and analytic network process. Three technical and five operational airworthiness criteria for civil aviation, three technical and nine operational airworthiness criteria for military aviation were defined, evaluated, prioritized and compared in terms of flight safety.

The most important technical factor is the “airworthiness status of the aircraft” both in civil (81.9%) and military (77.6%) aviation, which means that aircraft should initially be designed for safety. The most significant operational factors are the “air traffic control system” in civil (30.9%) and “threat” in the military (26.6%) aviation. The differences within factor weights may stem from the design requirements and acceptable safety levels (frequency of occurrences 1 in 10⁷ in military and 1 in 10⁹ in civil aircraft design) of civil and military aircraft with the mission achievement requirements in civil and military aviation operations. The damage acceptance criteria for civil and military aircraft are different. The operation risks are accepted in the military and acceptance of specific tasks and the risk levels can vary with aircraft purpose and type.

This study provides an acceptable rationale for safety programs and evaluations in aviation activities. The results of this study can be used in real-world airworthiness applications and safety management by the aviation industry and furthermore, critical factor weights should be considered both in civil and military aviation operations and flights. The safety levels of airlines with respect to our airworthiness factor weights or the safety level of military operations can be computed.

This is the first study considering technical and operational airworthiness factors as an MCDM problem. Originality and value of this paper are defining critical airworthiness factors for civil and military aviation, ranking these factors, revealing the most important ones and using MCDM methods for the evaluations of airworthiness factors for the first time. In civil aviation flight safety is the basic tenet of airworthiness activities in risk analysis, on the other hand in military aviation high levels of risks are to be avoided in peace training or operational tasks. However, even high risks have to be accepted during the war, if the operational requirements impose, as mission achievement is vital. The paper is one of a kind on airworthiness evaluations for flight safety.

The authors develop a framework to build an early warning mechanism in detecting financial deterioration of Chinese companies. Many studies in the financial distress and bankruptcy prediction literature rarely do they examine the impact of pre-processing financial indicators on the prediction performance. The purpose of this paper is to address this shortcoming.

The proposed framework is evaluated by using both original and discretized data, and a least absolute shrinkage and selection operator (LASSO) selection technique for choosing an appropriate subset of financial ratios for improved predictive performance. The financial ratios are then analyzed by five different data mining techniques. Managerial insights, using data from Chinese companies, are revealed by the methodology employed.

The prediction accuracy increases after we discretized the continuous variables of financial ratios. A better prediction performance can be achieved by including fewer, but relatively more significant variables. Random forest has the highest overall performance following closely by SVM and neural network.

The contribution of this study is fourfold. First, the authors add to the literature on defaults by showing variable discretization to be an essential pre-processing step to improve the prediction performance for classification problems. Second, the authors demonstrate that machine learning approaches can achieve better performance than traditional statistical methods in classification tasks. Third, the authors provide the evidence for the adoption of C5.0 over other methods because rules generated with C5.0 provide managerial insights for managers. Finally, the authors demonstrate the effectiveness of the LASSO technique for identifying the most important financial ratios from each category, enabling one to build better predictive models.

Refugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.

A spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.

The derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.

Some parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.

The model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.

The study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.