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Purpose: In supply chains, creating a secure space for data production, sending, storing, and analysis has always been a critical issue. The main goal of this research was to evaluate the importance of various security criteria in an intelligent supply chain system. Methodology: The main data collection method was the expert survey. Experts validat...
Citations
... The sustainable supply chain is the consideration of social, economic, and environmental issues in all organizational processes. These processes include the entire life cycle of the supply chain, from the purchase of raw materials to product design and development, warehousing, distribution, and delivery of the final product (Nozari, Fallah, Szmelter-Jarosz, & Krzemiński, 2021). ...
Supply Chain 5.0 is the latest change in the continuous management system of procurement and supply chain in the new era. This concept describes the use of transformative technologies such as artificial intelligence (AI), blockchain, the internet of things, and big data analytics to create a continuous network of self-managing supply chain nodes. This system is an ecosystem that combines different technologies simultaneously to create an integrated network and to create a sustainable, resilient, and human-centered process-oriented system. This research has tried to identify the main actors and provide an analytical framework by examining the dimensions and components of this intelligent system. Understanding this framework is always an effective guide for the powerful implementation of this smart system.
... Also, machine learning combined with AI can predict operational conditions and identify parameters that need to be changed to achieve desired results. As a result, IoT can reveal which data and processes are redundant and time-consuming, as well as identify tasks that are fine-tuned to increase efficiency [6]. In this research, by emphasizing the combination and simultaneous use of these two technologies, it has tried to provide an innovative framework in order to determine the cause-and-effect relationships of the elements affecting the resilient and sustainable supply chain. ...
Artificial intelligence (AI) in the Internet of Things (IoT), which is called artificial intelligence of things (AIoT), breaks the static streams of data and recognizes patterns that do not scale easily. The AIoT in addition to being a revolutionary technology for all industries has also shown its potential in processes (e.g., supply chain). Management, forecasting, and monitoring applications help the managers improve their company’s distribution operational efficiency and increase transparency in their decisions. Therefore, more than ever, the benefits of using these two technologies in the supply chain have been shown. This emerging technology not only causes change but is itself a response to the change toward sustainable development in industrial processes. This index along with other factors has a significant impact on the level of development. Thus, considering the importance of choosing the right technology in the success of development and economic growth of society, there are frameworks for implementing this hybrid smart technology in creating sustainability. This innovative framework has been created with an emphasis on combining these two valuable technologies. In this paper, the most important features and dimensions of sustainable and resilient supply chains based on these technologies are examined.
... The project's team consists of qualified personnel with assigned roles and responsibilities to complete the project. Researches demonstrate that team development improves the skills and technical ability of the company's human resources in the team environment and project performance (Nozari et al., 2021). Therefore, effective and efficient human resource development is a vital factor for the success of a construction project (Ng & Tang, 2010;Park, 2009). ...
... Human resource effectiveness depends on the knowledge, skills, and behavior that a person must have to fulfill his role (Liu et al., 2005). Accurate assessment of human resource skills, abilities, recognition of individual characteristics, and key behavior of individuals increase the chances of choosing a team that has the potential to succeed (Nozari et al., 2021). ...
As a part of human resource management, active companies in Iran's railway industry must determine the qualifications for appropriate project managers in railway construction. This research aims to consider the importance of Iran's railway construction projects due to the lack of staff with expertise and their high economic impact and budget. A decision-making model is presented. The project manager's qualifications are determined through questionnaires answered by experts in the field. The numerical model is created to determine the qualifications for project management in the railway industry. Therefore, Competency-Based Selection by Genetic Programming Multigenic Regression (CSPR) is proposed for project-oriented human resource management. Decision-making is done in three phases: Hierarchical analysis process for evaluating and determining the qualifications based on the questionnaires. The model is trained and validated by creating optimal coefficients and genes to determine the contribution of each of these qualifications in determining each candidate's final qualifications based on experts' questionnaires. Finally, the model's accuracy with optimal coefficients will be tested based on the questionnaires not used in the model training phase. The CSPR model provides a qualitative method and numerical optimization to evaluate and grade the project manager in Iran's railway construction projects based on the metaheuristic method.
... One of the most important features of this method is the calculation of the compatibility rate in the fuzzy state. The weights in this method are obtained from solving a non-linear optimization model [18]. ...
Key performance indicators are actually measurable variables based on which we can measure the success rate of an organization in reaching defined key goals. In order to create key performance indicators, steps, and standards must be passed, each of which is of great importance. Based on how the key performance indicator (KPI) is defined and determined, it is possible to measure the performance of a person, department, process, campaign, or strategic goals of a brand. In fact, KPIs can be considered for different industries and for different levels of each business. Considering the importance of football clubs and their high social impact, the purpose of this research is to investigate these key performance indicators in order to grow and improve their comprehensive performance. In order to extract data, a literature review was used. Data refinement and prioritization were done using the fuzzy decision-making method, and the opinions of active experts in clubs and football players were used. The results show that indicators based on infrastructure development are among the most important indicators and should be given special attention.
... Software programmers must write high security code to run on devices. Engineers responsible for deploying and managing IoT devices are required to take necessary measures to reduce security risks [13,14]. end users accessing data or systems through the Internet of Things; They should keep devices safe and avoid unauthorized access to users. ...
The connection of smart devices using the Internet has dramatically changed the way people live, and this concept has also been extended to the industrial sector. This practice not only provides more stable, faster, and safer communications but also makes it possible to realize the concept of the smart factory in the fourth industrial revolution. The Internet of Things uses a unique Internet Protocol to identify, control, and transmit data to individuals as well as databases. Data is collected through the Internet of Things, stored in cloud storage, and managed and calculated through analytical tools. Internet of Things security is a field of technology that focuses on protecting connected devices and networks in the Internet of Things (IoT). Ensuring the safety of networks with connected IoT devices is critical. Security in the Internet of Things includes a wide range of techniques, strategies, protocols, and measures aimed at mitigating the ever-increasing vulnerabilities of the Internet of Things in modern businesses. The simultaneous connection of objects also brings privacy concerns. For this reason, in this research, an effort has been made to examine and analyze the most important privacy requirements in the Internet of Things in digital businesses in Industry 4.0. In this regard, by using experts' opinions and literature review, privacy requirements were extracted and evaluated using fuzzy non-linear decision-making methodology. The results showed that acquired and intrinsic information has the highest importance.
... Kumar et al. [44] defined and ranked strategies for overcoming legal, organizational, social, strategic, and technical barriers to implementing Industry 4.0. Tripathi & Gupta [45] explored the impact and causal relationships among major challenges of applying Industry 4.0 initiatives to industrial SCs. Nozari et al. [46] focused on accessibility, awareness, confidence, resistance, and truthfulness aspects of intelligent IoT-based SCs to reveal the most important security indicators. ...
Industry 4.0 technologies embedded in the warehouse management system (WMS) are needed to improve the automation of material handling activities such as receiving, storing, picking, sorting, packaging, and delivering. This research aims to introduce a neutrosophic multi-criteria group decision-making tool that is intelligible in supporting the transition and upgrading of WMS with Industry 4.0-based solutions. This advanced two-stage model is based on the integration of the logarithmic percentage change-driven objective weighting (LOPCOW) method and the additive ratio assessment (ARAS) method under the type-2 neutrosophic number (T2NN) environment. In the first stage, T2NN-LOPCOW generates an objective importance vector of decision-making criteria. In the second stage, T2NN-ARAS based on the generalized weighted Heronian mean operator provides an advantageous order of Industry 4.0-based material handling technologies. T2NN-LOPCOW-ARAS brings the following novelties: (i) to straightforwardly represent and explore interconnection levels between weights of criteria, (ii) to provide wide-scoping insight into the stability of initial priority order, as well as a broad spectrum of flexible solutions, (iii) to control the normalization procedure and minimize distortions due to the double-normalization backbone. The real-life case study of a logistics company from the Serbian grocery retail sector illustrates the practical applicability of T2NN-LOPCOW-ARAS. A practical evaluation framework is defined to comprehensively assess automated guided vehicles (AGVs), collaborative robotics, and drones. The sensitivity analyses show the high robustness of the proposed framework. The comparative investigation shows that T2NN-LOPCOW-ARAS is superior to the extant methods. The research findings show that AGVs are the most favorable Industry 4.0-based material handling solution.
... With risk management, in addition to preventing an accident and incurring damages, you can also reduce its negative effects in the event of an accident. In fact, to create a relationship between producers and consumers, there are several processes that, with the expansion of the scope and volume of activities in this field, the realization of productivity-related indicators in creating this relationship becomes very complex [2]. ...
The Internet of Things (IoT) offers a modern solution in which the boundaries between real and digital realms are gradually blurred by the continuous transformation of each physical device into a smart object. Each of these intelligent objects plays a role in different realms of life, but at the same time leads to new challenges. In order to develop industries and make them smarter using transformational technologies, there are risks that slow down or prevent progress. As the risk increases in the processes, the task of managing and controlling the project becomes more difficult. Many of the failures that occur in business processes are due to risk and instability in the environment and within the supply chain structure. Therefore, a comprehensive quantitative relationship that can measure supply chain risk and take into account all dimensions of risk has not yet been proposed. IoT-based intelligent supply chains have always been studied as one of the high-risk sectors due to the presence of the Internet and network and huge data flow. This study examines and prioritizes the risk of implementing smart systems in IoT-based supply chains that have been prioritized using a nonlinear fuzzy approach. The results show that lack of knowledge and lack of maintenance of technical infrastructure is one of the most important risk factors in smart food chains, and for sustainable and efficient development, special attention should be paid to the risks resulting from these deficiencies.
... Strategy priority for IoT innovation adoption The samples consisting of millennial farmers and experts were determined using a purposive method [36][37][38]. This technique deliberately selected competent experts directly involved in IoT technology innovation and policymaking. ...
This research aimed to formulate priority strategies for adopting Internet of Things (IoT)-based innovation by millennial farmers in Central Java Province, Indonesia. This research uses quantitative methods. The two stages involved were exploring external factors using a political, economic, social, and technological approach and internal factors using the resource-based view approach including human resources, physical resources, and organizational resources. Interviews were conducted with 120 millennial farmers in ten regions of Central Java. This led to the formulation of alternative adoption strategies. Furthermore, the second stage was formulating strategic priorities using the analytical network process approach, involving purposively selected experts from policymakers in the government. According to the research, the most considered factors for IoT adoption strategies were relative advantage, social influence, and technology anxiety. In the relative advantage, the most prioritized sub-factor was business profit. The most prioritized technology anxiety sub-factor cluster was unfamiliar with using IoT. The most prioritized social influence sub-factor cluster was a personal relationship. The resulting strategic priorities were strengthening openness to change, IoT education to millennial farmers, optimizing the role of institutions, and socializing the benefits of IoT to millennial farmers. Openness to change motivates millennial farmers to achieve continuous and better innovation. Millennial farmers need to be prepared for the new experiences to come. Government support through education, intensive mentoring, and increasing the active role of farmer mentoring institutions accelerates the adoption of IoT by millennial farmers.
... By analyzing the capacity of the contract against the indicated capacity, the organization can find out which suppliers respond best to the order and which do not meet this need. Here, machine learning not only helps to make better decisions, but also increases transparency and clarifies important issues in the supply chain [13]. ...
The Internet of Things generates huge amounts of data through millions of devices. Machine learning-based artificial intelligence uses data and provides insights for optimal analysis. Machine learning uses past behaviors to identify patterns and creates models that help predict future behaviors and events. Artificial intelligence finds the power to gather responses and provides both creativity and the ground for intelligent action. Because the data delivered from the sensor can be analyzed with artificial intelligence, so businesses can make informed decisions. The Artificial intelligence of Things (AIoT) based on machine learning (ML-AIoT) succeeds in achieving agile solutions. It is under these circumstances that machine learning and artificial intelligence, along with IoT technology, become the world's superpowers of technology. In this case, machine learning can eliminate hidden IoT data patterns by analyzing large volumes of data using sophisticated algorithms. Machine learning inference can complement or replace manual processes with automated systems using statistical measures in critical processes. In this study, in order to understand the combination of these technologies and their applications in the real world, a framework for supply chain based on the combination of these superior technologies is presented. This framework shows how the combination of these technologies can help the intelligence of manufacturing and service businesses.
... The data collected by millions of IoT devices in medical procedures is so large that it is difficult to separate and extract useful information. To organize the unstructured data into a meaningful data set, artificial intelligencebased algorithms are used to remove useless data and maximize the use of the data in therapeutic processes (Nozari, Fallah, Szmelter-Jarosz, & Krzemi nski, 2021). ...
