نشریه نوآوری های صنعتی
سال دوم شماره 2 (پیاپی 6، تابستان 1403)

The purpose of this paper is to identify the internal factors that contribute to improving the development of manufacturing businesses in industry 4 and to provide a structural-interpretation model. In the first stage, internal factors were identified by reviewing the literature by the Meta-synthesis method. The codes of choice include organizational factors, intelligent supply chain, human resources and technology. In the next step, to present the model, effective relationships were determined using structural-interpretation modeling, which results in strategic planning, training and learning، digital capability and workforce updates are at the lowest level (Eighth level), and at the highest level, i.e., the first level, the cooperation between supplier and distributor، The transport system and commercial capability are located and the role of effective factors was determined by using MICMAC analysis. In the group of dependent variables, the factors affecting team creativity, business ability, efficient innovation and workforce updating are located. In the group of two-fold and key variables, data analysis factors, artificial intelligence, digital ability, intelligent human resources, the Internet of Things, robotics, employee empowerment are located. In the group of variables affecting strategic planning factors, training and learning, safety system, organizational culture, individual factors, as well as independent variables that include effective factors of supply chain management، transport and cooperation system between supplier and distributor. Finally, according to the extracted model, practical suggestions were presented to managers and policy makers of small and medium production companies to improve and develop their business by relying on industry 4. Also, suggestions were given to future researchers to conduct future research.

Increasing social awareness has raised expectations for companies to embrace their social responsibilities. Corporate social responsibility (CSR) is the concept that organizations take into account the interests of society through being responsible for the impact of their activities on customers, suppliers, employees, shareholders, and the broader community and environment in which they operate. This commitment goes beyond legal obligations, whereby organizations take meaningful steps to improve the quality of life of employees, customers, and other stakeholders. However, limited research has focused on integrating sustainability and corporate social responsibility in supplier selection literature. This research presents a group multi criteria decision making model to evaluate and rank contractors based on social responsibility criteria. This evaluation examines social responsibility from various dimensions of moral responsibilities, societal responsibilities, environmental responsibilities, and commitments to employees and customers. The proposed model is applied in a real case study at Shazand Petrochemical Company. The suggested approach is to develop a fuzzy analytical hierarchy process technique using fuzzy Z numbers. The key advantage of using fuzzy Z numbers in multi-criteria decision-making is the possibility of considering the confidence levels of various decision-makers in the calculations. Therefore, the proposed technique can be easily applied in developing other multi-criteria techniques as well as other applications that deal with diverse groups of experts with varying degrees of confidence. The results can help the organization in selecting socially responsible suppliers, and in this way helping in the achievement of sustainability goals and enhancing the company's competitiveness and reputation.

For many companies, effective business analytics is not an integral part of their work. However, as the complexity of companies' processes and activities increases, business analytics is a key competency for a company's success. Requirements management is the core competency for the success of a company's project and plan, poor performance occurs when companies do not mature in important business analytics processes and do not recognize the value it provides. Business analysis includes identifying needs, proposing solutions, and extracting, documenting, and managing requirements to deliver the expected benefits. This research examines the needs and expectations of the stakeholders of the Energy Industries Design and Engineering (EIED) Company in one of the company's departments and takes into account the requirements related to the processes related to needs assessment, then by determining the relevant needs and requirements, the process is identified through tools and by collecting the process data and verifying them, the analysis is done based on the data. Process improvement methods are used to measure the effectiveness of the relevant process indicators.

Deteriorating products require greater investment and higher expenditures compared to other goods. Enhancing shelf life and improving informational transparency are among the primary challenges for these products, particularly in the dairy industry. This article examines the role of advanced technologies, such as cold plasma and blockchain, in improving the supply chain of deteriorating products, with a focus on milk. Cold plasma extends product shelf life, while blockchain strengthens customer trust by providing clearer, more reliable information. This study investigates a two-level supply chain involving a manufacturer and a retailer, where the manufacturer utilizes cold plasma technology to extend shelf life, and the retailer bears the costs of blockchain to inform customers. The aim of this article is to examine the impacts of employing these two technologies in the supply chain of deteriorating dairy products, specifically milk, with a focus on the interactions between the manufacturer and retailer within a Stackelberg game framework. In this model, the manufacturer acts as the leader and the retailer as the follower, and the effects of technology-related costs on pricing decisions and profitability for each member are analyzed. Sensitivity analysis on the deterioration rate and shelf life reveals that the supply chain's profitability increases when deterioration rates are high, and customer sensitivity to deterioration is elevated, with cold plasma proving beneficial. Conversely, at lower deterioration rates, the high cost of cold plasma may reduce supply chain profitability. These findings underscore the importance of optimizing technology use based on the specific characteristics of the supply chain.

Today, the progress of industries depends on quality optimization, and improving production processes through setting controllable parameters of the process can greatly help this. The present research shows the practical experience gained in the development of the resistance welding quality of a pressure vessel, in which the well-known Taguchi method is used in the design of experiments to minimize the effect of uncontrollable factors on the target value. In this research, the pressure between the electrodes, the size and shape of the electrodes and the working surface (smoothness and cleanliness) were considered as the most important controlled variables. By conducting the designed test in 5 repetitions, the quality of the resistance welding improved significantly. It was observed that among the parameters affecting the quality of spot welding, the welding time has the greatest effect on the strength of the weld. The investigation of the current intensity showed that the increase of this factor as well as the welding time decreases the hardness of the welding center.