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Product design is an innovative process that, through the stages of problem statement, concept, and initial and detailed design, meets the needs of customers, the requirements of companies, and their limitations. In the current process of product design and development, formulating design objectives plays a crucial role in objective setting, project implementation, product needs and requirements specification, and performing activities validation. Currently, the role of the design and development stages in the sustainability of products, identifying strategies for improving this stage is of particular significance, and paying attention to the product planning and definition process group and focusing on establishing sustainability principles in the target book and defining new attributes can be regarded as one of the sustainable product development strategies. Consequently, this research is a case study in the field of applying the sustainability principles in the target book as one of the deliverables at the stage of product planning. The present study proposes strategies towards developing the attributes of the target book as a tool for exerting sustainability pillars in the process of design and development by means of surveying automotive industry experts, applying the DEMATEL and Fuzzy ANP combined method, and evaluating and determining new attributes.

Due to the importance of the commercial aviation system and, also, the existence of countless accidents and unfortunate occurrences in this industry, there has been a need for a structured approach to deal with them in recent years. Therefore, this study presents a comprehensive and sequential model for analyzing commercial aviation accidents based on historical data and reports. The model first uses the failure mode and effects analysis (FMEA) technique to determine and score existing risks; then, the risks are prioritized using two multi-attribute decision making (MADM) methods and two novel and innovative techniques, including ranking based on intuitionistic fuzzy risk priority number and ranking based on the vague sets. These techniques are based in an intuitionistic fuzzy environment to handle uncertainties and the FMEA features. A fuzzy cognitive map is utilized to evaluate existing interactions among the risk factors, and additionally, various scenarios are implemented to analyze the role of each risk, group of risks, and behavior of the system in different conditions. Finally, the model is performed for a real case study to clarify its applicability and the two novel risk prioritization techniques. Although this model can be used for other similar complex transportation systems with adequate data, it is mainly employed to illustrate the most critical risks and for analyzing existing relationships among the concepts of the system.

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

In the oil and gas industry, adopting policies that can reduce the negative environmental effect is vital. Environmentally Sustainable Supply Chain Management (ESSCM) is an approach to carrying out Supply Chain Management (SCM) in an eco‐friendly manner and according to environmental requirements. There are different environmental policies that companies can apply based on their resource availability. Therefore, this study aims to evaluate the impact of hard dimensions on Environmentally Adaptive (EA) and Mitigated Adverse Eco‐Effect (MAE) policies in the oil and gas industry. To rank the data, Bayesian Best‐Worst Method (BWM) and Ordinal Priority Approach (OPA) have been applied. Cause‐and‐effect relationships are then calculated by employing the Decision‐Making Trial and Evaluation Laboratory (DEMATEL) technique. The results indicate that the ranking of the hard dimensions varies based on the companies' business policies and their new product/technology development projects. In other words, the findings of this research demonstrate that ‘innovation’ is the crucial dimension in companies that are focussed on developing eco‐friendly products while ‘technologies for cleaner production’ is the most important dimension in the companies attempting to reduce destructive consequences on the environment. In both types of the company policies, ‘lean manufacturing’, ‘total quality management’, and ‘institutional pressures’ are the key dimensions for a successful implementation of ESSCM while the least important dimensions include ‘supplier relationship management’, ‘green purchasing’, and ‘green logistics’. The findings of this research can assist the decision‐makers in the oil and gas sector in prioritising and identifying the interrelationship of the dimensions that significantly impact the ESSCM.

Suppliers can achieve high levels of supply chain sustainability by improving the related factors. An agile supply chain can support sustainability. Identifying and ranking agility factors in the SAIPA company in Iran to reach a sustainable and green supply chain is the primary purpose of this study. SAIPA is an automotive company with an extensive supply chain. The data were quantitative, and the collection was completed by reviewing the literature and questioning experts. The FANP and the OPA methods were the tools used to analyze the data. These methods are proper for facing multiple-criteria decision-making problems, as in the case of this paper. We first identified the factors (capabilities, enablers, and attributes) using a literature review. After that, we gathered the data for ranking analysis by collecting the opinions of SAIPA’s organizational experts using a pairwise comparison questionnaire for the FANP and a prioritizing list for the OPA. Both methods showed that “Quickness” is the capability with the highest priority. “Customer Sensitivity” was the most critical enabler, and “Accurate customer-based measures” was the most significant attribute of the FANP analysis. The OPA results showed that “Information Management” was the first enabler, and “Efficient funds transfer” took first place among all the attributes. Managers should pay more attention to these factors to develop agile supply chains in the SAIPA company. The results also showed that the methods proposed for multi-attribute decision-making problems like the FANP have shortcomings, such as difficulties completing the pairwise comparison matrix due to burdensome data collection in cases similar to the one in this study with many factors.

Today, sustainability and its economic, social, and environmental pillars are the main elements of new product market development. Therefore, manufacturers, considering all stages of a product life-cycle, consider it necessary to have a framework for producing sustainable products. Moreover, new product design and development processes especially sub-process of product definition and detailed design processes can play a very critical role in developing product sustainability. This paper considers the role that the car platform can play in creating a family of products and with regard to various data related to the environmental, economic, and social pillars of each of the 15 modules of a car platform and also, using the best–worst method technique and multi-objective mathematical programming with the augmented epsilon constraint method, we were able to achieve a set of platforms with maximum sustainability. The present study explores the possibility of determining the most efficient combinations of modules for every one of the 23 cars’ platforms to produce a collection of products. Through these methods, the 54 efficient solutions in the Pareto front were achieved, and out of this number, after considering all the factors and weights of the objective functions, solution no. 51 was selected as the most efficient. Subsequently, all the 15 module variants of the 23 platforms were determined for solution no. 51. Finally, these variants were ranked on the basis of the SAW technique, and finally, sensitivity analysis is done.