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Supplier segmentation provides companies with suitable policies to control each segment, thereby saving time and resources. Sustainability has become a mandatory requirement in competitive business environments. This study aims to develop a clustering-based approach to sustainable supplier segmentation.

The characteristics of the suppliers and the aspects of the purchased items were considered simultaneously. The weights of the sub-criteria were determined using the best-worst method. Then, the K-means clustering algorithm was applied to all company suppliers based on four criteria. The proposed model is applied to a real case study to test the performance of the proposed approach.

The results prove that supplier segmentation is more efficient when using clustering algorithms, and the best criteria are selected for sustainable supplier segmentation and managing supplier relationships.

This study integrates sustainability considerations into the supplier segmentation problem using a hybrid approach. The proposed sustainable supplier segmentation is a practical tool that eliminates complexity and presents the possibility of convenient execution. The proposed method helps business owners to elevate their sustainable insights.

Ever since the bronze age, graphical representations have been employed to convey a message or a phenomenon. Graphical representations have been and continue to be a powerful tool to record events and changes throughout history. It is often helpful to use conceptual models to visualise problems, and in order to find possible solutions. Manufacturing planning and control (MPC) as a discipline is no exception to this rule. Many useful methods/models in this field including Gantt charts, CPM, PERT, and fishbone diagrams have been employed. However, representing complex multi‐variate problems through ordinary conceptual models can be quite arduous and the results may not be objectively accurate. This paper illustrates how a chain of “state‐space” models can be formed, based on the analytic hierarchy process (AHP), which can pertain to existing complex practical manufacturing circumstances in an objective manner.

Analytic hierarchy process (AHP) is a simple decision‐making tool to deal with complex, unstructured and multi‐attribute problems. Selection of the most suitable production planning system (push or pull systems) requires the development of a tool to address quantitative and qualitative parameters which influence success of push‐and‐pull systems’ implementation. This paper presents a multi‐criteria approach within AHP to classify the most appropriate production planning system, based on push, pull or hybrid systems’ methodologies.

The purpose of this paper is to compile and prioritize necessary strategies to produce electrical energy from fossil resources in the Iranian power industry.

In this study, the strengths, weaknesses, opportunities and threats (SWOT) and internal and external (IE) matrix have been applied in order to illustrate the main path long term planning and development. The benchmarking process has been performed by analyzing seven successful developing countries.

The results imply that Iran should implement six main strategies for increasing productivity and efficiency, reducing costs, renewing structure and making private, applying IT, increasing productivity of human resources, and environment protection. Benchmarking study shows that Iran has to speed up the environment protection program.

Although the use of the SWOT and IE matrix provide great advantages, the analysis should apply BSC in developing execution plans.

The results encourage the Iran power industry since five out of six main strategies have already been chosen for long‐term development.

The paper has provided considerable evidence to suggest that the proposed strategies are in line with benchmarked countries policies in the power industry.

This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R² = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.

This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R² = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.

The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R²” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.

The authors confirm the originality of this paper.

The purpose of this article is to develop a new model called strategy segmentation methodology (SSM) by combining the Kraljic portfolio matrix (KPM) and the supplier relationship model (SRM) so that the buyer company can effectively conduct its relations with its suppliers.

The importance weights of the criteria defining the dimensions of each model are calculated with the single-valued neutrosophic analytical hierarchy process (SVN-AHP) method. Subsequently, the derived importance weights are employed in the single-valued neutrosophic technique for order preference by similarity to ideal solution (SVN-TOPSIS) method to obtain the scores of the suppliers and their supplied items. In order to illustrate the feasibility of the proposed methodology, a case study in the machinery industry is performed with the related comparative analysis.

The implementation of SSM enables to formulate various strategies to manage suppliers taking into account the items they procure, their capabilities and performance and the supplier–buyer relationship strength. Based on the proposed strategies, it is concluded that the firm in the case study should terminate its relationship with six of its suppliers.

Although KPM has become the basis of purchasing strategies for various businesses, it neglects the characteristics of suppliers and the buyer–supplier relationship. In this study, KPM is integrated with the SRM approach presented by Olsen and Ellram (1997) to overcome these disadvantages of KPM. The novel integration of the two approaches enables the realization of a robust and reliable supplier classification model.

This research paper aims to identify and evaluate the enabling factors of agility capability in humanitarian relief chain network.

The research phases were implemented based on an integrated framework. First, a reference framework of the enablers has been constructed based on a literature review. Then, a hybrid evaluation approach is applied that combines fuzzy decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) to achieve reliable results. It provides a road map to identify and evaluate the interactions between the enabling factors and determines the weights correspond to their relative importance. This approach takes advantage of fuzzy set theory to deal with ambiguities, uncertainties and vagueness inherent in the evaluation process.

Relief chain agility is a vital determinant of the effectiveness to succeed humanitarian missions during and after natural and unnatural disasters such as earthquakes, epidemics and terrorist attacks. Results shed light on the essential enabling factors, relationships among them, and their importance for developing humanitarian relief chain agility enhancing the overall performance quality.

The integrated framework is implemented for the Red Crescent, a nongovernmental organization in Iran, which is trying to optimize the agility of their humanitarian relief chain network. In short, the findings are beneficial for identification and utilization of the essential prerequisites of agility in order to develop an agile humanitarian relief chain.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).

The proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.

A fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.

QFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

The purpose of this paper is to study the role of business intelligence (BI) in achieving agility in supply chain context by examining the relationship between BI competence, agile capabilities, and agile performance of the supply chain.

A theoretical framework is developed drawing on the resource-based view, the dynamic capabilities perspective, and the competence-capability relationship paradigm, as well as an extensive review of the literature. Structural equation modeling is employed to analyze the data collected from Iranian manufacturers in the automotive industry.

The empirical results support the conceptualization of supply chain BI competence as a multi-dimensional construct comprising managerial, technical, and cultural competence, and confirm that it is a key enabler of supply chain agility in terms of both agile capabilities and agile performance. The results also provide support for partial mediation of agile capabilities on the relationship between BI competence and agile performance of the supply chain.

This paper provides a response to the identified need for empirical evidence on the benefits derived from BI, especially in the supply chain context. It also contributes to the existing supply chain agility literature by providing insight into the value and role of BI in enhancing agile capabilities and performance in the inter-organizational supply chain.