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This paper aims to present a dynamic model based on casual relationships among the most important effective factors on business–IT alignment in Agile businesses by using system dynamics modeling approach.

To study the most important factors on agility and alignment, the data were collected by questionnaires filled by 201 experts and were analyzed by SPSS and PLS. Casual relationships among studied factors and efficiency coefficients of each factor were identified by fuzzy DEMATEL technique and analyzed by MATLAB and EXCELL. Finally, the dynamic model was plotted by VENSIM.

According to the results, only “learning IT capabilities” are the most important casual factor that has the highest influence on the other factors. “Business responding capabilities” take the highest effect from the system, and “business sensing capabilities” are in the next rank.

This study underpins effective IT deployment toward developing efficient IT capabilities to gain greater agility.

The dynamic capabilities view (DCV) has emerged as an influential theoretical and management framework in modern IS and agility researches. In this regard, we propose a conceptualization of dynamic capabilities in the form of an alignment model. Based on the dynamic capabilities, and on the alignment perspectives found in Henderson and Venkatraman’s seminal model, IT alignment is modeled as a process of reconfiguration of the firm’s IT and organizational resources, competencies and capabilities.

Conventional data envelopment analysis (DEA) models permit each decision-making unit (DMU) to assess its efficiency score with the most favorable weights. In other words, each DMU selects the best weighting schemes to obtain maximum efficiency for itself. Therefore, using different sets of weights leads to many different efficient DMUs, which makes comparing and ranking them on a similar basis impossible. Another issue is that often more than one DMU is evaluated as efficient because the selection of weights is flexible; therefore, all DMUs cannot be completely differentiated. The purpose of this paper is to development a common weight in dynamic network DEA with a goal programming approach.

In this paper, a goal programming approach has been proposed to generate common weights in dynamic network DEA. To validate the applicability of the proposed model, the data of 30 non-life insurance companies in Iran during 2013-2015 have been used for measuring their efficiency scores and ranking all of the companies.

Findings show that the proposed methodology is an effective and practical approach to measure the efficiency of DMUs with dynamic network structure.

The proposed model delivers more knowledge of the common weight approaches and improves the DEA theory and methodology. This model makes it possible to measure efficiency scores and compare all DMUs from multiple different standpoints. Further, this model allows one to not only calculate the overall efficiency of DMUs throughout the time period but also consider dynamic change of the time period efficiency and dynamic change of the divisional efficiency of DMUs.

The objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.

It is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.

A practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.

We propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.