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The purpose of this paper is to help industrial managers monitor and analyze critical performance indicators in real time during the execution of business processes by proposing a visualization technique using an extended formal concept analysis (FCA). The proposed approach monitors the current progress of ongoing processes and periodically predicts their probable routes and performances.

FCA is utilized to analyze relations among patterns of events in historical process logs, and this method of data analysis visualizes the relations in a concept lattice. To apply FCA to real‐time business process monitoring, the authors extended the conventional concept lattice into a reachability lattice, which enables managers to recognize reachable patterns of events in specific instances of business processes.

By using a reachability lattice, expected values of a target key performance indicator are predicted and traced along with probable outcomes. Analysis is conducted periodically as the monitoring time elapses over the course of business processes.

The proposed approach focuses on the visualization of probable event occurrences on the basis of historical data. Such visualization can be utilized by industrial managers to evaluate the status of any given instance during business processes and to easily predict possible subsequent states for purposes of effective and efficient decision making. The proposed method was developed in a prototype system for proof of concept and has been illustrated using a simplified real‐world example of a business process in a telecommunications company.

The main contribution of this paper lies in the development of a real‐time monitoring approach of ongoing processes. The authors have provided a new data structure, namely a reachability lattice, which visualizes real‐time progress of ongoing business processes. As a result, current and probable next states can be predicted graphically using periodically conducted analysis during the processes.

The purpose of this paper is to highlight the application of Interpretive Structural Modeling (ISM) to analyze the barriers in implementation of Total Productive Maintenance (TPM). TPM is explained in brief with emphasis on maintenance programs to improve quality of products, reliability of processes and reduction in cost. Barriers in implementation of TPM are also discussed. Concept of ISM and steps in developing ISM are described in detail. The authors then illustrate the research methodology which involves applying ISM to analyze barriers in TPM.

The paper starts off by describing the concepts of TPM and ISM. Barriers in implementation of TPM are discussed. It explains ISM as a methodology to understand the underlying interrelationship among the inhibiting factors. The authors draw up an action plan to carry out research on the usage of ISM to study the TPM inhibitors, to develop an integrated model to establish the relationship among the different TPM inhibiting factors and to suggest action plan to mitigate these factors.

Interpretive Structural Modeling (ISM) can be used to analyze the driving and dependence power of the variables inhibiting implementation of TPM. The barriers to implement TPM are described with detailed explanation. The complexity of the problem and the degree of interconnection among the variables can be found out. This will help Managers take action on mitigating the barriers.

By analyzing the interrelationships among the barriers and their strengths, management can chalk out the strategy to implement TPM in an organization. Management will become aware of the barriers which have the maximum influence and then can act accordingly to mitigate these barriers. This will help in implementing TPM faster and in an organized manner.

Many authors have used ISM to study various issues. A couple of authors have used ISM to determine barriers in implementation of TPM. The authors feel that most of the papers describe ISM in brief making it slightly difficult for readers to understand. This paper aims to explain elaborately step-by-step on how to develop an ISM making it easier for researchers to understand the ISM concept. Even though there are papers on TPM and difficulties in implementation of TPM, this paper explains the barriers in implementing TPM based on the experience of the corresponding author having worked in the refinery industry.

The purpose of this paper is to develop a comprehensive framework to identify and classify key medical tourism enablers (MTEs) and to study the direct and indirect effects of each enabler on the growth of medical tourism in India.

In this paper, an integrated approach using interpretive structural modeling (ISM) and Fuzzy Matrice d'Impacts Croisés Multiplication Appliquée á un Classement (FMICMAC) analysis has been developed to identify and classify the key MTEs, typically identified by a comprehensive review of literature and expert opinion. The key enablers are also modeled to find their role and mutual influence.

The key finding of this modeling helps to identify and classify the enablers which may be useful for medical tourism decision makers to employ this model for formulating strategies in order to overcome challenges and to become a preferred medical tourism destination. Integrated model reveals enablers such as medicine insurance coverage, international healthcare collaboration, and efficient information system as dependent enablers. No enabler is found to be autonomous enablers. The important enablers like healthcare infrastructure facilities and global competition are found as the linkage enablers. Research in medicine and pharmaceutical science, medical tourism market, transplantation law, top management commitment, national healthcare policy, competent medical and para-medical staffs are found as the independent enablers. Integrated model also establishes the direct and indirect relationship among various enablers.

The research provides an integrated model using ISM and FMICMAC to identify and classify various key enablers of medical tourism in India. In conventional cross-impact matrix multiplication applied to classification analysis, binary relationship of various enablers is considered. FMICMAC analysis helps to establish possibility of relationship among various enablers so that low-key hidden factors can be identified. The low-key hidden factors may initially exhibit marginal influence but they may show significant influence later on during analysis. The uncertainty and fuzziness of relationship among various enablers can be conveniently handled by FMICMAC and expert opinions can easily be captured. This research will help medical tourism decision makers to select right enablers for the growth of medical tourism in India.

The purpose of this paper was to argue that social networking service users should be vigilant in protecting the relationship between multiple accounts.

In this paper, the authors propose the use of account reachability, a measure of privacy risk which demonstrates the possibility of a stranger finding a user’s private account based on information in their public account. In addition, they present ARChecker, a tool to calculate the value of account reachability. ARChecker also provides advice on how to modify the user’s profiles and messages to decrease the privacy risk.

The system very simply checks account reachability and shows the result and reasons for accessibility to personal information. From the results, users can learn how to protect themselves from privacy risk by taking certain measures.

This paper proposes account reachability, a new measure of privacy risk, and presents ARChecker, a tool to calculate the value of account reachability.

This study aims to examine the associations of social networks with the sense of community (SOC) construct and spatial colocation or having an office. The study site was an institute for health-care policy research formed in 2011 by bringing together scientists from more than 20 different university units. Only 30% of the scientists were had an office or physical presence at the institute. Therefore, the institute was an ideal site to examine whether SOC was correlated with different dimensions of network position – connectedness, reachability and brokerage – even when the authors account for the lack of spatial colocation for the off-site scientists.

A two-part (sociometric and workplace) internet survey instrument was administered in 2014 to the institute’s population of 411 individuals. The sociometric data were used to create an undirected interaction network and the following dependent variables (DVs) or network centralities: normalized degree to measure connectedness; average reciprocal distance to capture reachability; and normalized betweenness to proxy brokerage. Separate node-level network regressions were then run with random permutations (N = 10,000) and listwise deletion for each of the DVs with SOC and spatial colocation as the independent variables, and variables that controlled for gender, organizational affiliation and job category.

SOC and spatial colocation are both positively and significantly correlated with network connectedness and reachability. The results suggest that both SOC and spatial colocation have a larger impact on reachability than connectedness. However, neither SOC nor spatial colocation are significantly associated with network brokerage. Finally, the findings show that SOC and spatial colocation are more reliable predictors of network connectedness and reachability than are key individual- and unit-level control variables, specifically the individual’s sex, job category and organizational affiliation. The controls were not significantly associated with any of the three network centralities, namely, connectedness, reachability and brokerage.

This exploratory study used social network analysis and node-level network regressions to examine the associations from SOC and spatial colocation to dimensions of network position. SOC is positively and significantly associated with network connectedness and reachability, suggesting that SOC is an important consideration when individuals are disadvantaged from the absence of spatial colocation. The findings have implications for work in the context of the COVID-19 pandemic as they imply that interventions based on the SOC construct could potentially lessen the negative effects of remote work on workplace social networks due to factors such as the reduction of social contacts.

This paper is a research note on “Quality framework in education through application of Interpretive Structural Modeling” (Sahney, S., Banwet, D.K. and Karunes, S. (2010), Quality framework in education through the application of Interpretive Structural Modeling: an administrative staff perspective in the Indian context, The TQM Journal, 22(1)). Sahney et al. applied Interpretive Structural Modeling (ISM) to prioritize, sequence and categorize elements critical to quality management in education; however, it seems that the final reachability matrix, and consequently the results, may not be true as some transitivity was not incorporated. So, the purpose of this paper is to apply a method to develop a transitive and compatible reachability matrix.

A counter-example was used to show that the final reachability matrix was incorrect, and then, based on Warfield’s studies, a transitive and compatible reachability matrix was developed directly.

As high priority should be placed on tackling the design characteristics which have a high driving power and thus possessing the capability to influence other elements, the correct analysis of ISM and its consequences is important to the priorities. The results from this study differed from the results of the Sahney et al. to a large extent. According to their analysis, all attributes were in the linkage area which has high driving power and high dependence. However, the authors reached different values for the relative importance and the interdependencies among the elements resulting in three different clusters.

This note clarifies and corrects the way the ISM methodology can be applied.

This paper aims to intend to help focal firms which are keen to develop a sustainable supply chain by identifying enablers, in knowing the interrelationships involved and in ranking the enablers.

Interpretive structural modeling and fuzzy MICMAC were used for the modeling and clustering of the enablers and fuzzy analytical hierarchy process has been used for the ranking purpose.

Awareness about sustainability incentives, pressure from stakeholders, support from supply chain partners and demand from customer for sustainable products were found very important for developing a sustainable supply chain.

This research will help practitioners to appreciate the importance of the enablers to focus on the making sustainability adoption feasible across the supply chain. This would also facilitate focal firm management to develop a sustainability culture across the supply chain.

Similar work has not been carried before in which interaction among enablers and their priorities were analyzed using hybrid methodologies in developing country context.

The aim of this study is to highlight the value of the success rate performance of a surgery while planning patient flow within a supply chain of a health care organization/hospital.

The paper has considered one of the common surgeries, cataract, and the complications that subsequently result from this surgery. The study employs interpretive structural modeling (ISM) approach to draw a roadmap to study various complications causing cataract that subsequently help in planning and coordination of patient flow.

The study finds that there is a hierarchy of causes and certain complications, the persistence of which gives a higher success rate performance in cataract surgery as compared to others.

The paper provides leverage to the decision maker while organizing the patient flow depending upon the information of hierarchy of complication of a disease, and accordingly ensures the availability of resources to the patient.

The study is of value in identifying the degree of complications from cataract surgery. Given the degree of complication, the patient logistics can be planned myopically in a health care organization which largely depends upon the degree of success rate. The paper attempts to suggest that the hierarchy obtained through ISM can be implemented in the modules of an enterprise resource planning (ERP) set up.

This study aims to investigate the hierarchical relationship between industry-specific attributes of supply chain (SC) strategies and information technology (IT) to improve the performance of firms in textile industry in Pakistan.

This study presents a mix of enablers through literature review and experts’ opinions. Next, the driving and dependence powers of enablers were identified, using the interpretive structural modeling (ISM) approach.

The ISM model suggests eight levels, with customer satisfaction at the top of the model. Process automation and TQM are placed at second and third level, respectively, whereas flexible sourcing and flexible capacity are positioned at fourth level. The key enablers of IT have high driving power, hence positioned at the bottom of ISM model.

This study has value for firms in garment manufacturing and exporting industry to invest into IT and sustainable SC design for competitive performance. The theoretical and managerial implications are provided.

Supply chain management literature recognizes that interorganizational networks provide resources that convey critical benefits, such as capital, competitive advantage and efficient strategy implementation. The purpose of this paper is to leverage network theory and identify technological innovations as the antecedents for organizations to achieve stronger interorganizational networks. Specifically, this paper investigates how supply chain technologies of its logistics affiliate, Cainiao Network (CN), affect Alibaba Group’s three fundamental network mechanisms reachability, richness and receptivity and how interorganizational networks subsequently drive Alibaba Group’s performance.

A case study approach was chosen as a methodology to develop an in-depth understanding of the proposed innovations-network-performance framework.

Results indicate that innovative technologies positively lead to network reachability, richness and receptivity. Stronger interorganizational networks directly lead to higher performance. In addition, CN is identified as a unique innovative business model.

The key contribution of this research is that it investigates Alibaba Group’s performance from a network and innovation perspective. It identifies technological innovations as a key driver for stronger interorganizational networks. Furthermore, three network mechanisms are introduced and investigated as the antecedents of organizational performance. This research also provides a comprehensive description of Alibaba Group and CN.