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As the number of web applications is increasing day by day web mining acts as an important tool to extract useful information from weblogs and analyse them according to the attributes and predict the usage of a website. The main aim of this paper is to inspect how process mining can be used to predict the web usability of hotel booking sites based on the number of users on each page, and the time of stay of each user. Through this paper, the authors analyse the web usability of a website through process mining by finding the web usability metrics. This work proposes an approach to finding the usage of a website using the attributes available in the weblog which predicts the actual footfall on a website.

PROM (Process Mining tool) is used for the analysis of the event log of a hotel booking site. In this work, authors have used a case study to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

This article first provided an overview of process mining, then focused on web mining and later discussed process mining techniques. It also described different target languages: system nets (i.e. Petri nets with an initial and a final state), inductive miner and heuristic miner, graphs showing the change in behaviour of the dataset and predicting the outcome, that is the webpage having the maximum number of hits.

In this work, a case study has been used to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

This paper investigates the overall system risk for a foodgrains supply chain capturing the interrelationship among the risk factors and the effect of risk mitigation strategies.

This paper first calculates the weight of risk factors using an integrated approach of failure mode, effects analysis and fuzzy VIKOR technique. Next, the weights are utilized as input for the weighted fuzzy Petri-net (WFPN) approach to calculate the system risk.

Two different WFPN models are developed based on the relationships among the risk factors, and both models demonstrate a higher risk value for the overall system.

The proposed methodology will help practitioners or managers understand the complexity involved in the system by capturing the interrelationship behaviour. This study also considers the concurrent effect of risk mitigation strategies for calculating the overall system risk, which helps to improve the system’s performance.

Precast wall lifting during prefabricated building construction faces multiple non-lean problems, such as inaccurate lifting-time estimation, unreasonable resource allocation and improper process design. This study aims to identify the pathways for improving lifting performance to advance lean construction of prefabricated buildings.

This study developed a methodological framework that integrates the discrete event simulation method, the elimination, combination, rearrangement and simplification (ECRS) technique and intelligent optimization tool. Two schemes of precast wall lifting, namely, the enterprise's business as usual (BAU) and enterprise-leading (EL) schemes, were set to benchmark lifting performance. Furthermore, a best-practice (BP) scheme was modeled from the perspective of lifting activity ECRS and resource allocation for performance optimization.

A real project was selected to test the effect of the methodological framework. The results showed that compared with the EL scheme, the BP scheme reduced the total lifting time (TLT) by 6.3% and mitigated the TLT uncertainty (the gap between the maximum and minimum time values) by 20.6%. Under the BP scheme, increasing the resource inputs produces an insignificant effect in reducing TLT, i.e. increasing the number of component operators in the caulking subprocess from one to two only shortened the TLT by 3.6%, and no further time reduction was achieved as more component operators were added.

To solve non-lean problems associated with prefabricated building construction, this study provides a methodological framework that can separate a typical precast wall lifting process into fine-level activities. Besides, it also identifies the pathways (including the learning effect mitigation, labor and machinery resource adjustment and activities’ improvement) to reducing TLT and its uncertainty.

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.

The purpose of this article is to conduct a bibliometric analysis of the cellular manufacturing system (CMS) literature published from 1982–2021 to identify key issues and trends for the future.

A six-stage methodology is used to conduct a literature review, which includes: (1) article collection; (2) inclusion/exclusion criteria; (3) reviewing the articles; (4) analyzing the articles; (5) framework development; and (6) future research directions. A total of 936 CMS-specific articles are reviewed. This paper made use of three software tools: the R package, VOSviewer and SciMAT.

According to the findings, the majority of CM researchers focused on cell formation and design. The USA, Iran and India are the top three leading publishers. Additionally, the gap and future direction of CM are discussed.

To the best of the authors' knowledge, the current study is the first attempt to investigate CMS evaluation through bibliometric and thematic analysis and provides a decisional framework as well as steps for CMS adoption. For individuals who are interested in understanding more about CMS and its evolution, this paper offers a starting point.

Despite the growing attention on the relevance of improved building management systems with cognition in recent years in the architecture, engineering, construction and operation (AECO) community, no review has been conducted to understand the human-environment interaction features of cyber-physical systems (CPS) and digital twins (DTs) in developing the concept of a cognitive building (CB). Thus, this paper aims to review existing studies on CPS and DTs for CB to propose a comprehensive system architecture that considers human-environment interactions.

Scientometric analysis and content analysis were adopted for this study.

The scientometric analysis of 1,042 journal papers showed the major themes of CPS/DTs for CB, and these can be categorized into three key technologies to realize CB in the AECO community: CPS, DTs and cognitive computing (CC). Content analysis of 44 relevant publications in the built environment assisted in understanding and evidently confirming the claim of this study on the integration of CPS and DTs for CB in construction by also involving the CC. It is found and confirmed that CB can be realized with CPS and DTs along with the CC. A CB system architecture (CBSA) is proposed from the three key technologies considering the human-environment interactions in the loop. The study discovered the potential applications of the CBSA across the building lifecycle phases, including the design, construction and operations and maintenance, with the potential promise of endowing resilience, intelligence, greater efficiency and self-adaptiveness. Based on the findings of the review, four research directions are proposed: human-environment interactions, CB for sustainable building performance, CB concept for modular buildings and moving beyond CB.

This study stands out for comprehensively surveying the intellectual core and the landscape of the general body of knowledge on CPS/DTs for CB in the built environment. It makes a distinctive contribution to knowledge as it does not only propose CBSA by integrating CPS and DTs along with CC but also suggests some potential practical applications. These may require expert judgments and real case examples to enhance reproducibility and validation.

The purpose of this study is to understand the strategy adopted by family firms in using social media for their business. Based on the social media usage, this paper attempts to segment family firms. To do so, a reactive – proactive – innovative (RPI) scale was developed for the study. Then, the family firms were categorised as reactive, proactive or innovative social media users. Further, based on the scale developed, clusters were created. Family firms were placed into different clusters based on the strategy that they had for using social media platforms for their business.

A pilot sample of 50 family firms and a main study of 256 Indian family firm entrepreneurs were surveyed through self-administered questionnaires. Factor analysis reduced the 12 scale-based questions to three distinct factors. Confirmatory factor analysis was then conducted on the main sample to confirm the constructs identified using exploratory factor analysis. Cluster analysis was used to build clusters of entrepreneurs who use social media as part of their digital marketing strategy.

Findings reveal that the Indian family firm market is largely divided into four main segments. These segments represent distinct behaviours with respect to the use of social media. The four segments of family firm entrepreneurs were named as high rollers, ignorant inhabitants, trend-setters, combative crowd based on their social media usage behaviour. These clusters give deep insights into the strategic usage of social media by family firms.

The limitation of this study is that entrepreneurs from all Indian states were not considered in the sample because of cost implications. This research study has only created the segmentation of the family firms as reactive, proactive or innovative social media users and also has created the clusters as high rollers, ignorant inhabitants, trend-setters and combative crowd. Also, the reasons for their behaviour and root cause for the strategic usage have not been studied.

This study reflects on current practices of family firms with respect to usage of social media and groups them into large identifiable clusters. Equipped with the findings from this study, the RPI scale developed for the study and the clusters created, entrepreneurs can now move towards better use of social media for innovation.

Although past studies have advocated the use of social media to spur innovation in firms, this study segments the current market based on their practices. It allows readers to gauge the proportion of family firms using social media for innovation and paves the way for a change in behaviour amongst these firms.

The purpose of this study is to develop an empirical model by understanding the relative significance of interactive technological forces, such as chatbots, virtual try-on technology (VTO) and e-word-of-mouth (e-WOM), to improve interactive marketing experiences among consumers. This study also validates the moderating role of the perceived effectiveness of e-commerce institutional mechanism (PEEIM) as a moderator between attitude and continued intention.

Data were collected through personal visits and an online survey. The link to the survey questionnaire was shared on different social media platforms and social networking sites. A total of 362 responses obtained in the online and offline modes were considered for this study.

e-WOM emerged as the strongest predictor of attitude, followed by chatbots and VTO. The results of this study revealed that PEEIM did not moderate the relationship between attitude and continued intention.

Using the self-determination theory and behavioral reasoning theory as theoretical frameworks, this study is an initial endeavor in the online shopping context to empirically validate interactive forces like chatbots, VTO, e-WOM and PEEIM as moderators together to arrive at a holistic framework. These forces, in turn, act as significant contributors to online shopping satisfaction.

This paper aims to prove the expediency and effectiveness of magnetic textiles use obtained by adding nanopowder synthesized on the basis of oxides of divalent and trivalent iron oxides, taking into account bacteriostatic, magnetotherapeutic and compressive properties.

The research includes methods of synthesis of nanoelements of bivalent and trivalent iron, methods of the theory of elasticity for determining the pressure between compression clothing and a limb, methods of creating an annular magnetic field with determination of its voltage, methods of determining the growth dynamics of mold bacteria and methods of approximation of experimental data.

On the base of the determination of the forces arising from the interaction of magnetic nanotextiles with a magnetic field, the expediency of using these materials in the creation of compression clothing has been proven. An additional medical value of magnetic textiles is the bacteriostatic effect. The content of magnetic nanoelements in the textile composition of 0.2% almost completely suppresses mold infections

Cotton samples with the addition of nanocomponents based on ferric and ferric oxides were studied.

Magnetotextile materials can be used in magnetotherapy, compression clothing, in textile products that provide bacteriostatic properties.

The use of magnetic textile materials is a perspective direction for the creation of medical textile products with complex properties.