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This paper shows how organizations can use learning clusters, Massive Open Online Courses (MOOCs) and free videos to improve organizational learning. The paper presents implementation steps through a case study with Indian micro, and small and medium-sized enterprises (SMEs).

As a part of the employee development strategy, most Indian organizations adapted structured training framework. However, employee skill and competency development are equally important for SMEs. An attempt was made to train employees of these sectors using MOOCs and free videos. Twenty Indian organizations were selected for the pilot study and segregated into two clusters for efficient implementation of the concept. The learning process was observed for the next six months to summarize the outcome.

The case study concluded that MOOCs and free videos made a difference in skill and competency development of SMEs using cost-effective internet and mobile handset. It also revealed the positive impact of this learning framework on productivity, the quality which eventually improved the revenue.

This study explores the usability of MOOCs and free videos for learning purpose. More studies on learning effectiveness are required to generalize results.

This study reveals the effectiveness of MOOCs and free videos for employee development. The foundation result will help the Learning and Development (L&D) professionals and senior management to think in a different way while making the employee development strategy.

This research increases the knowledge base related to the use of MOOCs and free videos for employee training.

This paper aims to describe the impact of personalized eLearning (PeL) using small videos and simulations. It sets out the results of a research project carried out across four Indian organizations.

This is an empirical study. The respondents completed product training using small videos and simulations as training content. The courses were assigned to learners using PeL concept. An online survey was conducted to capture feedback on learning content and process, followed by structural equation modeling (SEM) to explain the acceptance.

The research concluded that flexibility and engagement play an important role in competency development using eLearning. It also revealed the positive role of small videos, simulations, and PeL to improve product knowledge. The research findings are consistent with earlier studies.

The pilot study was a part of a thesis topic for a doctoral program. The study is limited to four domains, namely, aerospace engineering, biological science, thermodynamics, and nuclear research. More studies are required to generalize results. Data were collected through self-responses and focus group discussion. Hence, the “perception” of respondents has some influence on the overall outcome.

The foundation’s result will help learning & development (L&D) professionals and courseware designers to identify important factors for small video and simulation-based learning in an Indian context. The recommendations will help practitioners design effective PeL content for product training.

This research increases the knowledge base related to competency development using eLearning for product training in an Indian context.

The purpose of this paper is to determine the triggers of positive electronic word of mouth (eWOM) using real-time Big Data obtained from online retail sites/dedicated review sites.

In this study, real-time Big Data has been used and analysed through support vector machine, to segregate positive and negative eWOM. Thereafter, using natural language processing algorithms, this study has classified the triggers of positive eWOM based on their relative importance across six product categories.

The most important triggers of positive eWOM (like product experience, product type, product characteristics) were similar across different product categories. The second-level antecedents of positive eWOM included the person(s) for whom the product is purchased, the price and the source of the product, packaging and eagerness in patronising a brand.

The findings of this study indicate that the marketers who are active in the digital forum should encourage and incentivise their satisfied consumers to disseminate positive eWOM. Consumers with special interest for any product type (mothers or doctors for baby food) may be incentivised to write positive eWOM about the product’s ingredients/characteristics. Companies can launch the sequels of existing television or online advertisements addressing “for whom the product is purchased”.

This study identified the triggers of the positive eWOM using real-time Big Data extracted from online purchase platforms. This study also contributes to the literature by identifying the levels of triggers that are most, more and moderately important to the customers for writing positive reviews online.

The purpose of this paper is to incorporate prior knowledge in the artificial neural network (ANN) model for the prediction of continuous cooling transformation (CCT) diagram of steel, so that the model predictions become valid from materials engineering point of view.

Genetic algorithm (GA) is used in different ways for incorporating system knowledge during training the ANN. In case of training, the ANN in multi-objective optimization mode, with prediction error minimization as one objective and the system knowledge incorporation as the other, the generated Pareto solutions are different ANN models with better performance in at least one objective. To choose a single model for the prediction of steel transformation, different multi-criteria decision-making (MCDM) concepts are employed. To avoid the problem of choosing a single model from the non-dominated Pareto solutions, the training scheme also converted into a single objective optimization problem.

The prediction results of the models trained in multi and single objective optimization schemes are compared. It is seen that though conversion of the problem to a single objective optimization problem reduces the complexity, the models trained using multi-objective optimization are found to be better for predicting metallurgically justifiable result.

ANN is being used extensively in the complex materials systems like steel. Several works have been done to develop ANN models for the prediction of CCT diagram. But the present work proposes some methods to overcome the inherent problem of data-driven model, and make the prediction viable from the system knowledge.

The purpose of this paper is to develop an unsupervised classification algorithm including feature selection for industrial product classification with the basic philosophy of a supervised Mahalanobis‐Taguchi System (MTS).

Two novel unsupervised classification algorithms called Unsupervised Mahalanobis Distance Classifier (UNMDC) are developed based on Mahalanobis' distance for identifying “abnormals” as individuals (or, groups) including feature selection. The identification of “abnormals” is based on the concept of threshold value in MTS and the distribution property of Mahalanobis‐D².

The performance of this algorithm, in terms of its efficiency and effectiveness, has been studied thoroughly for three different types of steel product on the basis of its composition and processing parameters. Performance in future diagnosis on the basis of useful features by the new scheme is found quite satisfactory.

This new algorithm is able to identify the set of significant features, which appears to be always a larger class than that of MTS. In industrial environment, this algorithm can be implemented for continuous monitoring of “abnormal” situations along with the general concept of screening “abnormals” either as individuals or as groups during sampling.

The concept of determining threshold for diagnostic purpose is algorithm dependent and independent of the domain knowledge, hence much more flexible in large domain. Multi‐class separation and feature selection in case of detection of abnormals are the special merits of this algorithm.

The purpose of this paper is to utilize the power of Six Sigma, a disciplined approach to improve quality of product, process or service quality, for accident prevention in the manufacturing industry.

This paper presents the basic features of DMAIC methodology of Six Sigma and its application for the purpose of accident prevention; illustrates the set of tools and techniques to be used at different phases of DMAIC for accident data analysis; and outlines the DMAIC methodology by analyzing accident data from a large process industry in India.

The systematic and logical approach of Six Sigma problem solving could identify many root causes for accident and identification and deployment of corrective actions horizontally to relevant processes.

Six Sigma has been successfully implemented in improving manufacturing processes but its application for the purpose of accident prevention is still limited. This paper demonstrates that Six Sigma principle can resolve such problems, and can be used by any plant to solve similar problems of accident prevention.

The selection of right projects in a Six Sigma program is a major concern for early success and long‐term acceptance within any organization. One of the ever‐increasing challenges is to define and select right measure for improvement and appropriate problem definition. Many projects encounter the problem of no linkage with business objectives or customer needs, too large or high‐level project scope along with unclear problem and goal statement. Improperly, chosen metrics lead to sub‐optimal behavior and can lead people away from the organization's goal instead of joining them. This paper aims to propose a project selection methodology for different situations.

This research develops a model for project identification; ensuring well‐defined projects are selected having large impact on customer satisfaction or bottom line. The model is described for the situations: availability of performance data, balanced business score card implemented and no data is available.

A “top‐down approach” model is developed for project selection, since top management support for Six Sigma initiatives is absolutely critical to see tangible, significant results. The authors suggest establishing the linkage with data (either reactive or survey), otherwise through prioritization tool for project selection. Finally, factors influencing successful Six Sigma projects include management commitment; project selection and control skill, irrespective of whether this is a define, measure, analyze, improve and control or define, measure, analyze, design and validate/verify project.

This approach will help the organizations to select the specific project from multivariate organizational and customer needs. Three different methods for project selection are explained with examples and reasons for selection. Merits and demerits of each method are also highlighted.

Aims to investigate the causative factors and clinical applicability of spontaneous regression of malignant tumours without treatment, a really paradoxical phenomenon with many therapeutic potentialities. Analyses past cases to find that the commonest cause is a preceding episode of high fever‐induced thermal fluctuation which produces fluctuation of biochemical/immunological parameters. Using Prigogine‐Glansdorff‐Langevin stability theory and biocybernetic principles, develops the theoretical foundation of a tumour’s self‐control, homeostasis and regression induced by thermal, radiation or oxygenation fluctuations. Derives a threshold condition of perturbations for producing regression. Presents some striking confirmation of such fluctuation‐induced regression in Ewing tumour, Clear cell cancer and Lewis lung carcinoma. Using experimental data on patients, elucidates a novel therapeutic approach of multi‐modal hyper‐fluctuation utilizing radiotherapeutic hyper‐fractionation, temperature and immune‐status.