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The growth and prosperity of a nation depends upon its ability to innovate technologically and engineering has erupted as a fundamental chauffer of this essence, since last couple of eras. But recent decline in engineering admissions triggers the downfall in skilled labor, which can cause recession in industries or can ultimately lead to economic crisis. This study tries to illustrate the present status of engineering-related education, particularly in India and tends to skim the various parameters that affect the enrollment of students among higher education institutes, directly or indirectly.

A unique approach had been unleashed to tackle the nuisance of low admission among Indian engineering colleges or universities. Factors reviewed from the literature were further prioritized quantitatively after distributing suitable questionnaires among a relevant set of engineering aspirants. The “survey form” used was tactically designed on the basis of response surface methodology (RSM), which analyzed the data captured in Minitab statistical software and deducted logical inferences to optimize the “critical-to-admission” (CTA) factors, applicably.

A case study was successfully executed in a North Indian state to prove the efficacy of proposed methodology as far as downtrend in admission was concerned. This study was a rare blend of questionnaire-based work, where design of experiments principles had been utilized exclusively. It provided significant findings on how to earmark different admission-decisive factors along with their required prioritization.

It can further help the universities and higher education institutes to draft their indispensable professional policies and vision–mission statements, appropriately.

Quantitative studies in the service sector (like higher education) are quite rare to see. The present work is not only providing a roadmap for engineering institutions to boost their admissions in upcoming challenging times but it also acts as a light house for new students as it provides necessary guidelines for shortlisting colleges or universities while seeking admission for higher studies.

Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0.

The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies – Lot for Lot, Silver–Meal heuristic and Wagner–Whitin algorithm – are reviewed and analyzed. The suggested machine learning (ML)–based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production.

When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes.

The ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.

The purpose of this study is to study the impact of new product announcements on the shareholder value in India since; there is lack of perceptive results regarding the impact. Also, an attempt has been made to analyse the determinants of value creation, by industry type, which has so far escaped the attention of researchers.

First, standard event study methodology has been used to measure the abnormal gains/losses of the announcing firms for the new product introductions. Second, regression analysis has been conducted to find out the relationship between the shareholder value and the firm and industry characteristic variables.

The results of the study show that the announcing companies in India have got significant positive returns during the announcement of the new product. The value stands at 0.00455 for the event day. In the second part, the application of the regression test has found that firm size, R&D intensity, free cash flow, debt ratio and market size are significant variables in the determination of the shareholder value.

The present study goes a step further in establishing the reasons for value creation when new product announcements are made by the Indian firms. The analysis has been carried out industry wise to identify the determinants of shareholder value in different industries. This would guide the decision makers at the strategic level and players of the stock market at large in taking much more informed decisions.

This paper aims to conduct an inclusive bibliometric review of the International Journal of Lean Six Sigma (IJLSS) to elucidate the scholarly landscape, growth trends, impact, mappings, couplings, networking and thematic evolution within the field of Lean Six Sigma (LSS) research.

Using advanced bibliometric techniques, including network analysis and clustering, this study examines the publication output of IJLSS since its inception in 2010. The analysis focuses on identifying key contributors, mapping collaborative networks, tracing thematic evolution and exploring emerging research trends. The study is executed as per the proposed easy-to-understand methodology, containing well-structured nine segments hitting various critical-bibliometrics of IJLSS along with their respective implications.

The review reveals substantial growth in the publication output of IJLSS, with India emerging as a prominent contributor. Keywords such as “Lean”, “Six Sigma”, “Quality Management”, “Operational Excellence”, “Supply Chain Management”, “Industry 4.0” and “Sustainability” emerge as central themes, reflecting the journal’s focus on process improvement methodologies along with corresponding case studies. Collaborative networks among authors and countries are robust, indicating the global reach of LSS scholarship. Emerging research trends highlight areas of potential future exploration within the field.

Limitations of this study include the reliance on bibliometric data and the exclusion of nonindexed sources. However, the findings offer valuable insights into the scholarly landscape of IJLSS, providing researchers and practitioners with a comprehensive overview of LSS research inclinations and developments.

This paper contributes to the literature by providing a detailed analysis of LSS research published in IJLSS through a unique approach and future directions. The study adds to existing knowledge by mapping collaborative networks, tracing thematic couplings and identifying emerging research clusters within the ever evolving domain of LSS.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

The purpose of this paper is to identify the various barriers in the implementation of waste management techniques in manufacturing organizations.

In this study, 121 SMEs of the manufacturing sector have been extensively surveyed, to assess the relative impact of barriers in the waste management technique in a manufacturing organization. Further, analytical hierarchy process (AHP) has been used to identify the most significant barriers.

Major barriers in the implementation of a waste management technique in a manufacturing organization have been identified and their weightage has been calculated through the AHP model.

This study will assist the floor managers in manufacturing organizations to identify the major barriers and to plan accordingly for the adequate implementation of waste management technique.

The purpose of this paper is to examine and evaluate various Lean, Six Sigma and Lean Six Sigma (LSS) models proposed by various researchers, which have been implemented in manufacturing organizations.

The present study has followed three major steps in methodology. In the first step, pooling of various research articles has been done, followed by development of the primary results. At the final step, analysis and development of the LSS model have been performed.

This paper evaluated and examined various models of Lean Manufacturing, Six Sigma and LSS and developed an advanced LSS model that can be deployed in any manufacturing organization with the purpose of scrap reduction.

The findings of this study will assist shop floor managers to implement an LSS model in their organizations, which can effectively reduce the waste and enhance the overall productivity and quality of the manufactured products.

Before introducing new products, companies make announcements regarding the launch of the product which influences stock market yields of the announcing companies. Information content of the new product announcement has never been an exclusive focused stream of research. Therefore, an assessment of the impact of the content characteristics of the new product announcement on the shareholder value and the impact of source credibility (spokesperson) in making such announcements is a major gap in the existing literature. The paper aims to discuss these issues.

First, the standard event study methodology has been employed on the sample to measure the abnormal gains/losses accruing to the announcing firms. Second, moderated regression analysis (MRA) is employed to identify the characteristics of the new product announcement and to check the role of the spokesperson in creating shareholder value.

The results of the event study indicate that the abnormal returns are generated during the new product announcement. The results of MRA disclose the variables having a positive and a significant influence on the effective returns of the announcing companies. Likewise, the role of the spokesperson has come out brightly as a credible communicator.

The research provides a direction to the announcing companies regarding the content of the announcement leading to a positive perception among the investing community. Likewise, it also provides direction to the investor community about the characteristics of the announcement content they give weight age in forming a perception of strength in evaluating the new product announcement, to which they are largely unaware.

The purpose of this paper is to uncover the significance of quick changeovers in die‐casting foundry environments.

The paper gives set‐up instructions and guidelines to prepare the standardized set‐up procedure without ignoring actual constraints in foundries. It uses a case study in a medium scale piston foundry to generate an integrated set‐up reduction approach, utilizing single minute exchange of die (SMED)‐based industrial engineering tools to achieve faster set‐ups. It describes the feasibility of quick changeovers in foundry small and medium enterprises based on a “SMED” approach. Finally, the paper carries out empirical analysis of the financial/non‐financial benefits incurred from set‐up reductions.

Set‐up activities are a vital part of the production lead‐time of any product and so affect overall product cost. Tools like Pareto analysis, root‐cause analysis and method study have been used to analyze the existing procedure of set‐ups. A SMED approach can help eliminate unwanted activities, externalize the internal activities, if possible and reduce them by simplification or standardization. The application of other tools such as 5S, Poke‐Yoke and specific tool‐kits are suggested to further reduce set‐up times.

The paper demonstrates the practical application of SMED showing how it can bring real breakthroughs in productivity to small and medium scale foundries.

The purpose of this paper is to demonstrate a tactical approach to cope with the issues related to low availability of repairable machines or systems because of their poor reliability and maintainability. It not only explores the significance of availability, but also embarks upon a step-by-step procedure to earmark a relevant replenishment plan to check the mean time between failure (MTBF) and the mean time to repair (MTTR) efficiently.

The literature review identifies the extent to which availability depends on reliability and maintainability, and highlights the diversified challenges appearing among repairable systems. Different improvement initiatives have been suggested to avoid downtime, after analyzing the failure and repair time data graphically. Relevant plots and growth curves captured the historical deviations and trends along with the time, which further helps to create more robust action plans to enrich the respective reliability and maintainability of machines. During the case study, the proposed methodology has been tested on four SPMs and successfully validated the claims after achieving around a 98 percent availability at the end.

Graphical analysis is the key to developing suitable action plans to enhance the corresponding reliability and maintainability of a machine or system. By increasing the MTBF, the reliability level can be improved and similarly quick maintenance activities can help to restore the prospect of maintainability. Both of these actions ultimately reduce the downtime or increase the associated availability exponentially.

The work revolves around the availability of SPMs. Moreover, SPMs have been divided only into series sub-systems. The testability and supportability aspects have not been considered thoroughly during the fabrication of the approach.

The work focusses on the availability of systems and proposed frameworks that helps to reduce downtime or its associated expenditure, which is generally being ignored. As a case study-based work especially on SPMs in the auto sector this paper is quite rare and will motivate affiliated engineers and practitioners to achieve future breakthroughs.