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New product development (NPD) is necessary for business sustenance and customer satisfaction. Six Sigma and Design for Lean Six Sigma (DLSS) efficiently employ the repetitive stages for NPD, leading to quality performance and profitability. This study aims to map the quality performance through NPD attributes through the Lean methodology.

The data on NPD were collected from 267 respondents from manufacturing companies to map the relationship between Six Sigma and DLSS for NPD. Confirmatory factor analysis was employed to confirm model fit, while structural equation modeling was employed to analyze the empirical data for framework testing. The study included nine variables and fourteen hypotheses identified from the literature.

The statistical results of this study show that NPD attributes such as innovation, marketing, organization, customer, product and technology positively influence the Lean Six Sigma structured improvement process (LSSSIP) and DLSS. Moreover, integrating these attributes in Lean planning enhance quality performance. This empirical investigation's findings indicate that ten of the 14 hypotheses were supported, giving the study a strong foundation.

The data collection was limited to northern India; therefore, the results may not be generalizable to other areas of the world.

NPD involves handling technical issues and factors such as cost, operational bottlenecks, economic changes, competitors' strategy and company policy. This study helps understand the various NPD parameters and their relationship to Lean, which enables an effective NPD implementation strategy.

The current philosophy of NPD calls for a concurrent engineering approach; therefore, the entire organization must be part of this process. This study uses the holistic framework by optimizing NPD with Lean Six Sigma (LSS) principles. The study is unique in that, to date, research does not integrate NPD attributes with the objectives of LSS to develop an efficient NPD implementation strategy.

The study aims to add to the body of knowledge of open source tangible product management (also called open design). The objective is also to develop a guideline for efficient open source tangible product development and adoption.

The exploratory research design using secondary data (like newspapers, magazines, research articles, bogs, papers, etc.) is used to analyze open source tangible product design challenges and enablers. The success stories of Open Source Software projects (OSS) were studied for identification of critical success factors and further their relevancy was tested in the two popular cases of open source drug discovery (malaria and tuberculosis)

Open innovation has become a part of competitive strategy of current businesses. It requires an efficient intellectual property protection regime for its implementation. However, in a market dominated by proprietary benefits, the open source technology development can serve as remedy for innovation needs of neglected sectors. The OSS literature revealed managing two classes of factors, namely technology sponsor level factors and environmental factors for efficiency and effectiveness. The case study analysis in the context of applicability of these OSS critical factors showed their limitations in open source tangible products, and highlighted understanding additional challenges and remedies.

Open source innovation is a collaborative effort involving inputs from various/diverse players, hence monitoring the effort and motivation level of the contributors is a cumbersome task. Only the information that is available online and in print media is taken as research inputs in this work. Also the data taken were from two case studies; a lot more case studies in the open design domain can progress the theory. The implications of this study are far-reaching in the areas where profit motivated proprietary efforts lack in addressing societal need. It provides guidelines for addressing those unmet needs by developing products in a collaborative way without intellectual property hurdles.

The essence of open design is becoming more vital, and there is a pressing need to build theory to support it, which still is elusive and dispersed. The study fills the gap using secondary data and case study approach.

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

The purpose of this study is to identify important criteria for sustainable recovery of digital entrepreneurship from distress situation using shared resources. During pandemic disruption, the importance of sharing economy in managing business efficiency is reflected through this research.

The present study advances the knowledge on shared resources in business by integrating case study approach with multi criteria decision-making (MCDM) model. A fuzzy analytic hierarchy process approach is adopted to compute criteria weights, and a fuzzy technique for order performance by similarity to ideal solution (TOPSIS) technique is used to rank the sharing economy entrepreneurial ventures during COVID-19 pandemic in the context of emerging economy.

The present study identified five most important enablers (technological innovation, technology expertise, convergence of virtual and physical spaces, collaboration rather than competition, and benefits to underserved groups through transparency) for sustainable recovery of sharing economy ventures in emerging economy. For example, the study highlights online tutoring through shared intellect as the most sought after sharing economy venture during pandemic disruption, which fulfills the identified enablers.

The proposed framework provides an accurate decision support tool to rank the various identified potential enablers of sharing economy during disruptions. Further, the approach is practically relevant to sharing economy entrepreneurs in selecting the best approach to recover sustainability during pandemic.

The study is unique in addressing the need of sustainability for digital ventures via sharing economy approach in emerging economy (India). To develop a conceptual framework, the present study incorporates a case based approach together with the hybrid MCDM model. Further, the extant literature on disruptions is enhanced by prioritizing the enablers for sharing economy during pandemic.

This research presents a study on the supply chain process of an Indian apparel industry considering various parameters involved. The study aims to identify the main parameters to improve the supply chain process and develop a comprehensive structural relationship to rank them to streamline the apparel supply chain process and business environment.

The team of five experts from this apparel industry was made to give scores to multiple parameters. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) technique is used to develop the model for eleven key parameters and then rank them.

Based on the data analysis the planning, customer and warehouse storage have emerged as top three key parameters while the non-replenishment approach, push and pull strategy and manufacturing of the product are identified as the bottom three parameters from a hierarchy level. These parameters have been ranked based on their contributing attributes in this apparel supply chain process.

The study provides an overall ranking of parameters and the implications are in the direction of helping the industry to improve its supply chain performances rather than focus only on productivity. Further, the key parameters are identified as critical inputs and show that the firms are being more proactive and well prepared comprised of the industry.

The study indicates that the key parameters are identified by this apparel brand to improve its supply chain process. The key supply chain process involves planning, manufacturing, distribution, end customer and returns logistics of the goods, etc. So, this research also provides the focused parameters on the supply chain performance received by end customer from the supplier and rank them for effectiveness and improve their overall organizational performance. It also provides a critical observation of their supply chain process improvement which includes different brand uses, strategies and approaches.

A well-designed supply chain performance measurement system, should account for not only the capabilities and performance attributes of the focal firm but also its supply chain partners. The purpose of this paper is to help design a system that strikes a balance between the strategic objectives of the focal firm and its supply partners vis-à-vis the requirements of supply chain performance (cost, quality, speed and customer taste).

A theoretical framework on the strategic supply chain performance measurement system is developed based on existing literature and subsequently tested using a survey on 136 successful manufacturing organizations in India. The organizations were clustered into three strategy types and compared using analysis of variance on ranks to look for differences in preference for performance parameters.

The study examined the five dimensions of the supply chain practices, namely, strategic supply/distribution network, customer relationship, internal operations, information sharing and social and environmental responsiveness. The empirical results demonstrate the inclusion of business strategy orientation in designing today’s supply chain and hence its performance measurement system. Not supported hypotheses were addressed in the light of contextual factors.

The study is confined to finding preferences of non-financial aspects of supply chain performance and tier-1 suppliers. The research helps better design and benchmark supply chain performance metrics, based on the strategic choice of the firm.

This paper highlights the shortcomings in the existing performance measurement and gaps in the existing literature in the supply chain context. Further, it gives a holistic view of strategic supply chain performance measurement design.

The carbon emissions due to industrial production and market consumption activities are the major contributors to global warming. With the signing of UN Paris Accord 2016 on climate change, the world's major countries are devising measures to combat climate change and attain a sustainable, low-carbon future. Globalization demands companies not only to adopt greener manufacturing practices internally for reduced carbon footprint (CFP) but beyond its boundaries (i.e. its supply chain). This study aims to discuss the relationship between CFP and sustainable supply chain, as evident in the current literature and industry practices. It provides a total comprehension of past, present and future headings in the CFP area and its contribution to a sustainable supply chain.

A systematic literature review and analysis have been undertaken in supply chain sustainability and CFP. A bibliometric approach is adopted for this investigation, and one of the biggest computerized databases, “Scopus,” has been picked. In total, 37 articles have been zeroed in after a careful and watchful screening of firmly related topics.

Most researchers gave predominance to environmental impact among the three pillars of sustainability (economy, society and the environment) for a sustainable supply chain environment. Only a few researchers were motivated to cover social development and social responsibility aspects. This review highlights how managing a CFP is one of the significant attributes of sustainable development. Existing literature in the field of CFP and sustainability have been written on actual industry cases. Food, electricity and energy are some significant industries where supply chain sustainability successfully reduces carbon emission.

The theory-building strategy with recommendations on the conceptualization of a sustainable supply chain is limited in the literature. This study gives broad ideas on how organizations modified and redeveloped different tools and technologies to make their supply chain more sustainable. The strategic role of different carbon policies, environmental rules and regulations in the domain of CFP is also recognized in this work. This study highlights the biases of most of the researches toward applications than policy interventions. This study discusses the theoretical perspective about how CFP affects supply chain management and helps organizations and researchers develop a new set of approaches in handling CFP and other sustainability aspects.

The purpose of this study is to investigate the challenges before India for electric vehicle (EV) adoption by 2030. The study further looks into the measures taken by the Government of India (GOI) to promote research and development in EV sector and what is yet to be done.

In the present study, the challenges are identified allied to the commercialization of EVs in India. The data are collected, analyzed and compiled through secondary sources. The secondary data give a concise insight and comprehensive information regarding what is occurring around the globe as well as in the Indian context. Further, the challenges are investigated through a focus group study consisting of 11 participants from industry and academia.

The findings from the study are the critical roles of sharing economy and public utilities in the promotion of EV adoption, given the high cost of EV, lack of infrastructure and poor purchasing power of Indian customers. The sharing economy perspective provides various opportunities for the government to manage the resources (electric-powered transport system) optimally. Further, the study compares the global perspective in assigning the target figures.

The study highlights the facilitating role of the shared format in EV technology promotion but ignores the hurdles that can come in its implementations. Also, the focus group study has its limitation as it relies more on participants' perceptions and opinions.

The present study assists GOI and various stakeholders in having a realistic plan rather than daydreaming with overambitious goals. The diffusion of technology as a shared format (especially in the context of EV) has not been academically approached in the past literature.

Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain.

A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set.

The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time.

In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries.

The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Open-source innovation involving a diverse range of contributors can help provide affordable drugs to treat severe diseases that afflict poorer countries. Initiatives can be likelier to succeed if there is a strong community, technical expertise, transparency and compliance with regulations and standards. Securing financial and other support from various sources is likewise crucial.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.