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The purpose of the present work is to improve the industry performance by identifying and quantifying the risks faced by the Indian pharmaceutical industry (IPI). The risk values for the prominent risks and overall industry are determined based on the four risk parameters, which would help determine the most contributive risks for mitigation.

An extensive literature survey was done to identify the risks, which were also validated by industry experts. The finalized risks were then evaluated using the fuzzy synthetic evaluation (FSE) method, which is the most suitable approach for the risk assessment with parameters having a set of different risk levels.

The three most contributive sub-risks are counterfeit drugs, demand fluctuations and loss of customers due to partners' poor service performance, while the main risks obtained are demand, financial and logistics. Also, the overall risk value indicates that the industry faces medium to high risk.

The study identifies the critical risks which need to be mitigated for an efficient industry. The industry is most vulnerable to the demand risk category. Therefore, the managers should minimize this risk by mitigating its sub-risks, like demand fluctuations, bullwhip effect, etc. Another critical sub-risk, the counterfeit risk, should be managed by adopting advanced technologies like blockchain, artificial intelligence, etc.

There is insufficient literature focusing on risk quantification. Therefore, this work addresses this gap and obtains the industry's most critical risks. It also discusses suitable mitigation strategies for better industry performance.

The purpose of the study is to measure the performance of Indian hospitals, both operationally and financially, by using hospital KPI's. The assessment is predominantly done by linking it to the existing Lean practices in Indian hospitals.

An empirical study based on cross-sectional survey of hospital managers and specialists in various private healthcare facilities across India was conducted to validate the proposed Lean framework. From an extensive literature survey, the authors identified quality, delivery, efficiency, accessibility and patient centeredness to be the main operational performance (OP) indicators for hospitals. Business or financial performance was measured based on parameters which are average revenue per occupied bed (ARPOB), earnings before interest, tax, depreciation and amortization (EBITDA) and operating revenue. Confirmatory Factor Analysis (CFA) was carried out using a specialized technique, called Structural Equation Modelling(SEM) and an explicit factor structure was hypothesized.

Management commitment towards Lean in hospitals is statistically proven to have impacted operational and financial performance. However, leanness in technology and business processes showed no statistical significance on either operational or financial performance parameters. Hospital stakeholders showed statistical significance on though it had no impact on the financial performance. Results obtained from the statistical analysis indicate a positive impact of hospital Lean practices on timely delivery of services and improved service quality. Efficiency, accessibility of services and patient centered behavior in hospital operations could not be statistically proven to have impacted the financial performance.

Effectiveness of Lean management (LM) principles in improving hospital operations is largely dependent on patient centered behavior. Empowered employees who are trained to add value from a customer view point, make hospital operations safe and improved. Properly trained and communicated employees who are committed to quality improvements can make a positive impact on patients' quality of life and thus positively impact the society. The study lists ways to attain the required outcomes.

This paper is among the very few that has attempted to suggest ways to link implementation of Lean practices more effectively in Indian hospitals to improve hospital performance at operational and financial levels.

Small and medium-scale enterprises (SMEs) that operate with modest financial investments and commodities face numerous challenges to remain in business. One major philosophy used by SMEs these days is the implementation of lean manufacturing to get solutions for various issues they encounter. But is lean getting sustained over time? The purpose of this research is to design a Sustainable Lean Performance Index (SLPI) to assess the sustainability of lean systems and to pinpoint the variables that might be present as potential lean system inhibitors which hinder the sustainability of leanness.

A multi-level sustainable lean performance model is constructed and presented based on the literature research, field investigation and survey conducted by administering a questionnaire. Fuzzy logic approach is used to analyse the multi-level model.

SLPI for the SMEs is found using fuzzy logic approach. Additionally, the ranking score system is applied to categorise attributes into weak and strong categories. The performance of the current lean system is determined to be “fair” based on the Euclidean distance approach and the SLPI for SMEs.

This work is concentrated only in South India because of the country’s vast geographical area and rich and wide diversity in industrial culture of the nation. Hence, more work can be done incorporating the other parts of the country and can analyse the lean behaviour in a comparative manner.

The generalised sustainable lean model analysed using fuzzy logic identifies the inhibitors and level of performance of SMEs in South India. This can be implemented to find out the level of performance in the SMEs after a deeper study and analysis around the SMEs of the country.

The sustainable assessment of lean parameters in the SMEs of India is found to be very less in literature, and it lacks profundity. The model established in this study assesses the sustainability of the lean methodology adopted in SMEs by considering the lean and sustainability attributes along with enablers like technology, ethics, customer satisfaction and innovation with the aid of fuzzy logic.

This research study aims to introduce a maturity model based on capability maturity model integration (CMMI) that can assess the digital manufacturing maturity level of manufacturing companies.

A CMMI model for the manufacturing industry is designed to assess the digitalisation level of manufacturing industries. The model is developed exclusively for the process area “organisational process focus” (OPF), and the digitalisation level is quantified using fuzzy logic by employing a case study approach.

The CMMI is successfully employed to assess the digitalisation level of a manufacturing organisation using the fuzzy logic approach. The triangular fuzzy number of the Fuzzy CMMI Measure Index (FCMI) is obtained as (6.08, 7.33, 8.52). The transformation of FCMI into linguistic terms discloses the digitalisation level of the manufacturing organisation as “Capability Maturity Level 4” (CML 4).

The authors tested the suitability of CMMI in the manufacturing sector. The operational concept introduced in this research sets forth a unique framework to quantify the digitalisation level of manufacturing industries.

The purpose of this study is to develop a CMMI model for the manufacturing industry and to find an appropriate method to assess the CMMI level. The CMMI level indicates how well the processes are planned, performed, measured and controlled, thus revealing the performance level of an organization.

Among the various process areas of CMMI, “organizational process focus” (OPF) is selected for the study. The CMMI model for the process area OPF is designed based on the CMMI enablers, criteria and attributes. Based on this multilevel model, a case study approach is adopted and fuzzy logic is used to measure the CMMI level of an organization. The fuzzy performance importance index (FPII) and the ranking score are used to further analyze the attributes.

The proposed model has been successfully used to measure the CMMI level of the manufacturing industry in south India. The triangular fuzzy number of the fuzzy CMMI measure index (FCMI) is obtained as (2.077, 3.534, 5.000). The transformation of FCMI back into linguistic terms discloses the current CMMI level of the industry as “Capability Maturity Level 2” (CML 2).

The authors tested the suitability of an inter-disciplinary approach known as CMMI for the process appraisal in the manufacturing sector. The investigation sets forth a unique framework to quantify the performance of practices followed in a manufacturing organization and thereby help the industry to realize the present strength and weakness in terms of process assets.

Rice is an important grain in Indian scenarios, and the purpose of the research work is to identify the attributes which can be the possible barriers in the traditional rice supply chain network.

A multilevel conceptual model is developed based on the literature review, and a field study is conducted by administering a questionnaire from the experts. Fuzzy logic methodology and a ranking score method is applied to identify the rice supply chain performance and the barriers of the traditional rice supply chain network.

The rice supply chain performance index for the traditional rice supply chain network is obtained, and the performance of the existing rice supply chain is found to be “fair”. The “information flow” is the attribute that can be a critical weak attribute in the traditional rice supply chain network. A proposed model of the blockchain technology-enabled rice supply chain network is developed as a solution for the “information flow” barrier.

The present research work is focussed on the generalized rice supply chain model of the Indian scenario, and more detailed studies can be carried out based on the regional issues.

The rice supply chain plays an important role in Indian economic development, and hence the current research paper focusses on identifying the barriers and the performance of the existing rice supply chain network.

Lean implementation is a strategic decision. The capacity of organisation to be “Lean” can be identified before lean implementation by assessing leanness of an organisation. This study aims to attempt developing a holistic leanness assessment tool for assessing organisational leanness.

A neuro-fuzzy leanness assessment model for assessing the leanness of a manufacturing system is presented. The model is validated academically and industrially by conducting a case study.

Neuro-fuzzy hybridisation helped assess the leanness accurately. Fuzzy logic helped to perform the leanness assessment more realistically by accounting ambiguity and vagueness in organisational functioning and decision-making processes. Neural network increased the learning capacity of assessment model and increased the accuracy of leanness index.

The industrial case study in the paper shows the results in telecom equipment manufacturing industry. This may not represent entire manufacturing sector. The generic nature of the model developed in this research ensures its wide applicability.

The neuro-fuzzy hybrid model for assessing leanness helps to identify the potential of an organisation to become “Lean”. The organisational leanness index developed by the study helps to monitor the effectiveness and impact of lean implementation programmes.

The leanness assessment models available in literature lack depth and coverage of leanness parameters. The model developed in this research assesses leanness of an organisation by accounting for leanness aspects of inventory management, industrial scheduling, organisational flexibility, ergonomics, product, process, management, workforce, supplier relationship and customer relationship with the help of neuro-fuzzy hybrid modelling.

The purpose of this paper is to explore the adoption of lean practices by manufacturing companies in regions of low economic and technological development and to compare findings with previous studies from more developed regions highlighting important contextual differences. The paper uses the contingency theory to explore how contextual variables and scarce resources influence the adoption of lean practices.

A survey of 233 manufacturing firms was conducted in the State of Pará in the Amazon Region of Brazil.

The results demonstrate that six internal lean practices (single minute exchange of dies, human resource management, continuous flow, total productive maintenance, pull and statistical process control) and two external lean practices (supplier feedback and customer involvement) are implemented. However, the two external lean practices of just-in-time delivery by suppliers and supplier development were not implemented. Furthermore, from the 36 operating items comprised in eight lean practices that are being used, 13 were not implemented. As such, compared to developed regions, there is evidence for a more fragmented implementation in less developed regions. The results reveal empirical evidence explained by the contingency perspective, such as national, geographical, strategic context and culture.

There is broad evidence on lean implementation in developed and developing countries in the literature. However, little is known about lean implementation in poorer regions of developing counties. This is one of the first studies mapping lean implementation in a region with low economic and technological development. This has important implications for research and practice, especially to cross-country/cultural research on operation management.

Agility is the ability of an organization to adjust its supply chain tactics and operations to respond quickly against altering business environments such as fluctuating demand pattern, supply chain disruption and global competition. An agile organization must possess a promising capability of swiftly responding to dynamic conditions while being cost-effective without compromising the efficiency. Such high-performance adaptability necessitates the role of supply chain managers to maximize the agility of the supply chain through the efficient use of input resources. Therefore, the purpose of this study to reveal a new decision support tool that would allow the key decision-makers to maximize the agility of the supply chain while deploying the input resources more effectively.

In present study, an integrated approach of popular analytic hierarchy process (AHP) and goal programming (GP) has been adopted as a potential solution methodology. AHP has been implemented to allocate the local and global weights to decision variables, whereas GP incorporates the AHP weights into the desired model.

It was found that the proposed decision support tool restricts the value of the decision variables for maximizing the agility and optimizing the usage of input resources. The results obtained from the model validate the objective of achieving targeted agility level within the available resource limitations.

The decision support tool developed in the proposed study offers a systematic and effectively simple approach to supply chain managers with a goal of identifying the degree of focus under each decision variable in the respective manufacturing organizations.

A novel decision support tool has been developed known as an agility control system), which helps the decision-maker to achieve the required agility in the supply chain by controlling the decision variables.

With the emergence of the COVID-19 pandemic, the production shortage of personal protective equipment (PPE), such as surgical masks, has become increasingly significant. It is vital to quickly provide high-quality, hygienic PPE during pandemic periods. This comprehensive case study aims to confirm that Kaizen and 5S applications reduce wastage rates and stoppages, which as a result, created a more efficient and sustainable workplace in a small–mediumenterprise (SME) producing PPE in Turkey.

The method for this case is discussed with the help of a flowchart using the DMAIC cycle: D-define, M-measure, A-analyse, I-improve and C-control.

The total stoppages due to fishing line, gripper, piston and yarn welding have decreased by approximately 42.4%. As a result of eliminating wasted time and reduced changeovers, a total of 5,502 min have been saved per month. This increased production of approximately 10.55% per month, led to an addition of 506,184 units.

The use of lean manufacturing (LM), Six Sigma, Lean Six Sigma and continuous improvement methodologies are not common in textile SMEs. Based on the current literature reviewed, to the best of the authors’ knowledge, this is the first comprehensive case study that combines statistical tools, such as hypothesis tests and LM practices, in the production process for a PPE company operating as a textile SME.