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Due to the large-size, non-uniform transactions per day, the money laundering detection (MLD) is a time-consuming and difficult process. The major purpose of the proposed auto-regressive (AR) outlier-based MLD (AROMLD) is to reduce the time consumption for handling large-sized non-uniform transactions.

The AR-based outlier design produces consistent asymptotic distributed results that enhance the demand-forecasting abilities. Besides, the inter-quartile range (IQR) formulations proposed in this paper support the detailed analysis of time-series data pairs.

The prediction of high-dimensionality and the difficulties in the relationship/difference between the data pairs makes the time-series mining as a complex task. The presence of domain invariance in time-series mining initiates the regressive formulation for outlier detection. The deep analysis of time-varying process and the demand of forecasting combine the AR and the IQR formulations for an effective outlier detection.

The present research focuses on the detection of an outlier in the previous financial transaction, by using the AR model. Prediction of the possibility of an outlier in future transactions remains a major issue.

The lack of prior segmentation of ML detection suffers from dimensionality. Besides, the absence of boundary to isolate the normal and suspicious transactions induces the limitations. The lack of deep analysis and the time consumption are overwhelmed by using the regression formulation.

Defective parts in manufacturing is a serious issue faced by every manufacturer. Even after proper care in design, material selection and manufacturing of product, there exists a defective part. The purpose of this paper is to explore the quality of the manufacturing, and find the use of effective quality tools to reduce the part defect rate in an electrical parts manufacturing unit, thereby, reducing the replaced cost of defective parts.

With the help of quality initiatives, like total quality management (TQM) and Lean Six Sigma (LSS), the firms can produce quality product in each stage of production. The paper focuses on the primary data collected from the XYZ electric manufacturer.

The main finding of this case analysis is that by the effective use of quality tools, the defective part return rate can be reduced, because of which the firm can observe reduction in replaced cost of almost INR24 lakh. In addition, 10A switch part contributes more in replacement cost. Further, it adds to the 35 percent of the overall part rejection.

The study is more focused on particular type of switch product and can extend to other types of products. In addition, the analysis reveals the results of only 88 percent of the defective products.

The study provides results of the improved quality by effective use of quality tools and discusses the different types of defects in the electrical parts manufacturing. Introducing TQM and LSS to manufacturing can reduce the customer return rate to 1,300 parts per million (PPM) and even to 1,000 PPM in future.

The paper discusses the quality issues in the electrical manufacturer. Moreover, the case analysis briefs effective ways to improve the product quality and reduce the rejection rate.

In the apparel industry, suppliers play a significant role, directly affecting customer service levels and business profits. Integrating sustainable requirements into supplier selection not only aligns with global environmental goals but also enhances business performance, social responsibility and overall industry well-being. This study aims to design a multi-criteria model to evaluate and select the most sustainable suppliers in the fashion industry, trying to balance the conflicts in the set of sustainable development criteria.

The integration of analytic hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) offers advantages in sustainable supplier selection within the apparel industry. The AHP plays a crucial role in engaging multiple decision-makers with conflicting criteria to reach a consensus during the decision-making process. Conversely, the TOPSIS is used to compute alternative ratings. By simultaneously determining criteria weights and incorporating stakeholder preferences, hybrid models enhance decision-making strength and overcome limitations observed in classical multi-criteria decision-making techniques.

This research identified and classified 16 critical criteria impacting the selection of apparel industry suppliers, focusing on sustainable development. The criteria were weighted, providing a robust statistical foundation for the selection model. The results indicated that the most influential criteria were staff training, production capability, flexibility and practice of recycling. The proposed sustainable supplier selection model explains to decision-makers how criteria influence supplier ranking results compared to traditional models, supporting managers in making informed and sustainable supply chain decisions through continuous updates and enhancements.

This research provides new insight into the weighted impact of factors related to sustainable supplier selection in the apparel industry. The combination of a precise process and scientific knowledge will improve the quality of sustainable supplier selection.

The purpose of this paper is to assist ocean container carriers in their service quality improvement strategies to ensure breakthrough performance in India.

A total of seven container carriers have been involved in this study. To explore the list of service criteria, reviews of transportation literature, customer satisfaction survey questionnaires of container carriers, SERVQUAL battery, telephonic interviews and focus groups were conducted. For data collection, a shipper satisfaction questionnaire was administered. After data collection, a mean score analysis using SPSS 15 was taken up to assess the present service performance levels of the select container carriers. Then a performance gap analysis was carried out using the gap analysis formula found in the benchmarking literature.

Out of the 48 service criteria which decide the service quality of ocean container carriers, Maersk is the top performer in respect of 23 criteria, both Hanjin and MSC are top in eight criteria each, Evergreen is top in five criteria, APL is top in four criteria and CMA CGM is top in two criteria. Hapag has not scored top in any of the criteria. The gap analysis shows that APL needs to improve 44 areas in which it has shown negative gaps, CMA CGM needs to improve 47 criteria, Evergreen 45 criteria, Hanjin 47 criteria, Hapag 48 criteria, Maersk 40 criteria and MSC 43 criteria to become excellent.

This paper has enabled container carriers to understand the list of criteria that decide their service quality in the Indian container carrier industry. It has also informed them of their present service performance levels, and their areas of strengths and weakness. This will help them in efficient resource allocation. Understanding the areas and sizes of negative gaps, they can take appropriate steps to close them and become excellent.

This is the first service quality improvement study undertaken in the Indian container carrier industry and it has opened up enormous scope for future research.

The purpose of this paper is to propose a holistic approach for supplier evaluation and purchasing order allocation among the ranked suppliers who meet acceptable levels of economic, environmental and social measures.

A mixed research method of case study and analytical approach is adopted in this research. A fuzzy analytical hierarchical process (FAHP) is applied for ranking of suppliers. Supplier ranks are validated using judgements from multiple decision makers. Purchasing order allocation among the ranked suppliers is determined using cost minimization subject to multiple criteria of economic, environmental and social conditions. A cement manufacturing case example demonstrates and validates the proposed approach.

The research shows that both economic and environmental considerations are significant when suppliers are evaluated for sustainable procurement within the best practice of supply management process. Ranking of suppliers, based on experts’ opinions, indicates varying degrees of importance for each criterion. Adoption of sustainable procurement criteria for evaluating supplier in a cement manufacturing organization is explained by three organizational theories including resource-based, institutional and dynamic capabilities theories. Preferred suppliers from FAHP method are confirmed by judgements from multiple decision-makers. The analysis reveals that purchasing order allocation is different when suppliers are evaluated based on their relative importance and overall ranking.

Currently, individual performance measures and decision-makers are selected from a limited set. The purchasing allocation among ranked suppliers, subjected to cost minimization, incorporates environmental objective of acceptable carbon dioxide emission and social perspective of health and safety of workers, and provides a new approach for dual supplier evaluation and purchasing allocation problem in cement industry. Adopting the proposed supplier evaluation and order allocation approach in practice needs to be guided by the operational principles and an overall methodology which is appropriate for the specific industry with sustainability objectives.

This research enables decision-makers to incorporate sustainability analysis in the supplier evaluation as the basis for best practice with an industry-friendly holistic approach. Using organizational theories, the research re-enforces the importance of not only the energy consumption and environmental management systems of environmental dimension as driving forces/factors from Institutional theory perspective, but also pollution controls and prevention as purchasing capabilities from resource-based theory perspective. The proposed approach is expected to motivate decision-makers to consider sustainable perspectives in supplier evaluation and order allocation processes in a global supply chain and can become a benchmarking tool.

Suppliers’ information on health and safety of their truck drivers are used in order allocation, thus emphasizing the importance of social dimension and encouraging better conditions and benchmarking for delivery drivers.

This paper extends the contribution to the literature by providing guidelines for managers to set strategies, benchmarks and policies within broader sustainable supply chain practices and demonstrates the applicability of the approach using a cement-manufacturing scenario in an emerging economy.

Dimensional quality of sheet metal assemblies is an important factor for the final product. However, the part tolerance is not easily controlled because of the spring back deformation during the stamping process. Selective assembly is a means to decrease assembly tolerance of the assembly from low-precision components. Therefore, the purpose of this paper is to propose a fully efficient method of selective assembly optimization based on an improved genetic algorithm for optimization toolbox (IGAOT) in MATLAB.

The method of influence coefficient is first applied to calculate the assembly variation of sheet metal components since the traditional rigid assembly variation model cannot be used due to welding deformation. Afterwards, the IGAOT is proposed to generate optimal selective groups, which consists of advantages of genetic algorithm for optimization toolbox (GAOT) and simulated annealing.

The cases of two simple planes and the tail lamp bracket assembly are used to illustrate the flowchart of optimizing combinations of selective groups. These cases prove that the proposed IGAOT has better precision than that of GAOT with the same parameters for selective assembly.

The research objective of this paper is to evaluate the changes from rigid bodies to sheet metal parts which are very complex for selective assembly. The method of IGAOT was proposed to the selected groups which has better precision than that of current optimization algorithms.

Nowadays, firms are keen on improving the quality culture in the organizations. The proven success of Lean and Six Sigma has given rise to the synergetic Lean Six Sigma (LSS) approach that has been catching fire in the past one decade. However, there exists a gap between the understanding and implementation of LSS in the organizations, especially in the emerging economies. Taking this as a valuable opportunity, the purpose of this paper is to present a development of LSS training module.

This study starts with a literature review of LSS to reinforce the understanding of the research subject in scope of manufacturing sector. Then, an online questionnaire was designed and used to collect responses from 181 companies located in the Indian sub-continent. Subsequently, the results obtained from the survey were analyzed using COARSE approach.

This study reveals two key findings and associated contributions. First, it was found that the overall awareness of LSS within the responded manufacturing firms is about 70.4 percent. Second, there is no single standard training module that exists in any of the sampled firms to cater to their quality programs. Hence, in order to improve the LSS awareness which could subsequently help managers as a resource for creating an efficient workplace, this paper presents a structured LSS training framework.

Although this paper presents the importance of LSS and associated awareness level among the responded firms, more empirical evidence is required to generalize the model findings. Second, this study is scoped to firms that work out of the Indian sub-continent, and this provides a future opportunity to expand the scope of this research toward a global study for a comparison between emerging and developed economies. Third, this study is limited to manufacturing firms and hence paves an opportunity to research on a similar theme in services context as well.

Before embarking on an LSS journey, an organization can use the LSS training module proposed in this study to assess the employee awareness on LSS. Furthermore, organizations that already have a mature LSS practice can incorporate the LSS training module for periodic evaluation of the employees for effective change management.

The training module presented in this paper is the original contribution by the authors. This is no association to any single identifiable organization or associated funding. The direct practical implication of its application in real time is the value that managers could derive from the proposed LSS training framework.

This chapter aims to assess the scope of India's gastronomic tourism post-COVID-19 and discuss the utilisation of vlogs to promote India as a gastronomic destination. First, the evolution of gastronomic tourism is reviewed. Next, opportunities and challenges associated with India's gastronomic offerings, both from international and domestic tourism perspectives, are discussed. Then, the role of vlogging to position and promote India as a gastronomic destination is established. The chapter suggests recommendations for the Ministry of Tourism, Government of India on utilising vlogging to promote gastronomic tourism.

Due to stern management policies and increased community attentiveness, sustainable supply chain management (SSCM) performs a vast component in endeavor operation and production management. Sustainable circular supplier selection (SCSS) and evaluation presented the environmental and social concerns in the fields of circular economy and sustainable supplier selection. Choosing the optimal SCSS is vital for organizations to persuade SSCM, as specified in various researches. Based on the subjectivity of human behavior, the selection of ideal SCSS often involves uncertain information, and the Pythagorean fuzzy sets (PFSs) have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the multi-criteria decision-making (MCDM) procedure. Here, a framework is developed to assess and establish suitable suppliers in the SSCM and the circular economy.

This paper introduced an extended framework using the evaluation based on distance from average solution (EDAS) with PFSs and implemented it to solve the SCSS in the manufacturing sector. Firstly, the PFSs to handle the uncertain information of decision experts (DEs) is employed. Secondly, a novel divergence measure and parametric score function for calculating the criteria weights are proposed. Thirdly, an extended decision-making approach, known as PF-EDAS, is introduced.

The outcomes and comparative discussion show that the developed method is efficient and capable of facilitating the DEs to choose desirable SCSS. Therefore, the proposed framework can be used by organizations to assess and establish suitable suppliers in the SCSS process in the circular economy.

Selecting the optimal sustainable circular supplier (SCS) in the manufacturing sector is important for organizations to persuade SSCM, as specified in various research. However, corresponding to the subjectivity of human behavior, the selection of the best SCS often involves uncertain information, and the PFSs have a huge capability to tackle strong vagueness, uncertainty and inaccuracy in the MCDM procedure. Hence, manufacturing companies' administrators can implement the developed method to assess and establish suitable suppliers in the SCSS process in the circular economy.

Supply chain (SC) environment is surrounded by risk variables. This issue is regarded as an emerging and strategic problem which must be resolved by SC executives. The ability to measuring green supplier’s performance and affecting risk variables to demonstrating effective suppliers list has a potential contribution to be investigated. This paper aims to develop a decision-making model to assess green suppliers under legislation and risk factors. This leads to fewer disruptions in managing the SC and its impact to further improvement. It also presents research concepts forming a new approach for identification, prediction and understating relationship of supply risk.

At primal stage, different risk factors that influence green suppliers’ performance are indicated and their relationship is analyzed using decision-making trial and evaluation laboratory (DEMATEL) method. At the same time, failure mode and effect analysis is used to determine risk rating of each supplier. Finally, the evaluation based on distance from average solution (EDAS) method ranks suppliers and several comparisons and analysis are performed to test the stability of the results. The approaches include comparison to technique for order performance by similarity to ideal solution, multi-attributive border approximation area comparison, Vlse Kriterijumska Optimizacija I Kompromisno Resenje and complex proportional assessment methods, followed by analysis of rank reversal, weight sensitivity analysis and effect of dynamic metrics.

A real-time case study on green supplier selection (GSS) problem of a reputed construction company of Spain has been presented to demonstrate the practical aspects of the proposed method. In practice, though organizations are aware of various risks from local and global suppliers, it is difficult to incorporate these risk factors for ranking the suppliers. This real-case application shows the evaluation and incorporation of risk factors into the supplier selection model.

The proposed multi-criteria decision model quantitatively aids managers in selecting green suppliers considering risk factors.

A new model has been developed to present a sound mathematical model for solving GSS problems which considers the interaction between the supplier selection risk factors by proposing an integrated analytical approach for selecting green suppliers strategically consisting of DEMATEL, FMEA and EDAS methods.