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Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

This paper investigates how companies become resilient to supply chain (SC) piracy through using transactional and relational governance mechanisms to develop strategies effective in environments characterized by weak regulative institutions and mistrust.

This study developed case studies of nine large manufacturers with operations in Brazil.

The companies employed transactional and relational governance mechanisms to learn from past incidents, anticipate, and respond to the threat of SC piracy, becoming more resilient over time. Transactional governance mechanisms reduced risk triggers through technology, while relational governance mechanisms enhanced trust between SC and non-SC members, allowing the members to build social capital.

The authors provide practical guidance for managers and policymakers in developing risk management strategies based on technology and collaboration to reduce SC piracy in environments characterized by mistrust.

SC piracy is a serious problem for global operations and SCs in many low-cost manufacturing locations. Besides the cost and service level consequences, the authors also highlight worker safety consequences, including the potential for kidnapping, psychological trauma, injuries, and death.

This study focuses on the little-researched topic of SC piracy. The authors examine the negative effects of a weak institutional environment, while most prior research focuses on the positive effects of a strong institutional environment. The authors position transactional and relational governance mechanisms as essential elements of SC risk resilience.

Lean manufacturing (LM) concepts have been widely adopted in diverse industrial sectors. However, no literature review focusing on case studies describing LM implementation is available. Case studies represent the actual implementation and provide secondary data for further analysis. This study aims to review the same to understand the pathways of LM implementation. In addition, it aims to analyse other related review questions, such as how implementing LM impacts manufacturing capabilities and the maturity level of manufacturing organisations that implemented LM, to name a few.

A literature review of case studies that discuss the implementation of LM during the last decade (from 2010 to 2020) is carried out. These studies were synthesised, and content analyses were performed to reveal critical insights.

The implementation pattern of LM significantly varies across manufacturing organisations. The findings show simultaneous improvement in manufacturing capabilities. Towards the end of the last decade, organisations implemented LM with radio frequency identification, e-kanban, simulation, etc.

Reviewing the case studies documenting LM implementation to comprehend the various nuances is a novel attempt. Furthermore, potential future research directions are identified for advancing the research in the domain of LM.

President Trump's tenure was accompanied by a series of protectionist measures that intended to reinvigorate US-based production and make manufacturing supply chains more “local”. Amidst these increasing institutional pressures to localise, and the business uncertainty that ensued, this study investigates the extent to which manufacturers reconfigured their supply bases.

Bloomberg's Supply Chain Function (SPLC) is used to manually extract data about the direct suppliers of 30 of the largest American manufacturers in terms of market capitalisation. Overall, the raw data comprise 20,100 quantified buyer–supplier relationships that span seven years (2014–2020). The supply base dimensions of spatial complexity, spend concentration and buyer dependence are operationalised by applying appropriate aggregation functions on the raw data. The final dataset is a firm-year panel that is analysed using a random effect (RE) modelling approach and the conditional means of the three dimensions are plotted over time.

Over the studied timeframe, American manufacturers progressively reduced the spatial complexity of their supply bases and concentrated their purchase spend to fewer suppliers. Contrary to the aims of governmental policies, American manufacturers increased their dependence on foreign suppliers and reduced their dependence on local ones.

The research provides insights into the dynamics of manufacturing supply chains as they adapt to shifting institutional demands.