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Drawing on information processing view (IPV) theory, the objective of this study is to explore big data analytics (BDA) in pharmaceutical supply chain (PSC) for better business intelligence. Supply chain operations reference (SCOR) model is used to identify and discuss the likely benefits of BDA adoption in five processes: plan, source, make, deliver and return.

Semi-structured interviews with managers in a triad comprising pharmaceutical manufacturers, wholesalers/distributors and public hospital pharmacies were undertaken. NVivo software was used for thematic data analysis.

The findings revealed that BDA capability would be more practical and helpful in planning, delivery and return processes within PSC. Sourcing and making processes are perceived to be less beneficial.

The study informs managers about the strategic role of BDA capabilities in SCOR processes for improved business intelligence.

Adoption of BDA in SCOR processes within PSC is a step towards resolving the challenges of drug shortages, counterfeiting and inventory optimisation through timely decision. Despite its innumerable benefits of BDA, Australian PSC is far behind in BDA investment. The study advances the IPV theory by illustrating and strengthening the fact that data sharing and analytics can generate real-time business intelligence helping in better health care support through BDA-enabled PSC.

The pharmaceutical industry faces multiple risks that adversely affect its performance. Within these risks, some dependencies have been observed, which help in streamlining the mitigation efforts. Therefore, the present work identifies and categorizes various risks/sub-risks in cause–effect groups, considering uncertainty in the decision-making process.

An extensive literature review and experts' opinions were utilized to identify and finalize the risks faced by the pharmaceutical industry. For further analysis, data collection was done using a questionnaire focusing on finalized risks. Based on the data, the causal relation under uncertainty between various risks/sub-risks was identified using a multi-criteria decision making (MCDM) technique, i.e. intuitionistic fuzzy DEMATEL, in a pairwise manner.

The results show that the three most prominent risk categories are operational, demand/customer/market and financial. Also, out of the seven main risks, only supplier and operational are categorized within the effect group and the rest, i.e. financial, demand, logistics, political and technology within the cause group. The sub-risks within each category have also been categorized into cause–effect groups. The mitigation of cause group risks will help in economize the financial resources and improve the performance and resilience of the industry.

There is insufficient research on identifying the causality among the pharmaceutical industry risks. Additionally, an extensive discussion on the identified cause–effect groups is also missing in the literature. Therefore, in this work, efforts have been made to determine the prominent risks for the Indian pharmaceutical industry that will be helpful for channelizing the resources to mitigate risks for a resilient industry.

The recent pandemic caused by coronavirus disease 2019 (COVID-19) has significantly impacted the operational performances of pharmaceutical supply chains (SCs), especially in emerging economies that are critically vulnerable due to their inadequate resources. Finding the possible barriers that continue to impede the sustainable performance of SCs in the post-COVID-19 era has become essential. This study aims to investigate and analyze the barriers to achieving sustainability in the pharmaceutical SC of an emerging economy in a bid to help decision-makers recognize the most influential barriers.

To achieve the goals, two decision-making tools are integrated to analyze the most critical barriers: interpretive structural modeling (ISM) and the matrix of cross-impact multiplications applied to classification (MICMAC). In contrast to other multi-criteria decision-making (MCDM) approaches, ISM develops a hierarchical decision tool for decision-makers and cluster analysis of the barriers using the MICMAC method based on their driving and dependency powers.

The findings reveal that the major barriers are in a four-level hierarchical relationship where “Insufficient SC strategic plans to ensure agility during crisis” acts as the most critical barrier, followed by “Poor information structure among SC contributors,” and “Inadequate risk management policy under pandemic.” Finally, the MICMAC analysis validates the findings from the ISM approach.

This study provides meaningful insights into barriers to achieving sustainability in pharmaceutical SCs in the post-COVID-19 era. The study can help pharmaceutical SC practitioners to better understand what can go wrong in post-COVID-19, and develop actionable strategies to ensure sustainability and resilience in practitioners' SCs.

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 choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

The price of vegetables is not considered as the government sets the base price for the vegetables.

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.

The relevance of analytics to the healthcare supply chain is increasing with emerging trends and technologies. This study examines how analytics are used in the healthcare supply chain in the “new normal” environment.

A systematic literature review was conducted by extracting research articles related to analytics in the healthcare supply chain from Scopus. The author used a hybrid review approach that combines bibliometric analysis with a theories, contexts, characteristics, and methodology (TCCM) framework-based review to identify various themes of analytics in the healthcare supply chain.

The hybrid review strategy yielded results that focus on prevalent theories, contexts, characteristics, and methodologies in the field of healthcare supply chain analytics. Future research should explore the resulting antecedents, decision-making processes and outcomes (ADO) framework, which integrates technological, economic, and societal concerns and outcomes. Future research agendas could also seek to apply theoretical perspectives in the field of analytics in the healthcare supply chain.

The result of a review of selected studies adds to the current body of work and contributes to the growth of research in the field of analytics in the healthcare supply chain. It also provides new directions to healthcare supply chain managers and academic scholars.

This paper uses financial report information to analyze the accounting results of the COVID-19 vaccine development for Johnson & Johnson (J&J). This paper also uses stock price information to analyze the market reactions to the COVID-19 vaccine development and the state of clinical trials for J&J.

This instructional case investigates the interaction between J&J and the COVID-19 vaccine. This paper uses information from financial reports to analyze the accounting results of the COVID-19 vaccine development for J&J. This paper also uses stock price information to analyze the market’s reactions to the COVID-19 vaccine development and the state of clinical trials for J&J.

This case has been used in both undergraduate and graduate levels to highlight the application of accounting theories to practice and improve the understanding of financial statements, especially when Covid-19 has affected the global economy. Under this new context, students could explore new ideas from accounting aspect.

The case aims to investigate the interaction between J&J as a pharmaceutical company and COVID-19. It provides a context in which to discuss the consequences of COVID-19 vaccines from several financial perspectives, such as stock prices, accounting policies, earnings and cash flows:

LO1: Understand the responses of stakeholders to J&J’s COVID-19 vaccines.

LO2: Understand the accounting policies that J&J and its competitors follow regarding COVID-19 vaccines related to revenues, R&D expenditures and government funds.

LO3: Apply Ball and Brown’s theory to the impact of COVID-19 vaccine development on earnings quality of J&J and its competitors.

LO4: Assess the importance of COVID-19 vaccines in management decision-making through dividend policy and management compensation structure.

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.

The purpose of this study is to define and develop a new technological development path for latecomer firms in developing countries.

An analytical framework for development based on the technological capability (TC) dimensions is developed and examined in the drilling sector. Since the process of TC accumulation is dynamic, the case study approach is the best method for an exploratory theory-building study. Through a comparative case study of two Iranian drilling contractors, a new path for the technological development of latecomer oil service companies is proposed.

The study of two cases indicates that despite having similar scope and levels of TC, one of them demonstrated superior technical performance. To address this difference, the concept of operational efficiency is introduced which is considered the outcome of increasing the depth of TC.

Although upgrading the level of technological and innovation capability is an important path for technological development, latecomers that suffer from various disadvantages can perform their routine activities with superior performance and develop through their basic operational/production capabilities. Also, specialized indicators designed for assessing the level and depth of TC in the drilling industry have important insights for evaluating the technological and competitive position of oil service companies.

To the best of the author’s knowledge, this study takes the first step in defining and elaborating on the concept of depth of TC as a development path for latecomers. It also introduced a novel approach to the global operational/production efficiency frontier as a target for their catch-up.

The rapid growth of emerging market firms (EMFs) has been a topic of interest for the past two decades, especially in China. However, few studies have discussed how and why EMFs can impel the upgrading of their capabilities to quickly win competitive advantages in the global market. In this context, the purpose of this paper is to unravel the implausible upgrading phenomenon from the perspective of technological proximity.

This paper adopts a single case study, specifically that of a leading Chinese e-bike firm, with a special focus on the dynamic nature of the capability upgrading process and underlying mechanisms.

The results show that taking advantage of technological proximity is an important way for EMFs to climb the ladder of capability upgrading. The stage-based process reveals how capability upgrading is achieved through elaborate actions related to technological proximity. Furthermore, this study finds three learning mechanisms behind the technological proximity, which enable firms to successfully upgrade to higher levels of capabilities. In particular, the trigger role played by contextual conditions in guiding firms' capability upgrading is highlighted and characterized.

This study enriches traditional capability upgrading literature from a technological proximity perspective, especially the traditional static upgrading research related to EMFs. The authors also contribute to the conceptualization of technological proximity. However, the research setting is China's e-bike industry; therefore, the study's generalizability to other emerging markets and industries may be limited.

The results show that it is important to recognize the value of the transfer and sharing of technology between proximal industries for local governments. Also, appropriate policies should be developed to break down the technology barriers between these industries. Moreover, rather than catching up with the superior technologies of multinational corporations in advanced countries, focusing on products with high technological proximity in local or regional areas may be more helpful for EMFs' upgrading.

This paper investigates the capability upgrading process and mechanisms in EMFs, particularly with respect to the role played by technological proximity.