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The purpose of this study is to empirically analyse the fiscal revenue implications for oil-dependent African countries in the face of low-carbon energy transition (LET).

The study combined the novel fully modified ordinary least squares, dynamic ordinary least squares and canonical cointegrating regressions estimators to analyse secondary data between 1990 and 2020 for the three major oil-dependent African Countries (Algeria, Angola and Nigeria).

The result shows that LET reduces oil revenue and non-revenue for specific countries (Algeria, Angola and Nigeria) and the panel, suggesting that low-carbon energy transiting is lowering the fiscal revenue of oil-dependent African nations.

The seeming weakness of this study is its inability to broaden the scope to include all oil-producing African economies. However, since the study selected Africa’s top three oil-producing states, the sample can serve as a model for others with lesser crude oil outputs.

Oil-dependent African countries must urgently engage in sincere economic diversification in sectors like industry and manufacturing, the service sector and human capital development to promote economic transformation that will enhance fiscal revenue.

With the pace of energy transition towards low-carbon energy, it is not business as usual for oil-rich African countries (Algeria, Angola and Nigeria) due to fluctuating demand and price. As a result, it becomes worthy to examine how the transition is affecting oil-dependent economies in Africa. Also, this study’s method is unique as it has not been used in a similar study for Africa.

The purpose of the study is to examine the obstacle in doing business and determinants of credit adoption by the textile enterprises in India.

The study is based on World Bank’s Enterprises Survey, there are 571 enterprises involved in textile business. The enterprises survey has response on wide range of business obstacles which are categorized under three broad categories, namely, access to resource, business regulations and market externalities. Chi-square test and analysis of variance (ANOVA) have been used to examine the significant difference among firm’s profile and perceived business obstacles across the firm size. Furthermore, binary logistic regression model has been applied to explore the determinants of credit adoption by textile enterprises.

A statistically significant difference has been found in size of firms and legal status nature of establishment, gender of top manager, main product market and credit adoption from financial institutions. Majority of small- and medium-sized enterprises (SMEs) are sole proprietorship firm while large enterprises are limited partnership firms. Similarly, large enterprises have relatively more female as a top manager and international market for their product. ANOVA reveals equal degree of obstacles in doing textile business across the firm size. The logistic regression coefficient and marginal effects reveal that firm size, main market,gender of owner, number of establishment in the firms positive and significantly affects the credit adoption by 3 textile enterprises.

The study has some policy implications for various stakeholders such as textile business managers and promoters, government, investors and bankers for entrepreneurship development in textile sector. The study suggests that the government should incentivize small- and medium-sized businesses to increase their exports. The results show that despite government efforts to finance SMEs, fewer SMEs are receiving both short- and long-term credit. To help SMEs in the textile industry overcome financial difficulties and expand their main product market to both domestic and international levels, a soft loan should be provided based on the characteristics of textile enterprises.

The present study suggests the evidence-based understanding of textile business environment. The value and uniqueness of this study is to explore an ease of business textile sector using comprehensive enterprises survey data of World Bank.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.

Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating factors of Big Data operations in managing information and trust among several operations of SMSC. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from the internet of Things (IoT) devices, group behavior parameters, social networks and ecosystem framework.

Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management and Communication) are the constructs that this research first conceptualizes, defines and then evaluates in studying Big Data Analytics based operations in SMSC considering best worst method (BWM) technique.

To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.

The research outcome revealed that to handle operations efficiently a 360-degree view of suppliers, distributors and logistics providers' information and trust is essential.

In the Post-COVID scenario, the supply chain practitioners may use the supply chain partner's data to develop resiliency and achieve sustainability.

The unique value that this study adds to the research is, it links the data, trust and sustainability aspects of the Manufacturing Supply Chain (MSC).