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This paper investigates the impact of a monetary policy shock on the production of a sample of 312 industries in manufacturing, mining and utilities in the United States using a factor-augmented vector autoregression (FAVAR) model.

The authors use a FAVAR model that builds on Bernanke et al. (2005) and Boivin et al. (2009). The main assumption in this model is that the dynamics of a large set of macro variables are captured by some observed and unobserved common factors. The unobserved factors are extracted from a large set of macroeconomic data. The key advantage of using this model is that it allows extracting the impulse responses of a wide range of macroeconomic variables to structural shocks in the federal funds rate.

The results indicate that industries exhibit differential responses to an unanticipated monetary policy tightening. In general, manufacturing industries appear to be more sensitive compared to mining, and utility industries and durable manufacturing industries are found to be more sensitive than those within nondurable and other manufacturing industries to a monetary policy shock. While all industries respond to the policy shock, most of the responses are reversed between 12 and 22 months.

The implication of our results is that monetary policy can be used to impact most US industries for four years and beyond. The existence of disparate responses across industries underscores the difficulty of implementing a monetary policy that will generate the same impact across industries. As the effects of the policy are distinct, policymakers may want to attend to the unique impacts and implement industry-specific policy.

The study is important in the context of the current challenges in the US economy caused by the spread of coronavirus. For example, to tackle the current pandemic, the researchers are trying to come up with cures for COVID-19. A considerable response of the chemical industry that provides materials to pharmaceutical and medicine manufacturing to the monetary policy shock implies that an expansionary monetary policy may facilitate an invention and adequate supply of the cure later on. The same policy may not effectively stimulate production in apparel or leather product industries that are being hard hit by the pandemic.

The study contributes to the literature in broadly two aspects. First, to the best of our knowledge, this is the first paper that investigates the impact of a monetary policy shock on a sample of 312 industries in manufacturing, mining and utilities in the US. Second, to identify structural shocks and investigate the effects of monetary policy shocks on economic activity, the authors diverge from the literature's traditional approach, i.e. the vector autoregression (VAR) method and use a FAVAR method. The FAVAR provides a comprehensive description of the impact of a monetary policy innovation on different industries.

Integrating sustainability strategies with business processes is the most challenging task for industry professionals due to the lack of a proper understanding of sustainability concepts. At the same time, a lack of proper guidance restricts them from pursuing such activities. As far as the aspects of implementation are concerned, it is very tough to analyse and pick up key points to start with. The purpose of this paper is to utilize a text mining approach to analyse qualitative data and identify the critical issues for implementing sustainability in the manufacturing sector by focussing on turning processes based on the survey responses of researchers and industry professionals.

An integrated method employing principal component analysis (PCA) and the k-means clustering algorithm has been applied to extract useful information from a set of various suggestions provided by both the groups surveyed. The textual data has also been visualized using word clouds and, thus, it has been compared with the results of the text mining approach.

The results of the study indicate the importance of the role of government organizations and the need for a skilled workforce, which are crucial for enhancing aspects of sustainability in the manufacturing sector, as supported by both researchers and industry professionals. Besides this, researchers have highlighted the need to focus more on environmentally related issues, whereas industry professionals have raised performance-related issues.

The findings of the study present the important concerns of both the groups towards sustainability initiatives and, thus, will help to enhance the understanding of the underlying possibilities of negotiating jointly to enhance the performance of machining processes.

The novelty of this paper lies in its identification of important initiatives that are having a direct impact on the sustainable aspects of the machining process, based on the views of researchers and industry professionals.

Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.

Standard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.

Results show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.

This paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.

The development of corporate social responsibility (CSR) in the construction sector is slow, thereby leaving many opportunities for further development. To enable operators in the construction sector to effectively capitalise on the opportunities to promote the development of CSR in the sector, this study employs the practice viewpoint to take the stock of CSR activities in the sector. The purpose of this paper is to reveal the state of CSR practice in the construction sector. The study also draws from the development of CSR in the manufacturing, mining and banking sectors to inform the state of CSR practice in the construction sector.

This study carries out a systematic literature review of 56 journal publications that were published between the year 2000 and 2016. The deductive coding of the publications was done to identify four themes of CSR research that constitute the practice view of the state of CSR in the construction sector.

The implementation of CSR is the major emphasis in the state of CSR practice in the construction sector. The implementation of CSR is wrapped in the perception of operators about CSR potentials, dimensions of CSR implemented, strategies for implementation and the effects of the implemented CSR practices on performance. The sector characteristics and organisational structure are attributes for comparing the CSR practices between the construction sector and the manufacturing, mining and banking sectors.

This study provides a researchers’ view of the state of CSR in the construction sector. Additionally, the study draws from the development of CSR in the manufacturing, mining and banking sectors to inform the state of CSR practice in the construction sector.

Since works-in-process (WIPs) are highly vulnerable to defects because of the variety and complexity of manufacturing processes, the purpose of this paper is to describe how to utilize existing analytics techniques to reduce defects, improve production processes, and reduce the cost of operations.

Three alternatives for diagnosing causes of defects and variations in the production process are presented in order to answer the following research question: “What are the most important factors to be included in prognostic analysis to prevent defects?”

The key findings for the proposed alternatives help explain the characteristics of defects that have a great impact on manufacturing yield and the quality of products. Consequently, any corrective action and preventive maintenance addressing the common causes of defects and variations in the process can be regularly evaluated and monitored.

Although the focus of this study is on improving shop-floor operations by reducing defects, further experimentation with business analytics in other areas such as machine utilization and maintenance, process control, and safety evaluation remains to be done.

This study has been validated with several scenarios in a manufacturing company, and the results demonstrate the practical validity of the approach, which is equally applicable to other manufacturing sub-sectors.

This study is different from the others by providing alternatives for diagnosing the root causes of defects. Control charts, costs of defects, and clustering-based defect prediction scores are utilized to reduce defects. Additionally, the key contribution of this study is to demonstrate different methods for understanding WIP behaviors and identifying any irregularities in the production process.

Nicholas Georgescu‐Roegen’s analysis of the factory system is used to show how an expanded market makes possible increasing returns in manufacturing, which can in turn lead to the kind of cumulative growth process envisioned by Allyn Young. The analysis is then extended to take into account Nicholas Kaldor’s admonition to pay attention to the fact that manufacturing is a process that uses inputs that are the products of nature. It is found that the inability to use the factory system in agriculture limits the extent of increasing returns in manufacturing. Increasing returns in manufacturing and an accompanying pattern of cumulative growth require a continuing infusion of exogenous technological innovation in agriculture.

This study aims to find and evaluate the impact of COVID-19 in India’s mining activities. From the literature survey and input from experts, 14 important factors and ten green recovery strategies are identified. These 14 factors and 10 strategies are examined in a real industrial environment.

In this research study, fuzzy-complex proportional assessment is used to compare the identified strategies with the selected factors. Based on the outcome, this study identifies the optimum strategy that could assist the mining industry in handling both COVID-19 and sustainability issues.

The study’s outcome reveals that a large group of workers, collapse of demand and disruption, and suffered contractual workers are the top three factors that need to be considered regarding COVID-19 and sustainability issues. Effective strategies for eco-innovation practices, an emphasis on health and safety, and environmental awareness, education and training are the three primary strategies to be implemented for the greatest impact.

This research study is the first of its kind to coordinate both issues of COVID-19 and sustainability. In this regard, this study sheds a timely light on the precautions that need to be taken in tackling COVID-19 and sustainability issues. This study’s outcome will enhance the managerial capability in developing robust and effective strategies for handling difficult situations.

The purpose of this paper is to investigate the empirical relationships between changes in OECD output, commodity prices, the real exchange rate, real money supply, unit labour costs and manufacturing in South Africa. In particular, to test a version of the Dutch disease argument that increases in the prices of South Africa’s main commodity exports have had a negative effect on domestic manufacturing against the alternative hypothesis that there is a positive relationship between such changes in commodity prices and domestic manufacturing output.

Construction of a model including real manufacturing output in South Africa as the dependent variable and the following independent variables: OECD output, an international real metals price index, a real effective exchange rate index, real M3 money supply and manufacturing unit labour costs. The time series sample data comprise 124 quarterly observations for the period 1980-2010. The model equation was tested and estimated using a Johansen cointegration approach.

The main findings are: OECD output is the single most important determinant of domestic manufacturing output; while the real exchange rate has the predicted negative sign, rising commodity prices are associated with increases rather than decreases in domestic manufacturing and; large increases in unit labour costs since the early 1980s have dragged down manufacturing over the sample period.

The finding of a positive relationship between commodity prices and domestic manufacturing means that the Dutch disease argument must be revised when applied to South Africa. While rising commodity prices may lead to a negative exchange rate effect on manufacturing competitiveness, this is more than offset by the positive growth effects associated with upswings in the commodity price cycle.

Data mining (DM) is used to improve the performance of manufacturing quality control activity, and reduces productivity loss. The purpose of this paper is to discover useful hidden patterns from fabric data to reduce the amount of defective goods and increase overall quality.

This research examines the improvement of manufacturing process via DM techniques. The paper explores the use of different preprocessing and DM techniques (rough sets theory, attribute relevance analysis, anomaly detection analysis, decision trees and rule induction) in carpet manufacturing as the real world application problem. SPSS Clementine Programme, Rosetta Toolkit, ASP (Active Server Pages) and VBScript programming language are used.

The most important variables of attributes that are effective in product quality are determined. A decision tree (DT) and decision rules are generated. Therefore, the faults in the process are detected. An on‐line programme is generated and the model's results are used to ensure the prevention of faulty products.

In time, this model will lose its validity. Therefore, it must be redeveloped periodically.

This study's productivity can be increased especially with the help of artificial intelligence technology. This research can also be applied to different industries.

The size and complexity of data make extraction difficult. Attribute relevance analysis is proposed for the selection of the attribute variables. The knowledge discovery in databases process is used. In addition, the system can be followed on‐line with this interactive ability.

The purpose of this paper is to interpret current global events to extrapolate the issues of twenty-first century for consideration by African mining policy and decision-makers.

The high-level mining issues are identified to assess what lies ahead for the twenty-first century. Some of these require innovation, called beacons for twenty-first-century mining in this paper, so that decision-makers can consider policy instruments and management strategies to craft a more desirable future for governments and companies, without affecting other stakeholders negatively.

It is proposed that African mining should consider three cross-cutting elements as subsets for the existing policy themes and management decisions, namely, broad benefit, mine sustainability and business improvement. Digital technologies have the potential to significantly support the three elements for fast-tracking Africa’s sustainable economic development.

Although the findings can be applied to most of the developing world, the focus of this paper is on the African mining industry.

Practical considerations for policymakers in Africa.

This paper includes novel/original policy considerations that have the potential to become cross-cutting elements for the existing policy themes of the Africa Mining Vision.