Search results

The purpose of this article is to explore the new concept of TQM 4.0 as a way of adapting quality management (QM) in Industry 4.0 (I4.0), guiding industries to this new phase, which has generated adaptations in numerous areas, one of which is QM and human resources.

A systematic review of the literature was carried out. Methodi Ordinatio was applied to build the portfolio of articles with scientific relevance, which is the source of data collections and content analysis. To help out in the analysis, NVivo 12 and VOSviewer software programs were used.

The results demonstrate that when adapting the QM to the technologies of I4.0, the result is an ecosystem that supports the integration between technology, quality and people in the industrial scenario.

This article presents a systematic review of the literature, but without delving into specific issues such as the different industrial sectors and the culture of countries in which industries may be inserted, for example, which characterizes a limitation of this research.

This study provides an ecosystem model that can guide future research, regarding the concept of TQM 4.0, in addition to pointing out some ways of combining technologies, quality and people in the industrial context.

This is one of the first articles to employ a systematic review of the literature using Methodi Ordinatio to build a bibliographic panorama on the intertwining of the themes total QM (TQM) and I4.0, focusing on the emerging concept of TQM 4.0.

Smart scenarios related to industries or cities, characterized by intensive technology transfer and use of innovative and disruptive technologies, have been in the spotlight either on academic or organizational discussions, especially those with a technocentric focus. Among these technologies, artificial intelligence (AI) emerges as the most challenging one due to its complexity. Therefore, this chapter aims to address AI, in particular the future of the labor market, exploring the challenges regarding the skills required in the context of AI technology, addressing its uses, challenges, and benefits. In order to achieve this goal, a systematic review was conducted on the extant literature using the methodology Methodi Ordinatio. The results show that the current literature is gradually changing from a more critical and negative view of AI to a more optimistic one, with more positive approaches and expectations regarding its benefits. As practical implications, the findings can be used as a guide for governments to develop strategies aiming to deal with upcoming challenges, especially regarding future jobs and employability.

The purpose of this paper is to assess to what extent intraday data can explain and predict long-term memory.

This article analysed the presence of long-memory volatility in five Asian equity indices, namely, SENSEX, CNIA, NIKKEI225, KO11 and FTSTI, using five-min intraday return series from 05 January 2015 to 06 August 2015 using two approaches, i.e. conditional volatility and realized volatility, for forecasting long-term memory. It employs conditional-generalized autoregressive conditional heteroscedasticity (GARCH), i.e. autoregressive fractionally integrated moving average (ARFIMA)-FIGARCH model and ARFIMA-asymmetric power autoregressive conditional heteroscedasticity (APARCH) models, and unconditional volatility realized volatility using autoregressive integrated moving average (ARIMA) and ARFIMA in-sample forecasting models to estimate the persistence of the long-term memory.

Given the GARCH framework, the ARFIMA-APARCH long-memory model gave the better forecast results signifying the importance of accounting for asymmetric information when modelling volatility in a financial market. Using the unconditional realized volatility results from the Singapore and Indian markets, the ARIMA model outperforms the ARFIMA model in terms of forecast performance and provides reasonable forecasts.

The issue of long memory has important implications for the theory and practice of finance. It is well-known that accurate volatility forecasts are important in a variety of settings including option and other derivatives pricing, portfolio and risk management.

It could be said that using long-memory augmented models would give better results to investors so that they could analyse the market trends in returns and volatility in a more accurate manner and reach at an informed decision. This is useful to minimize the risks.

This research enhances the literature by estimating the influence of intraday variables on daily volatility. This is one of very few studies that uses conditional GARCH framework models and unconditional realized volatility estimates for forecasting long-term memory. The authors find that the methods complement each other.