Lr forex freedom extreme 4.0
This study offers practical insights and outcomes that will help researchers, decision-makers, and practitioners in unlocking the potential of I4. Introduction Industry 4. The I4. These technologies are helpful for autonomous and intelligent decision-making and integration of manufacturing processes through prediction, maintenance, fault rectification, and end-to-end control of operations in the intelligent factory Kang et al. In this pretext, India being one of the fastest developing countries and having understood the I4.
The soul objective remains the same, to promote the local industries to think global and act locally. Indeed, the Indian government is doing everything to facilitate the adoption of new technologies, which will help businesses, in the long run, to produce international quality products and services SAMARTH Udyog Bharat 4. The combined commitment by industry associations, policymakers, and the government has expedited the I4.
The industrial ecosystem has never been so conducive as it is today for the implementation of I4. Even though the government and industry associations have put in the best efforts, except few big businesses, most companies have not yet taken steps to adopt I4. Despite positive experiences and benefits harnessed by companies and strong political will, there is very little acceptance of the I4.
Past studies have found prominent barriers, like capital investment requirements and unclear cost—benefit analysis of the I4. Moreover, inadequate internet connectivity, insufficient data protection, and IT infrastructure converge into extreme challenges, which may create havoc if not attended to at the right time while adopting I4. Hence, there are high chances I4. While this is true, the fact remains that I4. Some research gaps identified by researchers are: 1 Minimal studies discussed the challenges and prospects of imbibing the I4.
As a repercussion of missing clarity about opportunities and challenges, many companies perceived I4. This condition has motivated researchers to forward this research agenda passionately. The research has considered the intervention of experts in the manufacturing sector as an add-on to demystify the complex relationship among I4. For becoming competitive and sustainable in the global market, the manufacturing industries must go beyond simply identifying barriers to I4.
So the urgent call necessitates devising the sustained research framework to examine and segregate identified prominent barriers in groups inhibitors using statistical approaches. Further, to derive the contextual interrelationships among these groups based on their driving and dependence power to address the research gap.
Hence, critically assessing and establishing interrelationships between these inhibitors is pertinent. This study has appropriately responded to all the earlier limitations while designing a properly fitting research methodology. Therefore, the research is a valuable contribution to the novel ideas. This will help to attain confidence among the manufacturing organizations while adopting I4.
The model intends to establish a pathway to developing an ecosystem that will promote the state-of-the-art ICT infrastructure, training, development facilities and customized yet progressive policies. In this context, the primary research objectives of this study are, RO1: To identify the crucial I4.
RO2: To converge the barriers into prominent inhibitors for the smooth implementation of I4. RO3: To perform an in-depth analysis of contextual interrelationships among these inhibitors based on their significance. RO4: To derive a framework and model for real-time decision-making by policymakers. This paper has five sections.
Section 2 has explored an exhaustive literature review. Results and discussions are elaborated in Sect. Section 5 highlights conclusions, managerial implications, and contributions to the new knowledge and recommends a pathway to counter post-COVID challenges in the manufacturing industry, and finally, presents future research recommendations. Literature review A systematic literature review is carried out to explore the theoretical background and multiple dimensions of I4.
This study has extensively used the electronic databases, i. The keywords used to reach pertinent literature relevant to the study are, i. Non-English language papers, editorials, and magazines have been omitted from the collected corpus. Finally, articles were identified through extensive screening, considering the theme, scope of the underline study, and relevance to the study objectives.
The review of these documents led to a sound understanding of the critical nature of impeding barriers hindering the I4. Industry 4. Another aspect of smart manufacturing is big data analytics. The analytics performed on the enormous data collected in real-time from machines improve processes and operations, decrease errors and defect costs, and offer opportunities to optimize resources, reduce waste, and rectify the problems beforehand Awan et al.
It has an incredible opportunity to render insights from maintenance practices, manufacturing operations, consumer dynamics, customer purchasing habits, new ways to minimize costs and facilitate more targeted decisions in manufacturing companies to satisfy customized consumer demand Ghobakhloo ; Wang et al. Because of a lack of clear directions and vision, several industries have been struggling to cope with I4.
In this line, some researchers have found that COVID has further worsened the condition, while others believe the pandemic has also bought some opportunities in the sustainable transformation of business functions Cohen ; Narayanamurthy and Tortorella Thus the substantial evidence around this compels us to believe COVID has expedited the digital revolution in society and industries.
The inspirations theme earlier has become the top agenda accepting the new transition as an only success mantra in the coming time assimilating AI applications, 3D printing, track and trace devices, etc.. The stand-still situation triggered by the coronavirus outbreak has forced manufacturing industries to devise alternate ways of doing business. Advanced intelligent automation is accepted as the most effective tool to acclimatize with new technologies, which unfortunately gave bad time to already hit the job market Sulkowski Another lesson learned is to design a fundamentally healthy industry ecosystem by imbibing agility, resilience, flexibility, automation, self-sustenance, and robustness in every industrial operation Ivanov and Dolgui However, the digitalization acceptance in just two months is equivalent to two years in a normal course, leading to the fast-tracking application of I4.
Definitely, first-mover companies have gained the absolute advantage, now leading the respective sectors. Thus, time is treated as an integer value and the days of extreme loss could occur upon a sequence of indivisible time units. The SEP-POT model can capture the self-exciting nature of extreme event arrival, and hence, the strong clustering of large drops in financial prices.
The triggering effect of recent events on the probability of extreme losses is specified using a discrete weighting function based on the at-zero-truncated Negative Binomial NegBin distribution. The serial correlation in the magnitudes of extreme losses is also taken into consideration using the generalized Pareto distribution enriched with the time-varying scale parameter.
In this way, recent events affect the size of extreme losses more than distant events. The accuracy of SEP-POT value at risk VaR forecasts is backtested on seven stock indexes and three currency pairs and is compared with existing well-recognized methods. The results remain in favor of our model, showing that it constitutes a real alternative for forecasting extreme quantiles of financial returns.
Keywords: forecasting market risk, value at risk, extreme returns, peaks over threshold, self-exciting point process, discrete-time models, generalized Pareto distribution 1. Introduction Forecasting extreme losses is at the forefront of quantitative management of market risk. More and more statistical methods are being released with the objective of adequately monitoring and predicting large downturns in financial markets, which is a safeguard against severe price swings and helps to manage regulatory capital requirements.
We aim to contribute to this strand of research by proposing a new self-exciting probability peaks-over-threshold SEP-POT model with the prerogative of being adequately tailored to the dynamics of real-world extreme events in financial markets. Our model can capture the strong clustering phenomenon and the discreteness of times between the days of extreme events.
Market risk models that account for catastrophic movements in security prices are the focal point in the practice of risk management, which can clearly be demonstrated by repetitive downturns in financial markets. The truth of this statement cannot be more convincing nowadays as global equity markets have very recently reacted to the COVID pandemic with a plunge in prices and extreme volatility.
The coronavirus fear resulted in panic sell-outs of equities and the U. For example, the German bluechip index DAX 30 plunged The COVID aftermath is a real example that highlights the strong clustering property of extreme losses. One of the most well-recognized and widely used measures of exposure to market risk is the value at risk VaR.
VaR summarizes the quantile of the gains and losses distribution and can be intuitively understood as the worst expected loss over a given investment horizon at a given level of confidence [ 1 ]. VaR can be derived as a quantile of an unconditional distribution of financial returns, but it is much more advisable to model VaR as the conditional quantile, so that it can capture the strongly time-varying nature of volatility inherent to financial markets.
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