In fact, IoT is another big player implemented in a number of other industries including healthcare. Until recently, the objects of common use such as cars, watches, refrigerators and health-monitoring devices, did not usually produce or handle data and lacked internet connectivity. However, furnishing such objects with computer chips and sensors that enable data collection and transmission over internet has opened new avenues. The device technologies such as Radio Frequency IDentification (RFID) tags and readers, and Near Field Communication (NFC) devices, that can not only gather information but interact physically, are being increasingly used as the information and communication systems . This enables objects with RFID or NFC to communicate and function as a web of smart things. The analysis of data collected from these chips or sensors may reveal critical information that might be beneficial in improving lifestyle, establishing measures for energy conservation, improving transportation, and healthcare. In fact, IoT has become a rising movement in the field of healthcare. IoT devices create a continuous stream of data while monitoring the health of people (or patients) which makes these devices a major contributor to big data in healthcare. Such resources can interconnect various devices to provide a reliable, effective and smart healthcare service to the elderly and patients with a chronic illness .
AI has also been used to provide predictive capabilities to healthcare big data. For example, ML algorithms can convert the diagnostic system of medical images into automated decision-making. Though it is apparent that healthcare professionals may not be replaced by machines in the near future, yet AI can definitely assist physicians to make better clinical decisions or even replace human judgment in certain functional areas of healthcare.
In order to analyze the diversified medical data, healthcare domain, describes analytics in four categories: descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics refers for describing the current medical situations and commenting on that whereas diagnostic analysis explains reasons and factors behind occurrence of certain events, for example, choosing treatment option for a patient based on clustering and decision trees. Predictive analytics focuses on predictive ability of the future outcomes by determining trends and probabilities. These methods are mainly built up of machine leaning techniques and are helpful in the context of understanding complications that a patient can develop. Prescriptive analytics is to perform analysis to propose an action towards optimal decision making. For example, decision of avoiding a given treatment to the patient based on observed side effects and predicted complications. In order to improve performance of the current medical systems integration of big data into healthcare analytics can be a major factor; however, sophisticated strategies need to be developed. An architecture of best practices of different analytics in healthcare domain is required for integrating big data technologies to improve the outcomes. However, there are many challenges associated with the implementation of such strategies.
Quantum algorithms can speed-up the big data analysis exponentially . Some complex problems, believed to be unsolvable using conventional computing, can be solved by quantum approaches. For example, the current encryption techniques such as RSA, public-key (PK) and Data Encryption Standard (DES) which are thought to be impassable now would be irrelevant in future because quantum computers will quickly get through them . Quantum approaches can dramatically reduce the information required for big data analysis. For example, quantum theory can maximize the distinguishability between a multilayer network using a minimum number of layers . In addition, quantum approaches require a relatively small dataset to obtain a maximally sensitive data analysis compared to the conventional (machine-learning) techniques. Therefore, quantum approaches can drastically reduce the amount of computational power required to analyze big data. Even though, quantum computing is still in its infancy and presents many open challenges, it is being implemented for healthcare data.
By giving all people opportunities to develop the skills they will need to participate fully in the future workplace, we ought to create more inclusive and sustainable economies and societies where no one is left behind. Industry 4.0 is about creating a unique life-long education system that ensures a future-ready workforce. Universities with a tradition of educating and training the world's most competent designers, engineers, technology specialists, consultants, operations professionals, and data analysts are in an exciting era to tackle these challenges quickly and collaboratively.
While many educational organizations and individuals might still wonder how Industry 4.0 could affect the education system, some are implementing changes today and preparing for a future when artificial intelligence (AI) and cyber-physical systems can connect their business globally. In this study, we focus our discussion on the reskilling and upskilling of the future-ready workforce in the age of Industry 4.0 and beyond. The following sections cover several key elements that contribute to training a future-ready workforce. Section 2 provides background information about the top skills needed for Industry 4.0. Section 3 discusses reskilling and upskilling of the workforce in different parts of the world. In Section 4, a life-long learning framework offers opportunities to reskill and upskill a future workforce. Finally, Section Five provides conclusions.
Industry 4.0 is a significant transformation to the digitization of manufacturing and the creation of a cyber-physical system. I4.0 connects production and process technologies, integrates vertical and horizontal value chains, and digitalizes product and service offerings to pave the way for new production and economic value chains. This transition has an enormous impact on higher education which has a role of training talents, leading scientific innovation, disseminating knowledge, as well as preparing a future-ready workforce.
The World Economic Forum has published several reports on the future of jobs and top skills that will play significant roles in future technology advancement (Schwab & Samans, 2016; Schwab & Zahidi, 2020). The authors summarized the perspectives of strategy officers and chief human resources managers from leading global companies about the current shifts in required skills, and recruitment across industries. These reports analyze skills needed for the labor market and track the pace of changes. A quick rate of technology adoption signals that in-demand skills across jobs will change over the next five years or longer; therefore, skill gaps will continue to be significant.
In the next ten years, both manufacturing and service firms will have to adapt to or adopt Industry 4.0 principles and technologies to survive the competition. The vast majority of business leaders (94%) now expect employees to pick up new skills on the job (Whiting 2021). They believe that investing in the right people and the right skillsets today ensures a favorable position well into the future. Based on the literature, we discuss seven vital disruptive technologies that require significant skill upgrade for a future-ready workforce. These technology groups are far from comprehensive, but they can serve as a guideline for organizations to formulate their technology portfolio and invest in reskilling and upskilling their employees and staff.
In a digital era, technologies, such as computer systems, the Internet, and smart devices, play a fundamental role in everyday life. However, while we enjoy the convenience and efficiency provided by the new technologies, we face new risks and threats caused by using technology. In recent years, businesses in all industries and of all sizes have experienced the increased frequency, volume, and sophistication of cyber-attacks (Lu & Xu, 2018). For example, on May 7, 2021, an American oil supply system, Colonial Pipeline, suffered a ransomware cyberattack that impacted the computerized equipment that operates the pipeline.
The developed countries have made a substantial investment in advanced technology in the Industry 4.0 era. In a study of analyzing the similarities and differences of supports for the development and diffusion of robotics and AI in the United Kingdom (UK) and Norway, Lloyd and Payne (2019) considered country effects by exploring the role of institutions and social actors in shaping technological change in the two countries. Drawing upon interviews with technology experts, employer associations, and trade unions, they examined public policies support for the development and diffusion of robotics and AI, along with potential consequences for employment, work, and skills. Consequently, both UK and Norway have provided more funding for R&D, including increased resources for universities and research institutes to train, upskill, and reskill the future workforce.
China is rebalancing its economic structuring by moving toward high value-add innovative industries, such as robotics, AI, and semiconductor products. However, some employees are not able to keep up with the change. A recent study on the first job insights (Li et al., 2018) indicated that the average time in the first job for the generation born in the 1990s in China was 19 months; the employees who were born in the 1980s spent 43 months in their 1st jobs, and those who were born in the 1970s stayed on their 1st job for 51 months. The average time on the 1st job has decreased exponentially over the past three decades. However, many Chinese employers lack comprehensive training programs. At the same time, some Chinese companies regard reskilling and upskilling as an expense rather than an investment in their human resources. 153554b96e