Critical Human Skills Requirements for the Digital Revolution

Dr Jolanda G. Tromp, Director Center for Visualization & Simulation, Duy Tan University

Dr Jolanda G. Tromp, Director Center for Visualization & Simulation, Duy Tan University

Adoption of Industry 4.0 technologies powered by AI and IoT (AIIoT) will involve transformational changes in the entire value chain and supply chain for manufacturing processes. From the human tasks, to the production of goods and the provision of services, disruptive change will affect how we do business,work and socialize. It is generally referred to as the“Fourth Industrial Revolution”, based on the exponential growth in new AIIoT application areas. Social innovation aimed at finding AIIoT solutions for pressing human and planetary problems, is a worldwide grassroots startup movement in many of the major cities and online, via hackathons and other design challenges.

AIIoT based configurations of new, smaller, more affordable networked sensors that communicate with all other sensors and processes in the supply chain. Autonomous robots on the ground and air-borne (drones) perform supply chain tasks with little to no human intervention or interaction. Human software engineers and human operators apply AI and Machine Learning (ML) to build and control these configurations via a network of communication-capable components. They have to decide how to transform the collected data into a single format for real time data analysis in data streaming business analytics systems. Since 2018, data science skills shortages are present in almost every country and large city in the world. Worldwide there has been a 29 percent increase in demand for data scientists since 2013 and is currently at a 344 percent increase. By the end of 2019, it is predicted that there will be about 26 million software developers, and this number is predicted to grow to more than 27 million by 2023. Due to the digital transformation of industry, we will lose an estimated 5 million workplaces. All companies, governments and countries worldwide will be significantly impacted by this; unprepared and unable to respond to these changes. The number of ML engineers, data scientists and big data engineers has grown by 650 percent since 2012. The global skilled worker's shortage is predicted to be more than 85 million by 2030.

The configurations and implementation of the networked sensors and the data analytics for business intelligence need to be tailor-made to the requirements of each representative human end-user group and the entire value chain and supply chain. This creates many diverse use-cases, which complicates the design process and challenges the designer-teams. The ML optimized value chain and supply chain configurations are blueprints for the best configurations. To respond rapidly to opimization reconfiguration requests, configurations need to be designed in a modular and flexible manner. All digitized supply chain processes and machines can be accessed, repaired and replaced by other machines, while being supervised,controlled and automated by the human engineers and operators.

Extended Realities (XR); Virtual Reality (VR) and Augmented Reality (AR), are tools to visualize the value chain and supply chain, displaying the virtual twins of supply chain machinery and processes and displaying the big data generated by the supply chain, in an interactive 3D world. New configurations of production processes, products and services can be visualized and controls can be mapped from the virtual representation to the local and remote physical machines. XR can also be used to deliver supply chain /knowledge and instructions, and collaborate with physically remote colleagues, objects and processes in these cyber-physical systems.

This computer-generated 3D mirror of the real world makes real-world objects visible in the virtual world by dynamically connecting via auto- IDs on the machines and updating the real-time visualizations of processes and data representations from the real world in the VR world, as events unfold over time. This creates an extended continuously updated network and visualization of data from the Operational Technologies (OT) and the Information Technologies (IT), converged into a system of physical components and cyber-twins;the 3D computer generated components interconnected with each other through “smart” information technology platforms. The raw IoT data output from these components is gathered, analyzed and visualized in 3D virtual space along with the virtual twin. This way the machines and machine interoperability throughout the value chain can be accessed in XR. This helps in reducing development costs and time.

In VR and AR, machine configurations can be tested before physical constructions start. Robots, chatbots, cobots, autonomous vehicles and drones can be trained in VR. AR and VR allow AI-driven operators and the human operators to share data, learn from each other and facilitate remote viewing and remote control of physical objects in distant or hazardous environments, via direct manipulation using computer vision and real-time visualization of the remote environment. The increasing convergence of real and virtual worlds and applications, facilitate remote monitoring, viewing and controlling of objects and operational procedures will become continuously more refined and are the main drivers of innovation and change in all sectors of the economy.

To achieve positive economics for investment, robots must replace humans on the work floor, rather than support them. Human workers must acquire advanced collaborative problem solving skills for tasks that require technical capabilities and soft skills – the essential human skills to manage errors and creative problem solving that machines cannot handle. To guide these developments, governments, educational instutions and companies must plan to accelerate the creation of industrial engineering jobs dedicated to 3D modelling, 3D simulations, big data analytics, ML, robotics. Guidelines and standards for the development of AIIoT systems with task-efficient, high-usability, low error prone human-computer interface designs,enabling the diverseand geographically distributed human operators to develop and customize integrations of AIIoT-driven robotics solutions, rooted in human-centered design practice.

The early adopters of the AIIoT technologies will be able to exponentially grow their productivity. The superior products and services will rapidly reduce the market demand for products and services that are outdated and lacking functionality or quality. Operations will systematically shut down due to competition, inefficiency and high costs. With this knowledge and power, also comes responsibility. There is a global governmental need for the international implementation of:

1. Government legislation - Interoperability between AIIoT devices and worldwide digital industrial transaction and transportation tracking.

Developing proprietary and collective data formats and valuation, and collective responsibility to use open architectures, creating collective wisdom, sharing best practices and by adopting common smart regulatory solutions for cross-border transactions and other international and global challenges that require international cross-cultural collaboration and innovation. Rigorous Human-Computer Interaction(HCI) design and evaluation methods are vital for quality control, health and safety.

2. Global certification - Global training and international exchange opportunities for skilled AIIoT workers with soft skills in addition to their tech skills.

Corporations can facilitate and enable citizens to develop the skills, products and services to create innovation for the benefit of all. AIIoT can be used to learn how to distribute skills and knowledge to where it is needed, how to avoid increasing inequality, how to create equal opportunities, identify best practices for innovation, lean development and affordable solutions for a sustainable planet and prosperous civilization.

3. Educational policies - Create solutions for the shortage of skilled workers, at the pace of the exponential growth in the need for more skilled workers.

AIIoT can be used to work out the best, most efficient, affordable and accessible, modular training. Training contents are conveniently delivered using AIIoT technologies, tailored and personalized, micro-sized and shaped to train targeted skills, using hand-held smart devices to consume byte-sized information anyplace, anytime.