The Autonomous Enterprise: Introduction

With the majority of the countries now trending towards some semblance of normalcy, the manufacturing sector is grappling with managing excess demands while still dealing with unprecedented workforce shortages and supply chain challenges. Adding to this complexity are disruptions driven by political turmoil, rising threats with cyberattacks, natural disasters, and environmental challenges. The convergence of these social, political, and economic factors underscores the increasing risks affiliated with over-reliance on human presence and human decision-making for business-critical operations.

The Autonomous Enterprise: Introduction

The COVID-19 pandemic starkly highlighted the liability factories and other human-dependent operations pose to business continuity. For instance, Ford, Chrysler, General Motors, Volkswagen, and many other automobile manufacturers employing millions of people around the world closed their plant doors amid the outbreaks and social distancing mandates, eventually leading to astronomically high car prices for consumers.

At the same time, many industrial organizations are undergoing a significant time of transition. A generation of highly-skilled operators, technicians, engineers, and other key personnel are reaching retirement age and leaving the workforce, taking with them decades of hard-won experience and tribal knowledge that the next generation will not have. While the new workforce may be more comfortable working with digital technology and applications, their expectations for work are shifting, including a significant growth in demand for remote and hybrid work-life models.

With these factors considered, global supply chains are seen as a greater risk, and there are trends to repatriate or localize manufacturing operations as a result. Doing this while retaining profitability in the face of higher labor costs and new labor regulations will require innovative and less labor-intensive manufacturing processes.

To address these social, political, and economic challenges, manufacturers are accelerating digital transformation efforts and prioritizing the application of artificial intelligence (AI) and machine learning (ML) to solve their complex production challenges. Enabled by the advancements in AI/ML, leading manufacturers across a wide variety of sectors have already expanded the role of AI to enable autonomous decision-making as well as augment the remaining human decision processes with context and decision support mechanisms.

According to the latest market reports, the Global Industrial AI market was a $16.9 billion market in 2020 and is expected to reach $102.2 billion by 2026. One aspect of this that is highly notable is that the impact of these sociopolitical challenges has accelerated the Compound Annual Growth Rate (CAGR) from 20% before 2020 to a forecasted growth rate of 35%. This post resurgence from Covid indicates a heightened interest and massive demand for the widespread development and adoption of AI capabilities.

This series will:

  • Define autonomous manufacturing
  • Explore key principles of autonomous systems
  • Review how technological breakthroughs are expanding the potential scope and maturity of autonomy
  • Explain how these common principles can be applied in an autonomous factory or extended across the value chain (into areas like R&D and supply chain) to create the autonomous enterprise
  • Introduce several key engineering concepts and challenges
  • Share challenges, opportunities and best practices from the field that enable autonomy and maximize business value

What to Read Next