Real-time automation for manufacturers: How providers can scale the market

The manufacturing vertical is expected to be the largest customer group for real-time automation solutions. This report outlines how ecosystem players, such as systems integrators and independent software vendors, can work together to leverage their strengths and better serve the sector.

Real-time automation involves the instant execution of tasks and processes without the need for human involvement. It involves two processes:

1. Real-time decision making which involves using data analytics, ML, and other AI algorithms to generate immediate insights at scale from existing data within a specific environment. Vast datasets from multiple sources can be immediately analysed as the data is generated, enabling prompt and informed decisions. For example, on a manufacturing floor, it provides instant notifications of changes and anomalies in machine performance and efficiency.
2. Intelligent automation which executes on the real-time decision-making to minimise downtime and increase overall performance and efficiency. It leverages AI and ML to implement the recommendations from real-time decision-making, enabling autonomous processes that surpass the capabilities of the traditional rule-based automation systems. For example, intelligent automation can respond to real-time information on equipment health, making adjustments that reduce machine breakdowns.

Real-time automation at scale is becoming increasingly viable given advancements in technologies such as edge computing, IoT, and AI which enhance each other’s capabilities.

• Edge computing: Edge provides highly performant compute capabilities that coordinate between IoT devices and support scalable AI processing. By enabling real-time data ingestion and analytics, edge computing facilitates timely decision-making for latency-critical IoT applications.
• IoT: IoT ensures devices coordinate with each other to achieve a common goal. Devices within an IoT platform can provide real-time source data, which is collected and processed at the edge. This source data can be leveraged by AI for more accurate training and inferencing.
• AI: Through real-time inferencing, AI applies outputs from trained ML models to deliver automated, effective responses across data-rich environments. AI also orchestrates the edge/IoT platform, enhancing resource consumption and data management.

Some manufacturers have already started to leverage new technologies to enhance their operational processes with several use cases, as demonstrated in the figure below.

Manufacturing use cases using advanced technologies

Regardless of recent technology advancement and use case applicability, there are still challenges associated with scaling real-time automation. There are three categories of barriers that are most pertinent:

1. Commercial and organisational: The nascent technology compounded by cultural risk aversion and fears of AI-induced job displacement, makes demonstrating ROI challenging. Additionally, enterprises often lack the necessary skills and training, while financial constraints limit the scale of deployments.
2. Technical: Outdated legacy infrastructure lacks the flexibility to accommodate changes in the production demand needed for scaling of new technologies. Moreover, disparities in technology standards between automation systems and legacy infrastructure can lead to integration complexities.
3. Legal and ethical: Rigorous and costly regulatory requirements are lagging behind the rapid progress in AI. Maintaining stringent data privacy standards will become more challenging as the temptation to leverage customer data for competitive advantage grows with expanding AI capabilities.

In this report, we leverage insights from a global research programme (including interviews and a survey of 371 enterprises) to explore the question:

How can ecosystem players collaborate and leverage their strengths to scale the adoption of real-time automation solutions and address the challenges associated in manufacturing?

Table of contents

• Note to the reader
• Executive summary
• Introduction
• Manufacturing is the leading vertical for real-time automation solutions
  • Solution providers can address manufacturers’ challenges through specialised innovation and collaboration
• Collaborative partners will drive scale and capture the opportunity
  • Systems integrators should be the primary partner for manufacturing enterprises
  • Systems integrators need support from the rest of the partner ecosystem
  • ISVs’ best-in-breed solutions can complement SIs
• Conclusion: What is next for solution providers?
• A message from our research partner
• Appendix

Related research

• Real-time automation: The future of transport & logistics
• Simplifying NaaS for easier enterprise consumption: Capturing the USD100bn opportunity
• SMBs: How telcos should approach them
• Applying the platform mindset: STL Partners’ enterprise manifesto

Jack Hurley

Jack Hurley

Consultant

Jack Hurley is a Consultant at STL Partners, specialising in edge computing and hyperscaler partnerships.