Shaping the Future of Maintenance Project Management With AI

Managing STOs (Shutdowns, Turnarounds, and Outages) effectively requires meticulous coordination of significant capital, labour, and resources. Given the limited window for repairs, inspections, and improvements, efficient stakeholder collaboration is crucial to minimise downtime.

In today's competitive industrial landscape, leveraging advanced technologies is essential for maintaining an edge. Predictive and generative AI solutions enhance productivity, optimise workflows, reduce costs, improve safety, and exceed customer expectations, delivering exceptional value.

An Enterprise Knowledge Twin is a digital representation of an organisation's knowledge, processes, and assets. By integrating data from various sources, it creates a comprehensive, real-time model of the enterprise. This model aids in visualising, understanding, and analysing information across different domain, thereby improving efficiency and decision-making.

AiSpanner excels in this area due to its foundation on a graph database, which maps the relationships between data, tasks, and personnel. Graph-structured data provides a flexible framework for describing and analysing entities and their interactions. This approach can reveal valuable insights from higher-dimensional representations of these entities and their relationships, which might otherwise be lost. However, the flexibility of graphs comes with the complexity of designing frameworks that can learn from this data and generalize across different domains.

Knowledge graphs enhance AI accuracy by connecting structured and unstructured data sources, providing a comprehensive view of complex, real-world scenarios. They offer an intuitive way to model intricate situations, with characteristics such as dynamic structure, highly interconnected data, and efficient access to data relationships. AI assistants and agents for project management can significantly boost productivity and streamline workflows.

With the rise of generative AI, the importance of knowledge graphs has increased. They are seen as the most efficient way to safely and securely apply generative AI to enterprise- specific knowledge and data, especially when combined with retrieval-augmented generation (RAG). Graph databases are particularly effective for schedule optimisation due to their ability to model complex relationships and constraints between tasks, resources, and time slots.

AiSpanner is collaborative AI systems designed to work alongside project team, providing real-time support and recommendations while leaving critical decision-making to humans. This symbiotic relationship will leverage the strengths of both AI and human intelligence, integrating AI tools into maintenance project management practices.

Predict Smarter, Faster, and More Accurately With AI

The AiSpanner AI Assistant serves as an interactive copilot, empowering maintenance teams with real-time access to crucial data and information. It enables team members to interact with data naturally and intuitively, using plain language queries. This makes it easier for planners, schedulers, engineers, and other team members to access relevant information without requiring deep technical expertise.

The AI Assistant provides access to essential business information, including Standard Operating Procedures (SOPs), product manuals, safety guidelines, engineering specifications, policy documents, and regulatory guidelines. This ensures users have the necessary information readily available at their fingertips.

By utilising Retrieval Augmented Generation (RAG) technology, the AI Assistant is optimised for enterprise environments. It provides contextually relevant responses that help users make informed decisions quickly.

The AiSpanner Advisor goes beyond mere on-demand assistance. It continuously monitors data streams and provides domain expert interpretation and actionable insights. These AI agents adapt to better support teams and organizations by learning best practices and shared processes from their enterprise data, helping users understand what advice means in the context of their enterprise.

As an always-on expert, the AiSpanner Advisor delivers in-depth analysis of recommendations and system performance, aiding users in interpreting complex data in real time. This includes specialised roles such as Safety Advisor, Schedule Advisor, Toolbox Meeting Advisor, and Risk Advisor. Subject matter experts can interact with the AI Advisor for advanced troubleshooting and scenario-specific guidance, which is particularly useful in high-stakes situations where in-depth analysis is required to make the best decisions.

Continuous improvement is a key feature of the AiSpanner Advisor. It provides ongoing feedback on process performance, helping teams refine their operations over time. By continuously learning from lessons learned, it helps optimise project management for better outcomes. Users can make data-driven decisions that enhance project efficiency, reduce waste, and improve overall productivity.

At an advanced level, AiSpanner empowers maintenance project professionals to enhance and develop new capabilities. This approach enables project managers to tackle more complex and strategic tasks tailored to the organisation or specific topics. For instance, they can create sophisticated automation for project management workflows, develop outstanding business cases for projects, and support complex decision-making processes involving multiple interdependencies and variables.

AiSpanner is designed to automate tasks of low complexity that require minimal human intervention. This includes generating real-time analysis, creating risk registers, performing real-time Earned Value Analysis (EVA), summarising post-shutdown notes, and analysing lessons learned. By automating these processes, project managers can create standardised prompts applicable across different projects, reducing the need for highly experienced project managers to verify results.

AiSpanner provides continuous, expert-level insights that drive proactive decision-making and continuous improvement.