The Challenges of Modern Maintenance

Manufacturers today, especially those in sectors like healthcare product manufacturing, face numerous challenges that demand attention:

➡️ Global Competition

Reducing costs and improving efficiency are paramount for maintaining competitiveness, particularly in industries like PET packaging.

➡️ Demand Variability

Fluctuations in demand necessitate flexible production and maintenance schedules to avoid downtime and ensure operational readiness.

➡️ Predictive Maintenance

Data analytics now enables predictive maintenance systems, where sensors monitor equipment conditions in real-time and identify early signs of failure. This approach allows for preventive interventions, preventing costly downtime.

➡️ Remote Maintenance

Innovations in remote maintenance systems have enabled technicians to diagnose and solve problems without being on-site, reducing costs and response times.

➡️ Regulations and Sustainability

Stricter environmental regulations are driving the need for sustainable packaging production, impacting both plant design and maintenance procedures.

➡️ Decentralized Repair Teams

Traditionally, companies with multiple facilities maintained costly onsite maintenance teams. However, with advancements in sensors and AR, decentralized repair teams are becoming feasible, allowing for optimized workforces and reduced costs.

➡️ More Agility and Less Downtime

Reduced downtime is timeless, but with increasing competition, businesses must also focus on agility. Lean practices like "just-in-time" inventory management and condition monitoring are essential for eliminating waste, improving productivity, and extending equipment life.

➡️ Technological Advancements

Integrating IoT and data analytics transforms predictive maintenance, enabling real-time monitoring that prevents costly equipment failures.

➡️ Labor Shortages

In 2024, the manufacturing industry faces a significant challenge due to a retiring workforce and a skills gap, particularly in automation. Recruiting skilled workers has become increasingly difficult as younger generations prioritize flexibility and remote work.

➡️ Virtual Training

Technological innovation has made distance learning possible for plant operators, ensuring that personnel are adequately trained and can be maintained effectively.

Market Dynamics

➡️ Supply Chain Issues

The recent crash of the container ship Dali into the Francis Scott Key Bridge in Baltimore disrupted the global supply chain, highlighting the need for robust supply chain management strategies to ensure access to raw materials in unpredictable environments.

➡️ Inflation

Rising raw materials and energy costs are squeezing profit margins, making it challenging for U.S. manufacturers to maintain quality while managing costs. Trevisani suggests improving internal efficiencies by eliminating waste and obsolete inventory and partnering with customers to streamline the flow of goods.

➡️ Economic Uncertainty

The unpredictable economic climate, driven by geopolitical tensions and fluctuating demand, poses significant challenges. However, anticipated government investments offer some stability, with record private sector investment in manufacturing.

How Process Mining Transforms Maintenance Operations

Maximo is a robust enterprise asset management system, yet maintenance managers can still struggle to optimize processes fully. This is where process mining becomes invaluable. By analyzing event logs, process mining reveals inefficiencies, compliance issues, and opportunities for automation, providing a clear roadmap for continuous improvement.

What is Process Mining?

Process mining leverages event logs to provide insights into how processes are executed. Maintenance managers can optimize operations by identifying bottlenecks and inefficiencies, enhancing overall performance.

What is SAP Signavio Business Process Transformation Suite?

SAP Signavio Business Process Transformation offers a comprehensive suite of tools designed to optimize business processes efficiently and precisely. Central to this suite are powerful solutions like SAP Signavio Process Insights and SAP Process Intelligence, which play a crucial role in uncovering actionable insights from complex data. SAP Signavio Process Insights enables businesses to swiftly identify inefficiencies and uncover potential improvements, delivering clear insights within just 24 hours of implementation. This rapid feedback is vital for organizations aiming to streamline operations, enhance performance, and maximize ROI by addressing critical process weaknesses​​.

Further enhancing this transformation capability, SAP Process AI leverages artificial intelligence to provide advanced process automation and predictive analytics. Embedded seamlessly within the SAP Signavio solutions, SAP Process AI is designed to provide instant answers to process questions, uncover hidden issues, and deliver actionable improvement recommendations, all while being future-proofed with continuous learning. Critical capabilities of SAP Process AI include:

➡️ Process Recommender

Empower your organization with instant best practice recommendations. Access a database of 5,000 best practices from SAP to quickly move from initial process exploration to design, accelerating process model development without consulting heavy services.

➡️ Process Analyzer (Text to Insights)

Democratize process mining with natural language processing. Users of any skill level can ask questions and receive immediate, relevant insights from data, making informed decisions faster.

➡️ Performance Indicator Recommender

Simplify process monitoring with quick recommendations on relevant process performance indicators (PPIs). This tool links business problems, affected processes, and metrics, enabling a self-service approach to define an initial process monitoring framework.

As industries are reshaped by breakthroughs in Generative AI, SAP Signavio is at the forefront, leveraging Large Process Models (LPMs) to enhance its process transformation suite. LPMs extend the capabilities of large language models (LLMs) by applying SAP’s extensive process and industry insights to Business Process Management (BPM). This innovative approach shortens time-to-insights, accelerates time-to-adapt, and improves process monitoring, reinforcing SAP Signavio's commitment to delivering unparalleled value to its customers.

Use Cases for Process Mining

Our Maintenance Excellence solution leverages process mining to optimize various aspects of maintenance operations:

➡️ Work Order Management

Identify inefficiencies in work order generation, assignment, and completion. Process mining can reveal misassignments that lead to delays, enabling corrective actions.

➡️ Preventive Maintenance

Optimize scheduling and eliminate redundant tasks, ensuring timely and cost-effective maintenance.

➡️ Inventory Management

Enhance inventory management by identifying bottlenecks and automating processes, ensuring necessary parts are always available.

Real-World Impact: A Case Study

A UK-based healthcare product manufacturing company faced significant challenges in managing its maintenance processes using IBM Maximo. By implementing our Maintenance Excellence solution, powered by SAP Signavio Business Process Transformation Suite, they achieved remarkable results:

➡️ Efficiency Gains

The company reduced work order processing time by 30% and increased equipment uptime by 10%, resulting in less downtime and higher productivity.

➡️ Cost Savings

Process mining identified unnecessary steps and opportunities for automation, leading to a 20% reduction in maintenance costs.

➡️ Compliance and Quality

Enhanced compliance monitoring helped the company avoid penalties and maintain high production standards.

Read more:

Maintenance Excellence Transformation

The Broader Benefits of Process Mining

Beyond specific use cases, process mining offers several overarching benefits:

➡️Cost Savings

By identifying and eliminating inefficiencies, companies can reduce maintenance costs and improve profitability.

➡️Improved Decision-Making

Real-time insights into maintenance processes enable more informed decision-making and proactive management.

➡️Enhanced Compliance

Process mining helps ensure adherence to regulatory requirements, reducing non-compliance risk.

➡️Improved Information Access

Enhanced maintenance management through standardized and automated data processes.

➡️Reduced Repair Time

A 25% reduction in the mean-time-to-repair (MTTR) due to AI-driven diagnostics and best practice recommendations.

➡️Seamless IT Integration

Integrating existing systems creates a comprehensive maintenance knowledge base.

➡️Knowledge System Establishment

Creating a "Four-Pool" knowledge system for equipment knowledge, training, talent evaluation, and expert support.

Conclusion

Leveraging SAP Signavio Business Process Transformation Suite, Ennuviz’s Maintenance Excellence Prebuilt Process Mining solution, designed specifically for IBM Maximo's predictive and reliability maintenance, can significantly enhance maintenance operations. By identifying inefficiencies, enabling automation, and improving decision-making, companies can optimize their maintenance processes to remain competitive in a challenging and evolving landscape. As demonstrated by our case study with a UK-based healthcare manufacturer, the transformative potential of process mining is undeniable. As manufacturers face increasing pressures from global competition, supply chain disruptions, and technological advancements, adopting these innovative approaches is essential for achieving long-term operational excellence and sustainability.