Issue

This Key Decision Document (KDD) serves as a comprehensive guide outlining critical decisions, considerations, and recommendations essential to the implementation and management of Asset Performance. It aims to clarify the rationale behind exploring and evaluating whether to extend advanced and data-driven approach to asset maintenance across Syensqo plants based on selected assets in comparison to standard SAP preventative maintenance process.

Asset Performance refers to the comprehensive management and optimization of physical assets throughout their lifecycle using advanced tools and technologies.

Key areas covered in this document include:

• Benefits and drawbacks of each solution, including factors such as efficiency, accuracy, safety, and compliance.
• Overview & Background
• Design Options 
• Evaluation
• Recommendation
• Business & Project Impacts

Overall, the purpose and structure of the KDD ensure clarity, transparency, and accountability throughout the process of adopting and utilising Asset Performance Management functionalities within Syensqo.

Recommendation

Based on the comprehensive evaluation using the decision matrix provided, extending S/4HANA Asset Performance Management (APM) across Syensqo plants as part of the ERP Rebuild is the recommended approach. This decision is based on the significant long-term benefits of operational efficiency, improved asset performance, and the ability to leverage both real-time and historical data. 

Background & Context

Syensqo currently employs preventive maintenance for a diverse range of assets across all their plants, including those critical to safety and production operations. However, asset management is handled individually at each plant, lacking a standardized approach. This leads to inconsistencies in how similar assets are proactively maintained, resulting in potential inefficiencies and varying maintenance standards.

Currently, only one plant utilizes advanced maintenance functionalities such as predictive maintenance and real-time asset monitoring. These advanced features allow for data-driven decision-making and proactive issue resolution. In contrast, the rest of the organization relies on standard SAP preventive maintenance, which focuses on scheduled tasks and routine inspections without leveraging advanced analytics or real-time data. This disparity in maintenance practices highlights the need for a cohesive and standardized approach across all plants to ensure consistent asset management and optimization throughout the organization.

As-Is Summary 

At present, only the Tavaux plant leverages the advanced functionalities of asset performance management, which encompass predictive maintenance, real-time monitoring, and comprehensive asset performance insights. The rest of the organization relies on standard SAP preventive maintenance, which focuses primarily on scheduled maintenance tasks without advanced analytics and predictive capabilities.

Opportunities

There is an opportunity to standardize and improve maintenance practices organization-wide, potentially closing the gap between strategy and execution.

Introducing strategies such as predictive maintenance, asset health monitoring, and risk-based maintenance, integrated with a program like SAP APM, can significantly enhance asset reliability, minimize downtime, and increase efficiency.

Assumptions

Data Availability: Not all plants will have sensors and real-time data available to integrate with APM. In cases where real-time data is not available, historical data will be used to feed S/4HANA APM with the necessary information to operate effectively.

Asset Types: The specific types of assets to be managed under the APM solution will be determined during the detailed design phase. This determination will influence the implementation approach and data requirements.

Constraints

Data Infrastructure: Variability in data infrastructure across plants may pose challenges for consistent data integration and utilization.

Asset Types Determination: The detailed design phase will need to identify and categorize the types of assets to be managed, which may impact the implementation timeline and complexity.

Impacts

Implementing S/4HANA Asset Performance Management (APM) as part of the ERP Rebuild Project will have various business and project impacts

Operational Efficiency: Enhanced maintenance strategies can lead to improved operational efficiency and reduced downtime.

Automation: Implementation of APM can automate routine maintenance tasks, reducing manual intervention

Licensing: APM - Asset Performance Management requires a separate license, based on number of objects (Equipment). A separate SAP IoT subscription to work with SAP Asset Performance Management is not required, as the SAP APM has an inbuilt subscription for SAP IoT with the service plan "OneProduct".

Data

• Integration: Need for seamless data integration across all plants.
• Cleansing: Significant effort may be required to cleanse existing data to ensure it is accurate and compatible with the new APM system.
• Migration: Migrating historical data to the new APM system can be complex and requires careful planning to ensure data integrity and continuity.

Languages: Multilingual will be required to accommodate users in different regions with diverse language preferences.

User Training: Extensive training required for users to adapt to new systems and processes.

System Complexity: Increased complexity in managing and integrating APM with existing systems and processes.

Business Rules

Data Utilization:

• Where real-time data from sensors is available, it should be integrated into the APM system to enable predictive maintenance and real-time monitoring.
• In the absence of real-time data, historical maintenance and performance data should be used to populate the APM system, ensuring all plants benefit from the system's capabilities.

Maintenance Scheduling: Predictive maintenance schedules to be generated based on data analytics from APM.

Options considered

Option A: S/4HANA - APM (Asset Performance Management)

This option involves extending the implementation of S/4HANA APM across the organization for selected assets, example Rotating Assets (Assets to be determined during Detail Design), whilst continuing to use standard SAP preventative maintenance in parallel. APM is a comprehensive solution designed to optimize asset reliability and performance through advanced analytics and strategic maintenance practices. It facilitates a holistic view of asset health, enabling organizations to implement effective maintenance strategies. Key functionalities include:

• Predictive Maintenance: Uses data and analytics to predict potential failures before they occur.
• Risk-Based Maintenance: Prioritizes maintenance activities based on asset criticality and risk assessments.
• Reliability-Centered Maintenance: Determines the most effective maintenance strategies to ensure asset reliability.
• Data Integration: Combines historical maintenance records, manual condition assessments, and real-time IoT data to provide actionable insights.
• Failure Mode and Effects Analysis (FMEA): Identifies potential failure modes, their causes, and effects for proactive maintenance planning.
• Lifecycle Cost Management: Tracks and manages the total cost of ownership of assets.
• Compliance Reporting: Facilitates compliance with regulatory requirements through better documentation and reporting of maintenance activities.
• Enhanced Collaboration: Improves collaboration between maintenance teams and other departments.
• Performance Benchmarking and KPIs: Defines and monitors key performance indicators (KPIs) and benchmarks for asset performance.

By improving collaboration among maintenance teams and offering tools for performance benchmarking, APM helps organizations minimize downtime, reduce maintenance costs, and extend the lifespan of their assets.

From a user experience perspective, SAP APM offers intuitive dashboards and detailed analytics that empower maintenance teams and decision-makers with actionable insights. 

It is important to note that while time series data significantly enhances the predictive capabilities of S/4HANA APM, the module still offers numerous benefits that can improve overall asset management, maintenance strategies, and operational efficiency. This allows Syensqo to build over time to introduce time series data as maturity increases.

High Level Capability Process 

Data - As shown in the below flow diagram, data is not required to be maintained separately in 2 applications. Master data held within S/4HANA is the primary source of truth and then replicated into APM through integration.

Data Flow Diagram

Option B: Standard S/4HANA Preventative Maintenance 

This option involves continuing with the standard SAP preventative maintenance approach currently used by most plants. Preventative maintenance focuses on scheduled maintenance tasks without incorporating advanced analytics or real-time monitoring capabilities. It operates on a schedule, using time-based or usage-based intervals to trigger maintenance activities. This approach ensures that assets are regularly checked and maintained to prevent unexpected breakdowns, thus enhancing asset reliability and operational efficiency.

The existing preventive maintenance approach is well understood by the maintenance teams, ensuring that they can operate efficiently and effectively. This familiarity can lead to higher compliance and better execution of maintenance tasks, further supporting asset reliability and performance.

Comparison Table 

Evaluation

The decision matrix provided below offers a structured approach to evaluating and comparing the options for Asset Performance Management (APM) and Standard Preventive Maintenance.

Despite the higher costs and implementation complexity, the benefits of APM in proactive maintenance, data utilization, resource optimization, and risk management make it a more advantageous option overall. Implementing APM will future-proof Syensqo, enhancing how asset performance and maintenance are managed.

Even without real-time data, APM offers more proactive and optimized maintenance through the use of historical data and predictive models. As real-time data is introduced, the capabilities will grow even further, providing greater benefits over time.

*The evaluation scoring system ranges from Low to Very High. In this system, a low score indicates a negative attribute, such as high costs.

See also

The following section describes relevant documentation:

Business Definitions (Glossary)

Change log

Workflow history