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 all plants based on selected assets in comparison to standard SAP preventative maintenance process.

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

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

Constraints

Impacts

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

• Licensing: APM - Asset Performance Management requires a separate license, based on number of objects (Equipment)

• Data Integration: Need for seamless data integration across all plants.
• 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

Currently, no specific business rules have been identified. Further updates may be determined during the detailed design phase.

Options considered

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

This option involves extending the implementation of S/4HANA APM across the entire organization for selected assets. 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.

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. 

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 

Option B: Standard S/4HANA Preventative Maintenance 

This option involves continuing with the standard SAP preventative maintenance approach currently used by most plants. It focuses on scheduled maintenance tasks without incorporating advanced analytics or real-time monitoring capabilities.

Option C: 

Option D: 

Evaluation

	Option A	Option B	Option C	Option D
Criterion 1	Pro Con	Pro Pro	Pro Con	Pro Con
Criterion 2	Pro Con	Con	Pro Pro	Con Con
Criterion 3	Pro	Con	Con	Pro

See also

Change log

Workflow history