Dexcent IDS

We are Industrial DataOps Practitioners!
3aV77UGRjo7uB7Sa3maYaE4j6VEsYTVqGdZnQmk0uzqFA6wt2YBVpr3zc4vedS4s.png

Operator-Driven Reliability Readiness Assessment

In the discipline of Asset Performance Management, Operator-Driven Reliability emphasises the importance of operating personnels’ insights into early identification of issues and optimizing asset performance. This assessment addresses the readiness of the organization’s operators to take on the additional responsibilities, recognizing they are the front line when it comes to extending asset life and improving overall efficiency.

Operator Empowerment : Ensures Operators are empowered to take ownership of their assets with the knowledge, training, and tools needed. These include understanding equipment function, identifying unexpected behaviour and performing basic maintenance.

Early Detection of Issues :Confirms there is a channel for Operators to promptly report subtle changes or anomalies in asset behaviour which can contribute early detection of potential issues before they become major outages.

Basic Maintenance Tasks : Ensures that Operators can proactively perform routine maintenance tasks, within the scope of their capabilities and training, with the goal of extending asset life and reducing in major failures.

Maintenance Team Communication : Ensures there is a method of communications between the Operators and the Maintenance Teams to facilitate the exchange of information about asset performance and unexpected maintenance needs.

Data Collection and Analysis : Assesses the ability of Operators collect and log relevant data during their operations to support the analyses needed to identify trends and patterns that reflect asset performance and health.

Skillset Development : Ensures that the channels and supports are in place to provide the proper training in equipment operation, troubleshooting, and basic maintenance techniques needed to take the organization to the next levels in APM.

Continuous Improvement : Recognizing that data driven decisions are part of the continuous improvement process, feedback from operators contributes to improvements in equipment design, maintenance practices, operational procedures, and overall operational reliability.

Benefits

Enhanced Operator Engagement and Empowerment : ODR readiness assessments encourage greater involvement and empowerment of frontline operators in asset reliability initiatives. This increased engagement can lead to a more proactive approach to maintenance, with operators contributing their expertise and insights into asset condition monitoring and management.

Improved Asset Performance Monitoring : By involving operators in reliability assessments, organizations can tap into their firsthand knowledge of asset operations. This facilitates a more thorough understanding of asset behaviours, allowing for better monitoring of asset performance and early identification of potential issues or failures.

Early Issue Identification and Resolution : Leveraging operator-driven strategies enables quicker identification of potential asset problems or inefficiencies. Operators, being intimately familiar with asset operations, can detect abnormalities or deviations from normal functioning, allowing for timely interventions to prevent failures or disruptions.

Knowledge Sharing and Collaboration : ODR readiness assessments promote a culture of knowledge sharing and collaboration between operators, maintenance teams, and other stakeholders. This facilitates the exchange of insights, best practices, and lessons learned, fostering a more cohesive and informed approach to asset management.

Optimized Maintenance Strategies : Assessing readiness for ODR helps in fine-tuning maintenance strategies by incorporating operator insights. This can lead to the development of more targeted and effective maintenance plans, optimizing resources, reducing downtime, and extending asset lifespan.

Cultural Shift towards Proactive Maintenance : Cultural Shift towards Proactive Maintenance: Engaging operators in reliability assessments contributes to a cultural shift towards proactive maintenance rather than reactive approaches. This shift can significantly improve overall asset reliability, reduce unplanned downtime, and enhance operational efficiency.

Scroll to Top

Table of Content

1. Purpose
1.1. Purpose and Goals
1.2. Why The Industrial DataOps Process Is Needed?
1.3. Industrial DataOps Practitioner Engagement
1.3.1. Oversee An Existing Industrial DataOps Program
1.3.2. High Data Secrecy Organizations
1.3.3. Full Engagement
1.4. Principles
1.4.1. Know Your Data
1.4.2. Curate Your Data
1.4.3. Unify Your Data
1.4.4. Analyze Your Data
1.4.5. Hardware, Software, and People Working Together
1.5. Lifecycle
2. Intention
2.1. Scope
2.2. Assumptions
3. Terminology & References
3.1. Definitions
3.2. Acronyms and Abbreviations
3.3. Industry References, Standards, Regulations and Guidelines
3.4. Site Related References, Standards, Regulations and Guidelines
4. Expectations and Responsibilities
4.1. Roles
4.2. Role Job Description
4.3. Role Assignment
5. Opportunity Identification
5.1. Need Initiated
5.2. Improvement Initiated
6.Discovery
7. Baselining
7.1. Data Rationalization
7.2. Data Justification
7.3. Data Impact
7.4. Data Flow
7.4.1. Data Producer
7.4.2. Data Path
7.4.3. Data Consumer
7.5. Data Good State
7.5.1. Failure Conditions
7.5.2. Warning Conditions
7.5.3. Abnormal Conditions
7.6. Data Processing Team
8. Target Confidence Factors
9. Critical Success Factors
10. Risk Analysis / Mitigation Plan
10.1. Risk Analysis
10.2. Mitigation Plan
11. Technology Selection
11.1. Hardware
11.2. Software
11.3. People
12. Project Execution
12.1. Project Synergy
12.2. Project Synergy
12.3. Resource Acquisition
12.4. Scheduling
12.5. Implementation
12.6. Training
12.7. Maintenance
12.8. Contingency
13. Evaluation Vs Baseline
14. Calibration & Sustainment
14.1. Training
14.2. Maintenance
14.3. Obsolescence
15. Continuous Improvement Process
15.1. Continuous Process Documentation
15.2. Audit
16. Management Of Change (MOC)
16.1. Applicability
16.2. Methodology