Asset Performance Management Best Practices. Joel A. Nachlas, Ph. D. Virginia Tech
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1 Asset Performance Management Best Practices Joel A. Nachlas, Ph. D. Virginia Tech
2 Survey Project sponsored by Meridium, Inc. Roanoke, Virginia
3 Project Team Andrew Henry & Joel Nachlas Virginia Tech Bob Francis Meridium Mike Dunlap Dunlap Marketing
4 The overall project objective is to define a map of company feature dependent best practices in the management of production asset maintenance.
5 Operational strategy Identify a large set of companies and or facilities that rely on capital intensive production resources. Contact managers or other high level executives of those firms to request participation in the survey and identify the specific people who would responsible for providing survey responses. In this process, use a screening questionnaire in order to determine whether contact is to receive version A or B of the survey.
6 The two versions of the survey are identical except for the section dealing with analytical methods. Companies indicating that they use formal analytical techniques received version A in which the analytics section inquired the techniques used to manage maintenance. Companies indicating that they do not have formal analytical techniques received version B in which the analytics section inquired about needed data resources.
7 Initial contact with target companies, implementation of the screening questionnaire and follow-up reminder phone calls were performed by Dunlap Marketing. Completed survey questionnaires were sent directly to Virginia Tech.
8 The screening questionnaire included a question concerning the business sector of the target firms. Survey responses received represent: Aerospace & Defense Chemical/Petrochemical Consumer Products Forest Products Metals & Mining Oil & Gas Pharmaceutical Power/Utility Transportation
9 Screening questionnaire responses included 300 companies that indicated they would complete the survey. Actual final response set included 53 completed surveys. Of these, 46 completed version A and 7 completed version B.
10 300 Phone Respondent Industry Breakdown Aerospace & Defense Chemical/Petrochemical Consumer Products 15 Forest Products Metals & Mining Oil & Gas Pharmaceutical Power/Utility Transportation Other/Blank
11 53 Respondent Industry Breakdown Chemical/ Petrochemical Forest Products Metals & Mining Oil & Gas Power & Utility Other/Blank
12 Respondents represented firms located in: 38 US states the District of Columbia and 6 Canadian provinces
13 Screening questionnaire Insights that were not initially intended are obtained from the responses to the screening questionnaire. For example, when asked if their firms perform quantitative analysis of maintenance or reliability data, the responses differed between A and B groups % 80.00% 60.00% 40.00% 20.00% 0.00% A B
14 Screening questionnaire Similarly, when asked if asset purchase decisions are based on life cycle cost analysis: 80.00% 60.00% 40.00% 20.00% 0.00% A B
15 Screening questionnaire Is reliability in design analysis used for capital expenditures? % 80.00% 60.00% 40.00% 20.00% 0.00% A B
16 Screening questionnaire Does your company report return on assets to management? 80% 60% 40% 20% 0% A B
17 Survey general The survey design included questions organized into 8 sections dealing with: Management practices Asset awareness Analytics Maintenance practices Information management Asset purchasing and renewal Spare parts inventories Respondent identity
18 Survey general Questions were organized to allow us to evaluate corporate success and the degree to which risk based quantitative analyses are used to manage production assets. Because the surveys were identified as focusing on asset management, responding firms assigned responsibility for the survey to plant managers, maintenance managers or reliability managers.
19 Survey general Success in asset management was also evaluated. The results described today are based on unplanned downtime as the measure of success. Related to this measure is the question of what events trigger maintenance effort. For the 53 survey respondents, we observed:
20 Survey general Maintenance triggers A Asset Failure (Reactive) Time Based Maintenance Schedule Asset Condition (Condition Monitoring) Predictive Modeling (Analytics)
21 Survey general Maintenance triggers B Asset Failure (Reactive) Time Based Maintenance Schedule Asset Condition (Condition Monitoring) Predictive Modeling (Analytics)
22 Survey A respondents Using unplanned maintenance as a measure of asset management effectiveness, we classified respondents and found that the 46 respondents to version A of the survey were distributed as: Unplanned downtime worst 28% best 33% moderate 39%
23 Survey A management practices Our management has a clear vision for physical asset performance and it is well known in our company % 80.00% 60.00% 40.00% 20.00% 0.00% yes
24 Survey A management practices Our organization enacts change: 1.Readily and effectively 2. Only with great expenditure of effort 3. Not well at all 80.00% 60.00% 40.00% 20.00% best moderate worst 0.00% 1 2 3
25 Survey A management practices Are problems addressed using a consistent methodology? % 80.00% 60.00% 40.00% 20.00% 0.00% yes
26 Survey A management practices Employee certification is: 1. A top management priority 2. Usually supported by management 3. Supported with sufficient justification 4. Rarely supported by management 5. Not supported by management 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% best moderate worst
27 Survey A management practices Compared to production, asset performance management is: 1. Less important 2. Equally important 3. More important % 80.00% 60.00% 40.00% 20.00% best moderate worst 0.00% 1 2 3
28 Survey A management practices An asset retirement plan is developed when? % of life % of life % of life 4. Beyond 75% of life 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% best moderate worst
29 Survey A management practices Capital expenditures are based on reliability in design and/or life cycle costs for what percentage of assets? 1. 0% % % 4. more than 75% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% best moderate worst 0.00%
30 Survey A asset awareness Asset status monitoring 1. Automated 2. Hand measurements 3. Visual inspection 4. Not at all 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% best moderate worst
31 Survey A asset awareness Knowledge of the age of your assets 1. We are fully aware of the age of all assets in our plant 2. Interval age is well maintained for roughly 75% of plant assets 3. Asset age is documented for less than 50% of plant assets 4. Asset age is not tracked or documented for any plant assets 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% best moderate worst
32 Survey A asset awareness The probability of breakdown is forecast for what percent of your assets? 1. 0% % % 4. More than 75% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% best moderate worst 10.00% 0.00%
33 Survey A asset awareness The probability of failure is calculated: 1. For each production asset 2. For groups of similar assets 3. For the whole plant 4. Not at all 70% 60% 50% 40% 30% 20% best moderate worst 10% 0%
34 Survey A asset awareness Breakdown probabilities calculated using: 1. Historical data 2. Manufacturer s specs. 3. Current asset status measures 4. Educated guess 70.00% 5. Not at all 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% best moderate worst
35 Survey A analytics Rank the maturity of your analytical processes. 1. Fully developed and up to date 2. Well established and improving 3. Developing 4. Embryonic and weak 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% best moderate worst 0.00%
36 Survey A maintenance practices Conditioning monitoring data for high cost production assets is: 1. Recorded and stored 2. Used to influence maintenance planning 3. Analyzed and used in a predictive or analytic model 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% best moderate worst
37 Survey A maintenance practices The most recent update of your asset maintenance plan occurred: 1. During the current year 2. Within the past 2 years 3. Within the past 5 years 4. More than 5 years ago 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% best moderate worst
38 Survey A information management Reports of unplanned breakdowns are: 1. Analyzed for root causes 2. Simply read and archived 3. Not generated or not reviewed % 80.00% 60.00% 40.00% 20.00% best moderate worst 0.00% 1 2 3
39 Survey A asset purchasing and renewal Does your firm have an asset retirement plan? 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% yes
40 Survey A spare parts inventories Spare parts inventory levels are: 1. Computed using an analytical algorithm 2. Specified by the maintenance manager based on experience 3. Set by management based on experience 4. Set based on industry practices 5. Not formally set 80.00% 60.00% 40.00% 20.00% best moderate worst 0.00%
41 Conclusions caveats There are many more results than those presented here. Analysis of the survey data is continuing. Results for the B surveys are sparse but appear to conform to those for the A surveys. The full study results should be available for publication by the end of the year.
42 Conclusions There seem to be several conclusions that are well supported. Some of these seem obvious but it is nice that the data supports them. 1. Management practices have a strong influence on asset management effectiveness. 2. A firm with a clear vision of how assets should and do perform get the most from their equipment. 3. Organizations that are flexible and can adapt have better asset performance. 4. Firms that use consistent methods to addressing problems have better asset performance.
43 Conclusions 5. Firms that encourage expansion in employee knowledge have better asset performance. 6. Firms that monitor and record asset status and that use their knowledge of asset condition to guide maintenance plans have better asset performance. 7. Firms that use analytical methods to model and study asset failure probabilities and to set maintenance plans have better asset performance. 8. Implementation of the analytical methods at the individual asset level enhance benefits.
44 Conclusions 9. Firms that use data to solve problems have better asset performance. 10. Firms that use life cycle cost analysis as a basis for asset management derive significant benefits from this. 11. Firms that have asset retirement plans in place have better asset performance. 12. Firms that maintain records and use accumulated data to address new problems have better asset performance. 13. Firms that emphasize sensitivity to asset status and the use of analytical methods experience significantly better asset performance.
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