You don t know, what you don t know.

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1 You don t know, what you don t know. Building an Enhanced CMMS to Prioritize Maintenance and Manage Risk - a Pilot Study at New York City s North River Wastewater Treatment Plant Gerald FitzGibbon 1, April Kelly 2, Arthur Spangel 2 1 Veolia North America, New York City, New York. 2 New York City Department of Environmental Protection, New York

2 Agenda 1. Objective of Pilot Project 2. Project tasks 3. What data is important and why? 4. Continuous Improvement Plan 5. Tracking and reporting 6. Next steps

3 2. Objective of Pilot Project 1. Maintenance optimization strategy 2. Enhance CMMS data capture, analysis & reporting to meet business needs 3. Pilot, Refine & Rollout

4 2. Improving CMMS is Step 1 in optimizing maintenance Step 1 Assess CMMS data quality & Plan to rectify data Yes Outputs OK? No Yes Fix CMMS? No STOP A need to address gaps in CMMS Collect Baseline data & metrics Establish baseline start date & duration Yes BWT Signoff No Change data/metrics DEP is here! Step 2 Optimize maintenance Yes Improved? No Abandon initiative STOP Optimize maintenance at NR and use baselines to measure impact Step 3 Determine Optimized workload Estimate workload requirements for all BWT plants Step 4 Develop organization to meet Maintenance Requirements 4

5 2. Enhance CMMS data to improve maintenance management & drive asset management Business Needs Needs based Maintenance Use age, condition and usage data to drive maintenance scheduling Improve CMMS data capture & reporting Improve Maintenance Management Operational Asset Management Track Maintenance Cost Improved KPI reporting Capture & Track asset condition Asset replacement Capturing accurate labor, parts and other direct maintenance costs to track maintenance performance By capturing a range of cost, condition, usage and asset performance data, a range of performance metrics can be used to optimize maintenance Use maintenance activities to capture and track asset condition data of selected assets (eg critical & high priority significant assets). CMMS data will allow BWT to model & prioritize asset replacement planning delivered through BWT engineering 5

6 2. Pilot at North River WWTP, refine approach and then rollout to all remaining 13 No. plants in NYC NUMBER OF PLANTS COVERED PILOT REFINE ROLLOUT 6

7 3. Project tasks 1. Nine tasks were undertaken & 50% through workshops involving some or all of the Steering Group and North River plant staff 2. Risk based approach 3. KPI based performance management 4. Continuous Improvement Plan

8 3. Pilot Project tasks (1 of 3) Task Task name 1 Risk & needs assessment 2 Performance measurement 3 Data dictionary mapping Notes Assess operational & organizations risks Map risk management into a statement of needs. Identify primary KPIs needed to measure and manage these needs to mitigate risk Review risk and select best performance measurement metrics and associated KPIs to manage performance. Prepare data requirement maps and the Data Dictionary

9 3. Pilot Project tasks (2 of 3) Task Task name 4 CMMS configuration 5 Data collection 6 Data conversion Notes Templates for data capture formats Setting up code tables for validation, and Defining business rules to provide access. Prepare a plan to collect required data Identify key processes needed and resource methods required to collect and maintain data elements. Prepare a plan to convert data existing within CMMS in formats that are not readily usable or that do not meet identified requirements into defined and configured data elements.

10 3. Pilot Project tasks (3 of 3) Task Task name 7 Data management practices 8 KPI measurement 9 Continuous improvement plan Notes Develop SOPs for the ongoing collection and management of required data Write KPI scorecards & dashboards. Develop a plan to define ongoing activity by BWT NR team to: monitor progress and success of overall CMMS Project, implement a process of continuous improvement, and ensure data integrity

11 4. What data is important and why?

12 4. A risk prioritization workshop was used to identify the most important risk and related data A risk prioritization workshop was held with senior management, operations and maintenance staff. Workshop was used to assess key organizational and operational risks and the means of mitigating such risks. The team calculated a priority score for each of the organizational CONSEQUENCE & operational risks identified. LOSS TYPE (1) Insignificant (2) Minor (3) Moderate (4) Major (5) Catastrophic Four (4) types of loss identified were: Safety Compliance Public Image Cost Impact Safety No risk to safety Audit Priority 3 Audit Priority 2 Audit Priority 1 Injury occurred Compliance Public Image No impact No impact Within 10% of limit Minor community interest - community complaints Notice of violation Public interest & broad adverse media coverage - Beach Advisory DEC Prosecution Loss of confidence/city investigation - Beach Closure US EPA/DOJ court order State Government investigation - national coverage Sewer backup/health impact Cost Impact No impact < $5K < $50K < $500K > $500K LIKELIHOOD RISK RATING Very Likely (< 1 year) Likely (1 year) Possible (every 2-5 years) Unlikely (6-20 years) Rare ( > 20 years) Extreme High Significant Medium Low

13 4. Highest ranked risk KPI was MTTR

14 5. Continuous improvement plan

15 5. Continuous improvement plan The Plan targets performance in three specific areas: Asset Data Quality Work Ticket Management Maintenance Management Improvements in these practices will provide the information needed to begin driving operational asset management by tracking larger, organizational metrics such as: overall trends in costs of maintenance, costs of labor and materials, and condition assessment scoring

16 5. Asset Data Quality One of the strategic business outcomes is to improve CMMS data capture and reporting. The improvements discussed in this section and their associated KPIs relate specifically to Asset Data Quality. Following the standardization of CMMS data configuration, collection of existing data, data conversion, and application of standardized specifications, asset data was reviewed to identify gaps and to measure the improvement in data quality and completeness needed to meet BWT s other business objectives.

17 5. Data collection & conversion tools (1 of 2) Data mapping tool All data may be entered into the data mapping tool and provided to the BWT CMMS team for bulk uploading into the CMMS. The Excel spreadsheet based tool is configured to provide quality assurance / quality control (QA/QC) and upload all data into the CMMS in the appropriate format Work management procedure Specification templates have been integrated into CMMS for Preventative Maintenance (PM) Work Orders (WO) to provide a means for maintenance staff to collect data during PM task interventions Dedicated data collection campaigns also detailed as an option to accelerate data collection. 17

18 5. Data collection & conversion tools (2 of 2) Manual specification conversion procedure and tool Nominated conversion manager reviews existing ASSET specification data and decides where to reallocate valid data into the newly implemented NAMEPLATE specification BWT CMMS team then perform bulk upload of data via the Work Management Procedure Advanced asset management practices procedure Detailed procedures were developed for data capture into the CMMS associated with work orders, operational data capture and asset condition inspection and assessment Various personnel implicated from maintenance staff to operations staff, management, asset management team, etc. 18

19 5. Work ticket management KPIs A key desired outcome of the pilot was to increase the knowledge of maintenance activities on an asset and organizational level. Several maintenance improvement areas were identified as important business outcomes including: Accurate and timely capture of the labor associated with maintaining, repairing and replacing assets, Accurate and timely capture of the cost of parts, materials, and other direct costs of maintaining assets, and Proper documentation on the cause of failure Performance Indicator WOs w/o Labor Hours (%) WOs w/o Spare parts or materials (1) CMs w/o Failure Code and Mode Rep. (%) PMs w/o Condition Inspection Results WOs w/ feedback (as determined by inspection results) WOs w/ operational data JOC WOs w/o cost information Req ts Contract WOs w/o cost information WOs w/o finishing comments Target Reduction No./Mo. No./Mo No./Mo No. /Mo No. /Mo No. /Mo No. /Mo No./Mo No./Mo

20 5. Maintenance Management KPIs Improved work order management provides valuable information needed to monitor the maintenance management processes. Maintenance Management improvement objectives are to: Increase use of KPIs to track Asset condition and reliability, and maintenance costs. Utilize asset maintenance costs, condition and other metrics to make informed Asset Management decisions. Measure overall maintenance function effectiveness Progress Goals KPIs Initial Frequency Target PMs Completed on-time ('+/- 10%) Increment M Mean time to repair x hrs Q Mean time between failure Stdy Q Equipment Availability 95% M; 12 month Open PM Work Tickets Tbd M; YTD Open CM Work Tickets Tbd M; YTD Work Ticket Backlog Tbd M; YTD Planned vs. Unplanned A; as Tbd Maintenance needed Regulatory excursions None TBD attributable to an asset

21 6. Tracking & reporting

22 6. Tracking & reporting using Crystal Reports The primary tool to track and report progress on data quality and maintenance management performance monitoring is a KPI report. Crystal Reports has limited flexibility to track trends but was considered a suitable tool to track at the outset of the project.

23 6. Tracking & reporting using Excel Dashboard WO DASHBOARD SELECT LOCATION BB CI HP JA MS MV NC NR OB OH PR RH RO SC TI UNKNOWN W6 WI NA AVERAGE TIME TO COMPLETE A WORK ORDER DAYS AVG CRAFT QUANTITY 1.3 AVG ORIGINAL ESTIMATE HOURS 12.8 AVG REVISED ESTIMATE HOURS 13.0 AVG CRAFT HOURS 9.3 AVG ORIGINAL ESTIMATE $423 AVG REVISED ESTIMATE $424 AVG LABOR DURATION AVG EXTERNAL COMMITED 35.2 AVG EXTERNAL COMMITED $1, AVG ACTUAL HOURS AVG ACTUAL AMOUNT AVG ACTUAL OVERTIME HOURS AVG ACTUAL OVERTIME 33.1 $ $59 TOTAL DATA POINTS CRAFT QUANTITY LABOR CRAFT HOURS ORIGINAL DURATION ESTIMATE HOURS ORIGINIAL ESTIMATE AMOUNT REVISED ESTIMATE HOURS REVISED ESTIMATE AMOUNT EXTERNAL COMMITTED HOURS EXTERNAL COMMITTED AMOUNT ACTUAL HOURS ACTUAL AMOUNT ACTUAL OVERTIME HOURS ACTUAL OVERTIME AMOUNT

24 7. Next Steps