Business Case 2012

SAFE/DW Business Analysis Deliverable Template

projectmanagement.com[Project Name] Business Case

[Project Name]Business Case

Yellow Highlighting = Text to be ReplacedBlue Highlighting = Example Text

Table of Contents

31. Introduction

1.1. Purpose31.2. Scope31.3. Distribution31.4. Intended Audience31.5. Copyright31.6. References42. Executive Summary42.1. Scope Statement52.2. Business Case Summaries53. Recommendations84. Cost Benefit Analysis104.1. Estimated DW Costs104.2. Business Case Value & Rankings114.3. Hard ROI Cost Benefit Analysis135. Business Cases165.1. Waste Water Treatment Analysis165.2. Treatment Process Regulatory Requirements Analysis305.3. Maintenance Scheduling Decision Support425.4. Other Notable Business Cases576. Prototype Results586.1. Prototype Overview586.2. Business Benefits606.3. Data Elements617. Attachment A Source Data Descriptions627.1. CMMS627.2. Source Control627.3. LIMS637.4. FIS637.5. CRISP647.6. Operations65

1. Introduction

1.1. Purpose

[Project Leader] is pleased to present this [Project Name] Business Case document to [Project Customer/Sponsor]. [Project Leader] appreciates the time [Project Customer/Sponsor] associates have spent with us to help us understand the critical business issues you face. The Business Case document captures the vision we have jointly created outlining how [Project Leader] can assist [Project Customer/Sponsor] in confronting their critical issues.

This document contains the deliverables that were produced for the [Project Name] Business Case project by [Project Leader].

1.2. Scope

This document includes the deliverables for the [Project Name] Business Case project for [Project Customer/Sponsor]. It does not include intermediate documents (e.g., the Project Tracking Documents).

This document does not include a detailed Work Breakdown Structure (WBS) for the next phase or detailed costing information. These items will be provided to [Project Customer/Sponsor] in a separate document to assist them in developing the next components of the [Project Name].

1.3. Distribution

NameCompanyProject Role

Chief Information Officer

Client Project Manager

***

Account Manager

Project Manager

Data Warehouse Specialist

Senior Business Analyst

1.4. Intended Audience

This document is intended to be used by [Project Customer/Sponsor] and by [Project Leader] staff. It will be the basis for budget justification, as well as the basis for continued project-related work at [Project Customer/Sponsor]s [location/division].

Project team members, project reviewers and project executive sponsors are the intended audience for this document.

1.5. References

[Project Customer/Sponsor] Strategic Information Plan (SIP)[Pub Date][Project Leader] Business Case Proposal

[Pub Date]2. Executive Summary

[Project Customer/Sponsor] engaged [Project Leader] to develop their [Project Name]-related business cases. The project goal was to define and prioritize the optimal three candidates for [project type] projects, selected from the recommendations of the Strategic Information Plan (SIP). The deliverables from this project provide the necessary information to budget and plan the first several [Project Type] projects.

In order for [Project Customer/Sponsor]s [Project Type] process to be fully successful it must be effectively aligned with the business needs, efficiently implemented with best practices and methodologies, and innovative in applying best of breed technology customized to [Project Customer/Sponsor]s business environment. The Business Case project specifically addresses the first criteria, which is ensuring that the [Project Type] plan is effectively targeted towards business justified strategic processes. The three business cases analyzed were:

Waste Water Treatment Analysis

Treatment Process Regulatory Requirements Analysis

Maintenance Scheduling Decision Support

[Project Customer/Sponsor]s mission is to protect the environment and public health by cost-effectively treating wastewater while complying with federal, state and local regulations. To align with the overall organization mission, the DW projects must support business processes that enable cost-effective treatment while maintaining health and compliance with federal, state and local regulations. Two of the candidate projects (Treatment Process Regulatory Requirements Analysis, Maintenance Scheduling Decision Support) address business processes targeted at cost savings through more efficient usage of labor, equipment and materials. The third candidate project, Treatment Process Regulatory Requirements Analysis, addresses compliance and political awareness of public and environmental health. Both individually and as a whole the DW projects will assist the district in obtaining improved cost effective business processes. Being more efficient will improve the agencies political standing on issues such as benchmarking, lowering the cost per million gallons of water, reducing odor complaints, protecting the community and environment health, and promoting community awareness and good will. Managing these political concerns will reduce the threat of privatization.

Prior to the DW business case project, [Project Customer/Sponsor] completed the Strategic Information Plan (SIP) which included the high level business cases for a DW and established the feasibility for applying DW technology to [Project Customer/Sponsor]s information technology requirements. The SIP was confirmed to be a current and valid representation of [Project Customer/Sponsor]s business requirements for information technology, and as such, the SIP was leveraged as a starting point for the Business Case project.

The three candidate projects were selected from the information provided in the Strategic Information Plan (SIP) and interviews with the project sponsors. The selection criteria used to determine the top three candidates included business justifications, anticipated return on investment (ROI) and perceived ability to complete successfully on do-ability. Through business user focus sessions the business justification, potential ROI and do-ability for each of the three candidate DW projects were refined. Based on the analysis performed in this phase, the Waste Water Treatment Analysis was selected as the first Data Warehouse population project. The other two business cases, Maintenance Scheduling Decision Support and Treatment Process Regulatory Requirements Analysis, would be accomplished by the next two population projects by adding the additional source systems and additional elements in the existing Data Warehouse.

Additionally, [Project Leader] has developed a Data Warehouse prototype, using actual [Project Customer/Sponsor] data, to demonstrate the Data Warehouse capabilities to [Project Customer/Sponsor] staff.

2.1. Scope Statement

The deliverables are focused on the three candidate projects. All business analysis and data research was directly related to these three project areas. Focus session attendees were chosen by the project sponsor based on the scope of the three candidate projects. All attendees expressed support for the applicability of the DW and many were enthusiastic proponents who are eagerly looking forward to the system being available.

It was noted by the [Project Leader] staff that conducted the focus sessions that there was a very strong cultural cohesiveness to the attendees of the sessions. The territorial frictions that often negatively effect focus sessions containing representatives from many departments was noticeably absent from the sessions conducted at [Project Customer/Sponsor]. This is usually indicative of a very positive and productive working relationship between departments. This collaborative relationship is a key factor in the success for a DW project.

The deliverables of the project are intended to provide clear business justifications together with user provided cost benefits and industry standard costing estimates for executing these projects. The estimates provide sufficient detail and accuracy for budgeting and approval purposes. All volumes, timings and other similar figures obtained in support of this business case are estimates only and are based upon the data that has been obtained from [Project Customer/Sponsor] during the focus sessions and follow on meetings. Additional task information, work breakdown structure (WBS) and costs will be provided in a separate document.2.2. Business Case Summaries

Significant business justifications exist for each of the three candidate DW projects. The merits of each case are detailed in Section 5, Business Cases. The following section provides a short description of the business cases summarizing the substantial business justifications.2.2.1. Waste Water Treatment Analysis

Treating wastewater is the primary function of [Project Customer/Sponsor] and all operational expenses are in some way related to this process.

The Waste Water Treatment DW will provide timely access to integrated lab sample data, CRISP operational metrics with FIS costing information to enable analysis of the treatment process and plant operations.

The business justifications are substantiated and include cost savings in the following areas:

Optimize the entire treatment process by closely tracking, trending and benchmarking the efficiencies within each plant process and the over-all cost efficiency of the entire plant. Enable a reduced labor force to work smarter with timely, accurate, accessible, integrated information which provides knowledge for timely tactical adjustments to the treatment process.

Accurately track, measure and benchmark cost benefits in plant process or chemical use adjustments. Enable management to work smarter with historical trend analysis and costing analysis providing strategic knowledge for medium to long term tuning of the entire plant process which will lead to more cost effective contracts for materials and capital projects.

Save chemical cost by reduced usage through discovery of efficiencies and the narrowing of tolerances.

Save chemical costs by improving delivery schedules, improved tracking of actual deliveries and actual quantities delivered by vendors and by using this knowledge as leverage to improve vendor contracts.

Reduce or eliminate duplicate data entry of lab samples, plant process information and other key data.

Reduce manual integration and reporting of lab and operational information.

Empower a more productive workforce with a DW providing the business knowledge base and a tool for analysis, reporting and decision support.

2.2.2. Treatment Process Regulatory Requirements Analysis

Meeting Environmental Protection Agency (EPA) regulatory requirements is a must-do business process for [Project Customer/Sponsor].

The Regulatory Requirements Analysis DW will support a majority of the EPA reporting requirements and enable analysis of wastewater treatment compliance through the integration of lab sample data with regulatory information.

The substantial business justifications include cost savings in the following areas:

Allow cost effective support of regulatory reporting, internal or external research, audits, lawsuit inquires and source data for plume graphs.

Reduce or eliminate duplicate data entry and manual research of lab reports which will automate the construct regulatory reports.

Enable a reduced labor force to work smarter with timely, accurate, accessible integrated information which provides knowledge for timely tactical adjustments to the treatment process where constituents approach EPA thresholds.

Enable management to work smarter with historical trend analysis providing strategic knowledge to tune the entire plant process to confidently maintain compliance while reducing costs.

2.2.3. Maintenance Scheduling Decision Support

Maintenance and Scheduling is a cross-functional process vital to ensuring undisrupted and effective processing of waste water.

The Maintenance Scheduling Decision Support DW will provide integrated CMMS maintenance information with SCADA/CRISP operational metrics such as fixed asset related runtime, temperature and analog measurements. While portions of the Maintenance & Scheduling process are addressed by specific systems (such as CMMS) the entire knowledge base of information required to plan efficient schedules does not exist. The DW will address the needs for comprehensive, integrated and historical information to support appropriate scheduling.


Eliminate ineffectual manual research of process flow information to determine maintenance requirements.

Produce maintenance cost savings by moving from a calendar interval based maintenance to an actual usage/need based maintenance.

Save the costs of unneeded maintenance on equipment with unexpected low usage.

Save the investment in fixed assets by triggering timely preventative maintenance on unexpected high usage equipment.

Validate or tune optimal calendar/interval based maintenance where actual usage is very close to expected planned usage.

Save near-end-of-life high maintenance costs by projecting optimal fixed asset replacement schedules. An average of 60% of the maintenance costs are incurred in the last 2 years. A majority of these cost will be saved by using the DW to determine the replacement schedules.

Save cost by negotiating more cost effective capital expenditure contracts by scheduling predictive replacement. Provide engineering and contractors lead time to more cost effectively planning capital replacements, retrofits and capital upgrades.

Save the costs of down-time, emergency fixes and potential permit violations due to equipment malfunctions.

Enable maintenance and scheduling management to work smarter in utilizing the labor, materials and equipment. Long term, the business can only be effectively supported by an optimal down-sized labor force if the labor is tasked with a right-sized work load. Empowered by the DW analysis and planning tools, management can reduce labor, materials and equipment costs while confidently meeting maintenance requirements.

3. Recommendations

The recommended [Project Leader] approach to the analysis, design and construction of a DW project for [Project Customer/Sponsor] is presented in this section. This approach is based on the [Project Leader] DW methodology that is utilized globally in the design and construction of data warehouses.

The road map below outlines the stages contained within the development of a DW. The arrow pattern illustrates which portions of the process are iterated. Once the overall DW Plan and Architecture have been developed and the scope defined during the Business Question Assessment, stages from that point forward are repeatable, constituting separate iterations of the Data Warehouse. [Project Leader] have completed the first four steps of the process for [Project Customer/Sponsor]. The next step in the process for [Project Customer/Sponsor] is Architecture Design and Review.

The remaining recommended process steps for [Project Customer/Sponsor] in the development of your Data Warehouse include:

Develop a Data Warehouse Architecture that covers Data, Application, Technology and Support Architectures.

Proceed with a Data Warehouse Tool Selection to determine what tools will be needed by the Data Warehouse.

Once the Architecture has been defined, develop the Iteration Implementation Plan which will identify in detail what the first three to four iterations of the Data Warehouse will include.

Proceed with the Detailed Design and Implementation of the first iteration of the DW.

The Iterations or Population Projects identified over a 19 month period will include:

Iteration #Time FrameBusiness Case SupportedSource Systems IncludedHistory LoadedNumber of Supported Users

1. (3 4 months)Waste Water TreatmentCRISP

LIMS

FIS

SOME EXCEL SSSix months12

2. (2-3 months)Operational Data Store and Enterprise Wide Data Warehouse IntroducedCRISP

LIMS

FIS

+ ADDITIONAL EXCEL SSSix months25

3. (3 4 months)Regulatory RequirementsCRISP

LIMS

FIS

EXCEL SS DATA

+ EPA/ECMOne Year50

4. (3 4 months)Maintenance & SchedulingCRISP

LIMS

FIS

EXCEL SS DATA

EPA/ECM

+ CMMSEighteen months75

5. (3-4 months)Source ControlCRISP

LIMS

FIS

EXCEL SS DATA

EPA/ECM

CMMS

+ PERMIT DATATwo Years75+

As the iterative process continues, additional elements will be selected from the existing input systems, in addition to the new source systems added. The order of the population projects above was determined from the analysis of the Business Cases reviewed during the current phase.

The Operational Data Store (ODS) and Enterprise Wide Data Warehouse (EWDW) are shown to be introduced after the first iteration. If [Project Customer/Sponsor] desires, the ODS and EWDW could be held back one iteration to allow for a second data mart to be constructed before them.

The order for the iterations is based on anticipated return on investment, overall organizational knowledge gained, and ease of implementation. It would be possible to change the order of implementation based on changing requirements from [Project Customer/Sponsor].

4. Cost Benefit Analysis

4.1. Estimated DW Costs

Industry cost figures for the design and implementation of enterprise wide data warehousing and data marts are difficult to obtain. Research indicates that some organizations are spending more than $10 million over five years. Most organizations are reluctant to release their budget and cost figures. Additionally, the uniqueness of each organization's hardware, software and labor requirements results in very different requirements and costs.

The figures presented below are considered by [Project Leader] to be representative estimates of a [Project Customer/Sponsor] DW project over the first five years of the project. The costs fall into three general categories: hardware, software and labor. The actual costs greatly depend on the [Project Customer/Sponsor] resources, tool selection, architecture and the detailed planning and design that will be accomplished as the DW project progresses. Based on the information gained during the project, all necessary features of a DW have been included in the comprehensive cost estimates. As the DW architecture is developed and tools are evaluated and selected, the costs will become firmer estimates.

The cost figures represent the initial design and construction of the enterprise wide DW and several Data Marts, and the intended support of the entire project through the five year point.

The costs reflected in the Total DW cost of Ownership chart are based on the following:

Hardware and software costs are based on current average costs. These estimates are obtained from [Project Leader] worldwide DW engagements of like construction, and [Project Leader] staff knowledge of our partner vendors costs.

Software Maintenance is based on the industry norm 15% of the purchase price, charged annually after the first 12 months of use.

Operational costs are based on industry norms.

Design and Implementation costs represent estimated costs for development labor.

4.2. Business Case Value & Rankings

Selection and prioritization of the DW population projects includes two key dimensions: business justifications and execution do-ability. These analysis areas were addressed by two main activities: (i) focus sessions to determine business justification and data requirements; and (ii) the source data analysis to determine the general availability of the source data. Those two areas have been cross-analyzed to determine the relative value of source data.

The first section below summarizes the relative rankings of the business cases, taking into consideration the availability and quality of the source data and considering the estimated DW project costs to address the business case.

The second section summarizes the relative rankings of the source data subject areas. It takes into consideration costs as compared to relative values that are defined by the supported business case justifications.

A pleasant, but not unanticipated conclusion is that the two subject areas (Lab and CRISP) are highly valued by many areas of the organization. A DW project that includes both subject areas will serve a wide range of business needs.4.2.1. Business Case Rankings

Summary Version of Rankings including Value Estimates.

Full Version of Rankings including Value Estimates with detail metrics.

Value of Source Data Based on Business Questions

4.3. Hard ROI Cost Benefit Analysis

These graphs illustrate the hard return on investment (ROI) analysis.

The basis for these graphs are the DW costs by iteration as compared with the Estimated Business Value (Hard ROI). The first graph uses the total cost for the enterprise wide data warehouse. The subsequent three graphs include the costs for building just one data mart, compared with the value of each business case.

These ROI justifications were based solely on estimated Hard ROI and do not reflect the priceless value of the many Soft ROI justifications for each business case.

1. Enterprise Wide DW Return on Investment

The ROI point at 24 months after beginning implementation is based on the combined value of all three business cases. The estimated yearly hard ROI values of each business cases were prorated over the months. ROI begins to accumulate after an iteration has been implemented into the production environment.

2. Waste Water Treatment Analysis Return on Investment

The graph shows where the cumulative value of the Waste Water Treatment Analysis DW exceeds the estimated cost of building the data mart.

3. Treatment Process Regulatory Requirements Analysis Return on Investment

The graph shows where the cumulative value of the Treatment Process Regulatory Requirements Analysis DW exceeds the estimated cost of building the data mart.The estimated $250,000,000 cost of building a full secondary treatment plant and the $11,000,000 yearly operations costs were not included as part of this ROI analysis.

4. Maintenance Scheduling Decision Support Return on Investment

The graph shows where the cumulative value of the Maintenance Scheduling Decision Support DW exceeds the estimated cost of building the data mart.

5. Business Cases

The following sections contain the full detail of business justifications for each of the three candidate data warehouse projects. Additionally, the focus session results are summarized.

5.1. Waste Water Treatment Analysis

5.1.1. Business Case Summary

Treating wastewater is the primary function of [Project Customer/Sponsor] and all operational expenses are in some way related to this process.

The Waste Water Treatment DW will provide timely access to integrated lab sample data, CRISP operational metrics with FIS costing information to enable analysis of the treatment process and plant operations.5.1.2. Business Benefits / Justification

The following sections define the business justifications in terms of the business needs expressed in the Strategic Information Plan (SIP) and the Focus Sessions.

While hard dollars are difficult to determine there are clear return on investment scenarios and business justifications.

Focus Session Business Questions

The business questions indicate the business value that the DW will provide to the users. The following business questions were directly applicable to the scope of the business case and were derived primarily from the specific focus session. A few questions are from separate interviews and other focus sessions.

How can chemical usage be managed to improve their application?

How can chemical inventory control be managed to improve their application?

How much does it cost to control odors in the plant?

Are we capturing all the costs associated with the operation on a process? Often we (operations) are asked and we have to substantiate (the cost of a treatment process).

Do changes to the operations processes affect the final effluent over time?

Can a relation be determined versus changes in the environment?

Proposed Prototype Questions

[Project Customer/Sponsor] users (mostly operations) proposed the following questions as possible prototype questions in the area of Waste Water Treatment Analysis. Although all of these did not fit within the prototype scope, they do all demonstrate the valuable knowledge a DW will provide to the business.

These queries exemplify how a DW will provide quick and accurate decision support for common business questions and business measures of success. Currently some of these questions are being partially answered via redundant manual data gathering, consolidation, validation and assembly.

1. Pounds of Cationic Polymer per cubic feet of sludge produced.

2. Efficiency of Primary Treatment.

3. Efficiency of Trickle Filter (secondary).

4. Efficiency of Activated Sludge (secondary).

5. Efficiency of Digester.

6. Efficiency of entire plant.

7. Concentration of Bio-solids (liquid %).

8. Concentration of H2S Hydrogen Sulfide (STINKS!).

9. Chart Increase chemicals & results in response to specific problem.

10. Source Trunk Constituent concentrations vs. same period last year. Provides a basis for decisions on plant 1 & plant 2 blend.

11. How much power was used over time:

vs. flow.

vs. power generated, exported, imported.

vs. digester gas used, produced.

vs. natural gas used.

12. Odor cost, (to prevent H2S release and related odors).

Caustic Soda use & soda cost in scrubbers.

Peroxide use & cost in influent.

Benefits / Value Statements

The business justifications are substantiated and include cost savings in the following areas:

Optimize the entire treatment process by closely tracking, trending and benchmarking the efficiencies within each plant process and the over-all cost efficiency of the entire plant. Enable a reduced labor force to work smarter with timely, accurate, accessible, integrated information which provides knowledge for timely tactical adjustments to the treatment process.

Accurately track, measure and benchmark cost benefits in plant process or chemical use adjustments. Enable management to work smarter with historical trend analysis and costing analysis providing strategic knowledge for medium to long term tuning of the entire plant process which will lead to more cost effective contracts for materials and capital projects.

Save chemical cost by reduced usage through discovery of efficiencies and the narrowing of tolerances.

Save chemical costs by improving delivery schedules, improved tracking of actual deliveries and actual quantities delivered by vendors and by using this knowledge as leverage to improve vendor contracts.

Reduce or eliminate duplicate data entry of lab samples, plant process information and other key data.

Reduce manual integration and reporting of lab and operational information.

Empower a more productive workforce with a DW providing the business knowledge base and a tool for analysis, reporting and decision support.

The following are the focus session value statements (Soft ROI) which were directly applicable to this business case.

1. No checks and balances covering deliveries of chemicals.

2. Bleach can go bad (perishable) if we do not use all that was purchased.

3. If too much chemical inventory on hand, there is an interest charge on payables.

4. Time spent ordering chemicals.

5. Without the trending information we have situations where we may order too much too little chemicals. This results in un-necessary delivery charges and related labor.

6. Currently the various units of measure make validations impossible manually. For example, the delivered quantity could be liquid weight, the billing quantity dry weight and the meter usage in gallons.

7. Currently there is no information to evaluate if plant process changes produced a true cost/benefit. To some extent results are seen, and separately costs, but no study of the cost/benefits of different treatment scenarios can be done.

8. A process improvement will have a ripple effect on cost savings. For example; improved Digesters would increase methane for electricity, save polymers, save truck hauling costs of Bio-Solids, and slightly increase the value of Boi-Solids concentration as a denser fertilizer since it is sold based on solids not tonnage. All of these cost saving process improvements also results in cleaner effluent to the ocean!

9. Better usage of:

Consumables Chemicals

Energy

Oxygen Systems

10. Reduce our concentrate of solids and reduce shipping charge.

11. Reduce building of process facilities through efficiency.

12. Increase production of methane.

13. Improve EMC research capabilities.

14. Improve assembly of TPOD reports for Operation 820 group.

15. Reduce or eliminate recalculating information for various reports.

16. Reduce reconciliation time to validate billing.

17. Reduce reconciliation time to validate various reports to include in larger reports (like the annual report).

18. In-plant air quality has a choice of processes they can employ. There is a potential for a change of process resulting in additional ROI.

Return on Investment

The following are the ROI scenarios expressed by the business users during the focus sessions and related interviews. It is understood that these net benefits will be very difficult to measure because both pre and post DW measurements are elusive. After implementing the DW, many of the benefits will be noticeable and quantifiable. A few of these ROIs are applicable to more than one business case.These numbers were directly used in the Ranking Spreadsheet to evaluate the relative values of the candidate business cases.

See section 4, Cost Benefit Analysis.

Hard ROI Estimates from the business Users

Potential to save $ 500,000 on chemical usage. Assumes 10% savings on $5 million/year on chemicals.

An additional 1 5% of chemical costs could be saved by more accurately tracking and verifying vendors deliveries of chemicals. Some vendor deliveries are not measured at all, while most deliveries are only eye-balled for verification. Some deliveries are billed in different units of measure which prevent manual verification.

Labor Cost Savings

15% of ECMs time is spent researching and collecting data.

Estimated 1 Full Time Equivalent employee (FTE) organization wide to do manual data collection, consolidation and validation.

At least 2 FTE saved across the various operations groups due to duplicate data entry, consolidation, verification and reviews of manually constructed reports. The 2nd FTE is directly related to creating the MSO report which includes about 7 sections, about 50 sheets, from all the different divisions.

10% FTE spent re-keying lab results

10% FTE spent on Calculation Sheet which for the most part could be printed from the DW, although a few manual steps and analysis remains.

Total 3.35 FTEs

Strategic Impact

Treating waste water is [Project Customer/Sponsor]s core business function. All expenditures and all operations are in some way directly related to this core business function. The DW will provide a wealth of information on [Project Customer/Sponsor]s core processes. The organizations goal is to operate the treatment process in the most cost effective methods while preserving the health of the community and environment.

The success of [Project Customer/Sponsor] in effectively meeting the needs of the EPA regulations and the Orange County community and industries depends on the processing running reliably. The DW will enable a large cross section of the entire organization to have timely access to lab results and plant processing information which is vital to their daily work load.

[Project Customer/Sponsor] faces the constant threat of privatization. The DW will provide the analysis tools necessary to identify efficiencies and accurately measure the effects of process improvements. Currently there is no tool for the analysis or measurement of efficiencies.

[Project Customer/Sponsor]s Critical Goals for FY 1997 include the following goals which are supported by the DW:

Competitiveness & Reorganization

Integrate computer system to link users and gain on-line access to data bases

Monitoring & Compliance

Negotiate changes in monitoring requirements in our ocean discharge permit to add flexibility, target data needs and improve cost effectiveness

Develop new information products to communicate Districts compliance performance.

Success / Do able

The critical success factors of a DW include:

Management Sponsorship

User Sponsorship

Business Justification & Budget Data Available, accessible. sufficient quality, appropriate level of detail and can be transformed to a view consistent with the business questions

Data Warehouse process: methodology, project management and skilled labor

The Business Case project assessed these success factors. The information provided in the focus session and interviews indicates that there is sufficient sponsorship and business justification for the DW. The users indicated that the data does exist. It is sufficiently accurate and available at the right level of detail to support a DW design, which transforms this information into a subject oriented decision support system. While the evidence indicates that these success factors are in place, they remain critical success factors to manage during the DW process.

It appears that the data sets coming directly from LIMS and CRISP will be accurate and can be directly transformed into a DW. The data from manual sources will be data quality risks that must be mitigated through quality controls. That data includes some chemical usage information and manually monitored meter readings. The process of building the DW with quality controls will assist the organization in improving the quality of the manually entered data.

Assuming the Critical Success Factors are met, the data quality risks are managed, and an appropriate DW process is executed this DW implementation will be successful.

Demand

The demand for the DW is supported by the following [Project Customer/Sponsor] organizations and business needs.

All Operations Groups

Assists Source Control by providing improved access and quality of the lab results data and the plant processing data.

Assists the Maintenance and Operations department by providing access and quality data for plant processes.

Assists ECM group by providing the lab results and plant process information.

Priority / Alignment with SIP Recommendations & Strategies

Below the SIP Investment Strategies and Recommendations which are directly related to this DW business case or are partially met by the DW business case.

This analysis was based solely on the information contained in the SIP document. The SIP summaries of the processes, applications, databases and strategies led to these conclusions and it is possible omitted details of these recommendations may have altered the conclusions.

Investment Strategy: 2, 5, 9, 10a, 12, 13, 18, 20a, 20b, 22

2.Manage Data Application This strategy is partially met by the DW because for the data subject areas the DW includes (LIMS, CRISP) the manage data requirements are met, such as providing historical data at a finer level of detail to evaluate trends and generating reports.

5.Meet Regulatory Requirements The DW partially supports this strategy by providing integrated LIMS and CRISP data which is used for regulatory reporting.

9.Plant Process Database The DW partially covers this strategy by providing the analysis and reporting needs that support the plant process operation, yet the DW does not cover the operational enhancements related to this strategy.

10a.Treat Wastewater Application The DW covers a majority of this strategy by providing integrated accessible treatment process information including both lab and CRISP data.

12.Discharge Wastewater Effluents Application Since the DW includes the effluent lab results the DW covers all the basic requirements for this strategy.

13.Sample/Analyze Wastewater Application Most of the requirements for this strategy is related to the DW supplied functionality including: analysis of large data sets, reporting, trending, graphs, etc.

18.Sample Database This strategy recommendation includes tracking and making available lab and Source Control Samples. This need is met by a combination of the three systems (LIMS, Source Control) and this DW.

20a.Control Influent Characteristics Application This strategy is partially met by the DW in the area of making influent lab results at a detail constituent level accessible to all areas of the organization.

20b.Effluent Database This strategy is mostly met by the DW in the areas of combining effluent lab results with ODES and FIS to provide integrated data with good reporting and analysis tools.

22.Influent Database This strategy is mostly met by the DW by integrating lab influent samples with CRISP process flows information for organization wide access and analysis.

Recommendation: 4, 9, 13, 14, 15, 18, 19 24, 26, 27, 28

These are the SIP recommendations which are at least partially addressed by the DW or would be integral to the DW. Several recommendations were not included in the list if they were simply data sources and not also integral to related business processes.

Many of these recommendations are directly covered by this DW Business Case. The Database Recommendations (13, 24, 27, 28) and Integrated Database Recommendations (4, 15) appear to be included in the DW. The analysis, reporting and decision support aspects of many process/application recommendations (9, 14, 18, 19, 26) also appear to be met by the DW.

4.Data Warehouse The candidate DW is a proposed phase to this recommendation.

9.Meet Regulatory Requirements Application The DW mostly covers this recommendation by providing integrated information from LIMS and CRISP which can be used to support regulatory analysis and reporting.

13.Plant Process Database The DW is integral to this recommendation. The DW provides the trending analysis to determine the operational enhancements and rules for automated adjustments. The DW covers a majority of this recommendation since it will model a majority of the relevant data.

14.Plant Automation (Treat Waste Water Process Application) Similar to the prior recommendation: the DW will provide the trending analysis to determine the operational enhancements and rules for automated adjustments. The DW would not include the actual operational automation which is covered by a system such as CRISP.

15.Integrating Plant, Maintenance, Lab and Financial Data The DW covers a majority of this recommendation by providing the integrated data store for these subject areas.

18.Discharge Wastewater Effluents Process Application The DW will likely cover all the required functionality for this recommendation by providing an accessible data store for all reporting and decision support on effluents.

19.Sample/Analyze Wastewater Process Application The DW will likely cover all of the required functionality for this recommendation by providing a decision support system including source data from LIMS, CRISP and ECM if necessary to meet this recommendation.

24.Sample Database Similarly to the prior recommendation, the DW will provide the data store for analysis of lab samples.

26.Control Influent Characteristics Process Application The DW is integral to the process of controlling influent. The DW will provide the analysis and knowledge base for personnel to make influent decisions and adjustments to operational systems.

27.Effluent Database The DW will likely cover all of the required functionality for this information by providing a decision support data store for effluents.

28.Influent Database The DW will likely cover all of the required functionality for this information by providing a decision support data store for influents.

Alignment with existing IT projects / applications / architecture

The DW will support (and not overlap) with the planned LIMS and CRISP upgrades and implementations.

The LIMS system will continue to expand to address the operational needs of the lab. The DW will use LIMS as a data source and integrate the lab data with plant process data in a subject oriented manner to allow appropriate cross trend analysis.

The SCADA/CRISP system will continually grow in the number of PLCs in place to monitor and control the treatment process. Currently there are sufficient PLCs in place to feed the DW operational metrics which will enable analysis and optimization of maintenance schedules. As new PLCs are deployed, the DW will expand to include analysis over a greater number of capital assets and a finer level of detail.5.1.3. Critical Success Factors and Inhibitors

Standard DW Critical Success Factors

Sponsorship at all levels of the organization is critical. Executives must sponsor the budget and encourage the organization to participate to ensure success. End users must be an integral part of the process to realize the benefits.

The quality and the source data must be ensured. The focus sessions determined that the data was perceived to be available and of the requisite quality, but that has not been confirmed by detailed analysis.

The DW is a new process for the information technology department. For the project to be successful they will need to augment their staff if DW skilled resources. They will also need to execute the process using a tried and true methodology and manage the process with sound project management techniques.

There must be a DW architecture in place. The DW process must fall within the guidelines of the organization's architecture. The DW architecture provides the framework for new iterations or enhancements to be built efficiently without re-inventing the wheel and without invalidating prior designs.

The DW is a new information technology tool for the organization. The DW process must include sufficient lead time for the users to become acclimated to the decision support tools and experience the learning curve from simple queries to true analysis.

The training costs on usage of DW DSS tools must be addressed. This can also be a benefit since users can learn one reporting environment instead of multiple systems with multiple views of the data and multiple reports/spreadsheets.

Specific Critical Success Factors for the Treat Waste Water Analysis DW

The chemical usage information and some critical meter readings are currently being obtained and tracked manually. As part of the DW process, these manual tasks must be included as part of the over-all data quality method. The data must be obtained, data entered on a timely basis, and validated.

Standard DW Inhibitors

Training costs on usage of DW DSS tools must be addressed. This can also be a benefit since users can learn one reporting environment instead of multiple systems, and multiple reports/spreadsheets.

Source system changes must be coordinated with the DW process. Source system bugs and down-time will affect the data quality and data flow to the DW. Source system design changes will affect data transformation / mapping for the DW. These are standard DW risk that can be mitigated by implementing DW methods such as data quality programs, enterprise wide data modeling techniques and metadata. The various systems are kept synchronized by utilizing an architected framework for the technology lifecycle.

Specific Inhibitors for the Treat Waste Water Analysis DW

There is sufficient information being tracking in CRISP to begin providing immediate benefits to data warehousing, but CRISP must be greatly expanded to include all of the data points that the various departments would like to analyze.

This DW is dependent on the LIMS application roll-out as a data source. LIMS went live in early January and as such is likely to have some bugs to be worked out. This could effect the quality and timely delivery of lab results data to the DW.

Source Control enhancements will continue and must be coordinated with the DW process by conforming with the DW architecture.

Manual Logs are included as a data source for the DW. These include manual readings for chemical levels, chemical usage and manual meter readings for meters that have not or can not be integrated with CRISP. Some of this data is consolidated to the Monthly Summary of Operations (MSO) reports and the MSO may be used as the direct source for the DW. Some manual processes will be necessary to ensure consistency of the format, quality of the content and possibly to manually trigger data loads when the data is complete.

Portions of the MSO Reports may be required source data for the DW. Since these are manually maintained, additional quality controls must be implemented to ensure consistent and accurate data for the DW. Some manual processes will be necessary to ensure consistency of the format, quality of the content and possibly to manually trigger data loads when the data is complete.

5.1.4. Assumptions

The information provided in the focus sessions and interviews is sufficiently accurate for the conclusions documented in this deliverable. It is further assumed that the estimated hard & soft return on investment statements represent reasonable estimates sufficient for budgeting.

Continuing the DW process requires that the Critical Success Factors and Inhibitors be addressed. This includes sponsorship, budget, data quality and an effective DW process.

The Cost Benefit Analysis was based on industry standard metrics for DW costing. The actual cost will vary based on actual scope, designs and suitability of the current technology infrastructure. The DW architecture process will refine these estimates through more detailed analysis of the requirements, scope and technology infrastructure gaps.

5.1.5. Business Process Model

Processes:

1. Bio-solid transport truck is weighed after loading. The trucks unloaded weight is subtracted to determine the load weight. The truck load report is sent to Accounting to trigger a payment.

2. The chemical vendor truck arrives and pumps chemicals into holding tanks. The delivery is visually confirmed by the level increase in the holding tank. The driver has a receipt outlining the weight or volume of the delivery. The Chemical Delivery Receipt is sent to Accounting and triggers an invoice payment. These chemicals are purchased under a blanket agreement with the vendor.

3. On the first of the month, staff in the plant take physical readings (flows, pressures, chemical levels). These are reported on the First of Month Readings Report. This report goes to the 820 group and is primarily used to reconcile with Accounting.

4. There is more than one type of Operators Log. They are used to record log information that is especially important to the process being monitored; pressures, levels, chemicals, and temperatures. They also capture verbiage about unique occurrences and abnormalities.

5. Verbiage from the Operators Logs provides information to the Plant Monthly Report.

6. Readings from the Operators Logs provide information to the Monthly Summary of Operations.

7. Manual Meter Readings that concern chemicals are recorded on the Chemical Inventory and summarized from there on the MSO.

8. Manual Meter Readings from the plant are one of the main sources of data for the MSO and the Night Report.

9. The Central Generation process gets information about digester gases produced from the manual meter readings. From this information, data from the gas company (imported natural gas), and internal instrumentation, measurements is used to calculate and report on the MSO:

Internal gas consumed

External gas consumed

Power produced

Power imported

Power exported

10. CRISP takes real-time readings in the plant: flows, levels, pressures, and temperatures. Some of these are recorded in CRISPs internal history files. Reports of this history are sent to the Control Center and entered into the MSO.

11. The Water District X and the Water District Y are contacted by phone to report reclaimed water usage for the MSO.

12. The Plant Monthly Report is a paragraph-form report that describes events in the plant. This is sent to the 820 group to eventually be included in the Operations and Maintenance Department Monthly Report.

13. The Monthly Summary of Operation is the assembled source of data about the plants operation, production, consumption and performance. The MSO includes LAB, CRISP, CenGen and manually compiled data. Some information from the plant enters a computer for the first time in this report. It is available online and is used throughout CSDOC. It is also sent directly to the 820 group for further analysis and to be included in the Operations and Maintenance Department Monthly Report.

14. The Night Information, also known as Process Information, is the assembled readings of log data from the plant. It is available online and is used as the first word in plant operations. Although it is mostly meter readings and measurements, there is a Note space at the bottom of the form that includes descriptive information about the plants condition entered from the Operators Logs.

15. The Operations staff in the Control Center does some additional analysis on the MSO numbers to illustrate efficiencies in the plant. This information is sent to the 820 group to assist the development of the TPOD (Treatment Plant Operational Data).

16. On regular intervals the lab takes samples from various areas in the plant and runs tests on them. The results of these tests are summarized on the Daily Lab Results Sheet and sent to the 820 group and plants (this is currently an Excel spreadsheet and will be migrated on to the LIMS system).

17. Using several of the reports that are sent from the plant operations, the 820 group reconciles usage information with payment information in Accounting. This includes consumables, chemicals and power usage.

5.1.6. Source Data Analysis

The DW will include data from the following sources. During user interviews it was confirmed that this data does exist at a sufficient level of detail and can be joined with other data sets to create a consistent business view.

LIMSLab Sample results (influents, in plant processing, effluents)

CRISPActual plant processes (run times, flows, meter readings)

FISCosts of materials / Chemicals and fixed assets

OperationsNon-CRISP readings come from manual data entry in operations, for Chemical usage and various meters.

5.2. Treatment Process Regulatory Requirements Analysis


Meeting Environmental Protection Agency (EPA) regulatory requirements is a must-do business process for [Project Customer/Sponsor].

The Regulatory Requirements Analysis DW will support a majority of the EPA reporting requirements and enable analysis of wastewater treatment compliance through the integration of lab sample data with regulatory information.5.2.2. Business Benefits / Justification

The following sections define the business justifications in terms of the business needs expressed in the Strategic Information Plan and the Focus Sessions.

While hard dollars are difficult to determine, there are clear return on investment scenarios and business justifications.



1. Are we operating the waste water process according to the EPA requirements on a continuous basis? Both in the operations and in maintenance. 2. ECM Are we reporting the information that we need to report in a timely fashion?

Are standard reports (Monthly, Quarterly, Yearly) on schedule according to permit to avoid fines?

Are violations and complaints reported within 3 days?

3. Do changes to the Operations processes affect the final effluent over time?

Can a relation be determined versus changes in the environment?

Effects that are caused could be in compliance but could cause a cumulative non-compliance.

4. We have a higher and increasing concentration of Cadmium but our influents dont reflect that. Why? We asked Operations and were told that it is because the Digesters have a longer retention time than other agencies, which cause a build up of heavy metals. Can we substantiate this with the trend analysis capabilities of a DW?

5. Would like to be able to look for specific trends for identified constituents.

6. Would be useful to use the DW to trigger flags or warnings so that they know to document something that has happened.

7. We are sitting on a gold mine of information, but cant get the pieces (nuggets) out.



Allow cost effective support of regulatory reporting, internal or external research, audits, lawsuit inquires and source data for plume graphs.

Reduce or eliminate duplicate data entry and manual research of lab reports which will automate the construct regulatory reports.

Enable a reduced labor force to work smarter with timely, accurate, accessible integrated information which provides knowledge for timely tactical adjustments to the treatment process where constituents approach EPA thresholds.

Enable management to work smarter with historical trend analysis providing strategic knowledge to tune the entire plant process to confidently maintain compliance while reducing costs.


1. It is harder to negotiate each permit cycle (5 years) because the customer base is increasing and therefore volumes increase to [Project Customer/Sponsor]. The EPA wants to write the waiver based on volume not concentration.

2. Need information to educate the EPA on how we effectively protect the public health and environment even though total volume of waste water may appear higher than what they perceive as being an issue.

3. Although direct fines may not be levied, providing the EPA with timely and accurate reporting builds a better relationship that can assist [Project Customer/Sponsor] with negotiating a more acceptable permit.

4. Effects that are caused could be in compliance but could cause a cumulative non-compliance.

5. Currently have about 2 years of history on-line, but the permit requires 5 years. The DW is the perfect repository for historical information and allows operational systems to run more efficiently by purging historical information that can be more effectively accessed in the DW.

6. Source Control is audited every year or two, plus the EPA and regional board call to question things on a regular basis. The DW would make these audits smoother by providing easy access to consistent information.

7. The DW could trigger flags or warnings so that they know to document something that has happened.

8. We are sitting on a gold mine but cant get the pieces out.


The following are the estimated return on investment scenarios expressed by the business users during the focus sessions and related interviews. It is understood that these net benefits will be very difficult to measure because both pre and post DW measurements are elusive. After implementing the DW, many of the benefits will be noticeable and quantifiable. A few of these ROIs are applicable to more than one business case.These numbers were directly used in the Ranking Spreadsheet to evaluate the relative values of the candidate business cases.

See section 4, Cost Benefit Analysis.

Hard ROI Estimates from the Business Users

Extensive labor costs in consolidating information.

15% of ECMs time is spent researching and collecting data.

Estimated 1 FTE organization-wide to do manual data collection, consolidation and validation.

At least 2 FTE saved across the various operations groups due to duplicate data entry, consolidation, verification and reviews of manually constructed reports. This includes at least one FTE in operations across employee name 1s group, employee name 2s area , employee name 3, employee name 4, and others each spending 20-30% of their time manually manipulation information that will be provided directly from the DW. The 2nd FTE is directly related to creating the MSO report which includes about 7 sections, about 50 sheets, from all the different divisions.

5% FTE in manual labor searching for data for the annual reports.

=>Total 3.20 FTEs

Fines for incorrect information reported.

Are consistently late but the EPA has been lenient and not levied the potential daily fines of up to $25,000.

Potential Clean Water Act, Air Quality violations. There are penalties defined.

Could lose waiver for discharge of secondary, which would cost the construction of a full secondary treatment plant.

Cost of full secondary treatment plant is $239,000,000 with an annual operation cost of $11,500,000. This is all in 1989 dollars.

Currently in the 8th year extension for the previous 5 year permit. Gave the info to the EPA in 1990.

Strategic Impact

[Project Customer/Sponsor] must have a strategy to leverage its relationship with the EPA (and related agencies). The EPA could have severe detrimental revenue and political impact on [Project Customer/Sponsor] through late reporting penalties, non-compliance penalties, raise poor political press, and the ultimate trauma revoke [Project Customer/Sponsor]s permit.

The DW will provide [Project Customer/Sponsor] with a reporting and knowledge base to pro-actively manage its relationship with the EPA. The DW will assist [Project Customer/Sponsor] in avoiding penalties by providing timely and accurate reporting. The DW will also provide a knowledge base for research, evidence and justification showing that [Project Customer/Sponsor] overachieves in compliance, protecting the environment, operating cost effectively and is a model treatment facility.

[Project Customer/Sponsor]s Critical Goals for FY 1997 include the following goals which are supported by the DW:

Competitiveness & Reorganization

Integrate computer system to link users and gain on-line access to data bases.

Monitoring & Compliance

Negotiate changes in monitoring requirements in the ocean discharge permit to add flexibility, target data needs, and improve cost effectiveness.

Develop new information products to communicate Districts compliance performance.

Success / Do able

The critical success factors of a DW include:

Management Sponsorship

User Sponsorship

Business Justification & Budget Data Available, accessible and of sufficient quality, appropriate level of detail, and can be transformed to a view consistent with the business questions.

Data Warehouse Process: methodology, project management, and skilled labor.

The Business Case project assessed these success factors. The information provided in the focus session and interviews indicates that there is sufficient sponsorship and business justification for the DW. The users indicated that the data does exist, is sufficiently accurate and available at the right level of detail to support a DW design. The DW transforms this information into a subject oriented decision support system. While the evidence indicates that these success factors are met, they remain critical success factors to manage during the process.

It appears that the data sets coming directly from LIMS and CRISP will be accurate and can be directly transformed into a DW. The data from manual sources will be data quality risks that must be mitigated through quality controls. That data includes some chemical usage information and manually monitored meter readings. The process of building the DW with quality controls will assist the organization in improving the quality of the manually entered data.

Assuming the Critical Success Factors are met, the data quality risks are managed, and an appropriate DW process is executed, this DW implementation will be successful.

Demand

The demand for the DW is supported by the following [Project Customer/Sponsor] organizations and business needs:

ECM regulatory reporting.

Lab trend analysis.

Source Control will use the DW as a source system for some information they already track and can also use it for new trend analysis.

Portions of the MSO reports will be covered by the DW.

Priority / Alignment with SIP Recommendations & Strategies

Below the SIP Investment Strategies and Recommendations which are directly related to this DW business case or are partially met by the DW business case.

This analysis was based solely on the information contained in the SIP document. The SIP summaries of the processes, applications, databases and strategies led to these conclusions and it is possible omitted details of these recommendations may have altered the conclusions.

Investment Strategy: 1, 2, 5, 7b, 10, 12, 13, 18, 20a, 20b, 22

1.Document Database The Regulatory Documents could be cross referenced from the DW.

2.Manage Data Application This strategy is partially met by the DW because the data subject areas that the DW includes (LIMS, CRISP) to manage data requirements are met. Examples include providing historical data at a finer level of detail to evaluate trends and generating reports.

5.Meet Regulatory Requirements The DW directly supports regulatory reporting and analysis

7b.Law/Regulation/Code Database The DW could include the strategy to provide all districts with access to basic regulation information.

10a.Treat Waste Water Application Since this DW will include both lab and CRISP information, the DW partially over-laps with this strategys requirements.

12.Discharge Wastewater Effluents Application Since the DW includes the effluent lab results the DW covers all the basic requirements for this strategy.

13.Sample/Analyze Wastewater Application Most of the requirements for this strategy are related to the DW supplied functionality including: analysis of large data sets, reporting, trending, graphs, etc.

18.Sample Database This strategy recommendation includes tracking and making available lab and Source Control Samples. This need is met by a combination of the three systems (LIMS, Source Control) and this DW.

20a.Control Influent Characteristics Application This strategy is partially met by the DW in the area of making influent lab results at a detail constituent level accessible to all areas of the organization.

20b.Effluent Database This strategy is mostly met by the DW in the areas of combining effluent lab results with ODES and FIS to provide integrated data with good reporting and analysis tools.

22.Influent Database This strategy is mostly met by the DW by integrating lab influent samples with CRISP process flows information for organization wide access and analysis.

Recommendation: 4, 9, 12, 13, 14, 15, 18, 19, 24, 26, 27, 28

These are the SIP Recommendations which are at least partially addressed by the DW or would be integral to the DW. Several Recommendations were not included in the list if they were simply data sources and not also integral to related business processes.

4.Data Warehouse The candidate DW is a proposed phase to this recommendation.

9.Meet Regulatory Requirements Application The DW appears to cover all of the requirements for this application by providing the knowledge base and reporting needs for the regulatory requirements business process.

12.Law/Regulation/Code Database The DW covers a majority of this recommendation by providing the database to track regulation/permit related metrics. The DW would also reference any document images that may be related to this recommendation.

13.Plant Process Database The DW compliments this recommendation by providing the over-all plant effectiveness measures. Since the DW will include plant process metrics from CRISP and LIMS results, the DW will directly support the analysis and reporting portions of this recommendation.

14.Plant Automation (Treat Wastewater Process Application) Similar to the prior recommendation, the DW will compliment this application by providing the trending analysis to determine the operational enhancements, and measure adjustments against regulatory goals and thresholds. The DW would not include the actual operational automation which is covered by a system such as CRISP.

15.Integrating Plant, Maintenance, Lab and Financial Data The DW partially covers this recommendation by providing the LIMS and CRISP data.

18.Discharge Wastewater Effluents Process Application The DW will likely cover all the required functionality for this recommendation by providing an accessible data store for all reporting and decision support on effluents.

19.Sample/Analyze Wastewater Process Application The DW will likely cover all of the required functionality for this recommendation by providing a decision support system including source data from LIMS, CRISP and ECM if necessary to meet this recommendation.

24.Sample Database Similarly to the prior recommendation, the DW will provide the data store for analysis of lab samples.

26.Control Influent Characteristics Process Application The DW will be integral to the process of controlling influent. The DW will provide the analysis and knowledge base for personnel to make influent decisions and adjustments to operational systems.

27.Effluent Database The DW will likely cover all of the required functionality for this information by providing a decision support data store for effluents.

28.Influent Database The DW will likely cover all of the required functionality for this information by providing a decision support data store for influents.

Alignment with Existing IT Projects / Applications / Architecture

The CRISP, LIMS and Source Control systems will continue to be enhanced to meet the operational needs of those departments. The DW addresses a separate need for an integrated view of the lab results and plant process information. The DW serves a cross section of users for each department who need to do cross-functional analysis.

The DW project is aligned with the following projects in ITs pipeline.

Data Integration

Strategic Information Architecture

FIS / Oracle Gateway

Data Extraction from Plant Control System

The DW will not overlap or interfere with the following projects in ITs pipeline. All of these projects will improve the source data available to the DW.

CMMS upgrades

LIMS upgrades

Source Control Upgrades

CRISP upgrades.

5.2.3. Critical Success Factors and Inhibitors

Standard DW Critical Success Factors

Sponsorship at all levels of the organization is critical. Executives must sponsor the budget and encourage the organization to participate to ensure success. End users must be an integral part of the process to realize the benefits.

The quality and the source data must be ensured. The focus sessions determined that the data was perceived to be available and of the requisite quality, but that has not been confirmed by detailed analysis.

The DW is a new process for the information technology department. For the project to be successful they will need to augment their staff with DW skilled resources. They will also need to execute the process using a proven methodology and manage the process with sound project management techniques.

There must be a DW architecture in place. The DW process must fall within the guidelines of the organizations architecture. The DW architecture provides the framework for new iterations or enhancements to be built efficiently without re-inventing the wheel and without invalidating prior designs.

The DW is a new information technology tool for the organization. The DW process must include sufficient lead time for the users to become acclimated to the decision support tools and experience the learning curve from simple queries to true analysis.

The training costs on usage of DW DSS tools must be addressed. This can also be a benefit since users can learn one reporting environment instead of multiple systems with multiple views of the data and multiple reports/spreadsheets.

Specific Critical Success Factors for the Meet Regulatory Requirements DW

The chemical usage information and some critical meter readings are currently being obtained and tracked manually. As part of the DW process, these manual tasks must be included as part of the over-all data quality method. The data must be obtained, entered on a timely basis, and be validated.

Standard DW Inhibitors

Training costs on usage of DW DSS tools. This can also be a benefit since users can learn one reporting environment instead of multiple systems, multiple reports/spreadsheets.

Source system changes must be coordinated with the DW process. Source system bugs and down-time will affect the data quality and data flow to the DW. Source system design changes will affect data transformation / mapping for the DW. These are standard DW risk that can be mitigated by implementing DW methods such as data quality programs, enterprise wide data modeling techniques and metadata. The various systems are kept synchronized by utilizing an architected framework for the technology lifecycle.

Specific Inhibitors for the Meet Regulatory Requirements DW

There is sufficient information being tracking in CRISP to begin providing immediate benefits to data warehousing, but CRISP must be greatly expanded to include all of the data points that the various departments would like to analyze.

This DW is dependent on the LIMS application roll-out as a data source. LIMS went live in early January and as such is likely to have some bugs to be worked out. This could effect the quality and timely delivery of lab results data to the DW.

Source Control enhancements will continue and must be coordinated with the DW process by conforming with the DW architecture.

Manual Logs are included as a data source for the DW. These include manual readings for chemical levels, chemical usage and manual meter readings for meters that have not or can not be integrated with CRISP. Some of this data is consolidated to the Monthly Summary of Operations (MSO) reports and the MSO may be used as the direct source for the DW. Some manual processes will be necessary to ensure consistency of the format, quality of the content and possibly to manually trigger data loads when the data is complete.

Portions of the MSO Reports may be required source data for the DW. Since these are manually maintained, additional quality controls must be implemented to ensure consistent and accurate data for the DW. Some manual processes will be necessary to ensure consistency of the format, quality of the content and possibly to manually trigger data loads when the data is complete.

5.2.4. Assumptions

The information provided in the focus sessions and interviews is sufficiently accurate for the conclusions documented in this deliverable. It is further assumed that the estimated hard & soft return on investment statements represent reasonable estimates sufficient for budgeting.

Continuing the DW process requires that the Critical Success Factors and Inhibitors be addressed. This includes sponsorship, budget, data quality and an effective DW process.

The Cost Benefit Analysis was based on industry standard metrics for DW costing. The actual cost will vary based on actual scope, designs and suitability of the current technology infrastructure. The DW architecture process will refine these estimates through more detailed analysis of the requirements, scope and technology infrastructure gaps.

5.2.5. Business Process Model

The process models depict the business processes which interact with the DW project.

[Model not available]

Processes:

1. Source Control sends the Source Control Monthly Report and the Source Control Annual Report to both the EPA and Regional Water Quality Control Board (RWQCB) as an NPDES requirement.

2. Source Controls permit holders send information about their effluent performance. Depending on the type of monitoring that Source Control is conducting for the permit holder, this could include:

Copy of current water bills.

Results of tests (self testing).

Information about business changes.

Other agencies that are users of the system are also monitored. This is done with a Memorandum of Understanding (MOU). These documents are similar to permits but are for agencies such as:

IRWD

SAWPA

XYZ Sanitation District.

3. The Lab provides most of the information to Source Control for reporting. Specifically this includes the Discharge Monitoring Report (DMR) and the Priority Pollutant Analysis Reports as well as test results for the Source Control samples.

4. Source Control uses selected items (such as Ammonia levels) from Operations for their reporting. This information arrives on the TPOD report.

5. Source Control tracks all trucking firms that move bio-solids from the plants. The list is sent to the Health Department to verify that the trucking firms are permitted to transport biological waste.

6. Operations, Maintenance and Solids Management Annual Report is an NPDES requirement. It is sent to Environmental Compliance and Monitoring (ECM) for distribution to the EPA and RWQCB.

7. Ocean samples gathered by ECM are sent to the Lab for analysis.

8. Outside contractors are also used to gather and analyze ocean samples. These results are returned directly to ECM.

9. Biological solids removal firms report back to ECM with information about the final destination and use of bio-solids. This is reported to the EPA.

10. When a spill occurs, it is generally reported to Operations from somewhere in the community.

11. When a spill is reported to Operations, they dispatch the Collections group. Collections go out into the field to gather information about the spill and create an action plan to deal with it.

12. Spill information is routed to Collections for response action. Once Collections has assessed the situation, the actions taken or help request is communicated to Operations.

13. Collections are the response team for spills. As such, they must keep ECM updated about the status of any spill that has occurred. This ranges from a report on the action taken to a request for additional outside services (i.e. Police, CalTrans, Office of Emergency Services).

14. Whenever a sewage spill occurs, the Orange County Health Department is notified immediately. This is a requirement but it is not reported on an official document. It is generally handled by phone.

15. Whenever a sewage spill occurs, the EPA is phoned for an initial report. The spill will eventually be documented if it is over 1000 gallons. The EPA may or may not request this report.

16. Whenever a sewage spill occurs, the RWQCB is notified immediately. This will eventually be reported in writing if the spill is over 1000 gallons.

17. During any sewage spill, if outside assistance is needed the following agencies are notified by phone:

Police: Safety, Public Assistance.

CalTrans: Roads.

PFRD: Storm Drains (Public Facilities and Resources Department).

OES:Emergency Service (Office of Emergency Services).

18. For every sewage spill over 1000 gallons, a spill report is written and sent to the RWQCB.

19. All spills are summarized on a monthly spill report to the RWQCB.

20. Operations provide information for reporting to ECM on a regular basis. This includes a variety of data such as:

Spill data.

Volumes of bio-solids produced.

Plant information.

21. There is a large amount of information that flows between the Lab and ECM.

The DMR goes to ECM for certification before the Lab publishes it to the EPA or RWQCB.

Lab results

Reports

Bio-solids information.

22. The Lab releases the DMR report to the EPA and RWQCB monthly. The report contains biological and chemistry data about influent and effluents for CSDOC.

23. The Ocean Monitoring Report (OMR) containing information about ocean impact is sent to the EPA annually.

24. The Ocean Monitoring Report (OMR) containing information about ocean impact is sent to the RWQCB annually.

25. The EPA requires an annual Bio-Solids Report from ECM. This report deals with the volume and disposition of the solid biological waste produced by the CSDOC dewatering process.

26. The Operations, Maintenance and Solids Management Annual Report is forwarded to the EPA by ECM. The report is constructed by Operations and Maintenance.

27. The Operations, Maintenance and Solids Management Annual Report is forwarded to the RWQCB by ECM. This report is constructed by Operations and Maintenance.

28. The Ocean Data Evaluation System (ODES) is an electronic transmittal of biological and chemical analysis to the EPA. This is similar to the information contained in the DMR. ECM transmits the information to ODES, but the data is fed by the Lab.

29. The Surveillance and Enforcement Monitoring System (SAEMS) is an electronic transmittal of Chemistry information to the RWQCB. It is sent directly from the Lab.

30. Although there are Air Quality Permit regulations (such as the release of Sulfides) the reporting is currently exercised during the application for new permits rather than on a fixed schedule.

31. The National Pollutant Discharge Elimination System (NPDES) is the vehicle that the EPA uses to regulate [Project Customer/Sponsor]. The EPA is unable to administer and enforce NPDES in every state so the administration is often pushed down to the local level. In this state, this task has been given to the State Water Resource Control Board who administers it through the Regional Water Quality Control Board. This is why most of the regulatory reports issued by [Project Customer/Sponsor] go to the RWQCB and the EPA.

5.2.6. Source Data Analysis

The DW will include data from the following sources. During user interviews it was confirmed that this data does exist at a sufficient level of detail and can be joined with other data sets to create a consistent business view.

LIMSLab Sample results (influents, in plant processing, effluents)

CRISPActual plant processes (run times, flows, meter readings)

OperationsNon-CRISP readings come from manual data entry in operations, for Chemical usage and various meters.

ECMThe permit limits can be manually entered into the DW

5.3. Maintenance Scheduling Decision Support


Maintenance and Scheduling is a cross-functional process vital to ensuring undisrupted and effective processing of waste water.

The Maintenance Scheduling Decision Support DW will provide integrated CMMS maintenance information with SCADA/CRISP operational metrics such as fixed asset related runtime, temperature and analog measurements. While portions of the Maintenance & Scheduling process are addressed by specific systems (such as CMMS) the entire knowledge base of information required to plan efficient schedules does not exist. The DW will address the needs for comprehensive, integrated and historical information to support appropriate scheduling.5.3.2. Business Benefits / Justification

The following sections define the business justifications in terms of the business needs expressed in the Strategic Information Plan and the Focus Sessions.

While hard dollars are difficult to determine, there are clear return on investment scenarios and business justifications.



1. How much is the total cost to control odors in the plant?

2. What is my O&M cost trend been for a given area at an asset level (e.g. pump station). Fixed asset cost, maintenance costs, utilities costs and replacement cost.

3. Notice of when process (meter) range is way out of range. The meter itself needs service.

4. What are the people/equipment/material requirements for a planned maintenance activity?

5. HR systems like safety/injury data for process areas to determine training costs and safety impacts for our processes.

6. What is the electrical load and requirements for existing electrical equipment?

7. How much do we electricity to we generate?

8. Trend analysis to determine actual Mean time between failure analysis will make PMs more cost effective.

9. Trend analysis to predict when major problems will occur. The Board has cut staff, when will the deferred maintenance cost more than the cost savings?



Eliminate ineffectual manual research of process flow information to determine maintenance requirements.

Produce maintenance cost savings by moving from a calendar interval based maintenance to an actual usage/need based maintenance.

Save the costs of unneeded maintenance on equipment with unexpected low usage.

Save the investment in fixed assets by triggering timely preventative maintenance on unexpected high usage equipment.

Validate or tune optimal calendar/interval based maintenance where actual usage is very close to expected planned usage.

Save near-end-of-life high maintenance costs by projecting optimal fixed asset replacement schedules. An average of 60% of the maintenance costs are incurred in the last 2 years. A majority of these cost will be saved by using the DW to determine the replacement schedules.

Save cost by negotiating more cost effective capital expenditure contracts by scheduling predictive replacement. Provide engineering and contractors lead time to more cost effectively planning capital replacements, retrofits and capital upgrades.

Save the costs of down-time, emergency fixes and potential permit violations due to equipment malfunctions.

Enable maintenance and scheduling management to work smarter in utilizing the labor, materials and equipment. Long term, the business can only be effectively supported by an optimal down-sized labor force if the labor is tasked with a right-sized work load. Empowered by the DW analysis and planning tools, management can reduce labor, materials and equipment costs while confidently meeting maintenance requirements.


1. Fixed assets and program expenditures are based on the cost analysis research. The total odor management cost analysis will lead to savings in labor, chemicals, instruments and materials.

2. Predictive maintenance analysis will enable identification of optimal replacement time prior to the final 2 years where a majority of the maintenance costs occur. See graphs below.

3. Enable management to schedule more effective maintenance providing the following benefits:

Optimal workforce.

Optimal inventory usage.

Optimal inventory replenishment.

Optimal equipment utilization.

4. Predictive maintenance will enable lowering Inventory on hand.

5. Assigning of labor more efficiently while extending useful life of asset.

6. Mean time between failure analysis will make PMs more cost effective.

7. If the wrong type of meter malfunctions (such as process feeding or chemical meter) then there is an additional cost during the time it is out of service.

8. If out of scope trends are captured, larger fixed assets could be saved by fixing problems before it is too late.

9. 60% of maintenance occurs in last 2 years of use for fixed assets. The sample graphs that we drew last week. Currently dont track costs at equipment level and dont track expected life of equipment.

10. Currently, O&M notices that maintenance has increased and then start a 3 year process to acquire the new equipment. End up keeping equipment 4 to 5 years too long.

11. It is estimated that [Project Customer/Sponsor] has about $1 billion in capital equipment that is maintained. Predictive maintenance based on actual usage together with predictive replacement will extend the useful life possibly save 10% or more in capital investments.

12. A DW in this arena will probably solidify standards. Example: For a 100hp pump, we need the following PLC TAG drops; vibration, temperature, runtime. This could help feed Engineering planning.

13. Determining actual Mean Time Between Failures and expected Life will provide knowledge for more cost effective negotiations with vendors.

14. Predictive maintenance will reduce stop and wait time and thus save on labor costs.

15. Prevent specific types of injuries and the associated costs.

16. By doing electricity load shedding during peak and critical situations and with proper maintenance, could possibly realize 10 25% savings of total energy costs.

17. Trend analysis to predict when major problems will occur. The Board has cut staff, when will the deferred maintenance cost more than the cost savings?

18. Increased efficiencies in cost per million gallons is good press / good politics for the board.

19. Increased efficiencies will also compare well on the benchmark studies thus reducing the chances of privatization.

20. The analysis will allow [Project Customer/Sponsor] to weed out equipment models which are under performing and either negotiate more appropriate pricing or replace them with higher quality models.

21. Maintenance & Scheduling IT Priorities (Scheduling and planning module for CMMS. Operational changes.

Warehouse integration. Operational changes.

Cost at equipment level, decision support. Business and strategic benefits. Major dollars here.

Business practices first, then the better tools (like hand held devices).

Restructure business practices, change work processes, then supply the best tools.


The following are the estimated return on inv

Date post:	09-Mar-2016
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