Expert Opinion Valuation Method to Quantify Digital Water Metering Benefits
Abstract
:1. Introduction
2. Research Method
2.1. Structured Interview Development
2.2. Structured Interviews
2.2.1. Interview Design
- 1 question on ‘Consent’ to participate;
- 8 questions covering participant profiling (1 open-ended, 2 multi-choice and 5 rating);
- 71 questions seeking participants’ opinions of the benefits (grouped for knowledge/expertise into 13 blocks, open-ended); and,
- 1 question to ‘Close’ the interview (open-ended)
2.2.2. Interview Approval, Testing and Recruitment
2.2.3. Data Cleansing and Analysis Procedure
2.2.4. Quantitative Analysis
3. Results
3.1. Descriptive Statistics of Participant Profiles
3.2. Agreement Levels for DWM Benefits
3.3. Benefit Quantification and Model Development
3.3.1. Initial Analysis
3.3.2. Examination of Benefit Value Assessments
- A single number was converted to a range by adding and subtracting 5% from the nominated value (less was added or subtracted when the number was near the extremes—100% and 0%). (e.g., (L15) reduce 80% interpreted to 75% to 85%)
- Where a range was indicated by phrase, but not specifically provided, 10% was added or taken from the limit value provided. For example, “25% or more” was interpreted as 25–35%, “up to 100%” was interpreted as 90–100%. When the nominated number was 10% or under, or 90% or over half the value to the limit was added or subtracted (e.g., (D15) reduce at least 95% interpreted as 95% to 97.5%)
3.3.3. Quantification of Benefit Value
3.3.4. Probability Distributions of Benefit Value
3.3.5. Benefit Value Modelling
4. Limitations
5. Discussion
- Call Centre impact—rather than an expected decrease in calls as suggested by the benefit questions, many participants expected calls to actually increase, due to increased information and questions raised in the minds of customers as to what the information meant.
- Meter reading costs—many participants were skeptical that the meter reading cost-savings could be achieved and mention the new costs of data communication, collection and storage.
- Failing meter detection—the operating life of batteries was often noted as a limiting factor when considering the extended life of meters and meter failure detection.
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Years in the Water Industry | Metro | Regional | Independent |
---|---|---|---|
0–2 years | 2 | 1 | 0 |
3–5 years | 5 | 3 | 0 |
6–10 years | 6 | 6 | 0 |
11–20 years | 10 | 3 | 5 |
more than 20 years | 6 | 3 | 2 |
Total | 29 | 16 | 7 |
Group | Expertise/Experience | Metro | Regional | Independent | Total |
---|---|---|---|---|---|
Customer | Customer Service | 16 | 13 | 6 | 35 |
Communications and digital 1 | 0 | 0 | 1 | 1 | |
ADR, complaints 1 | 0 | 0 | 1 | 1 | |
Water efficiency 1 | 1 | 2 | 1 | 4 | |
Technical | Engineering Planning | 11 | 3 | 2 | 16 |
Engineering Operations | 7 | 2 | 1 | 10 | |
Metering | 10 | 8 | 3 | 21 | |
Planning and Maintenance 1 | 0 | 1 | 0 | 1 | |
Integrated Water Management 1 | 1 | 0 | 0 | 1 | |
Contract Management, Stakeholder Engagement 1 | 0 | 0 | 1 | 1 | |
Support | Senior Management | 5 | 5 | 2 | 12 |
Finance | 4 | 1 | 0 | 5 | |
Legal/Regulation/Corporate Services | 4 | 1 | 0 | 5 | |
Human Resources | 0 | 1 | 0 | 1 | |
Information Technology | 1 | 1 | 1 | 3 | |
Data science 1 | 0 | 1 | 0 | 1 | |
R&D 1 | 0 | 1 | 0 | 1 | |
Academic 1 | 0 | 1 | 1 | 2 |
Level | Level of Experience | Metro | Regional | Independent | Total |
---|---|---|---|---|---|
High | Full rollout of DWM | - | 4 | - | 4 |
In progress rollout of DWM | 1 | 2 | - | 3 | |
Completed or in-progress trial(s) of DWM | 8 | 2 | 2 | 12 | |
Minimal | Planning a trial(s) of DWM | 6 | 4 | 2 | 12 |
Peripheral role in metering and/or digital metering area | 10 | 1 | 3 | 14 | |
Public information and presentations on DWM only | 1 | 2 | - | 3 | |
Zero | No current knowledge or experience/water industry general knowledge only | 3 | 1 | - | 4 |
Agreement Level Score | Theme (Meaning) | Sample of Codes |
---|---|---|
4 | Strong Agreement (interpreted as a higher enthusiasm level than YES or PROBABLY, etc) | Absolutely, Definitely |
3 | Agree (interpreted as better than 50% chance) | Yes, Probably, Should, Agree |
2 | Somewhat agree (interpreted as up to 50% chance) | Possibly, Potentially, Potential, Somewhat, To some extent, It will not be a significant benefit, Minimal impact, To be confirmed |
1 | Weak agreement (interpreted as more than no chance, but less than possibly) | Maybe, Perhaps, Could, Doubtful, Unsure, Difficult to say, Not necessarily |
0 | Disagree (interpreted as no chance) | No, Highly doubtful, Customers expect this, Would not expect so, Not applicable, Not particularly, Not to my knowledge, A dis-benefit |
−1 | NULL answer | Do not know, No opinion, Not Sure, Have not heard of, I do not understand, Not my area, Unable to answer, Have not considered, ‘-‘ |
Benefit Value Score | Theme (Meaning) | Sample of Codes |
---|---|---|
5 | Reduce/improve Substantially | Substantial, Reduce substantially, Reduce dramatically, Up to 100%, Up to 3 mths of leak, Should disappear, No readers no incidents, Eliminate |
4 | Reduce/improve Significantly | Significant reduction, Vastly improve, Reduce by nearly 90%, Reduce significantly 70%, Rapid engagement, Rich data, Faster identify, Easier identification, Early resolution, Far less, Highly satisfied, Contribute largely |
3 | Reduce/improve | Reduce, Improve, Less, Gain, Useful, Tailored, Reduce unrecorded, Much easier, Not major, Not drastic, Not large, More accurate, Measurable, Better, Assist responsiveness, Delay a few years, Help |
2 | Reduce/improve Marginal | Marginal, Reduce slightly, Small, Very low, Reduce some, Not big, Add little, |
1 | Reduce/improve Negligible | Negligible |
Context: | Cost of Water | Charges/Operational Costs | ||
---|---|---|---|---|
Theme (Score) | Start Pct (%) | End Pct (%) | Start Pct (%) | End Pct (%) |
Disagree (no benefit, 0) | 0 | 0 | 0 | 0 |
Reduce/improve Negligible (1) | 0 | <1 | 0 | <25 2 |
Reduce/improve Marginal (2) | 1 | <3 | 25 | <45 2 |
Reduce/improve (3) | 3 | <10 | 45 | <75 |
Reduce/improve Significantly (4) | 10 | <15 | 75 | <90 |
Reduce/improve Substantially (5) | 15 | 25 1 | 90 | 100 |
Agreement Level Score | |||||
---|---|---|---|---|---|
Context: Cost of Water | 1 | 2 | 3 | 4 | |
Benefit Value Score | 4/5 1 | 0 | 1 | 43 | 3 |
3 | 12 | 14 | 126 | 4 | |
2 | 11 | 4 | 18 | 1 | |
1 | 1 | 1 | 0 | 0 | |
Context: Charges/Operational Costs | |||||
Benefit Value Score | 4/5 1 | 0 | 5 | 86 | 2 |
3 | 12 | 14 | 115 | 7 | |
2 | 0 | 6 | 19 | 0 | |
1 | 0 | 2 | 0 | 0 |
Context: Cost of Water | Number Answers Scoring This Benefit Value | Number Including Imputed Score 2 | Cumulative Frequency | Relative. Cumulative Frequency | ||
---|---|---|---|---|---|---|
Benefit Value Theme | Start of Range % | End of Range % | ||||
Disagree (no benefit) 3 | 0 | 0 | 24 | 24 | 24 | 5 |
Negligible | 0 | <1 | 21 | 21 | 45 | 10 |
Marginal | 1 | <3 | 54 | 54 | 99 | 22 |
Reduce/improve | 3 | <10 | 158 | 302 | 401 | 88 |
Significant | 10 | <15 | 49 | 48 | 449 | 99 |
Substantial | 15 | 25 1 | 4 | 5 | 454 | 100 |
Context: Charges/Operational Costs | Number Answers Scoring This Benefit Value | Number Including Imputed Score 2 | Cumulative Frequency | Relative. Cumulative Frequency | ||
---|---|---|---|---|---|---|
Benefit Value Theme | Start of Range % | End of Range % | ||||
Disagree (no benefit) 3 | 0 | 0 | 40 | 40 | 40 | 10 |
Negligible | 0 | <25 1 | 2 | 11 | 51 | 12 |
Marginal | 25 | <45 1 | 25 | 38 | 89 | 21 |
Reduce/improve | 45 | <75 | 148 | 232 | 321 | 77 |
Significant | 75 | <90 | 59 | 60 | 381 | 92 |
Substantial | 90 | 100 | 34 | 35 | 416 | 100 |
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Monks, I.; Stewart, R.A.; Sahin, O.; Keller, R.; Low Choy, S. Expert Opinion Valuation Method to Quantify Digital Water Metering Benefits. Water 2020, 12, 1436. https://doi.org/10.3390/w12051436
Monks I, Stewart RA, Sahin O, Keller R, Low Choy S. Expert Opinion Valuation Method to Quantify Digital Water Metering Benefits. Water. 2020; 12(5):1436. https://doi.org/10.3390/w12051436
Chicago/Turabian StyleMonks, Ian, Rodney A. Stewart, Oz Sahin, Robert Keller, and Samantha Low Choy. 2020. "Expert Opinion Valuation Method to Quantify Digital Water Metering Benefits" Water 12, no. 5: 1436. https://doi.org/10.3390/w12051436