1. Why Value Stream Mapping Matters in Drinking Water
In the realm of drinking water operations, extraction throughput is not the same as compliant potable output. A plant may abstract, treat and pump 120 ML/day, yet deliver less usable value after backwash, filter-to-waste, storage constraints, distribution interruptions and compliance holds are considered.
Value stream mapping exposes this difference by connecting the complete flow:
- Source water and raw-water intake
- Treatment barriers that remove contaminants
- Clear water storage and resilience
- Pumping and distribution
- Laboratory, customer and regulatory information flows
The fundamental purpose is to define value from the customer’s perspective: safe, reliable and compliant water available at the required pressure and volume. The EPA Lean & Water Toolkit describes value stream mapping as a visual representation of material and information flows, supported by current-state and future-state maps.
For a water utility, the output is not simply water leaving the plant. It is compliant potable water delivered with evidence, reliability and controlled cost per kilolitre.
2. Scope Selection: One Product Family and Clear Boundaries
To make the map actionable, select one product family:
A 120 ML/day surface water treatment plant serving approximately 210,000 people.
Project boundaries
Start: Raw water entering the intake structure
End: Treated water entering the clear water storage and distribution network
The map includes treatment, quality verification, storage, pumping, reservoir monitoring, customer complaints and regulatory reporting. It excludes upstream catchment management, household plumbing and wastewater treatment.
This boundary is broad enough to expose system-level constraints while remaining suitable for a focused Lean Six Sigma project charter. It also follows the principles outlined in our guide to scoping Lean Six Sigma projects during the Define Phase.
3. Current-State Map: From Raw Intake to Network Supply
The following current-state map uses a worked operating example. Actual regulatory limits must be confirmed against the applicable jurisdiction. For reference, the U.S. EPA’s Surface Water Treatment Rule turbidity guidance identifies stringent filtered-water turbidity expectations, while the CDC treatment overview describes coagulation, flocculation, sedimentation, filtration and disinfection as common treatment barriers.
| Process box | Current-state observations and worked numbers |
|---|---|
| 1. Raw water intake and screening | Average demand is 120 ML/day, with a 1.25 peak factor. Screens remove debris before pumping to treatment. Intake flow and raw turbidity vary with rainfall and catchment conditions. |
| 2. Coagulation and flocculation dosing | Operators adjust coagulant and polymer doses using jar tests and experience. Over-dosing can require pH correction, increasing chemical use and process complexity. |
| 3. Clarification | Settled water moves forward after floc formation and sedimentation. Sludge withdrawal and settled-water turbidity are recorded, but not always linked directly to downstream filter performance. |
| 4. Filtration | Design filter run is 48 hours, while the actual average is 31 hours. Backwash and filter-to-waste account for approximately 9% of raw water intake. |
| 5. Disinfection | Disinfectant dose, residual, contact time, pH and temperature must remain within approved operating requirements. Manual checks supplement online readings. |
| 6. Clear water storage | Average storage is 42 ML, equivalent to only 8.4 hours of coverage at the 120 ML/day demand rate, against a 24-hour target. |
| 7. Water quality laboratory testing | Laboratory results can hold filter-run, release and incident decisions. Sampling, testing, review and documentation are separate information steps. |
| 8. Pumping to network | Energy intensity is 0.42 kWh/kL. Pumping schedules respond to demand and reservoir levels, while unplanned pump downtime is 3.5%. |
| 9. Reservoir monitoring | Levels, pressure, residual disinfectant and alarms are monitored across the network. Data is available, but escalation rules are not always standardised. |
| 10. Consumer complaint handling | Reports about taste, odour, appearance or pressure initiate investigation. Complaint information is not consistently connected to process data and reservoir conditions. |
| 11. Regulatory reporting | A turbidity excursion triggers a boil-water risk review. The plant experiences 11 excursions per year, with an average of 72 hours required for full compliance reporting. |
Takt-style flow and capacity exposure
At the average demand basis:
[
120\text{ ML/day} = 120,000,000\text{ L/day}
]
[
120,000,000 \div 86,400 = 1,389\text{ L/s}
]
Therefore, the planning flow requirement is approximately 1,389 L/s against rated capacity of 1,500 L/s:
[
1,389 \div 1,500 = 92.6%
]
This leaves only 7.4% nominal capacity buffer under the planning basis. Applying the 1.25 peak factor creates a stress demand of 150 ML/day, or approximately 1,736 L/s. That exceeds the rated flow and demonstrates why storage, demand levelling and rapid response controls are essential.
The map therefore distinguishes between extraction throughput and verified, compliant output. Water lost through backwash, held for testing or constrained by storage does not provide equivalent customer value.
4. The Eight DOWNTIME Wastes in This Water Stream
The DOWNTIME framework makes the current-state map more diagnostic.
- Defects: Filter-to-waste water is sent to drain after turbidity concerns, representing output that has consumed treatment resources without reaching customers.
- Overproduction: Treating water ahead of demand can increase storage volumes, pumping energy and residence-time management requirements.
- Waiting: Laboratory results hold a filter-run decision or delay the release of treated water.
- Non-utilised talent: Experienced operators spend time reconciling paper logs instead of analysing trends and improving control strategies.
- Transportation: Samples, forms and approval documents move between plant, laboratory and regulatory teams when digital transfer could reduce handoffs.
- Inventory: Excess coagulant, polymer or disinfectant stock ages in storage and increases handling and control requirements.
- Motion: Operators walk between panels, sampling points, chemical areas and control stations to assemble information for one decision.
- Excess processing: Coagulant is over-dosed, followed by pH correction, creating additional chemical use and process adjustments.
These wastes must be evaluated alongside safety and compliance. A step should not be removed simply because it consumes time; its control purpose must first be understood.
5. Future-State Build: Flow With Faster Evidence and Stronger Controls
The future-state map should preserve every required treatment barrier while reducing avoidable delay, variation and information gaps.
1. Introduce jar-test driven dosing control
Build dose-response curves that connect raw-water conditions to settled-water turbidity, pH and chemical dose. Operators can then adjust dosing using evidence rather than relying primarily on individual experience.
2. Optimise filter runs
Use head loss, filtered-water turbidity and flow data to identify the practical run-time window. A turbidity-triggered backwash rule can replace fixed or precautionary triggers where validated.
A realistic initial target is to move from 31 hours to 42 hours per run without weakening filtered-water quality controls.
3. Strengthen online analysis and Andon-style response
Online turbidity, disinfectant residual, pH and flow analysers should have reviewed alarm limits, clear ownership and escalation rules. An Andon-style visual signal can alert the team in real time when a critical parameter approaches an action limit.
The control plan should define:
- Who receives the alarm
- What immediate verification is required
- Which process adjustment is permitted
- When laboratory confirmation is needed
- How the event is documented and closed
4. Build 24-hour storage coverage
Demand levelling, reservoir operating rules and coordinated pump scheduling should increase average storage from 42 ML to approximately 120 ML, equivalent to 24 hours at 120 ML/day demand.
The objective is not simply to hold more water. It is to create controlled resilience without encouraging excessive residence time or compromising disinfectant residual.
5. Generate one compliance evidence pack per event
Each excursion should automatically assemble:
- Time-stamped analyser data
- Laboratory results
- Alarm history
- Operator actions
- Filter, dosing and pump status
- Risk assessment
- Corrective and preventive actions
- Regulatory submission fields
This can reduce reporting time while improving traceability. A digital command centre such as Ci Flow at ciflow.app should be considered separately from any other platform and evaluated for methodology fit, evidence governance and data-security requirements.
6. Link maintenance to asset criticality
Pump, analyser, valve and control-system maintenance should be prioritised by failure consequence, detectability and service impact. Preventative maintenance intervals should be informed by asset condition and criticality rather than applied uniformly.
6. Current-State Versus Future-State Data
The future-state figures below are improvement targets for this worked example, not guaranteed outcomes.
| KPI | Current state | Future state | Delta |
|---|---|---|---|
| Filter run duration | 31 hours | 42 hours | +11 hours |
| Backwash and filter-to-waste loss | 9% | 5% | −4 percentage points |
| Storage coverage | 8.4 hours | 24 hours | +15.6 hours |
| Unplanned pump downtime | 3.5% | 1.5% | −2.0 percentage points |
| Turbidity excursions | 11/year | 3/year | −8/year |
| Energy intensity | 0.42 kWh/kL | 0.36 kWh/kL | −0.06 kWh/kL |
| Full compliance reporting time | 72 hours | 24 hours | −48 hours |
| Customer complaints | 4.8 per 1,000 connections | 3.0 per 1,000 | −1.8 per 1,000 |
7. 90-Day Kaizen Sequencing
A disciplined sequence prevents the team from automating an unstable process.
Days 1–30: Establish the baseline
Actions
- Baseline backwash, filter-to-waste and total water loss
- Standardise filter logs and run-time definitions
- Review turbidity and analyser alarm limits
- Confirm measurement methods and data ownership
Owners
- Water Treatment Manager
- Laboratory Manager
- Process Improvement Lead
- Control Systems Engineer
Target KPI movement
- 100% standardised filter logs
- 95% data completeness
- Verified baseline for all eight future-state KPIs
- Alarm ownership assigned for every critical parameter
Days 31–60: Tune the process and storage system
Actions
- Develop coagulant dose-response curves through structured jar testing
- Trial a turbidity- and head-loss-based backwash trigger
- Define reservoir minimum, target and maximum operating levels
- Test demand-levelling and pump-scheduling rules
Owners
- Process Engineer
- Shift Operations Supervisor
- Distribution Network Manager
- Energy Manager
Target KPI movement
- Filter run average increased from 31 to 36+ hours
- Backwash loss reduced from 9% to 7% or less
- Storage coverage increased to 12–16 hours
- Energy intensity reduced to 0.39 kWh/kL
Days 61–90: Control and sustain
Actions
- Launch the automated compliance evidence pack
- Approve a criticality-based maintenance plan
- Finalise the control plan and monthly audit schedule
- Review customer complaints against treatment and reservoir data
Owners
- Compliance Manager
- Asset Manager
- Operations Manager
- Lean Six Sigma Black Belt or programme lead
Target KPI movement
- Reporting time reduced to 24–36 hours
- Unplanned pump downtime below 2.5%
- Turbidity excursions reduced to a run-rate of six or fewer per year
- Monthly audit compliance above 95%
8. Build the Capability to Improve Water Utility Performance
Value stream mapping provides the visual structure. Lean Six Sigma provides the analytical discipline to validate causes, quantify variation and sustain gains.
For water utility engineers and operations leaders, Green Belt training is the practical route to lead projects involving process capability, measurement systems, root-cause analysis, control charts, designed experiments and structured DMAIC delivery. Black Belt training develops the advanced capability required to lead complex compliance, energy, asset reliability and cross-functional transformation programmes.
Lean 6 Sigma Hub offers CSSC-accredited, self-paced online training with worked examples, real-world simulations, dummy data, charts and end-to-end DMAIC case studies. The training is designed to help professionals learn by doing and apply improvement methods in operational settings.
Enrol in Lean Six Sigma Green Belt or Black Belt training to build compliance confidence, reduce cost per kilolitre and lead a more reliable water value stream.
Kaizen. Kai-Care. Kai-Done. Lean Six Sigma
