The American Society for Quality (ASQ) defines Six Sigma as a method that gives organisations tools to improve the capability of their processes, and distinguishes it from Lean: Lean focuses on reducing waste, Six Sigma on reducing variation. Lean Six Sigma combines the two to reduce variation, waste and cycle time, and many implementations start with Lean and then apply statistical tools if process problems remain. ^[1]

Both approaches began in manufacturing, but the US Environmental Protection Agency describes Lean as a philosophy that has been extended to government and the service sector to help agencies identify and eliminate waste in their processes. ^[3] The difference in services is that the “product” is invisible: a patient file or licence application moving between offices, with nobody seeing how long it waits between one step and the next.

Where does waste hide in hospitals and government agencies?

The EPA guide uses the acronym DOWNTIME to remind teams of eight types of waste when observing day-to-day work. ^[3] The table below translates them into illustrative service examples:

The Institute for Healthcare Improvement (IHI) says the journey towards Lean begins with identifying the value-added and non-value-added steps in every process, and that waste includes money, time, supplies and goodwill. ^[2]

How do you apply DMAIC to a service project?

DMAIC is the most widely adopted Six Sigma methodology; its phases are define, measure, analyse, improve and control. ^[1] In services, each phase needs a practical translation:

• Define: state the problem in the service user’s terms, such as “time to issue a licence”, not in management language.
• Measure: record the baseline before any change; without it you cannot prove improvement.
• Analyse: walk with the file or the patient through the actual workplace (the gemba) and record where it waits, is returned or is reworked.
• Improve and control: test the solution on a small scale, then document it as standard work, the best currently known way of doing the job. ^[3]

What do documented hospital examples show?

In 2016 the American Hospital Association (AHA) published the case of a critical access hospital (a small rural hospital) that used Lean Six Sigma to improve patient flow in its emergency department. Mean time from the admission decision to the patient leaving the department fell by 40%, and patient and physician satisfaction rose by 12 percentile points. ^[4]

A study published in 2024 on an academic emergency department seeing around 100,000 patients a year mapped the value stream and redesigned the flow for less complex cases. Patients seen per eight-hour shift rose from 17 to 20.5, and the time from the attending physician’s assessment to the disposition decision fell significantly, as did the number of patients who left before treatment was complete or against medical advice. But the 9% fall in average length of stay was not statistically significant, and the authors noted that a single-centre before-and-after design cannot prove causation and that sustaining the new process after the trial ended was a barrier. ^[6]

The lesson is professional before it is statistical: separate what has genuinely improved from what only appears to have improved, and plan the control phase from the start.

What do government examples show?

The EPA’s 2013 guide documents results from US government bodies: ^[3]

• Florida Department of Environmental Protection: a kaizen event cut by 50% the time for a permit application to reach a processor.
• EPA Region 6: a value stream map of pesticides enforcement procedures cut total processing time by 53%.
• Minnesota Pollution Control Agency: a Six Sigma project raised the share of permits issued within 180 days from 9% to 75% and cut the backlog from nearly 50% to 8%.

The guide also notes that Minnesota state agencies held at least 265 kaizen and 5S events between 2008 and 2012, so improvement became a repeated practice rather than a one-off project. ^[3]

In a value stream mapping event as the guide describes it, the team maps the current state of the process from start to finish, identifies value-added time and waste, then maps the future state and draws up an implementation plan. ^[3] The common thread in these examples is that improvement came from rearranging steps and removing waiting and unnecessary approvals.

Which tool should you choose?

Not every improvement needs a full DMAIC project. If the cause is clear and the problem well defined, a kaizen event or even workplace organisation with 5S is enough. If the process is long and crosses several departments, start with a value stream map. If errors keep recurring and their cause is unknown, that is where Six Sigma’s statistical tools come in. ^{[1, 3]}

Hypothetical example: time to issue a trade licence

Hypothetical example: a local government body takes an average of 15 working days to issue a trade licence. Mapping the process, the team finds nine steps, three of them successive signatures on the same decision, and that the application spends most of its time waiting between steps rather than being worked on.

The team decides to merge the three signatures into one approval under delegated authority and to add a document checklist at intake to reduce returned applications, then measures the time weekly for three months. Results are reported as both average and spread, because applicants remember the longest wait, not the average.

What makes Lean Six Sigma succeed in services?

1. Leadership commitment: IHI stresses that a Lean culture starts at the top, with all staff involved. ^[2]
2. Leading at the front line: managers go to where the work is done and coach their teams rather than managing from reports alone.
3. Measure before and after: do not announce an improvement without a baseline and a test of whether the difference is significant. ^[6]
4. A process owner after the project: someone who tracks the indicators and updates the standard work.
5. Integration, not competition, between methods: Lean and other quality improvement models complement each other. ^[5]

Conclusion

Lean Six Sigma in services means seeing what is normally invisible: waiting, rework and unnecessary approvals. Start by mapping the process as it actually runs, measure before you change anything, choose the tool that fits the size of the problem, and do not close the project until you know who will sustain the gains.

Kaoru Ishikawa highlighted these tools in his guide to quality control because they let front-line staff solve most everyday problems without advanced statistics. The American Society for Quality (ASQ) lists seven: the cause-and-effect diagram, check sheet, control chart, histogram, Pareto chart, scatter diagram and stratification. Some newer lists replace stratification with the flowchart. ^[1]

Their value today is as a shared language: when a team in a hospital or government entity shows a Pareto chart, everyone sees where the biggest problem lies without a long explanation.

Which tool do you use, and when?

A common mistake is for a team to start drawing a fishbone before it knows which problem deserves analysis. Always start with a clear question, then choose the tool that answers it.

Check sheet and stratification: correct data first

A check sheet is a simple form for tallying how often each type of problem occurs. ^[1] It looks obvious, but its value lies in agreeing definitions: what counts as “late”? More than one working day, or beyond the published service time? If each employee records in their own way, every later analysis is built on sand.

Stratification means separating data by groups such as branch, shift or service type. ^[1] An error rate may look acceptable overall, then turn out after stratification to be high on the night shift alone.

Pareto: where to focus first

A Pareto chart ranks problems or causes from largest to smallest, based on the 80/20 rule: a few causes explain most of the effect. ^{[1, 3]} ASQ summarises how to build one: choose the categories, then the measure (count, cost or time), then the time period; collect the data; arrange the bars in descending order, grouping small categories as “other”; and, if you wish, add a cumulative percentage line. ^[3]

Watch the choice of measure. The most frequent cause may not be the most costly, so you may need one Pareto chart by number of cases and another by cost before setting priorities.

Fishbone: organising possible causes

The cause-and-effect diagram, or fishbone, puts the problem at the head and arranges its possible causes in main and secondary branches. ^[1] In services, branches might be people, procedures, systems, environment, measurement and inputs.

Remember that the diagram collects possible causes, not proven ones. You then need data to confirm or rule out each cause, which is where the scatter diagram, histogram or a return to the check sheet comes in.

Histogram and scatter diagram: understanding the data

A histogram shows the shape of the data’s distribution more clearly than a table. ^[1] If transaction times have two peaks, there may be two different processes operating under one name.

A scatter diagram plots two variables together to reveal a possible relationship, such as daily request volume and reply time. ^[1] But correlation does not prove causation; a third factor may be driving both.

Control chart: is the process stable?

A control chart plots a measured value over time against control limits calculated from the process’s own data. It separates two kinds of variation: common causes inherent in the process, and special causes arising from unusual events that make the process unstable and unpredictable. ^[2]

Out-of-control signals listed by ASQ include a point beyond the control limits, eight points in a row on one side of the centre line, or an obvious persistent pattern. The team then looks for the special cause and documents it. ^[2] If the process is stable but its performance is unacceptable, the answer is to change the process itself, not to chase each point.

A hypothetical example: complaints at a customer service centre

A service centre in a government entity is receiving more complaints. The team designs a check sheet of complaint types and collects four weeks of data, stratified by service channel. A Pareto chart shows that three types account for most complaints, led by slow replies to email.

The team draws a fishbone for that type, and possible causes include manual email routing and no reply templates. A scatter diagram confirms that reply time rises with the number of messages per shift. After introducing automatic routing and reply templates, the team tracks weekly average reply time on a control chart to confirm the improvement has held. The case is hypothetical, to illustrate the sequence.

Mistakes that spoil the analysis

• Drawing the tools with data collected under different definitions.
• Treating fishbone causes as facts without checking them.
• Treating every rise on the chart as a crisis when it is within normal variation.
• Putting the chart in a report without a decision or action following it.

Conclusion

Start with a question, then choose the tool that answers it: a check sheet to collect data, Pareto to set priorities, a fishbone to organise causes, and a control chart to tell whether an improvement has held.