A regional workforce plan can look consistent on paper while the teams underneath it face different hiring and retention pressures. Before carrying a policy from one market to another, I would ask: who will actually be available, experienced and ready to work?

Public labour data can help frame that question. It cannot answer it for an individual store, and it needs careful reading before it becomes an assumption in a labour model.

What Singapore’s 2025 figures show

Singapore’s Ministry of Manpower reports the following average monthly resignation rates, averaged across 2025:

Population Average monthly resignation rate
All industries and occupational groups in the survey 1.2%
Retail trade, all occupational groups 1.7%
Retail trade, clerical, sales and service workers 2.2%

The last group includes many customer-facing roles, but it is an occupational grouping, not a measure of every frontline worker. Its rate is around 1.8 times the survey-wide rate. That comparison does not establish a frontline-versus-head-office difference.

These are monthly rates, not the percentage of unique people leaving during the whole year. The survey covers private establishments with at least 25 employees and the public sector; it does not represent every small shop. Source: MOM, Labour Market Report, Fourth Quarter 2025, Table 7.4, page A22.

My reading: an industry total can hide differences between occupational groups. The figures do not tell us why employees left, their tenure, or whether scheduling caused the departures.

Australia: check what is being measured

The Australian Bureau of Statistics reports an annual job mobility rate of 7.7% for the year ending February 2025. Its retail trade rate, classified by the industry of the job left, is 9.3%. Retail also accounts for 10.9% of people who changed jobs; that last figure is a share of movers, not a turnover rate. Source: ABS, Job Mobility, February 2025, Charts 11 and 13.

These Australian annual job-change measures are not directly comparable with Singapore’s monthly resignation measures. They support asking a local question, not ranking the two countries using the percentages above. This is a comparison of specified 2025 releases, not a claim about the latest labour market conditions.

Turn the signal into a site-level investigation

I would use those figures to start three checks with HR and operations. These are implementation questions prompted by the data, rather than findings the data proves.

Can the schedule see the available skills? A stable headcount can conceal departures and replacements. Check skill sign-offs, time in role, supervision requirements and employee availability. A new starter and an experienced colleague may fill the same position without being interchangeable on every task.

Where does recurring training sit in the labour model? Measure induction, coaching and refresher work. Include trainer time as well as learner time, and make sure it is not already counted elsewhere. Use local hiring and training records to estimate the workload; a national resignation rate cannot tell you how often a particular store needs to onboard someone.

Which working arrangements do people actually want? Ask about notice, predictable hours, transport and availability. Test the operational trade-offs and listen to employee feedback. Do not assume that employees prefer part-time hours, or that a different contract mix will improve retention, simply because a market-level resignation rate is high.

The FT/PT experiment makes one part of that discussion visible: how contracted hours and reliable availability affect coverage. It does not model retention, employee preferences or recruitment success.

Keep the purpose consistent, validate the local design

Across a regional implementation, I would keep the intended outcome clear: reliable coverage, accurate records and rules people can use. Then work with local HR, payroll and operational owners to establish how contracts, employment requirements and actual work affect the design.

Document which requirements are shared, which vary by market, who owns each decision and how each rule will be tested. That gives the implementation team something more useful than either a rigid regional template or an unexplained collection of local exceptions.

A question for your next regional review: which workforce assumption have you checked against local evidence, and which are you still borrowing from another market?