How Should a Singapore SME Reduce Staff Overtime After the 8.8 and National Day Rush?

The fastest way for a Singapore SME to reduce staff overtime after the 8.8 and National Day rush is to stop treating overtime as a volume problem and start treating it as a process defect. Pull the last four weeks of overtime hours, attribute each block of hours to a specific task, and you will almost always find that the majority went to correcting information rather than serving customers — re-keying orders between systems, chasing missing delivery confirmations, reconciling stock that two systems disagree about, and answering enquiries whose answers already exist somewhere. Customer demand created the trigger; your admin workflow created the hours. Fix the second and the same peak costs materially less next time.

Why did overtime spike so much more than sales did?

This is the number worth checking before anything else. Take your peak-period revenue increase and your peak-period overtime increase and put them side by side. A 30% lift in orders that produced a 30% lift in overtime hours is arguably fair. A 30% lift in orders that produced a 70% lift in overtime hours means something in your operation scales worse than linearly — and that something is nearly always manual coordination.

The reason is structural. Order volume grows in a straight line. Coordination effort grows faster, because every additional order also multiplies the exceptions: the wrong SKU picked, the delivery window missed, the customer who replied to a WhatsApp thread instead of the enquiry form, the refund that needs three people to approve. During normal months your team absorbs exceptions in the gaps between tasks. During a peak there are no gaps, so the exceptions become overtime.

That distinction matters for what you do next. If overtime scaled with volume, you have a capacity question and the answer may genuinely involve part-timers or temporary hires. If overtime scaled faster than volume, hiring more people simply buys you more of the same rework at a higher cost base.

Where do the overtime hours actually go?

Most SME owners cannot answer this precisely, which is exactly why the problem recurs each year. You do not need a time-tracking system to find out. You need one week and a short conversation with the people who stayed late.

Ask each person who logged significant overtime during the peak to list the three tasks that consumed their extra hours. Do it individually and do it without judgement — you are diagnosing a workflow, not appraising staff. Then group the answers. In our experience across Singapore SMEs, the recurring clusters look like this:

• Re-entering the same data into a second system. Orders from a marketplace typed into an accounting package. Delivery details copied from WhatsApp into a spreadsheet. Quotes retyped as invoices.
• Chasing status nobody can see. Calling drivers to ask whether a delivery landed. Asking the warehouse whether stock moved. Every one of these calls interrupts two people, not one.
• Reconciling disagreements between systems. The stock figure in the system versus the stock on the shelf, resolved by counting after hours because counting during hours is impossible.
• Answering enquiries that have standard answers. Order status, delivery timing, return eligibility, warranty terms — asked hundreds of times, answered individually each time.
• Month-end catch-up. Paperwork deferred during the rush that has to be cleared before the books close, creating a second overtime wave in the following week.

Rank these clusters by total hours. The ranking is your work order, and it is usually not the ranking you would have guessed.

Which fixes actually remove hours rather than move them?

The test for any proposed fix is simple: does it eliminate the task, or does it relocate the task to someone else? A great deal of well-intentioned SME automation fails this test — a new dashboard that someone must now update, a new app that someone must now check.

Fixes that genuinely remove hours tend to fall into three categories.

Connect rather than re-key. If two systems hold the same information and a human moves it between them, that movement can almost always be automated. Marketplace orders into your order system, order data into accounting, delivery confirmations back into the customer record. This is unglamorous integration work and it produces the largest single reduction in overtime for most trading, retail and distribution SMEs.

Make status visible instead of askable. A shared order-to-delivery view that shows where every job stands removes the entire chasing category at once. It is worth noting that the value here comes from removing interruptions on both sides of every status call — the person asking and the person answering.

Answer the repeat questions once. A tracking page, an automated order-status message triggered on dispatch, or a returns portal converts a hundred individual replies into one configured rule. This is also the safest first place for an SME to use AI: the questions are bounded, the answers are known, and mistakes are low-consequence.

Deliberately left off this list: asking staff to work faster, adding an approval step to control overtime, and buying another SaaS tool to sit alongside the five you already have. The first burns goodwill, the second adds coordination cost to a coordination problem, and the third is how most SMEs arrived here.

How do you build the case to fix it before the year-end peak?

August is the right month to act because you have fresh evidence and a real deadline. The November–December peak is roughly four months away, which is enough time to implement and stabilise changes, and your FY2027 planning cycle is starting now.

The business case does not need to be elaborate. Take the overtime hours attributable to your top-ranked cluster, multiply by loaded hourly cost, and annualise across the peaks you run each year. Put the recovery cost next to it — implementation plus any ongoing platform cost. Most SMEs find the payback sits inside two peak cycles, which is a far easier conversation than a general appeal to efficiency.

Add one line the finance number will not capture: the retention effect. Staff who worked heavy overtime through a peak that felt disorganised are the staff most likely to leave in the following quarter. In a market where replacing an experienced operations person takes months, that risk belongs in the case.

What should you do in the next two weeks?

Run the diagnostic while memories are fresh. Compare overtime growth to revenue growth. Collect the three-task list from everyone who worked late. Rank the clusters by hours. Pick the single largest one and scope a fix for it — not all five, one. Then set a review date in early October, before the year-end build-up starts, to confirm the hours actually moved.

The businesses that come out of a peak season stronger are rarely the ones that sold the most. They are the ones that treated the strain as data and removed the cause before the next cycle.

Frequently Asked Questions

Is some overtime during peak periods unavoidable?
Yes, and the goal is not zero. Genuine demand-driven overtime — extra picking, packing and delivery during a surge — is a reasonable cost of a good sales period. What is avoidable is the administrative overtime that follows: rework, reconciliation, chasing and catch-up paperwork. In most Singapore SMEs that second category is the larger of the two, and it is the one that compounds year after year if left alone.

We only have 12 staff. Is this worth doing at a small scale?
It is often more worthwhile at small scale, because a 12-person business has no slack. When one operations person is tied up reconciling stock for three evenings, that is a meaningful share of your total capacity, and there is no one to absorb it. Smaller teams also implement changes faster — fewer stakeholders, fewer process variations, shorter testing cycles.

Do we need to replace our systems to fix this?
Usually not. The most common cause of admin overtime is disconnected systems rather than bad systems, and connecting what you already run is faster, cheaper and less disruptive than a replacement programme. Replacement becomes the right answer only when a core system genuinely cannot expose its data or enforce your process — and that should be a conclusion you reach after the diagnostic, not before it.