"Can You Work Saturday?" Times Twelve: Why Availability Collection Is the Worst Part of Hospitality Scheduling

Every Monday morning at a cafe I consult with in Sydney, the manager sits down with her phone, opens her messages, and begins the ritual. She sends twelve individual text messages to her twelve casual staff members:

“Hi [name], can you work any shifts this week? We need people for Thurs dinner, Fri lunch + dinner, Sat all day, Sun brunch.”

Then she waits. And waits. By Monday afternoon, five people have responded. Three said yes to some shifts, one is unavailable all week, and one asked a clarifying question about Sunday brunch timing. Seven people have not responded.

She sends follow-ups on Tuesday morning. Two more respond. Five remain silent. She calls two of them. One does not answer. One says they can work Friday dinner.

By Tuesday afternoon, she has enough information to build a draft schedule for Thursday through Sunday. Except that on Wednesday evening, one of the confirmed Friday dinner staff sends a text: “Sorry, something came up, can’t do Friday anymore.”

She starts the cycle again. Who is available Friday? She sends more texts. The schedule she published on Wednesday is already wrong on Thursday.

The manager told me she spends three to four hours per week on availability collection alone. Not scheduling — just finding out who can work. The actual schedule building takes another hour. The total time investment for managing a twelve-person casual workforce: four to five hours per week, every week, year-round.

The Casual Workforce Challenge

Casual staff are essential to restaurant economics. They provide the flexibility to scale labour up for busy periods and down for quiet ones. Without casual staff, restaurants would either be overstaffed (and unprofitable) during quiet periods or understaffed (and delivering poor service) during peak periods.

But casual staff come with a management overhead that is inversely proportional to their commitment level. A permanent employee has fixed hours. You know when they are working. The schedule for their shifts is set once and repeats until changed. A casual employee works when they are available, which changes week to week based on their other job, their studies, their social commitments, and their inclination.

The manager’s challenge is not building the schedule. It is collecting the raw data needed to build the schedule — who is available, when, and for how long. This data changes weekly and must be collected anew every cycle.

The Three Availability Problems

The availability challenge in hospitality is actually three distinct problems that managers conflate into one frustrating experience.

Problem 1: Collection. Getting casual staff to tell you when they can work. The current approach — individual text messages — puts the burden entirely on the manager. The manager initiates, follows up, and tracks responses manually. Every missed response requires additional effort.

The solution is to invert the process. Instead of the manager asking each person, the system asks everyone simultaneously, with a clear deadline and a clear consequence for not responding (you are marked unavailable and do not get shifts). This shifts the burden to the staff member, where it belongs — they want shifts, and communicating their availability is the price of receiving them.

Problem 2: Compilation. Once responses are collected, they need to be compiled into a single view that the manager can use for scheduling. When responses arrive as twelve individual text messages over a 48-hour period, the manager is mentally compiling availability while simultaneously doing everything else. Information gets lost, misremembered, or misattributed.

A structured response format — where availability is recorded in a grid (staff member x day) — eliminates the compilation problem entirely. The manager opens one sheet and sees every person’s availability for every day, ready for scheduling decisions.

Problem 3: Changes. Even after the schedule is built, casual staff change their availability. Someone gets sick. Someone’s other job changes their hours. Someone simply forgets they said yes. Each change triggers a cascade: the manager needs to find a replacement, which means contacting available staff, waiting for responses, and updating the schedule.

The Shift-Filling Bottleneck

The most time-sensitive availability challenge is the last-minute shift fill. A staff member calls in sick at 7am for an 11am shift. The manager has four hours to find a replacement.

In a manual system, the manager scrolls through their contacts, texts available staff one by one, and waits for responses. If the first two people are busy, the manager moves to the next option. Each exchange takes time. By the time someone confirms, an hour has passed and the manager has been distracted from opening prep.

Automated shift filling transforms this from a sequential process into a parallel one. The manager logs the open shift. The system simultaneously notifies all casual staff who indicated availability for that day. The first person to respond affirmatively gets the shift. Everyone else is notified that the shift has been filled.

The time from “shift open” to “shift filled” drops from sixty to ninety minutes to, in many cases, under fifteen minutes. And the manager does not touch their phone once — the system handles the entire process.

The Reliability Data Dividend

When availability collection is tracked systematically, a secondary benefit emerges: reliability data. Over time, the system accumulates data on each casual staff member’s response rate, acceptance rate, no-show history, and consistency.

This data is invaluable for workforce planning decisions. The manager can identify the most reliable casual staff (respond every week, accept shifts consistently, show up on time) and prioritise them for desirable shifts. They can identify unreliable staff (rarely respond, frequently cancel, occasionally no-show) and either have a performance conversation or reduce their shift allocation.

Without data, these decisions are based on gut feeling and recent memory. With data, they are based on patterns over months. The difference is the difference between “I think Jamie is pretty reliable” and “Jamie has responded to 94% of availability requests, accepted 78% of offered shifts, and has zero no-shows in the past six months.”

The Weekly Rhythm

The system works best when it follows a consistent weekly cycle:

Thursday 10am: Availability request sent to all casual staff for the following Monday-Sunday.

Friday 3pm: Reminder sent to non-respondents.

Friday 5pm: Deadline passes. Non-respondents marked unavailable. Availability summary generated for the manager.

Friday evening/Saturday morning: Manager builds the schedule using the compiled availability grid.

Saturday noon: Schedule published to all staff.

This rhythm becomes predictable for staff. They know when to expect the availability request. They know the deadline. They know the consequence of not responding. Predictability breeds compliance, and compliance reduces the manager’s workload.

The restaurant industry will always rely on casual staff. The flexibility is too valuable to sacrifice. But the administrative burden of managing that flexibility — the texting, the chasing, the compiling, the re-scheduling — does not have to scale with the number of casual employees. Automate the collection. Automate the reminders. Automate the shift filling. And spend those four hours per week doing something that actually improves the restaurant.