Due to cost considerations and a shortage of manpower, Industries with a high brain drain rate, like retail and F&B, have a prolonged demand for part-time staff to compensate for the loss of manpower from full-time employees. The cost of part-time recruitment is relatively lower than that of full-time employees with a faster and easier procedure, making it more flexible to adjust the labor force accordingly.
The biggest challenge lies in how to effectively use part-time employees and ensure the stability of the workforce. Being late, leaving early, or simply skipping work are common behaviors among part-time employees which greatly damage the company's productivity and even reputation. How to properly arrange part-time staff is therefore important for the company to turn the dangers of hiring part-time staff into opportunities.
Part-time employees have different preferences or behaviors in their working schedules: women with children who are responsible for housework prefer an early shift that enables them to arrive home before their children leave school; young college and university students who often turn night into day usually failed to wake up early for work, and hence middle or evening shift is more suitable for them. As long as you pay attention to their living habits and collect shift preferences holistically, you can easily compile a credible roster to ensure a sufficient labor force.
The AI Auto Rostering system developed by ME is exactly the antidote for this situation. By inputting the preference of part-time staff in advance, it can automatically generate rosters by month. In addition, AI can also automatically detect the peak sales period and the high consumption effect of holidays, then calculate the manpower required for the day based on the expected demand. This is how we achieve a technology-led rostering that no longer heavily relies merely on manpower, ultimately maximizing productivity.