Before considering improvements to workforce schedules, it's important to understand what constitutes a good roster. A schedule should adhere to both labor laws and company regulations, while also allowing for flexibility within other constraints. For instance, employee scheduling preferences, desired shift lengths, years of experience, mentoring team members, overtime, contract restrictions, etc. Different stakeholders within an organization can have very different opinions on what makes the best roster.
Therefore, the definition of a ‘good roster’ differs per organization. It will depend largely on the organization’s vision of workforce management. We recommend organizing one (or more) workshop(s) at the start with key stakeholders to come to a common definition of a good roster. This can result in multiple criteria, each with different priority. Creating a baseline using historical data helps establish a benchmark for the current quality of the schedule.

1. Efficient workload forecasting for shift allocation

A crucial first step before assigning people to shifts is to predict the required demand of work for the coming period. By leveraging historical data, machine learning can significantly enhance forecast accuracy. If this leads to peaks in workload, you might be able to flatten this when some tasks can be postponed. An optimizer can smooth out workload peaks and determine the optimal number of shifts, including their start and end times, while also considering the required qualifications.
Our experience shows that this approach improves the quality and efficiency of the schedule without incurring additional costs. For instance, suppose that in the past week, there were 3 days of overstaffing and 2 days of understaffing. Employees may not notice when you are overstaffed, but they certainly feel the stress of understaffing. With a more accurate forecast, shifts can be reallocated to different days, reducing both understaffing and the associated costs and stress.

"Should challenges persist, an AI-powered optimizer can impartially resolve issues, often meeting over 90% of employee preferences."

2. Respect rules and wishes

The current workforce generation desires more control over their schedules, emphasizing the necessity of a robust employee self-service app to accommodate individual preferences. Additionally, organizations are increasingly adopting self-scheduling practices, allowing employees to prioritize their preferences in the initial scheduling round. During subsequent rounds, teams collaborate to address any remaining conflicts. Should challenges persist, an AI-powered optimizer can impartially resolve issues, often meeting over 90% of employee preferences. This neutrality and efficiency surpass traditional manual planning methods considerably. As the importance of honoring personal preferences and fostering flexibility continues to grow, so does the demand for AI solutions.

3. Personalize your communications

A good employer offers transparent and personalized communication. Various topics relevant to the employee, such as company strategy, career paths, and personal support, must be communicated clearly and regularly. Personalized communication, based on preferences and employee characteristics, can help provide the right information at the right time. By utilizing machine learning, you can learn these preferences and characteristics from historical data. Integrating all apps into one employee app, including rostering opportunities, can simplify your tooling landscape. Employees should have easy options to exchange shifts, and AI can assist in computing suitable alternatives while respecting labor and company rules.

"Machine learning can uncover correlations, such as the relationship between roster health, adherence to employee preferences, and its impact on retention."

4. Learn from the data

If you store both planned and actual workforce schedules, valuable insights can be gained. For instance, significant differences between the actual and published schedules indicate substantial changes occurred, possibly due to employee requests or employer adjustments. The frequency of changes serves as a metric for assessing the initial schedule's quality. Machine learning can uncover correlations, such as the relationship between roster health, adherence to employee preferences, and its impact on retention. Furthermore, insights into employee preferences, ideal shift lengths, and self-scheduling behavior can be obtained. Emerging AI applications include generating new schedules based on past data, while the field of HR analytics utilizes comprehensive data to enhance HR processes.

Conclusion

These are just four practical examples of how AI can enhance your workforce scheduling process, with potential applications extending to various other operational areas. Harnessing the power of machine learning and mathematics not only improves employee satisfaction and efficiency but also enhances overall organizational processes, such as customer service and operations. This marks the beginning of an exciting new era in addressing the challenges of personnel shortages, presenting a promising opportunity for organizations to thrive in the modern workforce landscape.