As industry becomes more complex, and staff requirements, flexible working arrangements and transient workforces change the ‘traditional’ workforce paradigms, companies are finding it increasingly challenging to adequately manage the needs of servicing customers, while also managing the bottom line.
Downward pressure on HR and management to lower labor costs, when coupled with the ever-fluctuating demands of business can often lead to shortages in staff, qualified operators, and in many instances, may lead to lost revenue.
However, like all industries, Artificial intelligence and the application of complex – and award-winning – mathematical formulas have the answer when it comes to the management of your workforce, reduction of downtime, reduction of management hours and increasing your bottom line.
Predict labor demand using AI
Labor demand can be challenging at the best of times, seasonality, unscheduled events, and depending on your industry, this can result in lost sales revenue, missed opportunities & appointments or even more serious implications should your organization be in medical services or a hospital.
While demand planners can apply the best methodologies and excel spreadsheets, they can’t provide the insights and applied Algorithms that AI has successfully enabled thanks to WorkForce Optimiser.
When applied to the Acute Care Centre at the National University Hospital, they had the challenges of extensive work policies, procedures, and practices that needed to be managed. In addition, managing fairness in shirt assignments and leave requests. In a company with over 1250 beds, 70+ departments and 4000 staff, managing this is no mean feat.
However, through the application of the WorkForce Optimizer AI, they were able to successfully manage workforce, automate scheduling using mathematical techniques and planning tools, enjoy real-time tracking of attendance, automate claims processing and dynamic rescheduling of staff in case of unplanned events such as urgent or medical leave.
Through undertaking this process, the management team were able to effectively explore insights into labor data using advanced analytics, making informed and educated decisions not only on past data, but on forecast requirements.
What was the result?
From a staff point of view, they were able to become actively engaged and empowered in day-to-day management of staff rosters, through the innovative, real-time mobile app. Essentially allowing shift swaps with staff who meet the role specific requirements and meeting their shift related requests leaving staff happy, content at work not to mention more productive.
However, that was only the start, the NUH implemented a ‘bidding system’ allowing for staff to allocate their allotted points for changing shifts or requesting days off, rest day and even leave – again, putting the power back into their hands, and taking the burden of their managers.
The hospital realized a staggering 71% reduction in planning time, or 3,000 manager man-days saved in already the first year, which was previously allocated to the inefficient and antiquated management practices of the past in managing such complex requirements using spreadsheets. These also included the stamping out of subjective – often unilateral – management decisions, who may have favored one staff member over another, and compliance with the number of registered practitioners or staff of particular skill sets on the flood at any one time. (The solution ensured fairness while distributing shifts and managing staff requests. Something which was not possible using manual practices. Finally, the solution guaranteed compliance is being met when it comes to government and union regulations, as well as meeting organization specific requirements)
AI is changing the way we do business on so many levels; Siri and Alexa are changing how we find directions and order food, why should your workforce management system still be antiquated?
Speak to the team at WorkForce Optimizer today and realize real cost savings, an engaged workforce, and project delivery on time, every time.