The largest cost for businesses is often associated with labor costs, nearly 70% of all business costs are associated with labor. Many companies look to innovations to help gain better control over labor costs and increase efficiency. Artificial intelligence is the leading technology companies are using to help drive profits through better predictions. Companies that collect data and utilize AI are seeing increased profits and cost reductions. In 2015, McKinsey& Company outlined an activity-based approach for scheduling and budgeting. They outlined four main prerequisites for retailers to use to improve efficiencies.
The Four Prerequisites
In “Smarter Schedules, Better Budgets”, McKinsey identifies four main points for developing an activity-based approach; store-specific workload calculations, reliable forecasts of volume drivers, flexible workforces, and robust performance management. McKinsey states that workload calculations require an understanding of how much time certain tasks take to be completed. In manufacturing, these calculations are primarily associated with time-studies, where an analysis would identify how much time certain processes take as they are being manufactured and how parts are moved throughout a manufacturing plant. Some aspects of time studies include measuring the parts of work an employee spends the most time on, figuring out how long a task should take, and the location an employee spends most of their time working.
According to McKinsey, it is important to have reliable forecasts for each department per hour and understanding product flow. In the case of a grocery store, this would require forecasting the revenue per department, based on open hours and days of the week. The forecast would require utilizing regression analysis to help predict revenue.
For retailers, it is necessary to have a flexible workforce. Having seasonal, temporary, part-time, full-time employees is crucial for retailers to better control labor costs and complex schedules. Having a flexible workforce helps with developing better scheduling, and labor cost savings due to scheduling employees at the right time to drive revenue.
Performance management systems assist in productivity and accountability, it helps keep multiple stores aligned with a company’s overall objectives and plans. Some aspects would be objectives that drive customer satisfaction, service times, and other productivity items.
AI for Better Scheduling
In McKinsey’s article, they argue that improving workload calculations is key. Knowing the amount of time each task or process takes, will help determine where and when to schedule employees. Artificial intelligence can help improve processes, by automating and monitoring revenue and activities of various departments. According to McKinsey, most companies choose to build their own systems that are disconnected from their HR systems, using excel based sheets that take upwards of six months to develop and test. Today, we have systems like beepHR and beepShift that offers a seamless platform built on AI and blockchain that addresses all four of the prerequisites. In 2015, such systems were not available, but today seamless systems are better than an ad hoc system of disconnected systems. Using AI systems like beepShift and beepHR, will help automate a lot of functions leading to increased savings and improved customer service. AI will help create forecasts based on workload data and revenue. beepShift helps companies manage a flexible workforce by considering the skills each employee and scheduling them based on workload calculations. Also, beepShift will help with creating better forecasts based on actual revenue and make more accurate predictions. Applying AI helps solve many issues associated with determining better schedules and improving budgeting. Combining beepShift with beepHR a company has both an advance system for scheduling, and workforce planning, but a robust performance management system. To see how beepShift and beepHR can help digitally transform work, please contact us.