Every day, retailers around the world must solve the complex puzzle of scheduling – matching available staff, talent and skills with business demand to create an effective Schedule that maximizes productivity at store while minimizing labor costs.
Meanwhile market is becoming more complex, customers are demanding a better shopping experience, and today more than ever is key a custom customer service through all channels including the physical store. All this is reflected in data raised in recent market analysis; the 71% of retailers say that the amount of store workload has increased over last years.
Despite this, still most retailers are doing manually the scheduling process with no analytical support, almost 65% of retailers, facing overstaffed shift, shorthanded rushed and improvanble levels of productivity, which negatively impact in the business result.
If the right workers with the right skills are not in place at the right time, the organization cannot deliver a profitable engaging and satisfying customer experience. But building an accurate schedule to accomplish this goal can be a huge challenge particularly on operational managers.
There is no doubt that automated scheduling can improve the life of operational manages, but just the automation is not enough. If the generated schedule is not taking into account the customer visits forecast for instance, the schedule will be inefficient, generating an over cost during overstaffed shift or sales opportunity lose during understaffed shift. A bad schedule process automated will only produce disappointing results faster.
The success come when the schedules are generated with a performance based approach, enabling the schedules to be precisely aligned with the expected demand at store floor.
Retailers with higher levels of scheduling accuracy based on performance are doing a better job of ensuring they deliver a quality customer experiences. They are better able to anticipate business demand to deliver a quality service at an appropriate labor cost. At the end this is reflected archiving higher revenue per employee by improving productivity.
But implementing a performance based approach without tools and analytical support is almost impossible, and only the most advanced workforce management tools provide high analytical capabilities. This ones unite the predictive and prescriptive analytics value, giving a compressive business performance visibility and providing the necessary knowledge to improve the decision making process of a manager at the store.
The question are:
A performance based scheduling address different goals across the organization, linking each one for a global business result improvement.
In this sense, the main goals addressed are::
Just each of this point adds significant value ensuring an early ROI. In addition some of these tools are recently providing a Software as a Service delivery model which notably reduce the time required to recover the investment, being possible to achieve in the first month in the fastest cases.
And that’s not all, workforce analytics deliver new capabilities to improve business.
But Automating scheduling with a performance base approach not only improve efficiency and performance, it also allow organization to use scheduling data in new ways. Labor is a huge expense in retailers, with an accurate schedule and performance history, it is possible to generate dashboard with workforce and labor data, helping executives understand how labor cost is spent against business result, which allow a better executive decision making process about human resourc, talent needs, and investment.
Today more than ever retailers must take care of optimize and automate scheduling in the way to improve performance, business results, customer satisfaction and employee engagement, and nowdays this is reachable to any organization with a low investment thanks to the state of the art of advanced analytics.
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C007/20-ED. 2020 call for aid on technological development based on artificial intelligence and other digital enabling technologies within the framework of the strategic action of the digital economy and society of the state R&D program.