Sharing pools

A convenience store chain being operated under a franchise regime had several different dealers running the stores individually and autonomously, using their own workforce and labor management processes. The disparity that this situation brought was causing strong inefficiencies, among them a permanent need to overstaff each site in order to cope with increasing absenteeism and turnover rates. Moreover, it also fueled a declining employee loyalty culture, poor staff productivity and performance, and, ultimately, poor customer service levels. 

With a mounting competition, both on offer and footprint, and customer expecting more, many retailers were starting to look for ways to enhance customer service through better organization and productivity.

 

The ambition

How to grasp the big opportunity on store performance and avoid falling short by just looking into incremental improvements in cost and process?

In other words, the ambition was to take advantage of the situation and look beyond efficiency to find a turning point in performance, one that would deliver new practices, new habits, and a new culture.

A new way of doing things, based on the principle of sharing central resources, to provide not only a significantly better, lean, and flexible store management platform, but also more integration with the brand to benefit both the end consumer and the franchise.

 

Addressing it

The best practices in the shared services world provide a very useful transformation lever to turn over performance in different types of distributed environments, with this one being no exception.

In this case, a shared pool that combined technology, human resources, and flexible processes was arranged to serve as a platform to enhance value through store operations at site level. One that empowered all site personnel to create a more meaningful impact on the end consumer by providing automated proposals of task schedules, workload allocations, shift planning, and real-time measurements of customer demand and in-store activity.

From a technology standpoint, the platform provided activity-based, data-driven workload allocation across the staff, based on insights that pulled from actual demand data being captured by a display of cameras that counted customers on each site, and a digital twin model of the store operations that enabled accurate process monitoring. This enabled the right balance between service levels, measured with KPIs such as waiting times, and cost.

From a resourcing perspective, the solution kept staff assignments always consistent across stores, and provided a shared pool of people to cover for replacements due to planned leaves or unplanned contingencies, dynamically sized upon statistical absence data and an algorithm to learn from actual gaps. This enabled better anticipation to the uncertainty caused by human behaviors, keeping overtime needs, absenteeism rates, and impacts from staff turnover under control.

Process-wise, the platform modelled the store behavior using a Jackson network for the queues and a Markov probability model for the demand, among other factors. This enabled, among other things, new roles for store supervisors, with dedicated focus on the improvement of the core business, and a joint orientation towards securing a well-functioning in-store execution of the allocated workloads, productivity rates, customer service levels, and employee satisfaction.

 

Creating impact

A transformed organization of the store operations, based on task management and measurement as opposed to fixed job positions, delivering:

• 40% improvement in store operating costs, all across the chain, in the first pilot country after the first 6 months
• From a previous 49% of costs over gross margin per store to 30% in 6 months, in line with the industry benchmark top quartile

A flexible, demand-focused execution of store operations, enabling:

• Better service levels, improving average customer waiting times by +20%, from +120 seconds to 100 seconds