When you think of a retail chain, you think of its stores, the place where the “action” happens. Stores and their staff are the core of the business as they have crucial roles: sales and operational fulfillment.
Leading organizations are increasingly trying to improve their people management practices, acknowledging that the way they manage such strategic resources has a major impact on results, both regarding commercial effectiveness and client experience.
Nowadays, finding people to work in a store may look easy, but keeping them is a hard task.
One of the biggest problems for organizations is the difficulty to anticipate workload along time, which may frequently cause either overwork or idle time. This invariably leads to low retention rates, with a strong negative impact on productivity.
Store managers have long devoted considerable effort to managing its store-based staff. However, they have performed it in a decentralized and unstructured way.
Drawing a schedule, it’s an iterative task that store managers face frequently: forecast the demand, consider staff availability, functions, and needs to make the distribution of tasks while respecting legal restrictions.
These empirical approaches are typically unsuccessful in tackling the aforementioned retention and productivity issues.
What if the chosen schedule is not the best? Could the store manager make a better forecast or a better distribution of tasks?
Advanced analytics provide the tools to understand what the main productivity drivers are, what will be the workload along time and how to generate efficient schedules to tackle that workload. Everything should start with the mapping of the product or service delivery model, which comprises the set of tasks involved in preparing every item, with their necessary dependencies and precedencies. This helps clarify and structure what tasks are generated by each client order and define the roles and functions that will be impacted by it.
To generate a demand forecast for each store, it is pivotal to analyze past data of demand per unit of time, which can go as granular as a 15-minute timeframe. Considering the measured productivity of each task, it is then possible to estimate the expectable workload per task and, as a result, per function.
This leads to an estimation of sales, cost, and client service (mainly defined by average waiting time on queue) of each store, according to the number of workers available at each time.
Optimization models are then used to generate efficient schedules that meet both legal restrictions and employee needs, enabling the exploration of a huge range of possibilities in a short period of time.
The role of the manager is to define the main operation goal, whether it is cost reduction, client service, or the right balance between them. Respecting the preferences of workers and managers, considering their well-being and respecting the legal obligations are the concerns that needed to be addressed while pursuing that goal, which may carry difficult decisions.
The help of a decision-support tool is crucial to implement and sustain an analytical backbone for people management as store managers can give their input on the workload forecasting and refine iteratively the schedules. Such tools can help to reduce the workload of store managers, create a structured process of generating schedules, and foster better people management practices, avoiding overwork situations.
As the battle for talent and motivated workers persists, new practices for workforce planning are required to increase retention and thus enforce productivity. We believe that an analytical approach can place in store managers’ hands the tool they need to make better decisions and proactively anticipate the needs of a business operation.