Moving beyond empirical people planning practices

Advanced analytics to the rescue

When you think of a retail chain, you think of its stores, the places 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.

One of the biggest problems for organizations is the difficulty in anticipating workload over time, which may frequently cause either overwork or idle time. This invariably leads to low retention rates, with a strong negative impact on productivity.

Old planning and old results

Store managers have long devoted considerable effort to managing their store-based staff. However, they have performed it in a decentralized and unstructured way.

Drawing a schedule is 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.

Advanced analytics to the rescue

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 expected 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 in the queue) of each store, according to the number of workers available at each time.

The role of the manager is to define the main operational 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 need to be addressed while pursuing that goal, which may carry difficult decisions.

From a model to reality

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 the schedules iteratively. 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.