Our AI-generated summary
Our AI-generated summary
A leading European food retailer, MC, partnered with LTPlabs to rethink how it prepares its workforce for the future. Facing shifting consumer behaviors, new store formats, and rising pressure on personnel costs, the company needed a structured way to anticipate its staffing needs and align them with long-term strategic objectives. The goal was clear: create a forward-looking workforce plan that is flexible, cost-efficient, and grounded in data, with staff prepared for new technology and equipped with the right mix of hard and soft skills aligned with MC’s strategic priorities.
The Challenge
Traditional workforce planning was no longer enough. Workforce supply and demand often fell out of sync across stores and roles, and decision-making relied heavily on ad hoc analysis rather than systematized analytical evidence. The company needed to understand individual strengths and potential to extract the best from each person. This meant improving the allocation of people to functions, ensuring that the right profiles were hiredfor future needs.
At the same time, the organization had ambitious goals related to requalification, skill development, and the balance between part-time and full-time staff.
This context raised a central question: how can MC prepare its workforce for the store of the future in a way that is both operationally realistic and strategically sound?
Our Approach
LTPlabs built an end-to-end Strategic Workforce Planning solution that combines machine learning, segmentation, scenario simulation, and large-scale optimization using Gurobi. The starting point was a predictive model that estimates voluntary churn based on variables such as tenure, performance, contract type, skill profile, and local labor market factors. This allowed this food retailer to look ahead and anticipate workforce gaps before they occurred.
To better understand internal mobility potential, employees within each function were grouped into personas defined by performance, potential, technological proficiency and workload. This persona-based segmentation made it possible to identify employees ready for promotion, those suited for horizontal movements, and those requiring development, and cope with RGPD rules.
At the core of the solution lies a robust optimization model designed to generate multi-year workforce plans at minimum cost.
It considers compensation, hiring, internal mobility, and terminations while respecting operational and strategic constraints. These range from payroll-to-sales limits and education targets to part-time shares, mobility rules, and skill requirements.
The model also ensures that workforce trajectories are consistent with the strategic objectives defined for each year. By quantifying these objectives and linking them to concrete actions, it enables the anticipation of current movements, such as hiring or mobilities, needed today to achieve the desired workforce and capability profile in the future. The model enables users to simulate alternative scenarios and compare the effects of different strategic choices, such as salary growth, stricter mobility policies, higher qualification goals, among others.
Impact
MC gained the ability to produce optimized workforce plans in minutes, rather than weeks.
Each plan provides store-level guidance on how many people to hire, move, or requalify, along with the expected evolution of the workforce and the associated costs.
More importantly, these plans now anticipate future workforce needs, enabling the organization to act proactively rather than reactively.
With this forward-looking visibility, decision-makers can quantify trade-offs between competing objectives (for example, improving skills while containing costs, or increasing flexibility without compromising stability) and take timely actions that align today’s decisions with tomorrow’s strategic goals.
The solution has also strengthened company’s capacity to test “what-if” scenarios. Leaders can explore how external trends, from wage inflation to shifts in unemployment, may affect future availability, workforce composition, and budget needs. This foresight has transformed workforce planning from a reactive administrative task into a strategic capability.
The project demonstrated the power of combining predictive analytics with optimization to address complex workforce dynamics.
Our AI-generated summary
Our AI-generated summary
