IMPROVING THE PROCUREMENT AND SALES FORECASTING PROCESS

Subsidiary Mission

The general management of a French food industry company specializing in bakery and pastry products wanted to improve the supply and sales forecasting process. Following the diagnosis of the process and the resulting recommendations (short, medium and long-term actions), the general management wanted to return to performance with the main objectives, in the following six months, of increasing profitability and achieving an organizational performance that would enable the company to meet its growth challenges.

To achieve this, it requested our support for:

Determine the roadmap to achieve these objectives

  • Put the culture of performance measurement back at the heart of the business (rethinking the current Laboratory/Shop flow and finding a compromise between agility and industrial process);

  • Transform the supply model, by adapting it to the needs of the French stores and the capacity of the production plant;

  • Rely on a transversal working group (COPIL, Stores, Plant) towards a common goal.

Achieve their objectives

  • Quantitative: At least 250,000 euros of annual gain by reducing the percentage of losses of plant products in shops;

  • Qualitative: Obtain a stronger internal "agility" to generate efficiency, performance analysis, adapted processes, results and customer satisfaction.

Group mission

We assisted the group in order to take into account the management imperatives (which deadlines, which aggregates, which indicators) by taking into account the particularities of the sector and in particular the own/franchised shops/franchisees aspects and the variations in perimeter.

We then parameterized these models in the Group EPM tool in order to obtain:

  • Definition of a reporting and budgeting model;

  • Aggregation and consolidation of data;

  • Dashboards by level of aggregation (countries, brands, countries, etc.);

  • Analysis of anomalies and alert indicators;

  • Automatic production of Group reporting elements;

  • Predictive models.

Guest UserRetail