Decentralised forecasting & promotions: Demand Planning for Rigoni di Asiago
Rigoni di Asiago, an Italian leader in the production of high-quality organic products for over 90 years, produces certified organic honey, jams and spreads that are appreciated internationally.
The company stands out for its sustainable production model that combines tradition, quality and innovation.
CUSTOMER NEEDS
With four international branches (Italy, France, Belgium, USA) and increasingly complex commercial dynamics, Rigoni di Asiago needed to adopt a solution – in terms of forecasting methodologies and operational tools – that could:
- Improve forecast accuracy while reducing planning time;
- Integrate the impact of promotions on demand;
- Enable all subsidiaries to autonomously plan, while centralizing visibility at the headquarters;
- Encourage cross-functional collaboration through a shared platform.
In other words: transform demand into a strategic asset, not a forecast to chase.
The beanTech answer: powerful, secure and tailor-made demand planning
To meet Rigoni di Asiago’s needs, we designed a highly customised Demand Planning platform tailored to the company’s specific forecasting processes, built on the basis of a structured assessment involving all company stakeholders.
The solution supports a decentralised planning process, as it is managed directly by the group’s four branches within their respective areas of competence, but is still shared with the headquarters in Italy, which can view and monitor the output through the platform itself.
A platform that does not just collect data, but transforms it into value.
A new way to plan, share and decide
Discover in detail the methodology, architecture, implemented features and measurable benefits obtained by Rigoni di Asiago.
When we started structuring our medium-term planning process, we realized that accurate demand forecasting had to be the foundation.
beanTech’s Demand Planning solution enabled a collaborative forecast process — now, forecast data is at the heart of every supply chain decision.