Cafés Richard

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Here the coffee machine is becoming an intelligent point of sale lowering operating costs by avoiding unnecessary maintenance and generating higher revenue through enhanced quality.

Context & objectives

Cafés Richard is traditional coffee roaster specialising in coffee, tea and chocolate. It offers comprehensive business solutions throughout France with the motto of offering an “always perfect coffee” through its network of over 35,000 businesses selecting the brand from among all the market leaders. This mid-market company is therefore able to post annual revenue of some €130m via its 3 million cups of coffee drunk each day, produced using its 135,000 machines spread out across the country. This fire power is backed up by its undeniable strength: being its own and sole distributor it is able to control and guarantee the quality of its coffee throughout the value chain. The proliferation of competitors in its B2B market represents a threat that the group intends to face down by working in three main areas: the customer experience, machine optimisation in terms of cost and the exploitation of data as a key development factor.

Business Challenge

To reduce the maintenance costs of the machines made available to customers via preventive and predictive remote maintenance solutions
To better understand and monitor the customer experience in order to improve the quality of its coffee

Solutions & methods

Implementation of a proof of concept (POC) in order to gather data from two coffee machines supplied by two manufacturers, via two different modes of data transfer, and data summaries via an exploitable dashboard.
Implementation of remote preventive and predictive maintenance to provide real-time data on the number of cups drunk and to optimise restocking, and therefore revenue.
Exploitation of the data to cross-check the number of cups poured and the purchases of coffee, in order to verify that the coffee sold is indeed Cafés Richard coffee.
Management of coffee machines to ensure optimum coffee quality via exploitation of the data (water quality, temperature, pressure, etc.).

Key success factors