InnoSale aims to innovate today’s sales systems and processes for complex and variable industrial equipment, plants and services. The characteristics of these products make this task fundamentally different from the sales of products that can be selected easily in a shop system (e.g., at Amazon, Zalando, eBay).
The main goals of the project:
- Automated semantic transformation between domain-specific vocabularies used by customers and manufacturers (ontology-based creation of custom product configurations),
- Understandable system behaviour based on declarative rules that are easily extended by engineers or even learned by algorithms,
- Identification of similarities to former customer requests, offerings or projects by searching various heterogeneous data sources, like ERP, CRM, CAD systems and others,
- Efficient generation of specifications or models for several product alternatives and presentation using 3D enabled technologies,
- Principled scientific methods for estimating optimal prices, considering various customers and domain-specific challenges, regional criteria, and production capacities of the producer.
Some Project KPIs/Major InnoSale outcomes:
- A framework for deployment and execution of InnoSale platform components
- Artificial neural networks and a Q-Learning model providing best pricing
- An inference engine for processing rules, expert system and services proposing product variants, evolutional clustering model identifying similar customers
- Techniques combining deep learning systems with augmented reality techniques to improve user experience
- Machine-learning solutions for managing retail/commerce sector activities
- Three international demonstrators
Consortium Members:
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