Semantic Systems – Decision Support System Decision in Industrial Product Configuration
Semantic Systems
Sector: Industry
Business Case
Knowledge-based configuration, high qualification of personnel in the definition of the model and constraints governing the product configuration is required. The more complex the product, the longer the implementation time and the higher the learning cost for the configuration staff. Complicated and unintuitive configuration in view of the large number of product features User experience is not used to feed back into systems.
Objectives
Decision support for system users. Extract information in an orderly and flexible manner. Optimisation of product configuration by rule discovery based on performance patterns. Reduce training times for new users and therefore, reduce deployment and implementation times. Eliminate bottlenecks by allowing less experienced users to access the process. Reduce set up times and thus increase productivity.
Use case
Three lines of recommendation are established in the decision making process; Contrast knowledge-based rules with those discovered from configuration experience. Prioritise the features to be configured by optimising possibilities Suggest products that have been previously configured with these features
Infrastructure
Hybrid, On Premise / Cloud Deployment
Technology
Image recognition/processing Automatic or Deep Learning
Data
Datasets of order data to factory, user interaction monitoring Private datasets
Resources
Own resources, validation by the Client
Difficulties and learning
Product knowledge and modelling, unsupervised analysis of data to identify business rules
KPIs (business impact and metrics of the model)
Funding
Spri Group, Basque Government, CDTI
Collaborators, Partners
DeustoTech
