A Customer Success story.

Context Modelling



In a highly competitive market, manufacturers have the double challenge of building a sustainable business along with satisfying a wide array of customers quickly and efficiently has led to the concept of product customization, wherein companies strive to cater to the specific needs of their customers through a wide range of products to choose from. While this approach is beneficial for the customer, such design and manufacturing decisions can lead to adverse effects on production, distribution, and sourcing. 

Companies continue to struggle to ensure that mass customization does not lead to increased cost and reduced margins. Attempts to mitigate added costs of customization are in part achieved through product family hierarchy based decision tree, wherein products that satisfy the individual product functionality requirements are designed around a shared and efficient product architecture. The term product architecture is defined as a set of modules or components where use of commonality among product variants can translate into lower manufacturing costs associated with highly differentiated products through economies of scale.

The Challenges

The challenges facing product portfolio development process is two fold:

a.) Product designing and engineering require extensive knowledge about the product, its components, its ecosystem and also technical limitations of a product’s engineering design and manufacturing processes. Sharing design knowledge and product information are critical for designers to succeed in a collaborative environment. 

b.) Identifying candidate product concepts that have applicability or similarity in their functionality or use, with the design requirements is an arduous task. This involves an extensive search of all product variants which meet engineering requirements, price, and performance expectations. Having all the relevant information at one’s disposal is a dream but a rarity.

Our Approach

Building an easily searchable product master by classifying and linking engineering information along with customer-related product information across different dimensions and behavior such as technical specifications, supplier information, customers’ buying pattern, and commercial information, is key to democratizing product design knowledge.

For supporting product designers through the product design and development life cycle, we have adopted a cognitive model-driven architecture (see fig 1.0) using a multilevel (data, product model, product metamodel) approach. 


Benefits of using cognitive analytics in a product classification-based decision tree

The knowledge graph built using Parabole TRAIN enables design teams to understand the functional behavior of the products, their components, product attribute relationships, customer preferences along with commercial information including but not limited to vendors, the total cost of operations, associated with it. This allows for increased traceability and dependency awareness among consumer products, engineering products, BOM, raw materials, and suppliers. The generated product designs are then tested for engineering feasibility.

This approach enables stakeholders to define product models and relate them to physical instances. The data level represents real-world products, the model level describes product models and the metamodel level describes models of the product models. The metamodel is nothing but ontology enabling product designers to explore the semantics of product models in an engineering-friendly way. The interactions between these three levels are described to demonstrate how each level in the framework is used in a product engineering context.


An engineer-friendly real-time simulation and search engine enables users to understand the design/ product requirements and connect that design to the various components required for the design. This optimizes design and product life-cycle management, harmonizes manufacturing processes, ERP, PLM, and PIM in few clicks which otherwise used to be a manual and arduous task.

Contact us at info@parabole.ai to learn more about building a cognitive search engine for product designers.