The client manufactures a variety of sterile agitators, which must meet different requirements of their customers. Based on the customers’ requirements, different agitators are recommended to meet their criteria. Until now, an excel file was used to calculate and match the products to the given requirements. However, it is complex and hard to understand for internal employees. liquitec AG wishes to have a product database and webapp that automatically calculates the best-matching products for them.

The project was planned together with the client to understand their requirements and expectations towards the project. In a design phase, both the product database and webapp design were created. Furthermore, a technology stack based on the React framework Next.js was established. In the execution phase, the webapp was developed using the agile methodology Scrum to stay flexible with changes in customer requirements and to incrementally drive progress. In addition to regular testing, two on-site client meetings were held to train the employees and to test the webapp to receive feedback.

The final result is a product database (backend) together with a functioning and intuitive dashboard – in the form of a webapp – that suggests the ideal product based on different parameters or requirements of their customers. All requirements were fulfilled and the deliverable meets all success criteria defined together with the client. The webapp is available on all devices and screen sizes. An account is required to access the webapp’s content. Users with sufficient permission (admins) are able to manage the content of the webapp. Regular users can use the product finder to first enter the criteria to then receive two automatically calculated product recommendations that best match the requirements of the customer. The results can be added to favourites using a bookmarking functionality for later consultation. Product recommendations can be exported as a PDF or sent directly via e-mail to a specified address. The PDF generation is handled dynamically based on the content of the recommendation and is used by the client as product sheets that are sent to their customers.