Use Cases
We have years of experience in data science for a variety of different industries. Each project has had its unique goal, constraints and challenges. Therefore, there are different ways in which we as ZDS can add value to your project. Here you can discover some of the projects we have been involved in. Some details of these use cases were anonymised as some of the underlying projects are covered by non-disclosure agreements.
Medical Device Safety
A pharmaceutical company producing a medical device wanted to investigate whether the device can be safely used for longer that the currently FDA-approved period of 14 days.
| TOPIC | DESCRIPTION |
|---|---|
| Applied method | Simulation from an highly tailored model. |
| Major challenge | Finding an appropriate model based on the little data collected in the pilot study. |
 Added value |
This project implied getting in touch with the authors of a specific software (an R-package), which was easy for us through our academic links. This proved to be very efficient. |
| Insight | The statistical analysis revealed that the product could indeed be used longer than 14 days. |
Food Production Monitoring/Analysis
A food producer wanted to identify the causes of products being defective in their manufacturing site. The client also wanted to develop a monitoring system to stop the production machines when needed.
| TOPIC | DESCRIPTION |
|---|---|
| Applied method | Tuned machine learning. |
| Major challenge | Data quality and heterogeneity. |
| Added value |
The client was deeply involved in the data preparation process and could therefore clearly understand how to progressively improve the quality of the data in order to get valuable insights. |
| Insight | Some production machines used special software settings that lead to increased failure rate. These machines could be reprogrammed to avoid defective products. |
Optimal Frequency of Use of a Biocide
A governmental organisation wanted to test different treatment regimes (= different frequencies of use) for a biocide. ZDS designed the experiment, analysed the data and co-wrote the scientific publication.
| TOPIC | DESCRIPTION |
|---|---|
| Applied method | Design of experiments and generalised additive mixed models (GAMMs). |
| Major challenge | Finding a robust experimental design given the budget and logistic constraints. |
| Added value |
Our experimental design was very much different from “classical designs” and turned out to be much more powerful and allowed for much more robust and certain conclusions. |
| Insight | There was a clear best way (= optimal frequency) of how to use the biocide when taking all factors into account. |
Automated Reporting System
A client wanted to turn a periodical reporting process into an automated process. ZDS created a easy-to-use system that produces PDFs with embedded graphs, tables and data-driven comments.
| TOPIC | DESCRIPTION |
|---|---|
| Applied method | Dynamic documents (Quarto/Rmarkdown and dashboard). |
| Major challenge | Coordinating wishes and requirements from different clients parties. |
| Added value |
The client was trained to be able to use and modify the tool by themselves (i.e. enabling). |
| Insight | The automation of the periodical reporting process lead to a significant increase in efficiency. |