By enabling the integration of predictive quality applications into established processes and workflows, we empower existing business to use data-based decision support in quality management.
 

By analysing the manufacturing sequence in a method-based manner, we identify the potentials of data-based quality management. We uncover existing and required software as well as the necessary hardware interfaces as part of implementation projects and ensure their usability for the cross-process analysis of interactions between process and quality characteristics.

As business digitalisation increases, so does the availability of data. Empowered by this, we develop new approaches for acting proactively in contrast to reactive quality management. We use machine learning methods to predict process- and product-related quality and to identify and eliminate the causes of errors.

A strong data basis is indispensable for profitable data analysis. Using methodical approaches, we identify data gaps along the process chain, derive actions to improve the data basis and evaluate the context-specific suitability of the data.

Empowered by extensive knowledge of common sustainability standards, guidelines and laws, we support the establishment of a company-wide sustainability strategy. In doing so, we rely on data-driven sustainability management. Our focus is the development and integration of a sustainable business direction into existing company workflows.

The operational implementation of a sustainability strategy is accompanied by many challenges. Our experience in quality management within the production context makes it possible to channel defined sustainability strategies and targets into concrete fields of action. We define target systems based on key figures and control target projects on the basis of data. The conflicts between economic and ecological goals are countered with a visualisation of the conflicting goals, which enables data-based decision-making support and company-specific prioritisation of the sustainability targets.

For the quantitative evaluation of the products' and especially the processes' sustainability, key figures are used. Defining these in a way that suits the needs of the company is a challenge that we can support through our extensive cross-sector experience. Particularly in the area of cause-effect analysis we profit from our cross-functional cooperation and utilise our well-founded knowledge of data analysis from data-driven quality management.

In order to examine the life cycle of processes and products in the context of sustainability analysis, it is imperative to include heterogeneous data from different sources. We merge these to conduct a holistic evaluation of relevant variables and to increase the sustainability of the production processes, taking the entire life cycle into account.

By providing a wide range of services, we support companies in overcoming current challenges in the areas of quality management, sustainability management and digitalisation within the production environment. Our goal is to enable companies a direct access to the latest and most promising developments and consequently to empower the digital transformation of industry practice as well as the development of future-proof business models.

By systematically identifying areas of action and specifically initiating change initiatives, we enable companies to undergo digital transformation. Through targeted, methodical support, the projects' success as well as their integration into existing organisational structures and mechanisms is ensured.