DAVIE
Short description – DAVIE investigates the potential applications, design principles, drivers, and barriers of data-driven decision support systems. To this end, the project combines research perspectives from information systems, business administration, and management science. The project’s outputs include process models, methods, a demonstrator toolset, and guides for transfer and implementation. These resources are designed to help small and medium-sized manufacturing companies, in particular, initiate sustainable business process innovations by providing a better basis for decision-making.
General Information about the Project
Project Duration:
Okt 1, 2024 to Sep 30, 2027
Project Management:
Prof. Dr. Christoph Laroque (WHZ)
Partner:
Leipzig University
Total Amount of Funding:
688.550,60 €
Project Description
To better understand the necessary foundations for business process innovation, the DAVIE project brings together two disciplines: First, the foundations for action in business informatics are subsumed under the concept of “Entscheidungsunterstützungssysteme” (EUS). Examples of EUS include simulations of various production systems, dashboards with real-time data for better control of production processes, and early-warning systems for potential disruptions in supply chains. On the other hand, from an economic perspective, data-driven EUS are understood as “management innovation”. Well-known examples include Lean Management for optimizing business processes, the Balanced Scorecard for streamlining management processes, and Six Sigma for improving quality.
Despite existing approaches to EUS and management innovations, there are four significant research gaps: (1) From the perspective of the literature on EUS in business informatics, it remains unclear how a data-driven EUS must be adapted to the specific situations of a small and medium-sized enterprise. (2) There has been insufficient research into how a data-driven EUS must be designed so that it can cover all necessary bases for action in SMEs—from initial descriptive and diagnostic approaches through to predictive and prescriptive analytical methods. (3) From the perspective of the literature on management innovations in the field of economics, it is unclear which steps, activities, and capabilities are necessary for SMEs to use data-driven EUS effectively. (4) Furthermore, there is a lack of understanding regarding the diffusion of EUS—beyond the boundaries of an individual SME to the SME sector as a whole. Unless these research gaps are closed, it will be virtually impossible for the SME sector to widely adopt data-driven EUS and use it to initiate the necessary business process innovations to ensure sustainable competitiveness.
Against this backdrop, the DAVIE project will address the following research question: How can small and medium-sized enterprises in Saxony increase their business process innovations?
To answer this research question and address the aforementioned research gaps, four sub-questions arise:
1. How should data-driven EUS be adapted to the circumstances of small and medium-sized businesses?
2. How can the maturity level of data-driven EUS (descriptive, diagnostic, predictive, prescriptive) be gradually increased in small and medium-sized enterprises?
3. What are the necessary steps, activities, and skills required to implement data-driven EUS in small and medium-sized businesses on a practical and regular basis?
4. What are the drivers and barriers to the adoption of data-driven EUS in small and medium-sized enterprises?
The research questions are examined in DAVIE through the lens of business process innovations that play a role specifically in medium-sized manufacturing companies—the backbone of Saxony’s economy. Furthermore, the research focuses in particular on those business process innovations that contribute to the sustainability, future viability, and resilience of Saxony’s SMEs (e.g., Sustainable Development Goal SDG 8, “Decent Work and Economic Growth,” and SDG 9, “Industry, Innovation, and Infrastructure”).
Our Contribution
Through this project, the Industry Analytics research group and Professor Laroque aim to continuously expand expertise in the field of data-driven decision-making processes in small and medium-sized enterprises (SMEs) in order to promote cross-sector industrial collaborations between universities and industry. The project also focuses on industry-oriented support for early-career researchers through applied research—including the initiation and supervision of at least one collaborative doctoral thesis—as well as positioning this expertise within the national and international scientific community.
The objective is to develop EUS for small and medium-sized enterprises that can be applied to business process innovations. Through interviews with manufacturing SMEs in Saxony, the project aims to identify relevant fields of application and examine the current status of existing DSS within these sectors. Based on this, the requirements of SMEs will be systematized and translated into concrete functionalities, which will then be consolidated into DSS prototypes.
The dissemination of the results aims to raise awareness of data-driven EUS among small and medium-sized manufacturing companies in Saxony. To this end, a practical guide will be developed that explains the use of EUS for business process innovations. In addition, case studies will be published through various communication channels, and the EUS demonstrators will be made available online.
As the research project progresses, a maturity model for these EUS will be created to classify possible development stages (descriptive, diagnostic, predictive, prescriptive) in the context of business process innovations. This also includes defining the prerequisites and requirements for data, data preparation and models, analyses, algorithms, and visualizations of action options and their impacts, specifically for small and medium-sized enterprises. The prototype EUS will be further developed into demonstrators, which will be tested and validated in collaboration with companies.
The dissemination of the results aims to raise awareness of data-driven EUS among small and medium-sized manufacturing companies in Saxony. To this end, a practical guide is being developed that explains how to use EUS for business process innovations. In addition, case studies will be published through various communication channels, and the EUS demonstrators will be made available online.
Contact Persons
Prof. Dr. Christoph Laroque
Professor of Business Analytics
christoph.laroque[at]whz.de
Fabian Krabacz
Research Assistant
fabian.krabacz[at]whz.de
Jenny Rüffer
Research Assistant
jenny.rueffer[at]whz.de