Wirtschaft (MSB)
Refine
Year
Publication Type
- Conference Proceeding (86) (remove)
Language
- English (86) (remove)
Keywords
- Process-Driven Application (5)
- BPMN (4)
- 3D printing (1)
- Activ Investor (1)
- Activist Investor (1)
- Asset Stripping (1)
- BPM (1)
- Business Process (1)
- Continuous Software Engineering (1)
- Control-Flow Graph Analysis (1)
Faculty
In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions, are rarely addressed. Additionally, the integration of robotic process automation (RPA) to potentially increase the efficiency of customer journey analysis is not discussed in the current field of research. To fill this research gap, this paper introduces “customer journey mining”. Process mining techniques are applied to leverage digital customer data for accurate prediction of customer movements through individual journeys, creating valuable insights for improving the customer experience. Striving for improved efficiency, the potential interplay of RPA and customer journey mining is examined accordingly. The research methodology followed is based on a design science research process. An initially defined customer journey mining artifact is operationalized through an illustrative case study. This operationalization is achieved by analyzing a log file of an online travel agency functioning as an orientation for researchers and practitioners while also evaluating the initially defined framework. The data is used to train seven distinct prediction models to forecast the touchpoint a customer is most likely to visit next. Gradient-boosted trees yield the highest prediction accuracy with 43.1%. The findings further indicate technical suitability for RPA implementation, while financial viability is unlikely.
Against the setting of an increasing need for innovation and low margins, companies in the logistics
sector are facing highly competitive pressure. One field with high potential for optimization lies within
damage quotas. The use of big data analytics or data mining represents a promising approach to face
this challenge. However, within supply chain management, data mining is hardly being researched on
regarding damage quotas and thus not being utilized to its full possible extend. At the current time it
seems to predominantly be used for route and utilization optimization while the analysis of delivery
damages is hardly considered.
The aim of this research is therefore to showcase an initial approach for data mining in logistics to predict
delivery damage probabilities and to validate this by means of a multiple case study research. To create
a sound basis for evaluation, the groundwork is laid out based on CRISP-DM by the analysis of reference
data (German road-cargo market).
As a central result it is noted that data mining can systematically be used to help reducing the damages
by forecasting the probabilities of damages occurring during transport in dependence of different factors.
The approach can be utilized across different markets as long as sufficient data tracking delivery
damages is being collected within a company. Challenges arise in the field of air- and sea-freight.
Ecosystem Emergence and Founding Conditions - Lessions Learned from an Imprinting Perspective
(2022)
The rise of ecosystem prominence has provided several definitions of how we understand ecosystems nowadays. In this context, several scholars have considered influencing factors for ecosystem emergence. This paper addresses this consideration and analyzes the salient characteristics of different ecosystem types and their potential persistence since ecosystem founding to improve the understanding of emergence. We applied a three-step approach (1) identifying ecosystem types based on bibliometric analysis, (2) exploring salient characteristics per ecosystem type using qualitative content analysis and (3) deriving founding conditions from the salient characteristics following a conceptual approach. Based on a bibliometric analysis, we identified business/innovation, entrepreneurial and service ecosystems. In a second step, we developed salient characteristics within the themes of structure, power constellation/interdependencies and governance by inductive coding. As we identified a significant difference in alignment structure, we analyzed if alignment structure persists since ecosystem origin and explains why ecosystems differ. We analyzed potential pairings between alignment structure and their respective founding condition for every ecosystem type. With the alignment structures’ persistence, we can better understand why ecosystem types differ.
Automated regression tests are a key enabler for applying popular continuous software engineering techniques. This paper focuses on testing BPMN-based Process-Driven Applications (PDA). When evolving PDAs, the affected test cases must be identified and co-evolved as well. In this process, affected test cases can be overlooked, misunderstandings may occur during communication between different roles involved, and implementation errors can arise. Regardless of possible error sources, the entire test migration process is time-consuming. This paper presents a new semi-automated test migration process for PDAs. The concept builds on previous work on creating regression tests using a no-code approach. Our approach identifies the modifications of the PDA and classifies their impact on previously defined tests. The classification indicates whether existing test code can be migrated automatically or whether a manual revision becomes necessary. During an AB/BA experiment, the concept and the developed prototype proved a more efficient test migration process and a higher test quality.