Wirtschaft (MSB)
Refine
Year
Publication Type
- Conference Proceeding (111) (remove)
Keywords
- Process-Driven Application (5)
- BPMN (4)
- Logistics (2)
- 3D printing (1)
- Activ Investor (1)
- Activist Investor (1)
- Analytics (1)
- Artificial Intelligence (1)
- Asset Stripping (1)
- Augmented Reality (1)
Faculty
This study investigates the impact of Large Language Model (LLM) parameters, specifically
temperature and top P, on Supply Chain Risk Detection (SCRD). With a heightened focus
on Supply Chain Risk Management (SCRM) using AI, the research employs a Design of
Experiments (DoE) approach. The results reveal optimal temperature values for valid
assessments in SCRD applications. The study emphasizes the importance of tailored LLM
parameter settings, contributing insights for future research and practical applications in
enhancing supply chain resilience. Suggestions for incorporating Response Surface
Methodology (RSM) and refining the process are proposed for further investigation.
Assessing serious games within purchasing and supply management education: an in-class experiment
(2021)
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.
Experimental learning & reflection: how it promotes competence development in business education
(2019)
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.
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.
Startups have the potential to transform industries as they follow partly divergent business strategies and have the ability to develop new innovative products. The evolving fields of digitalization, sustainability and urbanization highlight the direction of change. Due to enormous time pressure and lack of knowledge, corporations rely heavily on external sources of knowledge to increase innovativeness. Therein, startups take a special role. Joint R&D projects, investments or strategic buyer-supplier agreements with startups grant corporations access to their innovative technologies. This paper gives insights into the organization of search processes to identify innovative startups and highlights approaches to initiate collaborations. Therefore, a multiple-case study among automotive OEMs and suppliers was conducted. The research ends with organizational structures, an identification process, and various instruments developed for the identification of startup innovations. Furthermore, propositions are made for a successful collaboration between startups and established corporations, displaying the role of purchasing in startup management, the need to take fast decisions, secure technical support by experts within their organization and build strong relationships with partners within their supply chain and new partners, as for example venture capitalists.
BPMN-based Process-Driven Applications (PDA) require less coding since they are not only based on source code, but also on executable process models. Automated testing of such model-driven applications gains growing relevance, and it becomes a key enabler if we want to found their development on continuous integration (CI) techniques.While process analysts are typically responsible for test case specifications from a business perspective, technically skilled process engineers take the responsibility for implementing the required test code. This is time-consuming and, due to their often different skills and backgrounds, might result in communication problems such as information losses and misunderstandings. This paper presents a new approach which enables an analyst to generate executable tests for PDAs without the need for manual coding. It consists of a sophisticated model analysis, a wizard-based specification of test cases, and a subsequent code generation. The resulting tests can easily be integrated into CI pipelines.The concept is underpinned by a user-friendly tool which has been evaluated in case studies and in real-world implementation projects from different industry sectors. During the evaluation, the prototype proved a more efficient test creation process and a higher test quality.
Innovative business models for data-driven B2B platforms evolve rapidly based on the prospects of digital technology. In addition to the platform provider, service providers on the supply side of the digital platform - the so-called complementors - play an important role in the process of value creation. This paper highlights the complementors’ perspective on the different facets of complementor relationship management (CoRM) and answers the following research questions: From the perspective of a complementor, what are the main fields of CoRM for data-driven B2B platforms? What factors of influence comprise the reason complementors join a platform?
Complementor relationship management for Data-driven B2B platforms: Towards a Holistic approach
(2021)
In the so-called ecosystem economy, new platform-based business models evolve rapidly based on the prospects of digital technology. Especially in the B2B context, data-driven platforms are highly relevant. Thus far, little research has been conducted on the supply side of data-driven platforms and especially on service providers, the so-called complementors. Therefore, this paper offers insights into the various facets of complementor relationship management (CoRM). The paper aims to develop a framework for the management of complementors of data-driven B2B platforms. For empirical evidence, we draw on 14 semi-structured expert interviews with platform managers and complementors. The findings outline two big areas of CoRM and discuss distinct characteristics of partner management and technology management. For partner management the differentiation into open and closed platform needs to be taken into account for complementor relationship management. Moreover, our study reveals the key factors of technology management which lead from platform infrastructure to digital applications like digital twins or predictive maintenance.
In the so-called ecosystem economy, new platform-based business models evolve rap-idly based on the prospects of digital technology. In the B2B context especially, data-driven platforms are highly relevant. Thus far, little research has been conducted on service providers, the so-called complementors of data-driven platforms. Therefore, this paper represents just a starting point for gaining deeper insights into the different facets of complementor management. For empirical evidence, we draw on semi-structured expert interviews with platform managers. The findings outline the distinct characteristics of open and closed platforms which need to be taken into account for complementor management. Moreover, the paper reveals a number of differences in managing suppliers compared to managing complementors. In addition, our study shows that the key factors influencing complementor management include platform openness, partnership intensity, strategic fit, and market structure respectively poten-tial.
Professional roles, including specific skills for each role, are a step towards higher professionalism and maturity within purchasing and supply management (PSM). The global development towards increasing digitalization, Industry 4.0, globalization, and increasing attention for corporate social responsibility force change within the purchasing organizations. Here, PSM's professional roles and skills are a good starting point to manage these changes by redefining professional roles organized by specific skills and responsibilities. For this reason, based on a systematic literature review and three World Cafés with 29 purchasing professionals, this study compiles a list of Industry 4.0 professional roles and skills in PSM.
This paper uses the findings from a literature review and series of expert interviews to develop a richer and Purchasing and Supply Management (PSM) context-specific perspective of the different key techniques, tools and principles that can be used to develop gamified learning to enhance the skills required by PSM professionals in dealing with current and future challenges, such as the transformation to Industry 4.0. It also provides further details of the different stages of implementing gamified learning, which can enhance the success of any such provision.
To increase maturity within purchasing and supply management (PSM), future purchasing skills are needed based on the technological development towards Industry 4.0. Past research, eg, the work of Bals, Schulze, Kelly, and Stek (2019), started to address this issue based on literature review and interview studies. However, a detailed description of these skills is missing. Utilizing a real-time Delhi study with 45 experts within the PSM field, nine future purchasing skills have been elaborated. Identified skills connect to the maturing and emerging technologies within purchasing and provide a guideline towards Industry 4.0 in purchasing based on a human-centric perspective.
Specifying roles in purchasing and supply management in the era of Industry 4.0: A Delphi study
(2021)
New technologies and systems within the field of purchasing and supply management (PSM) call forth responsibilities and require expertise. Moving towards Industry 4.0 in purchasing, increasing attention on specialization within talent and skills, where human capital is needed to exploit the full potential of technologies. Based on an internet-based real-time Delhi study with 47 experts within the PSM field, six future purchasing roles have been defined and elaborated. These future roles connect to the maturing and emerging technologies within the purchasing field and provide a guideline to further develop towards Industry 4.0 in purchasing based on a human-centered evolutionary approach.
While the service sector is growing rapidly, the purchasing of services has not yet received significant attention in theory or practice. Service purchasers face serious challenges, and existing purchasing practices for services are often non-strategic. We choose an exploratory–qualitative research approach to investigate the purchasing of IT, logistics and Maintenance, Repair, and Overhaul (MRO) services. In particular, we focus on the role of visibility and analyze how service purchasers can benefit from extensive knowledge about their service networks. We determine that visibility indeed adds significant value to service purchasing and can help service purchasers to decrease costs, mitigate risks and maintain competitiveness.
Process-Driven Applications flourish through the interaction between an executable BPMN process model, human tasks, and external software services. All these components operate on shared process data, so it is even more important to check the correct data flow. However, data flow is in most cases not explicitly defined but hidden in model elements, form declarations, and program code. This paper elaborates on data-flow anomalies acting as indicators for potential errors and how such anomalies can be uncovered despite implicit and hidden data-flow definitions. By considering an integrated view, it goes beyond other approaches which are restricted to separate data-flow analysis of either process model or source code. The main idea is to merge call graphs representing programmed services into a control-flow representation of the process model, to label the resulting graph with associated data operations, and to detect anomalies in that labeled graph using a dedicated data-flow analysis. The applicability of the solution is demonstrated by a prototype designed for the Camunda BPM platform.