Wirtschaft (MSB)
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Strategically Aligning Additive Manufacturing Supply Chains for Sustainability and Effectiveness
(2019)
This paper builds on a previously developed framework that integrated additive manufacturing, life-cycle analysis, and value creation (Feldmann & Kirsch, 2019) by exploring conditions related to the life-cycle approach that would require alignment among suppliers, additive manufacturing firms, and customers. This extension creates a bridge to aid implementation of taking a sustainability approach to additive manufacturing. In order to develop this extension, we distinguish between direct/indirect customers and internal/external customers and then create a matrix of incentives and cognitive frames that we believe will help companies interested in large-scale AM improve both the speed and the effectiveness of AM adoption. We provide an organizing framework that managers can use to create a supply chain that is aligned around closed-loop principles that will help speed adoption and move closer to sustainable goals that exist for AM technologies. These include reduced raw material use, reduced scrap and material overage, and reduced rework, and lower transportation costs. The goal is to attain often-conflicting goals of lower long-term costs and decreased environmental footprint. Using our extension, we believe we can provide a useful framework to help managers implementing advanced manufacturing technologies to achieve lower costs and greater environmental sustainability by creating a common supply chain framework around customized, on-demand products.
Robotic Process Automation in der betriebswirtschaftlichen Hochschullehre: Ein Erfahrungsbericht
(2022)
Robotic Process Automation (RPA) – Welche Prozesse lassen sich mit Software-Robotern automatisieren?
(2020)
Digitalization and sustainable development are goals of the global community, but can they also be achieved simultaneously? This article investigates the impacts of additive manufacturing (AM) on sustainable production and consumption. The use of AM technology as a means of digitalizing manufacturing processes is assessed through a qualitative life cycle analysis.
The model developed for this purpose provides a structure for an analysis of the general ecological effects of AM. The systematics of the life cycle model also supports a company-specific assessment.
AM can have a positive impact on achieving sustainable development with regards to ecological effects, particularly by reducing the consumption of resources in production and distribution. However, there are also negative ecological impacts of this technology, such as rebound effects and high energy consumption, which vary depending on the application and the printing process. It appears necessary for regulatory policy to intervene to maximize the opportunities for the positive effects of this technology. However, it is important to reduce the risks that contradict the objectives of the 12th Sustainable Development Goal of the UN: sustainability of consumption and production.
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.
Digitalisierungsstrategie
(2021)