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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.
The use of augmented reality (AR) in outbound logistics is associated with potentially strong stimuli for cost savings and throughput time. Nevertheless, the benefits of AR compared to conventional methods require a holistic analysis for investment decision making. Until now, research has only assessed case-study-related potentials and selected aspects of the technology. This paper answers the following research questions: How can the economic efficiency of AR in the packing process be quantified by utilizing a holistic model of value drivers? How can AR be technically implemented for packing processes in outbound logistics? What economic profit results from the use of AR technology in a case company’s packing process?
The presented model enables the investment decision to be supported based on economic value added (EVA), thereby providing an assessment of value drivers in packing systems. Cost drivers are identified on the basis of the Supply Chain Operations Reference (SCOR) process model. The technical and economic validation of the model was carried out by means of an empirical study: Expert interviews were conducted for validating the model elements. Data collection by a prototype at a mechanical-engineering company was used to calculate the value contribution. The mapping of cause-effect relationships within the framework of EVA driver trees has proven itself in both the expert interviews and the prototype validation. The field experiment at the case company demonstrated a positive value contribution of AR, in particular regarding employee productivity, length and variance of throughput time, quality aspects, volume utilization, and quantity of packing material used.
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.
AI-based chatbots as enabler for efficient external knowledge management in public administration
(2024)
This study addresses the pressing issue of staff shortages in German public administrations through the lens of digitalization, focusing on the potential of AI-based chatbots to solve this problem by replacing human labour. Employing a Design Science Research Process (DSRP) methodology, the research synthesizes theoretical foundations and regulatory frameworks to develop a robust chatbot concept. The artifact presented is a comprehensive architectural framework integrating user-centric design, linguistic processing, and regulatory compliance. The proposed artifact navigates complex federal structures and diverse IT infrastructures, promoting accessibility and inclusivity. Implications suggest enhanced efficiency and accessibility in public service delivery for potentially increasing citizen satisfaction and decreasing employee workload. The study underscores the importance of legal compliance and the evolving regulatory landscape in AI deployment. Future research will involve prototyping and evaluating the artifact's performance and applicability throughout the course of the DSRP, thus contributing to the advancement of digital transformation in public administrations.
