Reducing Wastage In Manufacturing Through Digitalization: An Adaptive Solution Approach For Process Efficiency

The transformation to digital manufacturing has become increasingly critical for companies to remain competitive and achieve efficient manufacturing processes. However, manufacturing operations are often plagued by suboptimal allocation of resources, which can lead to higher costs and lower productivity. Digitalization has the potential to address these challenges by enabling real-time data monitoring, reducing quality costs, and improving product quality. Previous studies have shown that digital manufacturing can improve the efficiency of manufacturing processes and lead to productivity increases in organizations. However, despite these advantages, many digital innovation projects in manufacturing fall short of their initial ambitions, often resulting in incremental improvements to an existing manufacturing system. This is partly due to the challenges faced by manufacturing companies in quantifying the added value versus the costs of digitization technologies. Therefore, the objective of this paper is to propose an adaptive solution approach that addresses the need of aiding the decision process in selecting and assessing digital technologies to reduce wastage in manufacturing processes. The approach combines the 'Makigami' methodology, an 'Activity Diagram' (AD) modelling methodology, and a simplified 'Flow Chart', representing an aggregated view of the more detailed AD via a custom modelling schema, into one coherent framework. We further introduce the 'Methods-Misallocation-Measure' (3M-Graph) framework, which maps methods onto elements of wastage and misallocation, and subsequently assigns potential countermeasures. This tripartite mapping facilitates the identification of wastage during process analysis, the allocation of digital optimization measures and eases the assessment of cost effectiveness. The proposed approach aims to improve process efficiency and reduce wastage in manufacturing through digitalization. We conduct a case study of the approach and its application to an industrial assembly station, comparing the initial and then optimized processes. Future work includes the identification of further improvements and extending the framework by methodologies for estimating cost effectiveness more concisely

NFDI4Ing - the National Research Data Infrastructure for Engineering Sciences

NFDI4Ing brings together the engineering communities and fosters the management of engineering research data. The consortium represents engineers from all walks of the profession. It offers a unique method-oriented and user-centred approach in order to make engineering research data FAIR &ndash; findable, accessible, interoperable, and re-usable. NFDI4Ing has been founded in 2017. The consortium has actively engaged engineers across all five engineering research areas of the DFG classification. Leading figures have teamed up with experienced infrastructure providers. As one important step, NFDI4Ing has taken on the task of structuring the wealth of concrete needs in research data management. A broad consensus on typical methods and workflows in engineering research has been established: The archetypes. So far, seven archetypes are harmonising the methodological needs: Alex: bespoke experiments with high variability of setups, Betty: engineering research software, Caden: provenance tracking of physical samples & data samples, Doris: high performance measurement & computation, Ellen: extensive and heterogeneous data requirements, Frank: many participants & simultaneous devices, Golo: field data & distributed systems. A survey of the entire engineering research landscape in Germany confirms that the concept of engineering archetypes has been very well received. 95% of the research groups identify themselves with at least one of the NFDI4Ing archetypes. NFDI4Ing plans to further coordinate its engagement along the gateways provided by the DFG classification of engineering research areas. Consequently, NFDI4Ing will support five community clusters. In addition, an overarching task area will provide seven base services to be accessed by both the community clusters and the archetype task areas. Base services address quality assurance & metrics, research software development, terminologies & metadata, repositories & storage, data security & sovereignty, training, and data & knowledge discovery. With the archetype approach, NFDI4Ing&rsquo;s work programme is modular and distinctly method-oriented. With the community clusters and base services, NFDI4Ing&rsquo;s work programme remains firmly user-centred and highly integrated. NFDI4Ing has set in place an internal organisational structure that ensures viability, operational efficiency, and openness to new partners during the course of the consortium&rsquo;s development. NFDI4Ing&rsquo;s management team brings in the experience from two applicant institutions and from two years of actively engaging with the engineering communities. Eleven applicant institutions and over fifty participants have committed to carrying out NFDI4Ing&rsquo;s work programme. Moreover, NFDI4Ing&rsquo;s connectedness with consortia from nearby disciplinary fields is strong. Collaboration on cross-cutting topics is well prepared and foreseen. As a result, NFDI4Ing is ready to join the National Research Data Infrastructure.</p