Capillary electrophoresis-mass spectrometry (CE-MS) is a powerful tool in various fields including proteomics, metabolomics, and biopharmaceutical and environmental analysis. Nanoflow sheath liquid (SL) CE-MS interfaces provide sensitive ionization, required in these fields, but are still limited to a few research laboratories as handling is difficult and expertise is necessary. Here, we introduce nanoCEasy, a novel nanoflow SL interface based on 3D printed parts, including our previously reported two capillary approach. The customized plug-and-play design enables the introduction of capillaries and an emitter without any fittings in less than a minute. The transparency of the polymer enables visual inspection of the liquid flow inside the interface. Robust operation was systematically demonstrated regarding the electrospray voltage, the distance between the emitter and MS orifice, the distance between the separation capillary and emitter tip, and different individual emitters of the same type. For the first time, we evaluated the influence of high electroosmotic flow (EOF) separation conditions on a nanoflow SL interface. A high flow from the separation capillary can be outbalanced by increasing the electrospray voltage, leading to an overall increased electrospray flow, which enables stable operation under high-EOF conditions. Overall, the nanoCEasy interface allows easy, sensitive, and robust coupling of CE-MS. We aspire the use of this sensitive, easy-to-use interface in large-scale studies and by nonexperts.
Robotic process automation is a disruptive technology to automate already digital yet manual tasks and subprocesses as well as whole business processes rapidly. In contrast to other process automation technologies, robotic process automation is lightweight and only accesses the presentation layer of IT systems to mimic human behavior. Due to the novelty of robotic process automation and the varying approaches when implementing the technology, there are reports that up to 50% of robotic process automation projects fail. To tackle this issue, we use a design science research approach to develop a framework for the implementation of robotic process automation projects. We analyzed 35 reports on real-life projects to derive a preliminary sequential model. Then, we performed multiple expert interviews and workshops to validate and refine our model. The result is a framework with variable stages that offers guidelines with enough flexibility to be applicable in complex and heterogeneous corporate environments as well as for small and medium-sized companies. It is structured by the three phases of initialization, implementation, and scaling. They comprise eleven stages relevant during a project and as a continuous cycle spanning individual projects. Together they structure how to manage knowledge and support processes for the execution of robotic process automation implementation projects.
Digital transformation forces companies to rethink their processes to meet current customer needs. Business Process Management (BPM) can provide the means to structure and tackle this change. However, most approaches to BPM face restrictions on the number of processes they can optimize at a time due to complexity and resource restrictions. Investigating this shortcoming, the concept of the long tail of business processes suggests a hybrid approach that entails managing important processes centrally, while incrementally improving the majority of processes at their place of execution. This study scrutinizes this observation as well as corresponding implications. First, we define a system of indicators to automatically prioritize processes based on execution data. Second, we use process mining to analyze processes from multiple companies to investigate the distribution of process value in terms of their process variants. Third, we examine the characteristics of the process variants contained in the short head and the long tail to derive and justify recommendations for their management. Our results suggest that the assumption of a long-tailed distribution holds across companies and indicators and also applies to the overall improvement potential of processes and their variants. Across all cases, process variants in the long tail were characterized by fewer customer contacts, lower execution frequencies, and a larger number of involved stakeholders, making them suitable candidates for distributed improvement.
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