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Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

The purpose of this paper is to identify and categorise problems in knowledge management of industrial maintenance, and support successful maintenance through adapting the SHEL model. The SHEL model has been used widely in airplane accident investigations and in aviation maintenance, but not in industrial maintenance.

The data were collected by two separate surveys with open-ended questions from maintenance customers and service providers in Finland. The collected data were coded according to SHEL model -derived themes and analysed thematically with NVivo.

The authors found that the adapted SHELO model works well in the industrial maintenance context. The results show that the most important knowledge management problems in the area are caused by interactions between Liveware and Software (information unavailability), Liveware and Liveware (information sharing), Liveware and Organisation (communication) and Software and Software (information integrity).

The data were collected only from Finnish companies and from the perspective of knowledge management. In practice there are also other kinds of issues in industrial maintenance. This can be a topic for future research.

The paper presents a new systematic method to analyse and sort knowledge management problems in industrial maintenance. Both maintenance service customers and suppliers can improve their maintenance processes by using the dimensions of the SHELO model.

The SHEL model has not been used in industrial maintenance before. In addition, the new SHELO model takes also interactions without direct human influence into account. Previous research has listed conditions for successful maintenance extensively, but this kind of prioritisation tools are needed to support decision making in practice.