Digital technologies change the way how manufacturing firms create offers for customers. These new technologies transform traditional Product-Service Systems into Digital Product-Service Systems. This paper investigates the use of digital technologies for the creation of new products and smart services in manufacturing firms. Additionally, this paper investigates the service orientation in Product-Service Systems. The data for this research were collected at the end of 2021 through the ASAP Service Management Forum in the manufacturing sector of the Republic of Serbia. The empirical results indicate that more than 60% of manufacturing firms use digital technologies for the creation of new products and less than 50% for the creation of new smart services. Moreover, results indicated that most services are product-oriented with 25% of share in a sample, followed by results-oriented services with 20% of share in a sample and use-oriented services with 11% of share in the total sample.

Due to the new remote working conditions driven by the consequences of the Covid-19 pandemic, we extend our previous work on sentiment-enabled business process modeling by including crowdsourcing capabilities with a web interface: SentiProMoWeb. These capabilities enable us to perform sentiment-driven business process re-design method with remote stakeholders from different locations. SentiProMoWeb implements an enterprise social information system to capture the feedback from stakeholders in a crowdsourced manner. We demonstrate the crowdsourcing capabilities of our approach with an illustrative scenario by using our SentiProMoWeb software. © 2022, IFIP International Federation for Information Processing.

This paper describes an example of an explainable AI (Artificial Intelligence) (XAI) in a form of Predictive Maintenance (PdM) scenario for manufacturing. Predictive maintenance has the potential of saving a lot of money by reducing and predicting machine breakdown. In this case study we work with generalized data to show how this scenario could look like with real production data. For this purpose, we created and evaluated a machine learning model based on a highly efficient gradient boosting decision tree in order to predict machine errors or tool failures. Although the case study is strictly experimental, we can conclude that explainable AI in form of focused analytic and reliable prediction model can reasonably contribute to prediction of maintenance tasks.