AI i svenska pappers- och massabruk : En fallstudie och framåtblick

Sammanfattning

Artificial intelligence (AI) has globally emerged as a trending technology promising organizational growth and efficiency, in addition to improved human-machine-data interactions. Despite these observations, reports on adoption of the technology within the Swedish pulp and paper industry indicate that AI is mainly used in pilot settings. Limited investments are made into exploration of the technology as the industry is uncertain its technology’s practical utility, and so, large volumes of industrial data remain unused. The Swedish forestry industry is a well-researched area, however, there is a gap in the research aimed specifically at AI-adoption in pulp and paper plants. Through a multiple case study involving five actors representing the manufacturer, supplier, and research perspectives, this study aims to contribute greater understanding of the current and future use of AI within the Swedish pulp and paper industry, in addition to actions and factors influencing its adoption. Semi-structured interviews were conducted with 10 respondents across a variety of operational roles within their respective organizations. Themes from the interview data were derived using thematic analysis, informed by the technology-environment-organization (TOE)-framework. Our results reveal that while the industry believes the technology holds many promising use cases within factory operations – including administrative tasks, predictive maintenance, and process optimization – its adoption is still immature, and the pace of adoption is low. There is a sophisticated and conservative technological tradition within the industry, both enabling and hindering the technological advancement of manufacturers and the industry’s technological suppliers. We identify key factors affecting adoption and recommend actions for managing and fostering the advancement of a technology whose development demands challenges previously established relations. Finally, we recommend further research including the manufacturer-supplier dependency, supplier perspective, manufacturer’s competence and knowledge transfer needs, and AI effects on current technological infrastructure.