Exploring the Perceived Potential of AI-Based Decision Support for Predictability in Industrial Planning : A Case Study in the Paper Manufacturing Industry

Sammanfattning

The rapid increase in data availability caused by digitalization has made artificial intelligence increasingly relevant for supporting complex planning and decision-making in industrial firms. In industrial B2B contexts, AI-based decision support is often discussed to improve forecasting, responsiveness and planning quality. However, less attention has been given to how such tools are perceived in market and demand planning within the paper manufacturing industry, where long planning horizons, demand variability, production constraints and strong dependencies between market, production, logistics and customer relationships make predictability particularly important. The purpose of this study is to explore how AI-based decision support is perceived to shape predictability, decision-making processes and relational outcomes in industrial B2B market and demand planning. The study is based on an exploratory qualitative single case study. Empirical data was collected through 13 semi-structured interviews with respondents involved in planning, forecasting, analytics, operations and management within the selected case organization. The data was analyzed through thematic analysis, guided by a theoretical framework including AI-based decision support, data-driven decision-making,Human-AI Collaboration Theory, demand forecasting, market planning and relational outcomes in industrial B2B relationships. The findings show that market and demand planningis challenged by fragmented data, manual preparation, demand variability and production constraints. These challenges limit predictability because planning depends not only on forecast accuracy, but also on the ability to coordinate information decisions and operational constraints across functions. The findings further suggest that data-driven decision-making is perceived to influence customer relationships and trust indirectly, through more reliable delivery information, earlier communication of changes and reduced planning disruptions. The study concludes that AI-based decision support is perceived as valuable when it supports scenario analysis, forecasting, visibility of planning consequences and cross-functional coordination. However, its value depends on data quality, system integration, human judgement, clear decision responsibilities and organizational readiness for change.