Mer Än En Algoritm : En studie om datadrivet värdeskapande & ruttoptimering i en nordisk logistikverksamhet

Sammanfattning

This study examines how data is used to create operational value through route optimization in a Nordic logistics organization, a Swedish state-owned postal organization operating in a sparsely populated northern region. Despite growing interest in data-driven logistics, limited research addresses how data concretely creates value in specific operative processes, particularly in Nordic postal contexts. The research question guiding the study is: how is data used to create operational value through route optimization in a logistics organization? A qualitative approach was adopted, based on semi-structured interviews across different organizational levels, analyzed through deductive content analysis using the DDDM framework by Brynjolfsson et al. (2011) as the primary theoretical lens. The results show that operational value creation through data is uneven and highly context-dependent. While the parcel operation benefits from a well-integrated iterative optimization chain, the letter delivery operation relies on a system not designed for its needs, producing outputs that require manual correction and consume more time than they save. Data quality issues, particularly inaccurate coordinate data, propagate through the entire optimization chain. KPI-based follow-up provides systematic feedback at the managerial level but fails to reach operational staff in an actionable form. The tension between algorithmic optimization and tacit knowledge emerges as a recurring theme, constrained by technological limitations, high staff turnover, and a dynamic environment that renders experiential knowledge temporally unreliable. The authors conclude that operational value arises not from data access alone, but from the interplay between well-designed systems, sufficient data quality, and human adaptability. Infrastructural inequalities within a single organization can produce fundamentally different conditions for data-driven value creation across business areas, a nuance largely overlooked in existing DDDM literature.