Organizational decisions are significant to an organizations' future. Research shows that many organizations tend to make decisions based on previous experiences, ‘gut feeling’ and assumptions rather than facts. This is troubling since decision-makers leave the organization’s future to the hands of chance. It is impossible to foresee the future, but accurate factual data can be of guidance towards a successful path. The food industry is one industry which must undertake tremendous efficiency efforts due to current climate change in order to sustain a growing population while resources are diminishing. Therefore, organizations must be efficient with resource allocation. Here accurate decisions must be made. Much research has been conducted into the food industry regarding sustainability practices, and data-driven approaches have become widely regarded as promising in sustainability practices due to the new industry paradigm of industry 4.0 and availability of data. Fact-based decision making based on the collection, storage, and analysis of data has been widely studied and coined Business Intelligence & Analytics (BI&A). This study is based upon Scandinavian food producers and processors and explores the current adoption and utilization of BI&A towards efficient production and its challenges. Through an explorative approach with interviews and analysis of organization reports, the current progress into BI&A towards efficient production and accompanying challenges were identified. Findings show that BI&A is applied to varying degrees for storage of data, and to monitor, analyze, and identify targets of action within energy consumption, food waste, material waste, and supply chain. Challenges identified were integration both internally within organizations and externally in the supply chain, economic, and leadership. To manage these challenges, managerial complications are provided. Further research could investigate the data collection processes in production, understanding the role of BI&A for top managers in decision making, investigate BI&A strategies towards efficient production, and provide empirical results from other parts of the supply chain.