Data and information flows in agro-food supply chains have exponentially increased due to diverse embedded tracking/sensing devices, ubiquitous usage of computer systems, and increasing data/information sharing among firms. This promises a huge potential value when the data are gathered and turned into information/knowledge that can be used to make better decisions. Focusing on agro-food logistics management, this PhD thesis investigates how to utilize available data/information in agro-food supply chains to improve logistics processes. Using the Dutch floriculture supply chain network as the case study platform, this research particularly deals with logistics challenges caused by the increasing trend of customer orders in the agro-food sector: high frequency, numerous order lines of small volumes, and short required delivery lead-times. The first part of the thesis helps readers gain a better understanding of the value of data/information in supply chain decision-making. First, it presents a generic framework to assess the value of information (VOI) in supply chain decisions along the four primary dimensions: “supply chain decisions”, “information”, “modelling approach”, and “supply chain context”. Second, it extends the “supply chain decisions” and “information” dimensions to propose a multi-level framework that explains how data and big data are actually linked to supply chain decisions at different levels, i.e., short-term or long-term and individual-firm level or supply-chain level. The second part of the thesis applies the VOI framework and the multi-level framework to the case of the Dutch floriculture sector. At the individual-firm level, the thesis examines the uses of common information types at agro-food suppliers and crossdocking facilities. For suppliers, the thesis proposes a data-driven process redesign, which uses historical customer orders to redesign the order fulfilment process for a higher delivery service level and a lower operational cost. For cross-docking distribution, the value of ...