Anticipatory shipping uses historical order and customer data to predict future orders and accordingly ship products to the nearest distribution centres before customers actually place the orders. It is a method to meet the increasing customer requirements on delivery service and simultaneously to reduce operational costs. This paper presents a case of anticipatory shipping in the context of agro-food supply chains. The challenge in these chains is the product perishability that leads to product obsolescence in the case of un-balanced supply and demand. This study introduces a data-driven approach that integrates product quality characteristics in data analytics to identify suitable products for anticipatory shipping at the strategic level. It also proposes process redesigns concerning production and transportation at the operational level to realise anticipatory shipping. Finally, using historical data from a Dutch floriculture supplier as input for a multi-agent simulation, the proposed approach and process redesigns are verified. The simulation output shows that anticipatory shipping could increase delivery service level up to 35.3% and reduce associated costs up to 9.3%.

The results presented in this article identify the role of costs in the scientific and grey freight terminal handling literature and analyses the handling costs of different terminal sizes. The literature review shows that handling costs only play a marginal role in the scientific research in intermodal rail freight terminals (IRT). This is remarkable given the large role costs occupy in decision-making in freight transport. Furthermore, the used cost levels show a wide range of proposed amounts and terminal sizes or handling technologies are seldom addressed. Finally, many of the scientific papers do not make it clear whether the average transhipment cost or market price is referred to. Next, the analysis of the investment in, and cost structure of, IRTs shows that IRT investments are very capital-intensive leading to relatively high average costs per handling. However, given the cost characteristics of IRTs, the average cost per handling represents the underlying cost structure and are – in this sense – representative. The cost analysis demonstrates that extra-large IRTs actually have the lowest average handling costs, followed by small IRTs.