A product is valuable only when it can be delivered to the user/customer at the right time, to the right place, and with the right quality. This is particularly essential in todayβs e-commerce environment, which demands quick response, and short product life cycle. Deviation of deliveries from the promised schedule can induce extra costs, which may be tangible like penalty costs, high inventory level and loss of market share or intangible like product depreciation, interruption of production and poor customer satisfaction. Efficient coordination of material suppliers, manufacturing plants, wholesalers, retailers, warehouses, and distribution centres, to deliver products as scheduled is the ultimate goal of supply chain management (SCM). In this connection, many industrialists and academicians are keen to develop optimization methodologies with artificial intelligence (AI) techniques that can optimize demand allocation problems, transportation policy, inventory management, etc. This special issue on βLogistics and SCM with AI Techniquesβ aims to present the recent developments and applications concerning global optimization with AI techniques for logistics and SCM. The papers selected for this issue comprise a cross-section of topics that reflect a variety of perspectives and disciplinary backgrounds covering performance measurement of supply chains; supplier evaluation; multi-agent design for SCM; supply chain scheduling and simulation; collaborative supply chain planning; resources allocation; and database systems. I believe the seven papers presented in this special issue adequately reflect these topics. The first paper, by Ohdar and Ray is a challenging problem in SCM, which relates to the performance measurement and evaluation of suppliers in supply chains. A genetic algorithm (GA) based methodology has been developed to evolve the optimal set of fuzzy rule-based system. The proposed methodology has been found to provide acceptable results in determining the suppliersβ performance score. The following paper by Jain et al., also tackles the problem in the evaluation of supplier performance, with evolutionary fuzzy system. The proposed methodology offers consistently good performance when applied to some benchmarking problems in the literature. The third paper by Daniel et al., presents a new approach for the SCM. This approach is based on the virtual enterprise paradigm and the application of multi-agent concept. The proposed architecture allows SCM to become more transparent to other participating members in the supply chain. The fourth paper by Nurmilaakso, proposes a methodology in the supply chain scheduling. This proposed scheduling methodology relies on distributed parallel forward simulation in which simple messages are exchanged between the agents periodically. Nurmilaakso concluded that although the proposed distributed simulation methodology could not optimise the schedules, at least it is capable of determining a feasible schedule for the practical implementation. In the fifth paper by Lau et al., the authors argue that the quality of information; the capability of providing the right information to the right person; and the utilisation of information in SCM are still immature. In this connection, they propose an infrastructure of a decision support system to aid to capture and maintain the business and resources allocation information, with the adoption of neural network concept. The proposed system is implemented in a shipping company to assist the allocation of containers. This is followed by a paper by Ip et al., which emphasises that the major characteristics of todayβs manufacturing information system must be able to both reconfigure and re-engineer operations in a cost-effective way. The authors propose an enterprise resource planning system based on the reconfigurable characteristics of material objects and finance objects. The results of a small case study demonstrated that the flattened organisational structure achieved a better communication and enhanced workflow reconfiguration. The final paper of this first special issue by Chiu and Lin, demonstrates how the concepts of collaborative agents and artificial neural networks (ANNs) can work together to enable collaborative supply chain planning in this global era. An errorminimising algorithm which models the agentsβ collaboration mechanism is used to train three ANNs. With a demonstrated example, the results showed that the ANN approach achieved complete order fulfilment and dramatically improved the resource utilisation of all the supply chain agents. I find great pleasure to announce that this special issue has attracted a great attention and response from researchers in the area of SCM. In order to accommodate all these good quality papers, there will be an additional issue dedicated to publish the second batch of accepted papers. In particular, these papers constitute state-of-the-art research-based contributes in the field of logistics and SCM with AI techniques. I sincerely hope you find the papers as useful and interesting as I did. I look forward to seeing another technological breakthrough in this area in the near future. Previously published in: Journal of Manufacturing Technology Management, Volume 15, Number 8, 2004