"Just-in-Time" (JIT) inventory systems are currently very popular in practice and in the operations management literature. These writings have generally assumed that all shipments are done by motor freight. We feel, however, that this assumption can be misleading. The main thrusts of a JIT strategy in production management need inventory control need not restrict the method of delivery as long as that mode can meet certain JIT-created characteristics. JIT provides railroads with real opportunities to tailor their services to meet the needs of the individual manufacturer or supplier. Possibilities include guaranteed delivery dates, prearranged pickup and delivery, short-term storage, and tardiness penalties. Regularly scheduled priority trains, the bypassing of time-consuming yard functions, close communication with shippers and consignees, and efficient freight consolidation are crucial. More importantly, JIT requires working directly with suppliers, manufacturers, shippers, and freight forwarders in order to fully exploit the characteristics imposed on freight transportation by such an environment. It is the purpose of this article to examine these criteria and constraints as they relate to rail freight systems, and, in particular, to suggest ways in which the railroad industry might better compete for its share of JIT transportation. We begin by examining important characteristics of a JIT system and the perceptions of rail freight by JIT manufacturers. Following this, we discuss specific strategies for rail service in a JIT environment, including the use of contracts, intermodal freight, and boxcar/distribution center combinations. Lastly, we present our conclusions and suggestions as to the "ideal" rail JIT system.

Shipment consolidation is a logistics strategy that combines two or more orders or shipments so that a larger quantity can be dispatched on a same vehicle. This paper discusses a discrete-time Markovian decision process (MDP) approach for determining when to release consolidated loads. We assume that the shipper controls the timing of each load dispatch. Thus, whenever a customer places an order, a choice must be made between dispatching this order (plus all others waiting) immediately, or continuing to consolidate until at least the arrival of the next order. Our MDP models of shipment consolidation consider movement by for-hire transportation (common carriage) or by a firm's own vehicles (private fleet). Small but realistic numerical examples illustrate the application of these models and the data-aggregation issues that must be resolved. Two minimization criteria are considered: cost per unit time, or cost per hundredweight per unit time. For private carriage, the optimal policy is of the control-limit type for common carriage, it may not be. These potential differences in form of the optimal policy are true for either objective function. The possibly constricting optimal polices are interpret in light of the costs encountered by an industrial firm's private fleet compared to the freight charges of a public trucking company.

It is well appreciated by logistics professionals that combining several small orders into one larger load can reduce the total transportation cost to the shipper. Logistics specialists all recognize that such a decision to consolidate must take into account the shipper's increased inventory carrying cost, and the possible impact on service to the customer (consignee), when an order is held for a longer time before dispatch. However, much of the literature on shipment consolidation is descriptive, not sufficiently quantitative to help a company develop a consolidation policy. This article is addressed to that end. Through a simulation mode, we consider three general strategies for dispatch of a consolidated shipment. These strategies are referred to a Time Policy, a Quantity Policy, and a Time-and-Quantity policy. Simulation results are discussed and understood in terms meaningful to distribution practitioners. For a large range of order-arrival rates and maximum holding times, we compare these policies on the basis of cost per load, cost per hundredweight, and average order delay. Appropriate consolidation policies are then suggested for various situations.