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Details an empirical study of the measurement of stockout costs in a distribution system and examines the incidence of stockouts in the supply of spare parts to the motor trade. Comprehensive studies were conducted in three major car workshops in Sweden and cost data collected from a variety of sources, including structured observation, quasi‐experiments, interviews and secondary data from internal information systems. On the basis of this research, a typology of stockout cost situations has been constructed and central concepts and measurement models developed. Results reveal that stockout costs are measurable in the system under review; information about stockout costs can have a significant impact on managerial decision making.

In calculations of inventory control costs, the effects of stockouts are often assumed or avoided because of the lack of accounting data for reasonable measurements. The authors describe the development of stockout cost models incorporating decisions made by consumers in an actual retail situation. Equations for calculating the revenue differences are based on the consumer decision alternatives. The results of a consumer survey, combined with retail prices for the product lines in question, enable the financial effects of stockouts to be calculated.

This article provides a discussion of key components for the decisionmaker concerned with the logistical issues of implementing a Just‐in‐Time (JIT) manufacturing philosophy. A JIT philosophy promotes reduced cycle times that provide benefits not normally considered in traditional inventory models and presents new concerns for the purchasing and logistics functions. The ramifications are investigated of a JIT implementation using an inventory‐theoretic modelling procedure modified and expanded to incorporate these considerations. The resulting cost comparisons indicate that the lead time variability associated with uncertain transit times in JIT is critical in the determination of order cycle time, order point, safety stock and the holding cost of the safety stock.

Consolidation, the grouping of several small shipments into one at a designated location, can reduce total logistics cost. Total logistics cost includes consolidation, transportation and inventory costs. Identifying where cost‐saving opportunities exist is often confused by the interrelated nature of these various costs.

Why is it important to use computers in physical distribution management (PDM)? The reason is simple. PDM covers almost all of the activities concerned with distributing a product. Some authorities claim that logically the functions of procurement‐and manufacturing should also be considered as part of the overall process of making goods available to customers at the right place, at the right time, and in the right quantity. These activities and functions are inherently complex, involving many stock‐keeping items (SKIs) different in size, colour and other product characteristics. In addition to multiple items, there are multiple options for producing and distributing these items. Think of the many combinations of plant locations, distribution centre locations, choices of transportation modes, inventory levels, and channels of distribution that the physical distribution manager must consider. Imagine having to compare the many possible alternatives by hand calculations, and you can get some feel for the necessity of using modern computers for planning, operating, and controlling physical distribution.

The choice of mode of transport used by a shipper to move his goods does not depend solely upon relative transportation costs. It should be viewed as one part of the complex process of deciding the method of distributing a product which, apart from transport, involves materials handling and packaging, inventory control and warehousing and order processing procedures. The transport operator providing a premium system of transport such as air freight may well be able to justify its use if appropriate economies can be obtained from other distribution activities. For example, a shipper would be prepared to incur higher transport costs if he were able to reduce the size of the inventory he had to hold to meet demand. Demonstrating in numerical terms the potential benefits from any changes in the distribution system is a difficult task because of the complexity of inter‐relationships involved. A computer‐based analysis is essential. However, because the results of such analysis must be easily understood it is also necessary that the system employed is simple to use, sparing in its input requirements, with a readily assimilated output: in short, it has to be efficient in the marketing sense, as well as an accurate model of the distribution system.

The purpose of this paper is to examine supply‐side disruptions in a supply chain, and to analyse the effectiveness of two inventory‐based policies for mitigating the impact of supply disruptions: maintaining strategic inventory reserves (the R‐policy), and using larger orders (the Q‐policy).

The paper assess the effectiveness of two inventory‐based mitigating policies implemented at a reseller when end customer demand is stable but supply can be disrupted. An analytical model is provided, and numerical experiments are conducted to evaluate the effectiveness of the policies for mitigating the impact of disruption under different disruption scenarios.

Results indicate that the R‐policy performs consistently better than the Q‐policy in terms of product availability measures, as tested under a wide range of frequency and duration of supply disruptions.

Supply chain trends of lean operations and global sourcing have exposed business organizations to a greater risk and have further raised the need to protect businesses against random supply disruptions.

The paper intends to contribute to the narrowing of the gap in the research of supply‐side disruptions. Further, the topic of inventory reserves has been discussed to date in only a very general sense; the paper proposes conditions for practical implementation and provides unique insights into the effectiveness of the use of strategic inventory reserves as a supply disruption mitigation policy.

This paper deals with developing new heuristic ordering rules for managing multi‐item, single vendor inventory systems with random demands. The inventory position for each item is continuously reviewed, and an order is placed when the projected stockout cost for all items exceeds a certain multiple of the average ordering cost. Rules are offered for determining which items to include in the order, and also for determining order‐up‐to level for each item. These rules involve two parameters, whose optimal values are estimated by simulation. Such systems are encountered in many real life situations, such as independent convenience stores, mom and pop grocery stores, ethnic grocery stores, neighborhood hardware stores, small independent retailers and family pharmacies. Many of these systems are grossly mismanaged from an inventory point of view. Ordering rules developed in this paper are the back‐bone of a proposed inventory and order management software required to support such small business operations.

This paper aims to introduce, apply and validate, through a realistic case study, an analytical cost model to support the design of the tow-train feeding system for mixed-model assembly lines managed according to the just-in-time concept. The fleet size and inventory level, minimizing the total annual cost, are the key model goals, while the tow-train shipping capacity and the service level are the decisional variables to set.

The model computes the material handling, inventory and stockout rising costs of the tow-train feeding system and looks for their minimization. It further computes the expected lead time between consecutive round-trips and the Kanban card number, distinguishing among parts and assembly lines, overcoming the simplifying hypothesis assuming a constant lead time for all parts. The model is validated against a dedicated case study stressing its strengths in terms of cost and inventory-level reduction.

The proposed approach is found to be effective if compared to the standard literature in the field of Kanban system design. The 10.76 per cent cost saving is experienced for the considered case study, and the inventory level is closer to the field-experienced profile.

The model adopts a practical perspective, making it easy and applicable to common operative industries.

The literature neglects to consider the differences in the part consumption when estimating the lead time between tow-train round-trips. The proposed model overcomes such limitations and strengthens the model applicability and performances.

Inventory management is an extremely important function to any business, since inadequacies in control can result in serious problems. If inventories are managed in an inefficient manner, it is likely that delays in production, dissatisfied customers, or curtailment of working capital will result.