Search results

In estimating desired inventory holdings business organizations must use some estimate of their cost of funds. While conceptually the overall or weighted average cost of capital would seem to be the appropriate measure, survey of senior U.S. and Canadian financial executives at large firms revealed that most of the responding firms used their cost of borrowed funds. As is demonstrated in the paper, use of the cost of borrowed funds rather than the overall cost of funds can cause inventory positions to be excessive during certain periods and deficient during other periods.

The purpose of this paper is to deal with the application of the stochastic inventory model to the three‐tier supply chain and verify the values obtained by mathematical model in physical simulation.

The paper investigates three‐stage serial supply chain with stochastic demand and fixed replenishment lead‐time. Inventory holding costs are charged at each stage, and each stage may incur a consumer backorder penalty cost charged by primary supplier to secondary supplier. The customer‐demand follows Poisson distribution. The base stock model is implemented for inventory control at both suppliers. Physical simulation is then designed in such a way that it satisfies all the assumptions for mathematical model. Simulation is run to verify the values obtained from mathematical model.

Computer simulation is designed to include all the assumptions made by mathematical model. Hence, mathematical base stock model and computer simulation model are comparable. Demand follows Poisson distribution in both cases. The backorder cost and inventory holding cost are calculated in each phase of simulation and summarized. The paper infers that the total inventory cost is optimum in phase II, in which reorder point is same as that calculated by mathematical model. In phase I, total inventory cost is more than that of phase II because of backorders. In phase III, excess inventory increased the total cost. Thus, the values obtained from mathematical model produce optimal inventory cost. Base stock model is effective when the demand is not deterministic and service factor assumed in mathematical model is 0.9, which is quite acceptable. Base stock model assumes replenishment order quantity as 1 and the total inventory cost decreases with replenishment lead time. Base stock model is beneficial for supply chains having short replenishment lead time. Computer simulation results indicate that discrete event simulations can be used to model stochastic systems like organizational supply chains and to validate the results from mathematical models.

The paper offers a review of simulation work aiming to support improvement of agility in the supply chain.

This chapter covers three main concepts: it provides an overview of supply chain management (SCM), introduces the concepts of procurement and what is entailed within this function, and explains how inventory is managed. In the first section, SCM is considered broadly but also in the context of hospitality. The key roles and objectives of SCM as well as the significance of supply chain risk and disruption are considered. In the second section, the concept of sourcing is discussed. Sourcing is a critical function in any organization: without this, an organization would cease to operate. The importance of supplier selection is explored, with methods to make the most appropriate selection and for subsequently managing suppliers. Finally, the third section focuses on how inventory management can be optimized. Concepts such as economic order quantity (EOQ) and ABC analysis are explored, along with alternatives to traditional inventory management methods.

In this paper we introduce a simulative model, designed and developed to optimally manage an integrated finished goods inventory system, and suitable for adoption in a wide range of make to stock manufacturing firms. The goal of the model is to manage optimally finished goods inventory levels, in order to minimize costs deriving from holding inventory and from adjustment to the master production schedule (MPS) to prevent stock‐outs. A trade off is sought between these costs components. The input variables of the model are: safety stock levels, assessed through coefficients k1, k2 and k3, and the stochastic distribution functions of products demand. The model performances are assessed considering the annual total cost for the inventory management. The model is then tested by a numerical application. The case considered refers to a major firm operating in the zootechnical feeds production industry.

A wide range of factors are included in this analysis of the conditions that favour an inbound consolidation strategy: the total logistics cost is calculated for consolidating and not consolidating in a case study.

Logisticians in the worldwide industry are frequently faced with the problem of measuring the total cost of holding inventories with simple and easy-to-use methodologies. The purpose of this paper is to look at the problem, and in particular illustrate the inventory holding cost rate computation, when different kind of warehousing systems are applied.

A multiple case study analysis is here developed and supported by a methodological framework directly derived from the working group discussions and brainstorming activities. Two different field of application are considered: one related to five companies with manual warehousing systems operating with traditional fork lift trucks; the other is among five companies operating with automated storage/retrieval systems (AS/RS) to store inventories.

The multi-case study helps to understand how the holding cost parameter is currently computed by industrial managers and how much the difference between manual and automated/automatic warehousing systems impacts on the inventory cost structure definition. The insights from the ten case studies provide evidence that the kind of storage system adopted inside the factory can impact on the holding cost rate computation and permit to derive important considerations.

The final aim of this work is to help industrial engineers and logisticians in correctly understanding the inventory costs involved in their systems and their cost structure. In addition, the multi-case analysis leads to considerations, to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the presented methodology, and with aid from the data from this paper.

The relevance of this work is to help industrial engineers and logisticians in understanding correctly the inventory costs involved in their logistics systems and their cost structure. In addition, the multi-case analysis lead to interesting final considerations, easily to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the methodology and extrapolating the data from this paper.

Inventory carrying costs represent one of the highest costs of distribution. Although they are a necessary input to the design of logistical systems, such costs are ignored by many companies and when they are used usually represent estimates or industry benchmarks. The authors present a methodology designed to provide managers with a practical framework for determining the costs of carrying inventory.

Humanitarian crises increase vulnerability of children to pneumonia, so aid agencies store pharmaceuticals in advance of this demand. Decisions on how much to store are plagued by many diverse challenges as is common in humanitarian contexts, so this study considers storing more medications to improve the relatively poor (∼80) demand coverage at a representative aid agency.

The paper combines inventory theory with health economics to calculate the impact inventory increases would have on the final cost of pneumonia treatment. It can then assess to what extent inventory can be increased while pneumonia treatment remains cost effective.

The study finds that more drug investment has only a small effect on the final treatment cost. Substantial drug inventory increases remain well within established guidelines for highly cost-effective treatments, so the agency should consider large increases as an efficient use of funding.

The study focuses on pneumonia treatment only to allow sufficient depth of analysis. Further research could look at many other treatments using the same approach, although some problem scenarios will include complicating parameters like drug perishability.

The level of pharmaceutical inventory at humanitarian warehouses is a high-value decision for the aid sector. The method shows the potential for health economics to provide practical decision support for a wide range of humanitarian and ministry of health warehouse operations. While large increases in inventory investment are within guidelines, there is an asymptotically increasing cost as demand coverage approaches 100%. As a result, decision makers may want to set a target demand coverage (e.g. 99%) and allocate remaining aid funding to other projects.

Many humanitarian supply chain decisions lack analytical support due to issues with complexity, scale or a lack of reliable input data, and this study is the first to provide analytical insights which can greatly improve the current approach to inventory control policies for pneumonia medications and beyond.

To develop and test three different inventory management strategies as applied to the complex emergency in south Sudan.

Quantitative modeling, simulation, and statistics.

This research identified critical system factors that contributed most significantly to inventory system performance, and identified strengths and weaknesses of each inventory management strategy.

This research represents a first step in developing inventory management systems for humanitarian relief. Future work would include modeling correlation among relief items, multiple items, and considering the impact of information.

In a domain that has seen limited application of quantitative models, this work demonstrates the performance benefits of using quantitative methods to manage inventory in a relief setting.

This research has value for relief organizations by providing a real‐world application of quantitative inventory management strategies applied to a complex emergency, and demonstrated performance advantages of quantitative versus ad hoc methods. This research has value for researchers by providing a new application of simulation and mathematical modeling (humanitarian relief).

Traditional inventory replenishment decisions in distribution systems have been reactive, but the availability of information throughout the distribution system means that other methods should be considered.