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We study an (R, s, S) inventory control policy with stochastic demand, lost sales, zero lead‐time and a target service level to be satisfied. The system is modeled as a discrete time Markov chain for which we present a novel approach to derive exact closed‐form solutions for the limiting distribution of the on‐hand inventory level at the end of a review period, given the reorder level (s) and order‐up‐to level (S). We then establish a relationship between the limiting distributions for adjacent values of the reorder point that is used in an efficient recursive algorithm to determine the optimal parameter values of the (R, s, S) replenishment policy. The algorithm is easy to implement and entails less effort than solving the steady‐state equations for the corresponding Markov model. Point‐of‐use hospital inventory systems share the essential characteristics of the inventory system we model, and a case study using real data from such a system shows that with our approach, optimal policies with significant savings in inventory management effort are easily obtained for a large family of items. [ABSTRACT FROM AUTHOR]

Within immunization programs supported by the World Health Organization, current practice is to use a vial as the basic unit for managing vaccine inventories at the clinic level. In addition to being inconvenient, this has the potential to induce errors in the ordering process. We propose alternative ordering policies based on using inner pack quantities corresponding to secondary vaccine packaging and draw upon visual management, simplified inventory tracking, better housekeeping, and other lean related concepts that have been used in the manufacturing sector to simplify and improve the vaccine inventory management system. We present analyses that show that the proposed polices only require very modest increases in fixed storage and transport requirements. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology for developing a spare parts inventory management system with a focus on the nuclear power sector. Often, demand for spare parts is highly intermittent and cannot be accurately forecasted through traditional methods. Examples include nuclear power generation equipment, ground space systems, and aircraft engine parts. We take a data-driven engineering management approach and develop a four-step methodology for spare parts management in such environments. These steps comprise an influence diagram for identifying relevant factors, weighting of influences through the analytic hierarchy process, grouping parts according to inventory criticality indices, and the development of base stock inventory policies for each group. This approach allows the system to be actively managed within a continuous improvement framework through employee engagement and input, and mathematical assumptions are not made in the models. To our knowledge, no such integrated, comprehensive methodology for spare parts has been developed. The techniques employed in this research can be effectively used together to holistically manage the entire spare parts process, or they may be used separately to manage portions of the process. This paper provides an overview of the methodology, and the entire approach is illustrated via a test bed nuclear power generation facility. [ABSTRACT FROM AUTHOR]

A remnant inventory distribution system is considered where a set of geographically dispersed distribution centers meet stochastic demand for a one-dimensional product. This demand arises from some other set of geographically dispersed locations. The product is replenished at the centers in a limited number of standard sizes, while the demand is for various smaller sizes of the product and arrives over time according to a Poisson process. There are costs associated with cutting and transportation and scrap can be profitably reclaimed. The combined production (cutting) and distribution problem is modeled as a network. A linear programming formulation is solved for a deterministic version of this problem using mean demand rates and the optimal dual multipliers are used to assign inherent values to remnants of various sizes. These values are then used to develop a price-directed policy that can be used in a stochastic environment. A simulation study shows that this policy significantly outperforms heuristic policies from the literature as well as other heuristic policies that have been used in the steel industry for a similar problem. Theoretical insights into the structure of the proposed optimization problem are provided along with proofs of several important results. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resource: Appendix] [ABSTRACT FROM AUTHOR]

Radio Frequency Data Communications (RFDC) technology is rapidly becoming a critical component of many traditional industrial engineering functions including materials tracking. inventory control, warehousing, order processing. shipping and database management. As a means of moving information. RFDC has many attractive features, such as speed. accuracy, reliability, convenience and low operating costs. When implementing RFDC systems a major problem is to quickly and efficiently determine the locations where transceivers should be placed so that effective radio communication can take place. The research described in this paper addresses this issue by developing a computerized layout simulation system that incorporates heuristic optimization methods to solve the placement problem. The effectiveness of this unique automated layout methodology is demonstrated by comparing it with the current method of utilizing manual site surveys, as well as with other placement methods. The methodology and solutions are validated by field-testing at actual facilities. [ABSTRACT FROM AUTHOR]