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Edmund Burke, the 18th century Irish orator said … ‘those who propose change should work hard to persuade a rational man that innovations or reforms would not end in damage or absurdity’.

To develop just‐in‐time (JIT) purchasing threshold value (JPTV) models for ready mixed concrete (RMC) suppliers to decide whether or not to switch from an economic order quantity (EOQ) approach to a JIT purchasing approach for the purchase of their raw materials, when a price discount is offered.

The existing economic order quantity (EOQ) with a price discount versus the JIT purchasing cost comparative models neglect some important cost components under the inventory management systems, for example, the out‐of‐stock costs and the impact of inventory policy on product quality and production flexibility. In addition, these models do not empirically study the capability of an inventory facility to hold the EOQ‐JIT cost indifference point's amount of inventory. These models suggest that the JIT purchasing approach is always preferred to the EOQ approach when the JIT purchasing approach can capitalize on physical plant space reduction. The JPTV models developed in this study overcome the two limitations of the existing EOQ and JIT purchasing cost comparative models.

By developing the JPTV models, this study suggests that the theoretical advantages of JIT purchasing may have been overstated.

The field studies conducted in the RMC industries in Chongqing, China and Singapore supported the propositions in this study. The JPTV models, if adopted, would help to enhance performance in the RMC industries in other cities as well.

The conventional Economic Order Quantity (EOQ) lot‐size model ignores the effects of damage costs. While producing an EOQ lot, the inventory may go “out of control”. An extension of the EOQ production model based on damage costs is presented and the relevant costs of damage and their effects on stockholding costs are discussed. A new EOQ formula including damage costs is developed and a comparison between the conventional concept and the new concept based on annual total cost is made. The analysis shows that, if the costs of damage to stock are taken into account, the computed EOQ is smaller, and the conventional EOQ is not the optimum solution.

Recent models comparing inventory costs under just‐in‐time (JIT) purchasing plans and economic order quantity (EOQ) purchasing plans have tended to favor EOQ purchasing in situations where annual demand of inventory is moderately large. Contends that these cost models are lacking dynamic cost components inherent in virtually all JIT purchasing plans. Presents a series of inventory purchasing cost models that extend prior methodology by Fazel by including relevant physical distribution cost savings. Additional comparative models are presented to further demonstrate how other relevant costs factors can be included in a comparative EOQ/JIT model. A cost comparison with an existing problem from the literature is used to illustrate the informational efficacy of new models.

The purpose of this paper is to investigate the impact of backroom storage in supply chain replenishment decision parameters: the order quantity based on the well-established economic order quantity (EOQ) model.

The authors develop an EOQ-type model to investigate the operational cost impact of the order quantity with backroom storage. Because of the discrete and discontinuous nature of the problem, a modification of an existing algorithm is applied to obtain an optimal order quantity. Numerical experiments derived from a leading retailer in Thailand are used to study the cost impact of the backroom.

The paper shows that the backroom storage will significantly affect the decision regarding the order quantity. If its effect is ignored, the cost increase can be as high as 30 per cent. The costs and operations of additional shelf-refill trips from the backroom must be carefully analyzed and included in the decisions of replenishment operations.

The model is a simplified version of the actual replenishment process. Validation from a real-world setting should be used to confirm the results. There are many additional opportunities to further integrate other issues in this problem such as shelf space decisions or joint order quantity between vendors and retailers.

The insights gained from the model will help managers, both retailers and vendors or manufacturers, make better decisions with regard to the order quantity policy in the supply chain.

Problems with backroom storage have been qualitatively described in the literature in the past decade. This paper is an early attempt to develop a quantitative model to analytically study the cost impact of backroom on order quantity decisions.

The purpose of this paper is to derive an economic order quantity (EOQ) for an inventory control problem where the inventory carrying cost and the order cost are uncertain, represented by fuzzy numbers. The fuzzy numbers used herein are most general so far, represented by adaptive trapezoidal fuzzy numbers. This paper attempts to use the most general form of fuzziness to represent the uncertainty of the parameters in the inventory model.

The fuzzy EOQ formula derivation is analytical. Given the inventory cost C_c and the order cost C_o as fuzzy numbers and the demand, a crisp number and instant replenishment of inventory, a fuzzy EOQ is derived. This is done by using the possibilistic mean and the possibilistic variance of the fuzzy total inventory cost. Then for practical implementation, this quantity is defuzzyfied using the middle of the maxima (MOM) of the fuzzy EOQ, in order to get the crisp value of the EOQ that minimizes the (fuzzy) total inventory cost.

The fuzzy EOQ model derived herein is the most general fuzzy model. It is then converted to a crisp optimal order quantity and a crisp order cycle. The model assumptions cover the uncertainties in estimating the order cost and the inventory carrying cost. However, the results that can be extended in case of the shortage in inventory stock are allowed.

Inventories by their nature are the basic part of consideration in any production, supply chain, warehousing and retail policies. The inventories consume a large part of budget, space, overheads and maintenance. Even though the problem considered in this paper is limited to single period and single item inventories, it can be extended to multiple items and multi‐period inventories. The paper gives an illustrative example and its solution at the end.

EOQ is the most fundamental concept in making inventory policies. However, in inventory literature, covering the risk of uncertainty in the various cost estimations such as carrying and order or shortage costs, is more recent and is not well developed.

This paper aims to present a literature review on models developed for the economic order quantity (EOQ) problem with incremental and all-units discounts, extending the work of Benton and Park (1996) which covered the most significant literature, from 1963 to 1994, about EOQ with discounts and that has identified four open areas in this field of study. The modeling of lot size with discounts wishes to give good solutions for realistic situations, such as those concerning the discounts offered by suppliers, to rises in the demand.

The research was carried out in papers published from 1995 to 2013, and indexed in databases as Scopus and ISI Web of Science. The papers were compared through objective function, constraints, discounts, developed algorithms, allowance of shortages or multiproduct, demand pattern and buyer or buyer–supplier perspective.

Results indicate two areas that still remain untouched, and probably the main cause is due to mathematical complexities. The authors have also identified an increasing trend of works that compared just-in-time with the EOQ with quantity discounts policy and also an increasing number of works that solved this category of problems with algorithms.

The research does not cover materials published in working papers, monographs, thesis, conferences or journals that are not indexed in those databases.

This manuscript fills a gap in the study of EOQ with incremental discounts, as it highlights the leading edge advances in this field and the main differences among models. As a whole, the new trends about modeling EOQ problems with quantity discounts were discovered.

The EOQ formula is definitely the oldest and best known single stage lot sizing technique. Its use reportedly dates back to 1904, even though it was not published until a later date. It is often looked upon with scepticism by practitioners and academicians alike, although the reasons for this may differ; it seems, however, to be the most widely used lot sizing technique overall.

Presents a mathematical model to assist companies in their decision to switch from the economic order quantity (EOQ) to the just‐in‐time (JIT) purchasing policy. Determines an upper limit for the JIT purchase price of an item below which the manufacturer will be better off using JIT purchasing. Also determines the annual demand level at which the costs of EOQ and JIT purchasing will be equal (the indifference point). For demand levels above this indifference point EOQ is the less costly method while JIT is preferable for demand levels below this point. The model also predicts that JIT will be preferred for inventory items with higher purchase price, holding costs, or ordering cost.

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.