EOQ FORMULA: IS IT VALID UNDER INFLATIONARY CONDITIONS?

The classical analysis of the economic order quantity (EOQ) problem ignores the effect of inflation. When a firm's cost factors are expected to rise at an annual rate of 10 percent or more, what adjustments in order quantities should the firm make to control its lot-size inventory (or cycle stock)? Using a model that includes both inflationary trends and time discounting, it is concluded that inflation brings no incentive either to increase or to decrease order quantities. In addition, order quantities can be computed using the classical EOQ formula under inflationary conditions, provided that the cost of capital invested in inventory is interpreted as an inflation-free cost. This interpretation implies that changes in the inflation rate should not affect the cost of capital that is utilized in the EOQ formula for determining order quantities.     

Concepts,Theory, and Techniques INFLATION,MAINTENANCE OF CAPITAL,AND THE IRR MODEL OF CAPITAL BUDGETING*

Businesses operating under inflationary conditions need capital-budgeting models that help them judge the adequacy of returns on their investments and also allow them to keep capital intact by considering the erosive effects of inflation. The model proposed in this paper computes a modified internal rate of return (IRR); if cash inflows from a project are divided between earnings and recovery of capital, total recovery equals that amount which the capital-budgeting concept adopted by the business specifies (such as the original investment in constant dollars or its replacement cost). Under this model, a project should be accepted only if this computed rate equals or exceeds a hurdle rate that consists of the inflation-free rate of return plus the effect of inflation on such a return. Other modifications to the IRR model suggested in the literature do not completely satisfy the objective of capital budgeting under inflationary conditions.     

A PROFIT-MAXIMIZATION MODEL FOR A RETAILER'S STOCKING DECISIONS ON PRODUCTS SUBJECT TO SUDDEN OBSOLESCENCE

A product has been formally denned as being subject to sudden obsolescence if its lifetime is negative exponentially distributed. Using an approximate model, Masters suggested that the traditional method of incorporating obsolescence cost as a component of inventory holding costs in the economic order quantity (EOQ) model was appropriate-for products subject to sudden obsolescence, provided that the obsolescence cost component was computed properly. Unfortunately, current practice of the EOQ model seriously underestimates the costs of sudden obsolescence. An exact model demonstrating that Masters' model also underestimated true lifetime costs and overestimated the optimal order quantity has been presented. Neither of these models addressed quantity discounts. Furthermore, with their cost-minimization focus, these models fail to identify situations when minimized costs exceed expected revenues. We extend Joglekar and Lee's model to focus on maximizing profits, rather than minimizing costs. This model answers such questions as whether to stock the product at all, whether to accept a quantity discount offer, and what order quantity to use. Numerical examples and sensitivity analyses suggest that Masters' model provides a significant improvement over the traditional model in moving toward true optimality. However, they also illustrate situations where both the traditional and the Masters' model accept a quantity discount that deserves to be rejected and stock a product that should not be stocked. In such situations, it seems important that a retailer uses the profit-maximization model presented here.     

The deterministic EPQ with partial backordering: A new approach

Several authors have developed models for the EOQ when only a percentage of stockouts will be backordered. Most of these models are complicated, with equations unlike those for the EOQ with full backordering. In this paper we extend work by Pentico and Drake [The deterministic EOQ with partial backordering: a new approach. European Journal of Operational Research 2008; in press] that developed equations for the EOQ with partial backordering that are more like those for the EOQ with full backordering to develop a comparable model for the EPQ with partial backordering.     

INVENTORY DECISIONS UNDER INFLATIONARY CONDITIONS

The standard approach to inventory policy ignores the effect of inflation. This paper investigates a model that includes both inflationary trends and time discounting. The paper compares this model with the standard EOQ model and the analysis for a one-time change, found in previous literature.     

The resource constrained single-item inventory problem with demand-dependent unit cost

In this paper, we present an economic order quantity (EOQ) with both demand-dependent unit cost and restrictions. An analytical solution of the EQO is derived using a recent and simple method, which isthe geometric programming approach. The EOQ inventory model with demand-dependent unit cost without any restriction and the classical EOQ inventory model are obtained.     

有限时域与采购价格上升情形下的最优订货策略

研究了有限时域下采购商面对价格上升时的订货策略问题.在分析问题的基础上提出一种新的最优采购策略,并分析了价格上升幅度对订货量的影响,以经典EOQ模型的总成本为基准,比较了本文提出的策略与文献已有策略在成本节约上的差异.本文对库存总成本的计算方法更加精确;分析表明在有限时域背景下采购商的临时订货量决定于价格上涨的幅度、在库库存以及时段长度.     

Multi-item EOQ inventory model with varying holding cost under two restrictions: A geometric programming approach

In this paper we provide a simple method to determine the inventory policy of multiple items having varying holding cost using a geometric programming approach. The varying holding cost is considered to be a continuous function of the order quantity. The EOQ inventory model with constant holding cost and the classical EOQ inventory model without constraints are derived.     

ECONOMIC ORDER QUANTITIES WITH QUANTITY DISCOUNTS: GRANDMA DOES IT BEST

We examine a new algorithm developed by Kuzdrall and Britney [5] for locating the optimal order quantity in the presence of quantity discounts. Their algorithm, based on a model for the supplier's formulation of the price schedule, involves a regression analysis to identify the supplier's variable cost per unit and the fixed cost that the supplier seeks to recover, followed by an iterative search for the optimum. The authors describe this method as a “convenient alternative to the aimless searching of traditional approaches” [5, p. 101]. We examine the allegation of superiority of their total setup lot-sizing model over the classical method and dispute their claim of superiority.     

INVESTMENT IN SETUP COST,LEAD TIME,AND DEMAND PREDICTABILITY IMPROVEMENT IN THE EOQ MODEL

We study an economic order quantity/reorder point (EOQ/ROP) model with stochastic demand and backorders where options of investing in reducing setup cost, lead time, and variance of demand forecast errors are available. The model is quite comprehensive relative to previous models since it simultaneously addresses the strategic decisions associated with these three investment opportunities as well as the tactical decisions of determining both the lot size and the safety stock. We develop a simple search procedure to obtain the optimal values of setup cost, lead time, variance of demand forecast errors, order quantity, and safety stock multiplier. Computational studies are performed to determine the sensitivity of the optimal solution of the model to changes in the model's parameters.     

Setups and effective capacity: the impact of lot sizing techniques in an MRP environment

This study investigates how lot sizing techniques influence the profit performance, inventory level, and order lardiness of an assembly job shop controlled by MRP. Four single-level lot sizing techniques are compared by simulation analysis under two levels of master schedule instability and two levels of end item demand. A second analysis investigates the influence of a multilevel lot sizing technique, the generalized constrained-K (GCK) cost modification, on the four single-level techniques at low demand and low nervousness. The analyses reveal a previously unreported phenomenon. Given the same inventory costs, the single-level lot sizing techniques generate substantially different average batch sizes. The lot sizing techniques maintain the following order of increasing average batch size (and decreasing total setup time):

economic order quantity (EOQ)

period order quantity (POQ)

least total cost (LTC)

Silver-Meal heuristic (SML)

The causes for different average batch sizes among the lot sizing techniques are analysed and explained. Demand lumpiness, inherent in multilevel manufacturing systems controlled by MRP, is found to be a major factor. The number of setups each lot sizing technique generates is the primary determinant of profit performance, inventory level, and order tardiness. EOQ, a fixed order quantity technique, is less sensitive to nervousness than the discrete lot sizing techniques. EOQ_, however, generates the smallest average batch size, and, therefore, the most setups. Since setups consume capacity, EOQ, is more sensitive to higher demand. SML generates the largest average batch sizes, and is, therefore, less sensitive to increased demand. At low demand, the other lot sizing techniques perform better on all criteria. They generate smaller batches and, therefore, shorter actual lead times. The GCK cost modification increases the average batch size generated by each lot sizing technique. GCK improves the profit and customer service level of EOQ the lot sizing technique with the smallest batches. GCK causes the other lot sizing techniques to generate excessively large batches and, therefore, excessively long actual lead times.     

Optimal Purchase and Transportation Cost Lot Sizing for a Single Item

A review of the literature indicates that the traditional approach for evaluating quantity discount offerings for purchased items has not adequately considered the effect that transportation costs may have on the optimal order quantity; despite the general fact that purchased materials must bear transportation charges. The transportation cost structure for less-than-truckload (LTL) shipments reflects sizable reductions in freight rates when the shipment size exceeds one of the nominal rate breakpoints. However, the shipper must also be aware of the opportunity to reduce total freight costs by artificially inflating the actual shipping weight to the next rate breakpoint, in order that a lower marginal tariff is achieved for the entire shipment. Such over-declared shipments result in an effective freight rate schedule that is characterized by constant fixed charge segments in addition to the nominal marginal rates. Over-declared shipments are economical when the shipment volume is less than the rate breakpoint, but greater than a cost indifference point between the two adjacent marginal rates. This paper presents a simple analytical procedure for finding the order quantity that minimizes total purchase costs which reflect both transportation economies and quantity discounts. After first solving for the series of indifference points that apply to a particular freight rate schedule, a total purchase cost expression is presented that properly accounts for the actual transportation cost structure. The optimal purchase order quantity will be one of the four following possibilities: (1) the valid economic order quantity (EOQ), QC; (2) a purchase price breakpoint in excess of QC; (3) a transportation rate breakpoint in excess of QC; and (4) a modified EOQ which provides an over-declared shipment in excess of QC. Finally, an algorithm which systematically explores these four possibilities is presented and illustrated with a numerical example.     

时变供需下基于商业信用的零售商最优订货策略

论文在时变供需环境下,即市场需求为斜坡式时间函数及补货率依赖于市场需求情境下,探讨了基于商业信用的供应链中零售商最优订货策略。首先,根据商业信用期和零售商补货周期的关系,分两种情形构建了零售商库存模型;其次,根据零售商的库存模型,证明了其最优解的存在,并给出相关的命题和算法来求解零售商的最优库存策略;最后,通过数值算例和灵敏度分析来论证文中的有关结论。研究发现:当供应商给予零售商的商业信用期较短时,此时市场需求仍处于增长期,随着市场需求稳定时间点的增大,零售商的最优订货周期逐渐减小,最优订货量和年费用也逐渐减小;当供应商给予零售商的商业信用期较长时,此时市场需求已趋于稳定期,随着市场需求稳定时间点的增大,零售商的最优订货周期逐渐增大,最优订货量和年费用也逐渐增大;当供应商生产与市场需求依赖性逐渐增大时,零售商的最优订货周期逐渐增大,最优订货量及年费用也逐渐增大。     

TOTAL SETUP LOT SIZING WITH QUANTITY DISCOUNTS*

Often, order quantity decisions are made by purchasers facing a price schedule of quantity discounts. Traditional solution procedures have consisted of the evaluation of total cost at numerous price-break points in search of the lowest total cost. This approach is tedious and not particularly informative, especially when one is faced with lengthy schedules. This paper presents a total setup lot-sizing model that reduces the computations required to find the least-total-cost quantity, given parameters from a supplier's price schedule. The parameters are first obtained by simple regression (graphical or computer) and in themselves can provide valuable insight for the purchaser's decision making. A total setup lot-sizing model is next developed to define a “critical interval” that contains the solution. The model and algorithm are tested under a variety of conditions. Their application offers the decision maker a convenient alternative to determine the best quantity to order from a tendered price schedule.     

An optimal lotsizing determination in a two-echelon purchasing/production system

Multi-stage optimal lotsizing problems are extremely intractable and hard to solve. This paper discusses an economical lotsizing problem in a two-echelon purchasing/production system, where material losses caused by deterioration and failure in stock and production stages are taken into consideration. With the aid of the techniques of first and second derivatives, economic order quantity (EOQ) can be expressed by means of economic production quantity (EPQ). It is possible to derive cost reductions in the purchasing/production system. This system was applied using data from a DPE (Developing, Printing and Enlarging) factory and numerical experiments demonstrated that the total cost was reduced by 22-4%.     

提前期、构建成本和短缺量拖后率均可控的EOQ模型

传统库存模型通常将提前期和构建成本视为不可控制。事实上可以通过追加投资缩短提前期和降低构建成本。缺货期间,为减少订单丢失量和补偿顾客的损失,供应商会给予一定的价格折扣。现实库存系统中,容易得到需求的期望值和标准差,但较难得到其分布规律。基于此,考虑短缺量拖后率与价格折扣和缺货期间库存水平相关,提出了一种需求为任意分布且提前期和构建成本均可控的EOQ模型,证明了模型存在唯一最优解,给出了一种寻优算法。数值仿真分析表明,一般情况下,压缩提前期和降低构建成本能降低订购批量和安全库存,降低库存总成本;短缺量拖后系数和缺货概率对库存总成本影响较大,企业应尽量降低缺货概率,尤其在短缺量拖后系数较小时。     

A note on “Khouja and Park,optimal lot sizing under continuous price decrease,Omega 31 (2003)”

Khouja and Park [1] analyze the problem of optimizing the lot size under continuous price decrease. They show that the classic EOQ formula can lead to far from optimal solutions and develop an alternative lot size formula using the software package Mathematica. This formula is more exact, but also more complicated. In this note, we study the net present value formulation of the model, and thereby gain an insight that leads to the proposal of a modified EOQ formula. The modified EOQ formula, albeit not as accurate, is a good alternative to the formula developed by Khouja and Park, especially if mathematical complexity may hamper implementation.     

Multi-item inventory model with quantity-dependent inventory costs and demand-dependent unit cost under imprecise objective and restrictions: A geometric programming approach

A multi-item inventory model with constant demand and infinite replenishment is developed under the restrictions on storage area, total average shortage cost and total average inventory investment cost. These restrictions may be precise or imprecise. Here, it is assumed that inventory costs are directly proportional to the respective quantities, and unit purchase/production cost is inversely related to the demand. Restricted shortages are allowed but fully backlogged. First, the problem is formulated in crisp environment taking the deterministic and precise inventory parameters. It is solved by both geometric programming (GP) and gradient-based non-linear programming (NLP) methods. Later, the problem is formulated with fuzzy goals on constraints and objectives where impreciseness is introduced through linear membership functions. It is solved using the fuzzy geometric programming (FGP) method. The inventory models are illustrated with numerical values and compared with the crisp results. A sensitivity analysis on the optimum order quantity and average cost is also presented due to the variation in the tolerance of total average inventory investment cost and total average shortage cost following Dutta et al., 1993, Fuzzy Sets and Systems, 55, 133-142.     

“A JOINT ECONOMIC-LOT-SIZE MODEL FOR PURCHASER AND VENDOR”: A COMMENT*

Banerjee [1] developed a joint economic-lot-size model for the case where a vendor produces to order for a purchaser on a lot-for-lot basis under deterministic conditions. The assumption of lot-for-lot bases is restrictive in nature. In this note, a more general joint economic-lot-size model is suggested and it is shown to provide a lower or equal joint total relevant cost as compared to the model of Banerjee.     

SOME STATISTICAL ISSUES IN THE ESTIMATION OF A SIMPLE COST-VOLUME-PROFIT MODEL*

This paper presents point and interval estimators of both long-run and single-period target quantities in a simple cost-volume-profit (C-V-P) model. This model is a stochastic version of the “accountant's break-even chart” where the major component is a semivariable cost function. Although these features suggest obvious possibilities for practical application, a major purpose of this paper is to examine the statistical properties of target quantity estimators in C-V-P analysis. It is shown that point estimators of target quantity are biased and possess no moments of positive order, but are consistent. These properties are also shared by previous break-even models, even when all parameters are assumed known with certainty. After a test for positive variable margins, Fieller's [6] method is used to obtain interval estimators of relevant target quantities. This procedure therefore minimizes possible ambiguities in stochastic break-even analysis (noted by Ekern [3]).