Demand uncertainty and lot sizing in manufacturing systems: the effects of forecasting errors and mis-specification

This paper proposes a methodology for examining the effect of demand uncertainty and forecast error on lot sizing methods, unit costs and customer service levels in MRP type manufacturing systems. A number of cost structures were considered which depend on the expected time between orders. A simple two-level MRP system where the product is manufactured for stock was then simulated. Stochastic demand for the final product was generated by two commonly occurring processes and with different variances. Various lot sizing rules were then used to determine the amount of product made and the amount of materials bought in. The results confirm earlier research that the behaviour of lot sizing rules is quite different when there is uncertainty in demand compared to the situation of perfect foresight of demand. The best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. In addition the choice of lot sizing rule between ‘good’ rules such as the EOQ turns out to be relatively less important in reducing unit cost compared to improving forecasting accuracy whatever the cost structure. The effect of demand uncertainty on unit cost for a given service level increases exponentially as the uncertainty in the demand data increases. The paper also shows how the value of improved forecasting can be analysed by examining the effects of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high forecast error variance, improved forecast accuracy should lead to substantial percentage improvements in unit costs

The (r,q) policy for the lost-sales inventory system when more than one order may be outstanding

We study the continuous-review (r; q) system in which un_lled demands are treated as lost sales. The reorder point r is allowed to be equal to or larger than the order quantity q. Hence, we do not restrict our attention to the well-known case with at most one replenishment order outstanding, but our modeling streamlines exact analysis of that case. The cost structure is standard. We assume that demand is Poisson, that lead times are Erlangian and that orders do not cross in time (lead times are sequential). We determine the equilibrium distribution of the inventory on hand at the delivery instants from the solution (obtained by the Gauss-Seidel method) of the equilibrium equations of a Markov chain. To optimize r and q we develop an adapted version of the algorithm suggested by Federgruen and Zheng for the backorders model (BO). The results obtained in our numerical study show that the suggested procedure dominates standard textbook approximations. In particular, the reductions in the average cost of a simple Economic Order Quantity policy are in the range of 3-14%. Except when lead times are long and variable or when the unit cost of shortage is low, the optimal BO policy provides a fair approximation to the average cost of the best policy.inventory/production; operating characteristics; policies; probability; Markov processes; Area of review; Manufacturing; Service; Supply Chain Operations

Simple heuristics for push and pull remanufacturing policies

Inventory policies for joint remanufacturing and manufacturing have recently received much attention. Most efforts, though, were related to (optimal) policy structures and numerical optimization, rather than closed form expressions for calculating near optimal policy parameters. The focus of this paper is on the latter. We analyze an inventory system with unit product returns and demands where remanufacturing is the cheaper alternative for manufacturing. Manufacturing is also needed, however, since there are less returns than demands. The cost structure consists of setup costs, holding costs, and backorder costs. Manufacturing and remanufacturing orders have non-zero lead times. To control the system we use certain extensions of the familiar (s,Q) policy, called push and pull remanufacturing policies. For all policies we present simple, closed form formulae for approximating the optimal policy parameters under a cost minimization objective. In an extensive numerical study we show that the proposed formulae lead to near-optimal policy parameters.inventory control;remanufacturing;heuristics

Accounting estimates as cost inputs to logistics models

Production Function;econometrics

Impact of Variable Ordering Cost and Promotional Effort Cost in Deteriorated Economic Order Quantity (EOQ) Model

The instantaneous economic order quantity (EOQ) profit optimization model for deteriorating items is introduced for analyzing the impact of variable ordering cost and promotional effort cost for leveraging profit margins in finite planning horizons. The objective of this model is to maximize the net profit so as to determine the order quantity and promotional effort factor. For any given number of replenishment cycles the existence of a unique optimal replenishment schedule are proved and further the concavity of the net profit function of the inventory system in the number of replenishments is established. The numerical analysis shows that an appropriate policy can benefit the retailer, especially for deteriorating items. Finally, sensitivity analyses with respect to the major parameters are also studied to draw managerial decisions in production systems

A periodic review inventory model with stock dependent demand, permissible delay in payment and price discount on backorders

In this paper we study a periodic review inventory model with stock dependent demand. When stock on hand is zero, the inventory manager offers a price discount to customers who are willing to backorder their demand. Permissible delay in payments allowed to the inventory manager is also taken into account. Numerical examples are cited to illustrate the model

An EOQ model for time-dependent deteriorating items with alternating demand rates allowing shortages by considering time value of money

The present paper deals with an economic order quantity (EOQ) model of an inventory problem with alternating demand rate: (i) For a certain period, the demand rate is a non linear function of the instantaneous inventory level. (ii) For the rest of the cycle, the demand rate is time dependent. The time at which demand rate changes, may be deterministic or uncertain. The deterioration rate of the item is time dependent. The holding cost and shortage cost are taken as a linear function of time. The total cost function per unit time is obtained. Finally, the model is solved using a gradient based non-linear optimization technique (LINGO) and is illustrated by a numerical example

Design of a network of reusable logistic containers

In this paper, we consider the management of the return flows of empty logistic containers that accumulate at the customer’s sites. These containers must be brought back to the factories in order to sustain future expeditions. We consider a network composed of several factories and several customers in which the return flows are independent of the delivery flows. The models and their solutions aim at finding to which factory the contain- ers have to be brought back and at which frequency. These frequencies directly define the volume of logistic containers to hold in the network. We consider fixed transportation costs depending on the locations of the customers and of the factories and linear holding costs for the inventory of logistic containers. The analysis also provides insight on the benefit of pooling the containers among different customers and/or factories.supply chain management, returnable items, reverse logistic, economic order quantity, network design