Credit Policy and Inventory Control

Credit policies are shown to be important in the basic economic order quantity inventory model. An economic order quantity model is derived which explicitly considers possible credit periods allowed by suppliers. This model is shown to be very sensitive to the length of the credit period, and to the relationship between the credit period and inventory level. It is also shown to be more sensitive to estimates of demand for inventory items and less sensitive to order costs than the basic economic order quantity model. A practical example illustrates this sensitivity, shows how inventory costs may be considerably reduced by taking the existence of a credit period into account, and demonstrates the implications for inventory and credit policies.     

Practical Inventory Replenishment Policies for a Linear Trend in Demand Followed by a Period of Steady Demand

Inventory policies are considered for the situation of a deterministic linear trend in demand followed by a period of constant demand: an idealised form of a common situation in spares provisioning. An analytic optimal solution is derived and its practical shortcomings discussed. The performance of practical inventory policies for this situation are compared and a policy based on the well known economic batch quantity, suitably modified for linear trend, is shown to give small cost penalties when compared to the analytic solution. The good performance of the policy based on the economic batch quantity is maintained, even when used on examples which simulate situations where the parameters of demand are not known exactly. Indeed the method is then superior to all others tried, including the "optimal" analytic method.     

Optimal Myopic Allocation of a Product with Fixed Lifetime

Allocation policies of a perishable product from a regional centre to n locations in the region are analysed. Optimal myopic rules are derived for two general classes of policies: rotation policies, where unused product that is not outdated is returned to the centre; and retention policies, where returns to the centre are not possible. Costs are charged for every unit short or outdated. It is shown that the optimal myopic rule minimizes both shortage and outdate costs for one period, it is simple to implement in a realistic environment and is independent of the unit costs. Analytic solutions are given for several demand distributions. Finally, an example of application in blood management is presented.     

MANAGEMENT OF A RENEWABLE RESOURCE WITH A FINITE DEMAND ELASTICITY

Optimal management policies are derived for a renewable resource when the demand for output has finite elasticity. The analysis modifies traditional views of this problem by allowing for the possibility that static optimizing policies, which maximize myopic net revenues, may be superior to “Golden Rule” policies which account for longer-run stock effects.     

A Note on Dave's Inventory Model for Deteriorating Items

In this paper an attempt has been made to generalise Dave's results on an inventory model for deteriorating items. He has derived the results for optimal order level and lot size, considering a deterministic constant demand throughout the scheduling period. Here, a variable known demand is taken to show that the optimality conditions for the optimal initial stock do not depend on the nature of the demand. A numerical example is used to illustrate the analysis.     

Back Ordering in Inventory Control

An approach to determining the empirical relationship between time out of stock and back ordering is outlined. Using this relationship the expected lost sales in a reorder level system of inventory control are derived for continuous and block demand depletion, fixed and variable lead times. Their applicability to a generalized inventory model is discussed.     

Optimal policies for inventory systems with two types of product sharing common hardware platforms: Single period and finite horizon

In this paper, we consider an inventory system whose products share a common hardware platform but are differentiated by two types of software. Choice of different software results in different installation cost and different selling price of the whole product. Product with different software also faces different customer demand. We investigate the optimal proportion of an order to be installed with software 1 or 2, that maximizes expected profit in the single and multiple period scenarios, respectively. The optimal policy is analytically obtained and proved to be an order-up-to policy in each scenario. Our investigation reveals that whether to replenish, and how much to replenish each product depend not only on its own initial inventory level, and system parameters, but also the initial inventory level of the other product. We perform numerical experiments using the optimal policies we have derived in the paper.     

Inventory models with a mixture of backorders and lost sales under fuzzy cost

Continuous review and periodic review inventory models in which a fraction of demand is backordered and the remaining fraction is lost during the stockout period are considered under fuzzy environment. Fuzziness is introduced by allowing the cost components imprecise and vague to certain extent. Trapezoidal fuzzy numbers are used to represent these characteristics. The optimum policies of these models under fuzzy costs are derived. Numerical results highlighting the sensitivity in the decision variables are also described.     

An inventory system with unit demand and varying ordering levels

An inventory system with unit demand, varying ordering levels and random lead times is considered in this paper. Ordering level is determined by the number of demands during last lead time. The ordering quantity will be such as to bring back the inventory level to S at the ordering epoch. No backlog is permitted. The time dependent probability distribution of the inventory level is obtained. Correlation between the number of demands during a lead time and the length of the next inventory dry period is obtained and it is illustrated by an example.     

Stock Control Using the Stock Level Frequency Distribution

Stock level frequency distributions are derived for continuous review stock control policies in which the lead time demand is normally distributed. The percentage points of the distributions have been tabulated, and the table can be used to determine the reorder point that will meet a specified customer order quantity from stock with a specified probability. The results can also be used to determine the order up to point for a periodic review policy and the reorder point for a mixed policy.     

Base stock policies for production/inventory problems with uncertain capacity levels

In this paper we consider a single item, stochastic demand production/inventory problem where the maximum amount that can be produced (or ordered) in any given period is assumed to be uncertain. Inventory levels are reviewed periodically. The system operates under a stationary modified base stock policy. The intent of our paper is to present a procedure for computing the optimal base stocl level of this policy under expected average cost per period criterion. This procedure would provide guidance as to the appropriate amount of capacity to store in the form of inventory in the face of stochastic demand and uncertain capacity. In achieving this goal, our main contribution is to establish the analogy between the class of base stock production/inventory policies that operate under demand/capacity uncertainty, and the G/G/1 queues and their associated random walks. We also present example derivations for some important capacity distributions.     

A Simple and Effective Component Procurement Policy for Stochastic Assembly Systems

We examine the component procurement problem in a single-item, make-to-stock assembly system. The suppliers are uncapacitated and have independent but non-identically distributed stochastic delivery lead times. Assembly is instantaneous, product demand follows a Poisson process and unsatisfied demand is backordered. The objective is to minimize the sum of steady-state holding and backorder costs over a pre-specified class of replenishment policies. To keep the analysis tractable, we impose a synchronization assumption that no mixing occurs between sets of component orders. Combining existing results from queueing theory with original results concerning distributions that are closed under maximization and translation, we derive a simple approximate solution to the problem when lead time variances are identical. In simulations, our derived policy is within 2% of optimal and significantly outperforms policies that ignore either component dependence or lead time stochasticity. It is also quite robust with respect to various model assumptions, except the synchronization one.     

Optimal inventory management for a retail chain with diverse store demands

Item demands at individual retail stores in a chain often differ significantly, due to local economic conditions, cultural and demographic differences and variations in store format. Accounting for these variations appropriately in inventory management can significantly improve retailers’ profits. For example, it is shown that having greater differences across the mean store demands leads to a higher expected profit, for a given inventory and total mean demand. If more than one inventory shipment per season is possible, the analysis becomes dynamic by including updated demand forecasts for each store and re-optimizing store inventory policies in midseason. In this paper, we formulate a dynamic stochastic optimization model that determines the total order size and the optimal inventory allocation across nonidentical stores in each period. A generalized Bayesian inference model is used for demands that are partially correlated across the stores and time periods. We also derive a normal approximation for the excess inventory from the previous period, which allows the dynamic programming formulation to be easily solved. We analyze the tradeoffs between obtaining information and profitability, e.g., stocking more stores in period 1 provides more demand information for period 2, but does not necessarily lead to higher total profit. Numerical analyses compare the expected profits of alternative supply chain strategies, as well as the sensitivity to different distributions of demand across the stores. This leads to novel strategic insights that arise from adopting inventory policies that vary by store type.     

Inventory models with ramp type demand rate,partial backlogging and Weibull deterioration rate

In this paper, an inventory model with general ramp type demand rate, time dependent (Weibull) deterioration rate and partial backlogging of unsatisfied demand is considered. The model is studied under the following different replenishment policies: (a) starting with no shortages and (b) starting with shortages. The model is fairly general as the demand rate, up to the time point of its stabilization, is a general function of time. The backlogging rate is any non-increasing function of the waiting time up to the next replenishment. The optimal replenishment policy for the model is derived for both the above mentioned policies.     

Managing Dynamic Inventory Systems with Product Returns: A Markov Decision Process

This paper presents a Markov decision process for managing inventory systems with Markovian customer demand and Markovian product returns. Employing functional analysis, we prove the existence of the optimal replenishment policies for the discounted-cost and average-cost problems when demand, returns, and cost functions are of polynomial growth. Our model generalizes literature results by integrating Markovian demand, Markovian returns, and positive replenishment lead times. In particular, the optimality of the reorder point, order-up-to policies is proved when the order cost consists of fixed setup and proportional cost components and the inventory surplus cost is convex. We then make model extensions to include different cost components and to differentiate returned products from new ones. Finally, we derive managerial insights for running integrated closed-loop supply chains. At the aggregate level, returns reduce effective demand while many structural characteristics of inventory models are intact. A simple heuristic for managing systems with returns is to still utilize literature results without returns, but effective demand is lower than customer demand.     

Optimal pricing and production in an inventory model

This paper deals with the problem of simultaneously determining the optimal price policy and production rate over a given planning horizon. For nonlinear demand functions and convex inventory and shortage cost functions the optimal solution paths are derived by using optimal control theory. The treatment of linear nonsmooth cost functions requires the use of a generalized maximum principle. The solution method is a phase portrait analysis providing insight into the optimal pricing and production policies as well as the resulting inventory paths. Moreover, it is shown that in the case of nonsmooth piecewise linear cost functions the equilibrium is approached within finite time although the model is nonlinear in the control variables. Finally it is illustrated that exogenous fluctuations in the demand rate (seasonal demand pattern) amount to cyclical optimal solutions.     

Forecasting demand variation when there are stockouts

This paper addresses the common problem of forecasting demand when there are a large number of stockouts. The well-known single period stochastic inventory (or ‘newsboy’) problem provides the optimum, single period, stocking level for a product subject to stochastic demand. There are many situations where repetitive ‘newsboy’ solutions are implemented to guide stocking of repeat, but related, products, such as newspapers, magazines, or perishable groceries. Implementation of the ‘newsboy’ solution requires forecasts of the distribution of demand, although there are many plausible cost parameters that lead to optimum stocking policies where there is a high probability of a stockout. The company is, therefore, faced with the problem of attempting to forecast demand when a high percentage of the available sales data reflects the stock available for sale, rather than the true demand.A procedure has been developed¹ to improve estimates of the mean and variance of the distribution of demand when there are stockouts, but this procedure fails when the percentage of stockouts increases above 50%. A modified stockout adjustment procedure is presented in this paper, and it is shown that use of this new procedure can lead to greatly improved estimates of demand parameters, and greatly improved profitability, when there are a high percentage of stockouts.     

A multi-product continuous review inventory system in stochastic environment with budget constraint

Common characteristics of inventory systems include uncertain demand and restrictions such as budgetary and storage space constraints. Several authors have examined budget constrained multi-item stochastic inventory systems controlled by continuous review policies without considering marginal review shortage costs. Existing models assume that purchasing costs are paid at the time an order is placed, which is not always the case since in some systems purchasing costs are paid when order arrive. In the latter case the maximum investment in inventory is random since the inventory level when an order arrives is a random variable. Hence payment of purchasing costs on delivery yields a stochastic budget constraint for inventory. In this paper with mixture of back orders and lost sales, we assume that mean and variance of lead time demand are known but their probability distributions are unknown. After that, we apply the minimax distribution free procedure to find the minimum expected value of the random objective function with budget constraint. The random budget constraint is transformed to crisp budget constraint by chance-constraint technique. Finally, the model is illustrated by a numerical example.     

产能配置的市场传导机制与政策效应——来自中国钢铁行业的证据

通过构建两区制的MSVAR模型,探讨不同市场状态下钢铁行业产能配置的市场传导机制及财政支出的作用效应,研究发现:产能过剩是行业快速发展过程中市场传导机制引发的客观结果,这是由市场传导机制的非对称性以及政策的差异性作用导致的。在市场扩张时期,行业需求与财政政策均能促进对投资与产能配置水平的提高,但市场需求的诱导作用要明显强于财政政策的刺激作用;在市场下滑时期,行业需求对投资与产能配置的作用明显减弱,财政政策对投资的调整作用显著增强,对产能配置的影响不显著。不同市场状态下市场传导机制的非对称性正是钢铁行业产能过剩形成且难治理,进而导致供需结构失衡的主要原因。上述研究表明,仅通过市场调节机制是无法有效改善经济运行状态的,加强财政政策与市场传导机制的结合才是政府如何改善供给结构和需求变化不匹配问题的关键。     

Multi-period modeling of two-way price commitment under price-dependent demand

This paper examines the use of price-commitment policies in dynamic contracting in multiple-period, finite-time horizons. Two specific forms of price commitment are considered: one on the part of the retailer through a retail-fixed-markup contract and one on the part of the manufacturer through a price-protection contract. Optimal policies for each form of price commitment are analytically derived, as are optimal policies for the traditional price-only and centralized supply chain scenarios that we use as comparisons. We prove that optimal retail price and order size solutions exist in each period under the assumption of non-increasing price-dependent demand. We show that the existence of retailer inventory between periods causes the optimal policies to differ from a static single-period model. Further, we show that a supplier offers a price-protection policy as a signal to the retailer to resolve the gaming that naturally occurs under price-only; this effectively decouples the multi-period dynamic contracting setting into repeated single-period scenarios. However, the resulting behavior can actually inhibit supply chain performance. On the retail commitment side, we find that retail-fixed-markup policies are quite effective in improving supply chain efficiency. We show that such policies can lead to Pareto-improvement over price-only contracts and can even coordinate the supply chain in some situations.