Inventory decisions for substitutable products with stock-dependent demand

We study a retailer’s inventory policy for two products. The products are substitutable and have inventory dependent demand, so a higher inventory level of a product increases its sales. We model the joint effect of demand stimulation and product substitution on inventory decisions by considering a single-period, stochastic demand setting. We provide the first order optimality conditions for the profit maximizing order quantities and interpret them using marginal analysis. We also consider two heuristic solutions that separately account for either demand stimulation or product substitution. Our numerical analysis reveals that the optimal policy by appropriately using sales information that quantifies substitution and demand stimulation can produce significantly higher profits. The profit benefits are lessened under certain circumstances, such as when the two products have similar critical fractile values, suggesting that in such instances the heuristics may be used effectively.     

Modified critical fractile approach for a class of partial postponement problems

This paper studies a single period partial postponement problem, which is motivated by an inventory planning problem encountered in Reebok NFL replica jersey supply chain. We consider a group of regular products each facing a random demand. There are two stocking options. One is to procure the regular products. The other is to stock a common component which can be customized later to any one of the regular products, after demand realization.We obtain an insightful interpretation of the optimality conditions for this class of problems, and use it to obtain rules of thumb that practitioners can incorporate into their inventory models to determine the stocking levels to minimize the supply chain cost. Instead of proposing a numerical procedure to obtain the optimal solution, we propose an adaptation of the classical critical fractile approach for this class of partial postponement problem. The closed-form formula obtained are surprisingly effective. Our numerical results suggest that this simple approach to inventory planning often comes close to the performance of the optimal solution obtained from numerical method.     

Retailer policy, uncertainty reduction, and supply chain performance

We investigate how the retailer’s inventory policy affects the total cost of a serial supply chain. When the retailer uses the locally optimal (s,S) policy, there is randomness in order time and order quantity to the supplier whereas the supplier sees randomness only in order quantity for the suboptimal (R,T) policy and only in order time for another suboptimal (Q,r) policy. Using an extensive computational study, we find that the suboptimal policies perform better from the total supply chain perspective. The benefit of policy changes is magnified when the retailer costs are low, when the supplier costs are high, and when there is information sharing.     

Optimal inventory and pricing policies for remanufacturable leased products

In this paper we consider a company which leases new products and also sells remanufactured versions of the new product that become available at the end of their lease periods. When the amount of end-of-lease items in stock is not sufficient to meet the demand for remanufactured products, the firm may purchase additional cores from a third-party supplier. We develop a dynamic programming formulation for determining the optimal price of remanufactured products, and optimal payment structure for the leased products. Our objective is to maximize the discounted system-wide profit over a finite horizon. The profit function consists of revenues that are obtained from remanufactured product sales and leasing, remanufacturing and manufacturing costs, inventory holding and shortage costs. We consider a consumer choice based demand model for mapping a potential customer into one of the product segments (a remanufactured product customer or a customer for a leased product with a particular lease period) for a given price/lease payment vector. We explore several properties of the discounted profit function and provide insight on the behavior of pricing and inventory policies. We also investigate the effect of key product characteristics such as deterioration in age, cost of shortage in remanufacturable product inventory, and key market characteristics such as relative willingness-to-pay for buying a remanufactured product and relative willingness-to-pay for leasing a new product on optimal pricing policies through a computational study.     

An inventory control model with consideration of remanufacturing and product life cycle

This paper investigates inventory control policies in a manufacturing/remanufacturing system during the product life cycle, which consists of four phases: introduction, growth, maturity, and decline. Both demand rate and return rate of products are random variables with normal distribution; the mean of the distribution varies according to the time in the product life cycle. Closed-form formulas of optimal production lot size, reorder point, and safety stock in each phase of the product life cycle are derived. A numerical example is presented with sensitivity analysis. The result shows that different inventory control policies should be adopted in different phases of the product life cycle. It is also found that the optimal production lot size and reorder point are not sensitive to the phase length and the demand changing rate.     

The impact of expiration dates labels on hedonic markets for perishable products

This study draws inferences from a pre-registered field experiment to investigate how expiration dates impact consumer preferences for food products with different ages. Based on a power analysis, we recruited 373 adult participants for the experiment. The results demonstrate that expiration dates largely influenced the perceived freshness of a food product as they seem to indicate a quality standard. When no explicit expiration date was present, consumers believed that the product’s quality decreased monotonically as time passed. However, in the presence of an expiration-date label, consumers initially perceived the product as more acceptable, but then that perception changed quickly after the date of expiration to levels more consistent with what they would perceive if the expiration date was not present. These results support policies that label expiration dates for consumers, as these labels benefit consumers, producers, and the public, especially for times prior to the expiration date. These results also suggest that it is important to craft policy and market strategies for products that are past the expiration date, but are still safe to consume. Policies that set the expiration dates to the time where the taste quality begins to worsen would likely benefit both consumers and producers, while policies that set the expiration dates to the time where the food is no longer safe probably improve the public outcomes as it minimizes food waste.     

Considering stochastic lead times in a manufacturing/remanufacturing system with deterministic demands and returns

In this paper we study a manufacturing/remanufacturing system with stochastic lead times and a constant demand. We base our approach on previous research in which we have developed models to describe an inventory system with stochastic lead times. In this paper, we first adopt this method to manufacturing/remanufacturing situations, where there are essentially two supply sources for replenishing serviceable inventory. We then provide a solution procedure when a cycle ordering policy is used. Secondly, we investigate the possibility to use a dual sourcing ordering policy in which each order is split between a manufacturing and a remanufacturing process. Finally, we compare the two ordering policies and illustrate how the lead-time patterns influence the economic consequences.     

Optimal production–inventory strategies for a HMMS-type reverse logistics system

The aim of the paper is to find optimal inventory policies in a reverse logistics system with special structure. It is assumed that demand is a known continuous function in a given planning horizon and return rate of used items is a given function. There is a constant delay between the using and return process. We investigate two stores. The demand is satisfied from the first store, where the manufactured and remanufactured items are stored. The returned products are collected in the second store and then remanufactured or disposed. The costs of this system consist of the quadratic holding costs for these two stores and the quadratic manufacturing, remanufacturing and disposal costs.The model is represented as an optimal control problem with two state variables (inventory status in the first and second store) and with three control variables (rate of manufacturing, remanufacturing and disposal). The objective is to minimize the sum of the quadratic deviation from described inventory levels in stores and from described manufacturing, remanufacturing and disposal rates. In this form, the model can be considered as a generalization of the well-known Holt et al. (Planning Production, Inventories, and Work Forces, Prentice-Hall, Englewood Cliffs, NJ, 1960) model with two warehouses. After solving the problem, we give some numerical examples to represent the optimal path in dependence of the demand rates.     

Consolidating distribution centers can reduce lost sales

In many industries, products are sold out of inventory. When inventory shortages are possible, expected product sales will fall short of expected product demand. We find that this shortfall is proportional to the standard deviation of demand. As we show, this implies that consolidating retailer units – which leads to a lower standard deviation for the aggregate demand – can increase sales.     

A discounted cash flow approach to the base stock inventory model

A widespread approach to inventory modelling is to associate costs with measures of system performance and determine the control policy which minimises the long run average cost per unit time. This type of approach ignores the impact of a control policy on the timing of the cash flows associated with payments to suppliers and revenue streams from customers. The approach in this paper is to concentrate on cash flows and determine the control policy which maximises the expected net present value of the cash flows associated with a demand, valued at the time when that demand occurs. There is a Poisson demand process, a fixed lead time, unsatisfied demand is backordered and the system is controlled using a base stock policy. A solution procedure is given and a comparison is made with an equivalent simple interest model and with the standard cost model with linear holding and shortage costs.     

Postponed product differentiation with demand information update

This paper studies the problem of how to coordinate postponed product differentiation and forecast update to improve manufacturing efficiency. We consider a two-stage model of multiple products with a common component. In stage 1, the manager obtains a prior demand distribution of each product and decides the production quantity of the common component. In stage 2, the demand forecast is updated and the common component is differentiated into various final products. Then the final demand of each product is realized and inventory leftover (shortage) is assessed. We use stochastic programming to model this problem, and propose an optimal bundle-type algorithm to solve it. Furthermore, we develop some simple and effective approximation algorithms for several special cases. Extensive numerical experiments are conducted to show the effectiveness of the approximation algorithms, to compare the performance between the traditional production model and the postponement production model, and to examine the impact of parameters on the performances of the two systems.     

Replenish-up-to inventory control policy with random replenishment intervals

The multi period inventory problems have been studied under two main assumptions. Continuous review assumption where an order can be made at any time depending on inventory position and periodic review assumption where an order can be initiated only at discrete time epochs. In this study, we analyze a multi period inventory problem that falls under neither of these two assumptions. In the case we consider, there are periodic replenishments but the replenishment intervals are taken to be i.i.d. random variables. This setting represents the real life cases where a supplier visits a retailer with random inter arrival times and the retailer replenishes his inventories based on a replenish-up-to-level inventory control policy. We also assume that only a certain fraction of unmet demand is backordered and the rest of it is lost.In this setting under general distribution between replenishment epochs, we show the concavity of the expected profit function and give the condition that must hold for the optimal replenish-up-to-level. We also present the specific solutions and analysis under two different distributions, namely, uniform and exponential distributions, together with some numerical examples.     

Introduction to design and analysis of production systems

In a two-party VMI channel, the vendor operates the basic stocking and delivery functions and makes inventory replenishment decisions while the retailer is responsible for customer acquisition and in-store services. This paper focuses on such retail channel and proposes an analytical model for the partners in supply channel to determine the inventory policy with the objective of optimizing system net profit. The model explicitly incorporates issues from both the vendor and the retailer in order to derive a policy for mutual benefits. To illustrate and obtain insights from the proposed solution procedure, we devise a set of numerical analyses based on various scenarios. Factors such as shelf-space-dependent demand, shelf-space capacity, demand pattern, logistics characteristics, and disparity between holding costs of the warehouse and the retail store are also investigated.     

Joint determination of inventory replenishment and sales effort with uncertain market responses

An optimal joint operational and marketing decision is crucial for robust supply chain management. This paper addresses concurrent determination of inventory replenishment and sales effort decisions such as price, incentives to salesforce, and short-term promotions, or a combination of them. Market responses to sales efforts are typically highly uncertain, and demand in each period has its distribution dependent on the selected sales effort. In each period a replenishment order may be issued, which incurs both fixed and variable ordering costs, and at the same time the sales effort is also determined, the execution of which may incur costs. For such a model, the previously developed methods which are used for the joint inventory-pricing models become inadequate. A computational procedure for obtaining an optimal joint policy is addressed, and the conditions for the optimality of that policy are identified.     

Ordering and pricing policies in a manufacturing and distribution supply chain for fashion products

In the paper, we develop a model of manufacturing and distribution supply chains that are operating to meet price-sensitive random demand for products with short life cycles such as fashion products. Two specific scenarios are considered. The manufacturer-controlled scenario is one where the distributor shares price-sensitive random demand with the manufacturer, and the manufacturer controls the supply chain stocking decisions and bears the risk of overstocking costs. The distributor-controlled scenario works in the opposite direction. Prevailing wisdom suggests that the manufacturer should control supply chain decisions (e.g., via vendor-managed inventory). Our results indicate that such an arrangement is against the interest of a distributor selling short life-cycle products. Furthermore, we find that the total supply chain profit is generally higher when the distributor controls the supply chain stocking decisions and bears the risk of overstocking costs.     

An investigation of setup instability in non-stationary stochastic inventory systems

In stochastic inventory systems unfolding uncertainties in demand lead to the revision of earlier replenishment plans which in turn results in an instability or so-called system nervousness. In this paper, we provide the grounds for measuring system nervousness in non-stationary demand environments, and gauge the stability and the cost performances of (R,S) and (s,S) inventory policies. Our results reveal that, both the stability and the cost performance of inventory policies are affected by the demand pattern as well as the cost parameters, and the (R,S) policy has the potential to replace the cost-optimal (s,S) policy for systems with limited flexibility.     

Economic lot-sizing and dynamic quantity competition

We study a problem of dynamic quantity competition in continuous time with two competing retailers facing different replenishment cost structures. Retailer 1 faces fixed ordering costs and variable procurement costs and all inventory kept in stock is subject to holding costs. Retailer 2 only faces variable procurement costs. Both retailers are allowed to change their sales quantities dynamically over time. Following the structure of the economic order quantity (EOQ) model, retailer 1 places replenishment orders in batches and retailer 2 follows a just-in-time (JIT) policy. The objective of both retailers is to maximize their individual average profit anticipating the competitor's replenishment and output decisions. The problem is solved by a two-stage hierarchical optimization approach using backwards induction. The second-stage model is a differential game in output quantities between the two retailers for a given cycle length. At the first stage, the replenishment policy is determined. We prove the existence of a unique optimal solution and derive an open-loop Nash equilibrium. We show that both retailers follow contrary output strategies over the order cycle. The EOQ retailer, driven by inventory holding costs, decreases his market share whereas the output of the JIT retailer increases. Moreover, depending on the cost structure, the EOQ retailer might partially be a monopolist. At the first stage, the EOQ retailer determines the cycle length, anticipating the optimal output trajectories at the second stage.     

Production planning and inventory allocation of a single-product assemble-to-order system with failure-prone machines

We consider the optimal production and inventory allocation of a single-product assemble-to-order system with multiple demand classes and lost sales. Each component is replenished by a dedicated machine that is subjected to unpredictable breakdowns. We find that the machine state not only influences the production and allocation decisions on its own component but also influences the decisions on the other components. Specifically, the optimal component production policy is a base-stock policy with the base-stock level non-decreasing in the inventory levels of the other components and the states of the other machines. The optimal component allocation policy is a rationing policy with the rationing level non-increasing in the inventory levels of the other components, the states of the other machines, and its own machine state. We use an exponential distribution to approximate the distribution of the total processing times and propose two heuristic policies to address the production and allocation decisions. The importance of taking machine failures into consideration is revealed through computational experiments.     

Inventory service-level optimization within distribution network design problem

We propose a sequential heuristic approach to optimize inventory service levels in a two-stage supply chain. The proposed approach deals with service level and inventory decisions, simultaneously with network design decisions, and incorporating unfulfilled demand costs in a previous inventory-location model. A two-step formulation is considered, where the first step optimizes service level and the second step addresses location and inventory decisions. Each algorithm iteration solves an inventory-location model for a fixed service level, and then the service level is updated in order to reach an equilibrium condition between operating system and unfulfilled demand costs. The algorithm converged in three iterations for a set of sample instances, obtaining the same outcome in comparison with a more intuitive, exact, but more time-consuming search procedure.     

A comparison of the constant-order and dual-index policy for dual sourcing

We analyze a single-product, periodic-review, stochastic demand inventory model with backorders and two supply options, a regular and a more expensive expedited one, with deterministic, offsetting lead times. Since the optimal policy for such a problem is generally unknown, several simpler policies have been proposed in the literature, e.g., the single-index (SIP), dual-index (DIP), or constant-order policy (COP). In previous research the DIP has been found to perform closely to the optimal policy in specific numerical settings. The COP, on the other hand, is very appealing from a practical point of view. We explore the relative cost performance of these two policies in a numerical study to gain insights into appropriate policy choices. We find that in settings, where dual sourcing is most valuable, the simpler COP delivers satisfactory results, sometimes even better ones than the DIP, and therefore represents a legitimate policy alternative for practical situations.