Optimal pricing and inventory policies: Centralized and decentralized decision making

The paper studies an optimal control problem of pricing and inventory replenishment in a system with serial inventories. Consumer demand for a specific product at a retail outlet depends on price as well as the in-store stock of the product. The hypothesis is that for certain consumer products, a large volume of displayed goods leads consumers to buy more than if the stock is small. In addition to the stock that is on display in the store, there is an inventory of the product in a central warehouse. First we consider a setup in which management of the two stocks is decentralized such that pricing decisions are made by the store manager who also decides on the level of in-store inventory. The warehouse manager makes the replenishment decisions concerning the stock in the warehouse. Next we study the problem where stock management and pricing decisions are centralized. Optimal trajectories for inventories, replenishment rates, and retail price are derived by using phase diagrams and a formal synthesizing procedure.     

Operationalizing technology improvements in product development decision-making

Achieving competitive advantage and price premiums in many technology-based markets requires the incorporation of current technology in new products. To do so, firms in hyper-competitive environments increasingly plan and design their products concurrent with the independent development and validation of underlying technologies. Simultaneous validation of a core technology has important implications for a company’s product positioning and launch sequence decisions making these traditional marketing decisions relevant to operations managers. Prior research has shown that to minimize cannibalization in the absence of such improvements in technology, a firm should not launch low-end products before high-end products. However, concurrent evolution of technology can make it desirable and even necessary to introduce low-end products before high-end products. This is because in technology-based industries, improvements in technology delay the introduction of a high-end product, and a firm must trade-off the benefit of launching the low-end product earlier (greater discounted profits) against the cost of cannibalization of high-end product sales. High-end product cannibalization can be further reduced by offering the customer an option to upgrade from the low-end to high-end product, with important implications for the firm’s product positioning and introduction sequence decisions. Based on our study in the high technology industry, we model the product positioning and introduction sequence decisions under the simultaneous evolution of technology. Our analysis indicates that it may be optimal in a variety of circumstances for a firm to launch products in an increasing order of performance, even in the absence of network externalities. Besides presenting analytical results for product positioning and profit from different introduction sequences, the paper also makes a contribution to managerial practice by providing insights in the form of a conceptual framework.     

需求受库存水平影响的短生命周期物品延迟订货的库存管理

像计算机、电视机、空调等这类具有物理变质的可能性很小,但生命周期较短、不断更新换代、价值不断贬值的电子产品会发生无形变质的现象.在假设无形变质率与需求率负相关,同时在产品的存储过程中,考虑库存水平对销售量的影响情况下,研究需求受库存水平影响、且缺货时存在延迟订货的短生命周期物品的库存管理问题.创新之处在于考虑了产品的缺货问题;在允许缺货的条件下,建立了短生命周期物品的库存模型;运用数值算例进行了求解和验证;并对各参数进行了敏感性分析.     

On the Effect of Product Variety in Production–Inventory Systems

In this paper, we examine the effect of product variety on inventory costs in a production–inventory system with finite capacity where products are made to stock and share the same manufacturing facility. The facility incurs a setup time whenever it switches from producing one product type to another. The production facility has a finite production rate and stochastic production times. In order to mitigate the effect of setups, products are produced in batches. In contrast to inventory systems with exogenous lead times, we show that inventory costs increase almost linearly in the number of products. More importantly, we show that the rate of increase is sensitive to system parameters including demand and process variability, demand and capacity levels, and setup times. The effect of these parameters can be counterintuitive. For example, we show that the relative increase in cost due to higher product variety is decreasing in demand and process variability. We also show that it is decreasing in expected production time. On the other hand, we find that the relative cost is increasing in expected setup time, setup time variability and aggregate demand rate. Furthermore, we show that the effect of product variety on optimal base stock levels is not monotonic. We use the model to draw several managerial insights regarding the value of variety-reducing strategies such as product consolidation and delayed differentiation.     

Supply chain coordination through integration of innovation effort and advertising support

This study integrates firms’ innovation and advertising decisions in a two-echelon supply chain, where a monopoly manufacturer sells products to ultimate consumers through an autonomous retailer. Considering that both innovation and advertising contribute to the product demand, we first investigate the optimal equilibriums of channel members under two different game structures: the non-cooperative and cooperative. In the non-cooperative structure, the manufacturer controls the innovation effort and wholesale price while the retailer controls the advertising rate and retail pricing. In the cooperative structure, the manufacturer agrees to share part of retailer’s advertising expenditure. We find that both the optimal operation and marketing decisions are sensitive to effects of innovation and advertising on demand as well as the manufacturer’s cost reduction coefficient due to innovation. Further, we find that the manufacturer always prefers cooperation. Meanwhile, only when the firms’ investments significantly contribute to the market mechanism, does the retailer have incentive to implement a cooperative program. In addition, we further propose a new two-way subsidy policy to coordinate channel members’ business functions.     

Optimal ordering and pricing policies in a single-period environment with multivariate demand and markdowns

This paper investigates a single-period inventory model in which the demand of the product is a deterministic, multivariate function of price, time, and level of inventory. Models are formulated for the basic pricing case and the case with a price markdown during the season. Solution methodologies are presented for each case when the pricing decisions are predetermined and when they are decision variables. Comments on the practical use of this model are presented, and sensitivity analysis is conducted on the decision variables and demand parameters.     

A two-stage queueing network on form postponement supply chain with correlated demands

To ease the conflict between quick response and product variety, more and more business models are developed in supply chains. Among these, the form postponement (FP) strategy is an efficient tool and has been widely adopted. To the supply chain with FP strategy, the design mostly involves two problems: determination of customer order decoupling point (CODP) position and semi-finished product inventory control. In this paper, we develop a two-stage tandem queueing network with MAP arrival to address this issue. Particularly, we introduce a Markov arrival process (MAP) to characterize the correlation of the demand. By using of matrix geometric method, we derive several performance measure of the supply chain, such as inventory level and unfill rate. Our numerical examples show that both the variance and the correlation coefficient of the demand lead to more delayed CODP position and more total cost.     

Stocking and quality decisions for deteriorating perishable products under competition

In this study, we address the joint inventory and quality management in a Cournot duopoly, for a seasonally produced, perishable product whose quality deteriorates over time. The sales of the product occur over two periods, namely the season (first period) and the off-season (second period). Apart from the stocking quantities for the two periods, firms must decide the quality levels of the units to stock for the second selling period. Firms incur a cost to maintain particular quality levels. The equilibrium policies of the firms are characterized, and we discuss the impact of the firms’ quality costs on their inventory and quality decisions. We identify the conditions of the quality costs when competition ceases to exist in the second period, and analyse the impact of the quality costs on inter-temporal price fluctuations and product availability. Using the unconstrained equilibrium policy, we frame the firms’ inventory disposal policies when production yields are exogenous.     

Incorporating manufacturing lead times in joint production-marketing models: A review and some future directions

In many industries the pricing of a product over time can be used to manage demand for the product. Lead time, or promised delivery time is often a significant factor in price negotiations. However, the production planning literature has largely treated pricing decisions as exogeneous while focusing on the allocation of production capacity between products over time. On the other hand, the marketing literature has generally ignored the effects of capacity constraints and focused on the effects of pricing. In this paper, we begin by reviewing the existing literature on integrative production-marketing research, focusing on those models that consider lead times and capacity. We suggest a number of directions for future research that take advantage of recent developments in production planning models, as well as explicit modeling of feedback loops governing key parameters, which suggest a broader view of the problem.     

Finding and identifying optimal inventory levels for systems with common components

In this article, we consider the problem of finding the optimal inventory level for components in an assembly system where multiple products share common components in the presence of random demand. Previously, solution procedures that identify the optimal inventory levels for components in a component commonality problem have been considered for two product or one common component systems. We will here extend this to a three products system considering any number of common components. The inventory problem considered is modeled as a two stage stochastic recourse problem where the first stage is to set the inventory levels to maximize expected profit while the second stage is to allocate components to products after observing demand. Our main contribution, and the main focus of this paper, is the outline of a procedure that finds the gradient for the stochastic problem, such that an optimal solution can be identified and a gradient based search method can be used to find the optimal solution.     

Production planning via scenario modelling

Several Linear Programming (LP) and Mixed Integer Programming (MIP) models for the production and capacity planning problems with uncertainty in demand are proposed. In contrast to traditional mathematical programming approaches, we use scenarios to characterize the uncertainty in demand. Solutions are obtained for each scenario and then these individual scenario solutions are aggregated to yield a nonanticipative or implementable policy. Such an approach makes it possible to model nonstationarity in demand as well as a variety of recourse decision types. Two scenario-based models for formalizing implementable policies are presented. The first model is a LP model for multi-product, multi-period, single-level production planning to determine the production volume and product inventory for each period, such that the expected cost of holding inventory and lost demand is minimized. The second model is a MIP model for multi-product, multi-period, single-level production planning to help in sourcing decisions for raw materials supply. Although these formulations lead to very large scale mathematical programming problems, our computational experience with LP models for real-life instances is very encouraging.     

Joint pricing and production decisions for new products with learning curve effects under upstream and downstream trade credits

Today's leading companies generate a substantial portion of their sales from new products. For example, Apple Inc. generates almost 60% of its sales revenue from products launched in the past four years. Hence, the importance of new products cannot be overestimated. Pricing strategy is particularly important during the introductory stage of a new product, when the learning curve effect is most pronounced. Although the learning curve effect reduces production cost significantly during the introduction period of a new product, most researchers have assumed that the production cost remains constant throughout a product's lifecycle. In our opinion, failing to consider this learning curve phenomenon may lead to biased solutions. Furthermore, suppliers often offer manufacturers a short-term interest-free loan (i.e., trade credit) to stimulate sales, while manufacturers adjust price to influence consumer purchasing decisions. However, most researchers have not considered the combined effects of pricing strategy on demand, learning curve effect on production cost, and trade credit influence on lot size. Therefore, we propose an inventory model to determine optimal lot-sizing and pricing strategies with both upstream and downstream trade credit, manufacturer's production cost which follows a learning curve effect, and production quantity influenced by both selling price and trade credit. Then we derive the conditions of the optimal solution. Numerical examples and sensitivity analysis are performed to examine results and provide managerial insights. These results show that the learning curve effect significantly lowers selling price, while tremendously increasing both profit and demand.     

Price and lead time decisions in dual-channel supply chains

Manufacturers today are increasingly adopting a dual channel to sell their products, i.e., the traditional retail channel and an online direct channel. Empirical studies have shown that service quality (we focus on the delivery lead time of the direct channel) even goes beyond product price as one of the major factors influencing consumer acceptance of the direct channel. Delivery lead time has significant effects on demand, profit, and pricing strategy. However, there is scant literature addressing the decision on the promised delivery lead time of a direct channel and its impact on the manufacturer’s and retailer’s pricing decisions. To fill this gap, we examine the optimal decisions of delivery lead time and prices in a centralized and a decentralized dual-channel supply chain using the two-stage optimization technique and Stackelberg game, and analyze the impacts of delivery lead time and customer acceptance of a direct channel on the manufacturer’s and retailer’s pricing behaviours. We analytically show that delivery lead time strongly influences the manufacturer’s and the retailer’s pricing strategies and profits. Our numerical studies reveal that the difference between the demand transfer ratios in the two channels with respect to delivery lead time and direct sale price, customer acceptance of the direct channel, and product type have great effects on the lead time and pricing decisions.     

Fuzzy pricing,marketing and service planning in a fuzzy inventory model: A geometric programming approach

In the real world markets, demand is influenced by different parameters. Recently, many researchers have been interested in integrated production and marketing planning strategies in inventory models where demand depends on different parameters such as price and/or marketing expenditure. The quality of services that are offered to customers of a product is one of the most important parameters that affects demand in the real markets and has not been considered in development of inventory models. On the other hand, the cost parameters in real inventory systems and other parameters such as price, marketing and service elasticity to demand are imprecise and uncertain in nature. So, the notion of fuzziness can be applied to cope with this uncertainty. In this paper, a new fuzzy profit maximization inventory model with shortages is proposed. The demand is considered as a power function of price, marketing expenditure and service expenditure. Furthermore, unit cost is determined as a power function of order quantity. Since the proposed model is in a fuzzy environment, a fuzzy decision should be made to meet the decision criteria, and the results should be fuzzy. Therefore, the proposed model is formulated and solved using geometric programming and fuzzy optimization techniques to derive an approximation of the results’ membership functions. The model is illustrated with a numerical example and finally a case study is provided for evaluation and validation of the results of model.     

Newsvendor with multiple options of expediting

We consider a manufacturer facing single period inventory planning problem with uncertain demand and multiple options of expediting. The demand comes at a certain time in the future. The manufacturer may order the product in advance with a relatively low cost. She can order additional amount by expediting after the demand is realized. There are a number of expediting options, each of which corresponds to a certain delivery lead time and a unit procurement price. The unit procurement price is decreasing over delivery lead time. The selling price is also decreasing over time. In this paper, we assume that the manufacturer must deliver all products to the customer in a single shipment. The problem can be formulated as a profit maximization problem. We develop structural properties and show how the optimal solution can be identified efficiently. In addition, we compare our model with the classical newsvendor model and obtain a number of managerial insights.     

On the cooperation of recycling operations

This paper addresses two important issues that may affect the operations efficiency in the recycling industry. First, the industry contains many small-scale and inefficient recycling firms, especially in developing countries. Second, the output from recycling a waste product often yields multiple recycled products that cannot all be sold efficiently by a single firm. To address these two issues, this paper examines how different firms can cooperate in their recycling and pricing decisions using cooperative game theory. Recycling operations under both joint and individual productions with different cost structures are considered. Decisions include the quantity of waste product to recycle and the price at which to sell each recycled product on each firm’s market. These decisions can be made jointly by multiple cooperating firms to maximize total profit. We design allocation schemes for maximized total profit to encourage cooperation among all firms. Managerial insights are provided from both environmental and economic perspectives.     

Inventory competition in a dual-channel supply chain with delivery lead time consideration

Aimed at the inventory competition of perishable products in a dual-channel supply chain with consideration of the delivery lead time in the online direct channel, we extend the Newsvendor model considering stock-out-based consumer switching behavior to include the delivery lead time. We examine the retailer's optimal order quantity decision in the retail channel and the manufacturer's optimal inventory level decision in the online direct channel, explore the manufacturer's optimal delivery lead time decision in the online direct channel, discuss the impact of the product price and consumer switching behavior on the optimal decisions of supply chain members, and compare the optimal decisions between decentralized and centralized scenarios. The results show that, compared with the centralized scenario, at least one of the supply chain members will overstock in the decentralized scenario and that consumers in the online direct channel enjoy a shorter delivery lead time and hence better service in the decentralized scenario. Finally, we present numerical examples to analyze the impact of relevant parameters on the supply chain members’ profits and the supply chain efficiency.     

Bundle pricing of inventories with stochastic demand

We consider a retailer selling a fixed inventory of two perishable products over a finite horizon. Assuming Poisson arrivals and a bivariate reservation price distribution, we determine the optimal product and bundle prices that maximize the expected revenue. Our results indicate that the performances of mixed bundling, pure bundling and unbundled sales strategies heavily depend on the parameters of the demand process and the initial inventory levels. Bundling appears to be most effective with negatively correlated reservation prices and high starting inventory levels. When the starting inventory levels are equal and in excess of average demand, most of the benefits of bundling can be achieved through pure bundling. However, the mixed bundling strategy dominates the other two when the starting inventory levels are not equal. We also observe that an incorrect modeling of the reservation prices may lead to significant losses. The model is extended to allow for price changes during the selling horizon. It is shown that offering price bundles mid-season may be more effective than changing individual product prices.     

Temporary price increase during replenishment lead time

Pricing and inventory management make up together revenue management, which is a significant effort to boost revenues out of available resources. Firms use various forms of dynamic pricing, including personalized pricing, markdowns, promotions, coupons, discounts, and clearance sales, to respond to market fluctuations and demand uncertainty. In this paper, we study a temporary price increase policy, a form of dynamic pricing, for a non-perishable product, a practice used by several giant retailers such as Amazon, Walmart, and Apple. We develop a continuous review inventory model that allows for joint replenishment and pricing decisions, where the lead time is not zero. A replenishment decision controls supply, while a pricing decision controls demand. A manager exercises a temporary price increase to slow demand and avoid a stock-out situation while waiting for a shipment, which may not necessarily increase revenues, but decrease stock-out costs. The problem is to solve for the optimal replenishment and the pricing policy parameters that maximize the long-run expected profit. That is, when and how much to order and when to raise the price. In this paper, the inventory level and time trigger a price increase. We solve many numerical examples and perform extensive sensitivity analyses. Our results show that compared to a model that focuses on fixed pricing, our model brings an additional increase in profit of about 13%.     

The shape of the yield and its impact on inventory decisions

We consider an inventory model with stochastic demand, positive lead time and random yield where ordering decisions are made according to a linear inflation rule. In case of a positive lead times the complexity of such inventory systems increases distinctly. Due to positive lead times, the inventory position contains no longer a term for outstanding orders but the estimated quantity of goods to be delivered after a known positive lead time period, which differ from the realized deliveries. Thus, a forecast error occurs in each period. In previous research this forecast error was assumed to be normally distributed which is not an appropriate assumption in case of symmetric yield. Since yield skewness can’t be neglected, we propose to fit a skew normal distribution or a generalized extreme value distribution on the forecast error to account for the yield skewness. A numerical study reveals that the proposed approaches are excellent and outperform existing ones.