Inventory Policy with Parametric Demand: Operational Statistics,Linear Correction,and Regression

In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements.     

Newsvendor Pricing Problem in a Two‐Sided Market

We study the pricing problem of a “platform” intermediary to jointly determine the selling price of the platforms (hardware) sold to consumers and the royalty charged to content developers for content (software), when the demands for content and for platforms are interdependent. Our model elucidates the impact of supply chain replenishment costs and demand uncertainty on the strategic issues of platform pricing in a two‐sided market.     

行为库存管理研究综述及前景展望

库存管理是运作管理学科的研究重点之一.由于其在经济活动中具有重要的地位,库存管理在行为运作管理这一新兴学科中再次成为学术界关注的焦点.本文首先简述了行为运作管理的行为理论基础以及行为经济学的发展,根据行为运作管理的研究目标构建了行为库存管理的整体研究框架,进而在这一框架下对现有的行为库存管理相关研究进行系统综述,并进一...     

Decision Making and the Price Setting Newsvendor: Experimental Evidence

We present an experimental study of the price‐setting newsvendor problem, which extends the traditional framework by allowing the decision maker to determine both the selling price and the order quantity of a given item. We compare behavior under this model with two benchmark conditions where subjects have a single decision to make (price or quantity). We observe that subjects deviate from the theoretical benchmarks when they are tasked with a single decision. They also exhibit anchoring behavior, where their anchor is the expected demand when quantity is the decision variable and is the initial inventory level when price is the decision variable. When decision makers set quantity and price concurrently, we observe no significant difference between the normative (i.e., expected profit‐maximizing) prices and the decision makers’ price choices. Quantity decisions move further from the normative benchmarks (compared to when subjects have a single decision to make) when the ratio of cost to price is less than half. When this ratio is reversed, there is no significant difference between order levels in single‐ and multi‐task settings. In the multidecision framework, we also observe a tendency to match orders and expected demand levels, which subjects can control using prices.     

Joint Mail‐In Rebate Decisions in Supply Chains Under Demand Uncertainty

We study the joint decisions of offering mail‐in rebates (MIRs) in a single‐manufacturer–single‐retailer supply chain using a game theoretic framework. Either party can offer an MIR to the end consumer if it is in his best interest. The consumer demand is stochastic and depends on the product price and the amount of MIRs. When the retail price is exogenous, we show the existence of a unique Nash equilibrium under both additive and multiplicative demand functions and characterize it completely. We show that any of the following four scenarios can be the equilibrium: both parties offer MIR, only one party offers MIR, none offers MIR. When the retail price is a decision variable for the retailer and the rebate redemption rate increases with the amount of MIR, we once again prove the existence of a unique Nash equilibrium where both the retailer and the manufacturer offer MIRs. Using a numerical study, we show that the average post‐purchase price of the product is higher not only than the perceived pre‐purchase price but also than the newsvendor optimal price without an MIR. This implies that an MIR makes a product look cheaper while the consumers actually pay more on average.     

资金约束下损失规避零售商的订购和广告协同策略

在损失规避下,探讨了零售商的订购和广告协同决策问题。零售商的初始资金状态存在三种情形：资金充足、资金相对短缺（不需融资）、资金短缺（对外融资）,分别在三种情形下刻画了零售商的最优运营策略,结果表明：零售商用于商品采购和广告的资金产生的边际效用与边际负效用之比相等,且介于1与单位融资资金的使用成本之间;用于两项活动的最后一单位资金产生的边际效用相等。探讨了损失规避、初始资金、融资利率、商品价格及广告促销的敏感性对运营策略的影响：在资金充足或资金短缺下,商品订购量和广告强度随影响因素的变动方向通常相同;在资金相对短缺下,商品订购量和广告强度随影响因素的变动方向则相反。讨论中揭示了一些管理启示。     

Inventory placement problems in dynamic supply chains

Summary  In this paper a basic model for analyzing the inventory placement problem in a supply chain is developed. The problem will be studied for a capacitated, multi-stage supply chain facing a continuous, stochastic demand for a single period for a single product — a specialty item with a very short selling season. The objective is to maximize the probability of achieving a set profit level. We prove that finding an optimal stock investment at the stages entails solving a mixed binary integer linear program. We characterize properties of the stock investment, examine two interesting cases where the stock investments have a simple structure, and develop a branch and bound approach for solving the more general case.

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Zusammenfassung  In diesem Beitrag wird ein Grundmodell entwickelt, um das Problem der Lagerplatzierung in einer Lieferkette analysieren zu k?nnen. Das Problem wird für eine kapazitierte mehrstufige Lieferkette untersucht, wobei unterstellt wird, dass es um die Nachfrage nach einem einzigen Produkt für eine einzige Periode (einen Sonderartikel mit sehr kurzer Verkaufszeit) geht und diese Nachfrage stetig und stochastisch ist. Unter diesen Bedingungen soll die Wahrscheinlichkeit maximiert werden, ein vorgegebenes Gewinnniveau zu erreichen. Die Suche nach der optimalen Vorratsinvestition auf den Stufen der Lieferkette führt zur Formulierung eines gemischten ganzzahligen linearen Programmierungsproblems mit Bin?rvariablen. Die Eigenschaften der Bevorratungsinvestition werden charakterisiert, zwei interessante F?lle mit einer einfachen Struktur der Bevorratungsinvestition untersucht und ein Branch-and-Bound-Ansatz zur L?sung eines allgemeineren Problemfalls entwickelt.

     

需求不确定环境下多个零售商竞争的鲁棒随机优化模型

在需求不确定环境下构建了由一个制造商和多个零售商组成的供应链系统,考虑不同产品的可替代性,建立了多个零售商竞争的随机优化模型。利用鲁棒优化方法研究了需求不确定环境下多个零售商竞争的绝对鲁棒优化问题、偏差鲁棒优化问题和相对鲁棒优化问题。最后通过数值算例比较分析了不同产品替代率下的绝对鲁棒优化解、偏差鲁棒优化解及相对鲁棒优化解。     

Cross‐Training with Imperfect Training Schemes

Cross‐training workers is one of the most efficient ways of achieving flexibility in manufacturing and service systems for increasing responsiveness to demand variability. However, it is generally the case that cross‐trained employees are not as productive on a specific task as employees who were originally trained for that task. Also, the productivity of the cross‐trained workers depends on when they are cross‐trained. In this work, we consider a two‐stage model to analyze the effects of variations in productivity levels on cross‐training policies. We define a new metric called achievable capacity and show that it plays a key role in determining the structure of the problem. If cross‐training can be done in a consistent manner, the achievable capacity is not affected when the training is done, which implies that the cross‐training decisions are independent of the opportunity cost of lost demand and are based on a trade‐off between cross‐training costs at different times. When the productivities of workers trained at different times differ, there is a three‐way trade‐off between cross‐training costs at different times and the opportunity cost of lost demand due to lost achievable capacity. We analyze the effects of variability and show that if the productivity levels of workers trained at different times are consistent, the decision maker is inclined to defer the cross‐training decisions as the variability of demand or productivity levels increases. However, when the productivities of workers trained at different times differ, an increase in the variability may make investing more in cross‐training earlier more preferable.     

A Newsvendor Who Chooses Informational Effort

We study a newsvendor who can acquire the services of a forecaster, or, more generally, an information gatherer (IG) to improve his information about demand. When the IG's effort increases, does the average ex ante order quantity rise or fall? Do average ex post sales rise or fall? Improvements in information technology and in the services offered by forecasters provide motivation for the study of these questions. Much depends on our model of the IG and his efforts. We study an IG who sends a signal to a classic single‐period newsvendor. The signal defines the newsvendor's posterior probability distribution on the possible demands and the newsvendor uses that posterior to calculate the optimal order. Each of the possible posteriors is a scale/location transform of the same base distribution. When the IG works harder, the average scale parameter drops. Higher IG effort is always useful to the newsvendor. We show that there is a critical value of order cost. For costs on one side of this value more IG effort leads to a higher average ex ante order and for costs on the other side to a lower average order. But for all costs, more IG effort leads to higher average ex post sales. We obtain analogous results for a “regret‐averse” newsvendor who suffers a penalty that is a nonlinear function of the discrepancy between quantity ordered and true demand.