Demand Forecast in Retail Assortment Optimization—Based on an Empirical Analysis of Beverage Sales

This paper focus on establishing the demand forecasting model to optimize product assortments from a set of SKUs in the same category. The aim of the model is to achieve revenue maximization. Based on the attribute level, the demand model considers the consumers’ preference and the possibility of substitution between different attributes. Then it divides the product’s specific attributes and multiplies these attributes effects. Furthermore, one beverage case was applied to the demand model to do empirical analysis. Top beverage categories were selected and e-commerce sales data were collected to represent the pre-sale of whole categories. Moreover, a store named S with some beverage SKUs is assumed and applied to the model, which predicted sales volume of each existing SKU and the total revenue

How to measure competition? The role of price dispersion in B2B supply markets

Since the formation of close relationships with suppliers requires a considerable amount of resources, the capacities for such relationships are limited. Thus, recently, research points into the direction that it might not be conducive to unconditionally engage in strategic buyer-supplier alliances. Specifically, in those cases where there is a vivid competition within the supply market, it might not be necessary to cooperate closely. However, a convenient measurement method for competition has been missing in the literature so far. Accordingly, this conceptual paper translates insights from the field of economics for an application in purchasing and supply management. It is recommended to evaluate the product price dispersion of supplier quotations in order to assess the intensity of competition in supply markets. As a consequence, this conceptual paper paves the way for future research on competition between suppliers. For managers, the proposed method could support the development of efficient purchasing strategies

An Integrated Framework For Configurable Product Assortment Planning

A manufacturer\u27s assortment is the set of products or product configurations that the company builds and offers to its customers. While the literature on assortment planning is growing in recent years, it is primarily aimed at non-durable retail and grocery products. In this study, we develop an integrated framework for strategic assortment planning of configurable products, with a focus on the highly complex automotive industry. The facts that automobiles are highly configurable (with the number of buildable configurations running into thousands, tens of thousands, and even millions) with relatively low sales volumes and the stock-out rates at individual dealerships (even with transshipments) are extremely high, pose significant challenges to traditional assortment planning models. This is particularly the case for markets such as the U.S. that mostly operate in a make-to-stock (MTS) environment. First, we study assortment planning models that account for exogenous demand models and stock-out based substitution while considering production and manufacturing complexity costs and economies-of-scale. We build a mathematical model that maximizes the expected profit for an Original Equipment Manufacturer (OEM) and is a mixed-integer nonlinear problem. We suggest using linear lower/upper bounds that will be solved through a Modified-Branch and Bound procedure and compare the results with commercial mixed-integer nonlinear solvers and show superiority of the proposed method in terms of solution quality as well as computational speed. We then build a modeling framework that identifies the optimal assortment for a manufacturer of automotive products under environmental considerations, in particular, Corporate Average Fuel Economy (CAFE) requirements as well as life-cycle Greenhouse Gas (GHG) emission constraints. We present a numerical experiment consisting of different vehicle propulsion technologies (conventional, Diesel, and hybrid) and study the optimal shares of different technologies for maximizing profitability under different target levels of CAFE requirements. Finally, we develop assortment planning formulations that can jointly identify optimal packages and stand-alone options over different series of the product model. Our numerical experiment reveals that product option packaging has a considerable effect on managing product complexity and profitability

オムニチャネル小売業におけるファッション商品の品切れに対する対応のモデル化分析

筑波大学 (University of Tsukuba)201

Data-Driven Optimization in Revenue Management: Pricing, Assortment Planning, and Demand Learning

This dissertation studies several problems in revenue management involving dynamic pricing, assortment selection, and their joint optimization, through demand learning. The setting in these problems is that customers’ responses to selling prices and product displays are unknown a priori, and the only information the decision maker can observe is sales data. Data-driven optimizing-while-learning algorithms are developed in this thesis for these problems, and the theoretical performances of the algorithms are established. For each algorithm, it is shown that as sales data accumulate, the average revenue achieved by the algorithm converges to the optimal. Chapter 2 studies the problem of context-based dynamic pricing of online products, which have low sales. For these products, existing single-product dynamic pricing algorithms do not work well due to insufficient data samples. To address this challenge, we propose pricing policies that concurrently perform clustering over products and set individual pricing decisions on the fly. By clustering data and identifying products that have similar demand patterns, we utilize sales data from products within the same cluster to improve demand estimation for better pricing decisions. We evaluate the algorithms using regret, and the result shows that when product demand functions come from multiple clusters, our algorithms significantly outperform traditional single-product pricing policies. Simulations with both synthetic and real data from Alibaba show that our algorithm performs very well, and a field experiment at Alibaba shows that our algorithm increased the overall revenue by 10.14%. Chapter 3 investigates an online personalized assortment optimization problem where customers arrive sequentially and make their choices (e.g., click an ad, purchase a product) following the multinomial logit (MNL) model with unknown parameters. We develop several algorithms to tackle this problem where the number of data samples is huge and customers’ data are possibly high dimensional. Theoretical performance for our algorithms in terms of regret are derived, and numerical experiments on a real dataset from Yahoo! on news article recommendation show that our algorithms perform very well compared with benchmarks. Chapter 4 considers a joint assortment optimization and pricing problem where customers arrive sequentially and make purchasing decisions following the multinomial logit (MNL) choice model. Not knowing the customer choice parameters a priori and subjecting to a display capacity constraint, we dynamically determine the subset of products for display and the selling prices to maximize the expected total revenue over a selling horizon. We design a learning algorithm that balances the trade-off between demand learning and revenue extraction, and evaluate the performance of the algorithm using Bayesian regret. This algorithm uses the method of random sampling to simultaneously learn the demand and maximize the revenue on the fly. An instance-independent upper bound for the Bayesian regret of the algorithm is obtained and numerical results show that it performs very well.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155268/1/semiao_1.pd

Spare Parts Management of Aging Capital Products

Spare parts are critical for operations of capital products such as aircraft, refineries, trucks, etc/, which require maintenance regularly. Original Equipment Manufacturers (OEMs) bear the responsibility of undisrupted maintenance service and spare parts flow for their capital products. Due to various factors OEMs lose their spare parts suppliers occasionally and these losses threaten the reliability of their maintenance service and capital products. In this thesis, we consider supply risk in management of spare parts inventory. The thesis consists of two parts: First we develop advance indicators for future supply problems of spare parts and suggest a model utilizing those indicators for inventory control of spare parts. Our results indicate that OEMs can save significantly by utilizing those indicators together with our model in their daily business. Second, we consider secondary markets and their effects on spare parts supply chains of OEMs. Secondary markets are chap supply sources for spare parts needs of OEMs. Therefore effective usage of them yield significant cost savings and boost service rate of OEMs. Furthermore, secondary markets are sources of competition since low prices on those markets attract some customers of OEMs. These two factors are considered from the perspective of spare parts inventory control. In the second part, we conclude that for OEMs it is beneficial to use secondary markets as a supply source as long as they adjust their selling prices accordingly