The Effect of e-Business on Supply Chain Strategy

Internet technology has forced companies to redefine their business models so as to improve the extended enterprise performance - this is popularly called e-business. The focus has been on improving the extended enterprise transactions including Intraorganizational, Business-to-Consumer (B2C) and Business-to-Business (B2B) transactions. This shift in corporate focus allowed a number of companies to employ a hybrid approach, the Push-Pull supply chain paradigm. In this article we review and analyze the evolution of supply chain strategies from the traditional Push to Pull and finally to the hybrid Push-Pull approach. The analysis motivates the development of a framework that allows companies to identify the appropriate supply chain strategy depending on product characteristics. Finally, we introduce new opportunities that contribute and support this supply chain paradigm

Uplift Modeling with Multiple Treatments and General Response Types

Randomized experiments have been used to assist decision-making in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods

A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling

Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem of constructing such models from randomized experiments data is known as Uplift Modeling in the literature. Many algorithms have been proposed for uplift modeling and some have generated promising results on various data sets. Yet little is known about the theoretical properties of these algorithms. In this paper, we propose a new tree-based ensemble algorithm for uplift modeling. Experiments show that our algorithm can achieve competitive results on both synthetic and industry-provided data. In addition, by properly tuning the "node size" parameter, our algorithm is proved to be consistent under mild regularity conditions. This is the first consistent algorithm for uplift modeling that we are aware of.Comment: Accepted by 2017 IEEE International Conference on Data Minin

Online Pricing with Offline Data: Phase Transition and Inverse Square Law

This paper investigates the impact of pre-existing offline data on online learning, in the context of dynamic pricing. We study a single-product dynamic pricing problem over a selling horizon of $T$ periods. The demand in each period is determined by the price of the product according to a linear demand model with unknown parameters. We assume that before the start of the selling horizon, the seller already has some pre-existing offline data. The offline data set contains $n$ samples, each of which is an input-output pair consisting of a historical price and an associated demand observation. The seller wants to utilize both the pre-existing offline data and the sequential online data to minimize the regret of the online learning process. We characterize the joint effect of the size, location and dispersion of the offline data on the optimal regret of the online learning process. Specifically, the size, location and dispersion of the offline data are measured by the number of historical samples $n$, the distance between the average historical price and the optimal price $\delta$, and the standard deviation of the historical prices $\sigma$, respectively. We show that the optimal regret is $\widetilde \Theta\left(\sqrt{T}\wedge \frac{T}{(n\wedge T)\delta^2+n\sigma^2}\right)$, and design a learning algorithm based on the "optimism in the face of uncertainty" principle, whose regret is optimal up to a logarithmic factor. Our results reveal surprising transformations of the optimal regret rate with respect to the size of the offline data, which we refer to as phase transitions. In addition, our results demonstrate that the location and dispersion of the offline data also have an intrinsic effect on the optimal regret, and we quantify this effect via the inverse-square law.Comment: Forthcoming in Management Scienc

Competition in the supply option market

This paper develops a multi-attribute competition model for procurement of short life cycle products. In such an environment, the buyer installs dedicated production capacity at the suppliers before the demand is realized. Final production orders are decided after demand materializes. Of course, the buyer is reluctant to bear all the capacity and inventory risk, and thus signs flexible contracts with several suppliers. We model the suppliers' offers as option contracts, where each supplier charges a reservation price per unit of capacity, and an execution price per unit of delivered supply. These two parameters illustrate the trade-off between total price and flexibility of the contract, and are both important to the buyer. We model the interaction between the suppliers and the buyer as a game in which the suppliers are the leaders and the buyer is the follower. Specifically, suppliers compete to provide supply capacity to the buyer and the buyer optimizes its expected profit by selecting one or more suppliers. We characterize the suppliers' equilibria in pure strategies for a class of customer demand distributions. In particular, we show that this type of interaction gives rise to cluster competition. That is, in equilibrium, suppliers tend to be clustered in small groups of two or three suppliers each, such that within the same group all suppliers use similar technologies and offer the same type of contract. Finally, we show that in equilibrium, the supply chain inefficiencies, i.e., the loss of profit due to competition, are in general at most 25% of the profit of a centralized supply chain, for a wide class of demand distributions.supplier portfolio; supplier competition;

Improving supply chain efficiency through wholesale price renegotiation

In a decentralized supply chain, double marginalization is an important source of inefficiency. We suggest in this paper a simple mechanism to reduce it that uses a wholesale price contract and renegotiation. Our mechanism only requires repeated interaction, and rational behavior from the players. Specifically, over T rounds of negotiation, the supplier proposes different prices in each round, and the buyer places orders at the quoted price. Even though prices are decreasing in time, the buyer places a positive order, to force the supplier to reduce its price in the following round. This interaction results in higher profits for both supplier and buyer. We solve the buyer and supplier problems and show that, as T increases, supply chain efficiency tends to 100%, and the sub-optimality gap decreases with 1/T. Finally, we discuss how these results can be applied to design negotiation processes.strategic customer; dynamic pricing; supply chain;