On the learning benefits of resource flexibility

Resource flexibility, arguably among the most celebrated operational concepts, is known to provide firms facing demand uncertainty with such benefits as risk pooling, revenue-maximization optionality, and operational hedging. In this paper, we uncover a heretofore unknown benefit: we establish that resource flexibility facilitates learning the demand when the latter is censored, which could, in turn, enable firms to make better-informed future operational decisions, thereby increasing profitability. Further, we quantify these learning benefits of flexibility and find that they could be of the same order of magnitude as the extensively studied risk-pooling benefits of flexibility. This suggests that flexibility’s learning benefits could be a first-order consideration and that extant theories, which view flexibility only as the ability to act ex post, could be underestimating its true value when learning the demand is desirable, for example, when it enables managers to make better ex ante capacity, assortment, or pricing decisions in future periods

Demand Management in E-Fulfillment

Internet retailers are in a unique position to adjust, in real-time, the product and service offering to the customer and to change the corresponding prices. Although this flexibility provides a vast potential for demand management to enhance profitability, standard practices and models to support the decision makers are lacking as of to date. This thesis aims to contribute to closing this gap by systematically investigating demand management approaches in e-fulfillment. We identify relevant novel planning issues through an in-depth case study at a Dutch e-grocer. We focus particularly on attended home delivery, where the Internet retailer applies delivery time slots to coordinate the reception of the purchased goods with the customer. The main levers to manage customer demand in such an environment are the offered time slots and the corresponding delivery fees. The Internet retailer may apply both of these options, slotting and pricing, at different moments in the sales process, either off-line prior to the actual order in-take or real-time as demand unfolds. The thesis presents several decision-support models for time slot management, both forecast-based and in real-time. The computational studies on real-life data demonstrate the viability and the merits of these methods. The results show that a more dynamic and differentiated demand management approach can lead to considerable cost savings and revenue gains

Data-Driven Algorithms for Stochastic Supply Chain Systems: Approximation and Online Learning

In the era of Big Data, with new and emerging technologies, data become easily attainable for companies. However, acquiring data is only the first step for the company. The second and more important step is to effectively integrate the data through the learning process (mining the data) in the decision-making process, and to utilize the information extracted from data to improve the efficiency of the company’s supply chain operation. The primary focus of this dissertation is on multistage stochastic optimization problems arising in the context of supply chains and inventory control problems, and on the design of efficient algorithms to solve the respective models. This dissertation can be categorized into two broad areas as follows. The first part of this dissertation focuses on the design of non-parametric learning algorithms for complex inventory systems with censored data. We address two challenging stochastic inventory control models: the periodic-review perishable inventory system and the periodic-review inventory control problem with lost-sales and positive lead times. We assume that the decision maker has no demand distribution information available a priori and can only observe past realized sales (censored demand) information to optimize the system's performance on the fly. For each of the problems, we design a learning algorithm that can coverage to the best base-stock policy with tight regret rate. The second part of this dissertation focuses on the design of approximation algorithms for stochastic perishable inventory systems with correlated demand. In this part, we consider the perishable inventory system from the optimization perspective. Different from traditional perishable inventory literature, we allow demands to be arbitrarily correlated and nonstationary, which means we can capture the seasonality nature of the economy, and allow the decision makers to effectively incorporate demand forecast. For this problem, we develop two approximation algorithms with worst-case performance guarantees. Through comprehensive numerical experiments, we have shown that the numerical performances of the approximation algorithms are very close to optimal.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138697/1/zhanghn_1.pd

Efficient Real-time Policies for Revenue Management Problems

This dissertation studies the development of provably near-optimal real-time prescriptive analytics solutions that are easily implementable in a dynamic business environment. We consider several stochastic control problems that are motivated by different applications of the practice of pricing and revenue management. Due to high dimensionality and the need for real-time decision making, it is computationally prohibitive to characterize the optimal controls for these problems. Therefore, we develop heuristic controls with simple decision rules that can be deployed in real-time at large scale, and then show theirs good theoretical and empirical performances. In particular, the first chapter studies the joint dynamic pricing and order fulfillment problem in the context of online retail, where a retailer sells multiple products to customers from different locations and fulfills orders through multiple fulfillment centers. The objective is to maximize the total expected profits, defined as the revenue minus the shipping cost. We propose heuristics where the real-time computations of pricing and fulfillment decisions are partially decoupled, and show their good performances compared to reasonable benchmarks. The second chapter studies a dynamic pricing problem where a firm faces price-sensitive customers arriving stochastically over time. Each customer consumes one unit of resource for a deterministic amount of time, after which the resource can be immediately used to serve new customers. We develop two heuristic controls and show that both are asymptotically optimal in the regime with large demand and supply. We further generalize both of the heuristic controls to the settings with multiple service types requiring different service times and with advance reservation. Lastly, the third chapter considers a general class of single-product dynamic pricing problems with inventory constraints, where the price-dependent demand function is unknown to the firm. We develop nonparametric dynamic pricing algorithms that do not assume any functional form of the demand model and show that, for one of the algorithm, its revenue loss compared to a clairvoyant matches the theoretic lower bound in asymptotic regime. In particular, the proposed algorithms generalize the classic bisection search method to a constrained setting with noisy observations.PHDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145995/1/leiyz_1.pd

Internet of Things-Enabled Dynamic Performance Measurement for Real-Time Supply Chain Management - Toward Smarter Supply Chain -

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. Park, Jinwoo.Supply chain performance measurement has become one of the most important and critical management strategies in the pursuit of perfection and in strengthening the competitive edges of supply chains to face the challenges in todays global markets. To constantly cope with the resulting rapid changes and adopt new process designs while reviving supply chain initiatives and keeping them alive, an effective real-time performance-based IT system should be developed. And there are many researches on supply chain performance measurement system based on the real-time information system. This thesis proposes a standard framework of a digitalized smart real-time performance-based system. The framework represents a new type of smart real-time monitoring and controlling performance-based IT mechanism for the next-generation of supply chain management practices with dynamic and intelligent aspects concerning strategic performance targets. The idea of this mechanism has been derived from the main concepts of traditional supply chain workflow and performance measurement systemswhere the time-based flow is greatly emphasized and considered as the most critical success factor. The proposed mechanism is called Dynamic Supply Chain Performance Mapping (DSCPM), a computerized event-driven performance-based IT system that runs in real-time according to supply chain management principles that cover all supply chain aspects through a diversity of powerful practices to effectively capture violations, and enable timely decision-making to reduce wastes and maximize value. The DSCPM is proposed to contain different types of engines of which the most important one is the Performance Practices and Applications Engine (PPAE) due to its involvement with several modules to guarantee the comprehensiveness of the real-time monitoring system. Each of these modules is specified to control a specific supply chain application that is equipped with suitable real-time monitoring and controlling rules called Real-Time Performance Control Rules (RT-PCRs), which are expressed using Complex Event Processing (CEP) method. The RT-PCRs enable DSCPM to detect any interruptions or violation smartly and accordingly trigger real-time decision-making warnings or re-(actions) to control the performance and achieve a smart real-time working environment. The contributions of this dissertation are as follows: (1) building a conceptual framework to digitalize the supply chain, based on their strategic performance targets, deploying IoT technologies to convert its resources to smart-objects and therefore enable a dynamic and real-time supply chain performance measurement and management. (2) Demonstrating the feasibility of the DSCPM concerning performance targets by developing some practices and tool modules that are supplied with RT-PCRs (e.g., Real-time Demand Lead-time Analysis, Real-time Smart Decision-making Analysis (RT-SDA), Real-time Supply Chain Cost Tracking System (RT-SCCT), etc.). (3) Verifying the effectiveness of RT-PCRs in RT-SDA and RT-SCCT modules by building simulation models using AnyLogic simulation software.Chapter 1. Introduction 1 1.1 OVERVIEW 1 1.2 PROBLEM STATEMENT AND MOTIVATION 4 1.3 RESEARCH OBJECTIVES 7 1.4 THESIS OUTLINE 11 Chapter 2. Background and Literature Review 12 2.1 INTRODUCTION 12 2.2 SUPPLY CHAIN PERFORMANCE MEASUREMENT 13 2.3 PROCESS-ORIENTED SCPM AND SCOR MODEL 25 2.4 IOT AND SCM 31 Chapter 3. Performance-based IoT Deployment for Digital Supply Chain Transformation 40 3.1 INTRODUCTION 40 3.2 DIGITAL SC TRANSFORMATION FRAMEWORK 42 3.3 FRAMEWORK DEMONSTRATION USING A THEORETICAL CASE STUDY 65 3.4 CONCLUSION 71 Chapter 4. IoT-enabled Dynamic Supply Chain Performance Mapping based on Complex Event Processing 73 4.1 INTRODUCTION 73 4.2 REAL-TIME ENTERPRISE INTEGRATION 74 4.3 INTEGRATION OF DSCPM IN REAL-TIME SUPPLY CHAIN INFRASTRUCTURE 76 4.4 DYNAMIC SUPPLY CHAIN PERFORMANCE MAPPING FRAMEWORK (DSCPM) 77 4.5 CONCLUSION 107 Chapter 5. DSCPM-enabled Smart Real-time Performance Measurement Environment 109 5.1 DSCPM-ENABLED REAL-TIME TIME AND PERFORMANCE-BASED ANALYSIS FRAMEWORK 109 5.2 DSCPM-ENABLED REAL-TIME SC COSTS TRACKING SYSTEM 132 Chapter 6. Managing Perishability in Dairy Supply Chain using DSCPM Framework (a case study scenario) 152 6.1 INTRODUCTION 152 6.2 ASSUMPTIONS AND NOTATION 153 6.3 SIMULATION EXPERIMENTS 158 6.4 RESULTS AND DISCUSSION 161 6.5 A NEW APPROACH, FOR DESIGNING AND MANAGING PERISHABLE PRODUCTS INVENTORY SYSTEM 168 6.6 DECISIONS SENSITIVITY ANALYSIS 172 6.7 IOT COSTS-BENEFITS ANALYSIS 173 6.8 CONCLUSIONS 176 Chapter 7. Conclusions 179 7.1 CONCLUSION 179 7.2 FUTURE RESEARCH 182 Bibliography 184Docto

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