An integrated decision making model for dynamic pricing and inventory control of substitutable products based on demand learning

Purpose: This paper focuses on the PC industry, analyzing a PC supply chain system composed of onelarge retailer and two manufacturers. The retailer informs the suppliers of the total order quantity, namelyQ, based on demand forecast ahead of the selling season. The suppliers manufacture products accordingto the predicted quantity. When the actual demand has been observed, the retailer conducts demandlearning and determines the actual order quantity. Under the assumption that the products of the twosuppliers are one-way substitutable, an integrated decision-making model for dynamic pricing andinventory control is established.Design/methodology/approach: This paper proposes a mathematical model where a large domestichousehold appliance retailer decides the optimal original ordering quantity before the selling season and theoptimal actual ordering quantity, and two manufacturers decide the optimal wholesale price.Findings:By applying this model to a large domestic household appliance retail terminal, the authors canconclude that the model is quite feasible and effective. Meanwhile, the results of simulation analysis showthat when the product prices of two manufacturers both reduce gradually, one manufacturer will often waittill the other manufacturer reduces their price to a crucial inflection point, then their profit will show aqualitative change instead of a real-time profit-price change.Practical implications: This model can be adopted to a supply chain system composed of one largeretailer and two manufacturers, helping manufacturers better make a pricing and inventory controldecision.Originality/value: Previous research focuses on the ordering quantity directly be decided. Limited workhas considered the actual ordering quantity based on demand learning. However, this paper considers boththe optimal original ordering quantity before the selling season and the optimal actual ordering quantityfrom the perspective of the retailerPeer Reviewe

Optimal Learning Algorithms for Stochastic Inventory Systems with Random Capacities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156225/2/poms13178_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156225/1/poms13178.pd

Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning

We consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon. Demand in each period is stochastic and price-dependent, and unsatisfied demands are backlogged. At the beginning of each period, the firm determines its selling price and inventory replenishment quantity, but it knows neither the form of demand dependency on selling price nor the distribution of demand uncertainty a priori, hence it has to make pricing and ordering decisions based on historical demand data. We propose a nonparametric data-driven policy that learns about the demand on the fly and, concurrently, applies learned information to determine replenishment and pricing decisions. The policy integrates learning and action in a sense that the firm actively experiments on pricing and inventory levels to collect demand information with the least possible profit loss. Besides convergence of optimal policies, we show that the regret, defined as the average profit loss compared with that of the optimal solution when the firm has complete information about the underlying demand, vanishes at the fastest possible rate as the planning horizon increases.http://deepblue.lib.umich.edu/bitstream/2027.42/116388/1/1294_Ahn.pd

Learning Algorithms for Stochastic Dynamic Pricing and Inventory Control.

This dissertation considers joint pricing and inventory control problems in which the customer's response to selling price and the demand distribution are not known a priori, and the only available information for decision-making is the past sales data. Data-driven algorithms are developed and proved to converge to the true clairvoyant optimal policy had decision maker known the demand processes a priori, and, for the first time in literature, this dissertation provides theoretical results on the convergence rate of these data-driven algorithms. Under this general framework, several problems are studied in different settings. Chapter 2 studies the classical joint pricing and inventory control problem with backlogged demand, and proposes a nonparametric data-driven algorithm that learns about the demand on the fly while making pricing and ordering decisions. The performance of the algorithm is measured by regret, which is the average profit loss compared with that of the clairvoyant optimal policy. It is proved that the regret vanishes at the fastest possible rate as the planning horizon increases. Chapter 3 studies the classical joint pricing and inventory control problem with lost-sales and censored demand. Major challenges in this study include the following: First, due to demand censoring, the firm cannot observe either the realized demand or realized profit in case of a stockout, therefore only biased data is accessible; second, the data-driven objective function is always multimodal, which is hard to solve and establish convergence for. Chapter 3 presents a data-driven algorithm that actively explores in the inventory space to collect more demand data, and designs a sparse discretization scheme to jointly learn and optimize the multimodal data-driven objective. The algorithm is shown to be very computationally efficient. Chapter 4 considers a constraint that only allows the firm to change prices no more than a certain number of times, and explores the impact of number of price changes on the quality of demand learning. In the data-driven algorithm, we extend the traditional maximum likelihood estimation method to work with censored demand data, and prove that the algorithm converges at the best possible rate for any data-driven algorithms.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120721/1/boxchen_1.pd

Data Driven Optimization: Theory and Applications in Supply Chain Systems

Supply chain optimization plays a critical role in many business enterprises. In a data driven environment, rather than pre-specifying the underlying demand distribution and then optimizing the system’s objective, it is much more robust to have a nonparametric approach directly leveraging the past observed data. In the supply chain context, we propose and design online learning algorithms that make adaptive decisions based on historical sales (a.k.a. censored demand). We measure the performance of an online learning algorithm by cumulative regret or simply regret, which is defined as the cost difference between the proposed algorithm and the clairvoyant optimal one. In the supply chain context, to design efficient learning algorithms, we typically face two major challenges. First, we need to identify a suitable recurrent state that decouples system dynamics into cycles with good properties: (1) smoothness and rich feedback information necessary to apply the zeroth order optimization method effectively; (2) convexity and gradient information essential for the first order methods. Second, we require the learning algorithms to be adaptive to the physical constraints, e.g., positive inventory carry-over, warehouse capacity constraint, ordering/production capacity constraint, and these constraints limit the policy search space in a dynamic fashion. To design efficient and provably-good data driven supply chain algorithms, we zoom into the detailed structure of each system, and carefully trade off between exploration and exploitation.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150030/1/haoyuan_1.pd