Confidence Intervals for Data-Driven Inventory Policies with Demand Censoring

We revisit the classical dynamic inventory management problem of Scarf (1959b) from the perspective of a decision-maker who has n historical selling seasons of data and must make ordering decisions for the upcoming season. We develop a nonparametric estimation procedure for the (*S; s*) policy that is consistent, then characterize the finite-sample properties of the estimated (*S; s*) levels by deriving their asymptotic confidence intervals. We also consider having at least some of the past selling seasons of data censored from the absence of backlogging, and show that the intuitive procedure of first correcting for censoring in the demand data yields inconsistent estimates. We then show how to correctly use the censored data to obtain consistent estimates and derive asymptotic confidence intervals for this policy using Stein’s method. We further show the confidence intervals can be used to effectively bound the difference between the expected total cost of an estimated policy and that of the optimal policy. We validate our results with extensive computations on simulated data. Our results extend to the repeated newsvendor problem and the base-stock policy problem by appropriate parameter choices

Online Learning Algorithms for Stochastic Inventory and Queueing Systems

The management of inventory and queueing systems lies in the heart of operations research and plays a vital role in many business enterprises. To this date, the majority of work in the literature has been done under complete distributional information about the uncertainties inherent in the system. However, in practice, the decision maker may not know the exact distributions of these uncertainties (such as demand, capacity, lead time) at the beginning of the planning horizon, but can only rely on realized observations collected over time. This thesis focuses on the interplay between learning and optimization of three canonical inventory and queueing systems and proposes a series of first online learning algorithms. The first system studied in Chapter II is the periodic-review multiproduct inventory system with a warehouse-capacity constraint. The second system studied in Chapter III is the periodic-review inventory system with random capacities. The third system studied in Chapter IV is the continuous-review make-to-stock M/G/1 queueing system. We take a nonparametric approach that directly works with data and needs not to specify any (parametric) form of the uncertainties. The proposed online learning algorithms are stochastic gradient descent type, leveraging the (sometimes non-obvious) convexity properties in the objective functions. The performance measure used is the notion of cumulative regret or simply regret, which is defined as the cost difference between the proposed learning algorithm and the clairvoyant optimal algorithm (had all the distributional information about uncertainties been given). Our main theoretical results are to establish the square-root regret rate for each proposed algorithm, which is known to be tight. Our numerical results also confirm the efficacy of the proposed learning algorithms. The major challenges in designing effective learning algorithms for such systems and analyzing them are as follows. First, in most retail settings, customers typically walk away in the face of stock-out, and therefore the system is unable to keep track of these lost-sales. Thus, the observable demand data is, in fact, the sales data, which is also known as the censored demand data. Second, the inventory decisions may impact the cost function over extended periods, due to complex state transitions in the underlying stochastic inventory system. Third, the stochastic inventory system has hard physical constraints, e.g., positive inventory carry-over, warehouse capacity constraint, ordering/production capacity constraint, and these constraints limit the search space in a dynamic way. We believe this line of research is well aligned with the important opportunity that now exists to advance data-driven algorithmic decision-making under uncertainty. Moreover, it adds an important dimension to the general theory of online learning and reinforcement learning, since firms often face a realistic stochastic supply chain system where system dynamics are complex, constraints are abundant, and information about uncertainties in the system is typically censored. It is, therefore, important to analyze the structure of the underlying system more closely and devise an efficient and effective learning algorithm that can generate better data, which is then feedback to the algorithm to make better decisions. This forms a virtuous cycle.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149894/1/aschenwd_1.pd

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

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

Deep Neural Newsvendor

We consider a data-driven newsvendor problem, where one has access to past demand data and the associated feature information. We solve the problem by estimating the target quantile function using a deep neural network (DNN). The remarkable representational power of DNN allows our framework to incorporate or approximate various extant data-driven models. We provide theoretical guarantees in terms of excess risk bounds for the DNN solution characterized by the network structure and sample size in a non-asymptotic manner, which justify the applicability of DNNs in the relevant contexts. Specifically, the convergence rate of the excess risk bound with respect to the sample size increases in the smoothness of the target quantile function but decreases in the dimension of feature variables. This rate can be further accelerated when the target function possesses a composite structure. Compared to other typical models, the nonparametric DNN method can effectively avoid or significantly reduce the model misspecification error. In particular, our theoretical framework can be extended to accommodate the data-dependent scenarios, where the data-generating process is time-dependent but not necessarily identical over time. Finally, we apply the DNN method to a real-world dataset obtained from a food supermarket. Our numerical experiments demonstrate that (1) the DNN method consistently outperforms other alternatives across a wide range of cost parameters, and (2) it also exhibits good performance when the sample size is either very large or relatively limited

Data-driven inventory optimization

The recent explosion of data availability opens up opportunities for companies to make better decisions. However, it is not clear, in general, how to get from data to a good decision. Exploiting these data for improved decision making requires adequate method- ologies. Inventory management decisions are a particularity important set of decision problems for virtually every company that buys, produces, distributes, or sells physical products. In this dissertation, we investigate the question of how to get from data to a good decision in inventory management problems. To this end, we revisit three fundamental inventory management problems, propose new data-driven methodologies, and measure their impact on inventory performance. Chapter II covers the newsvendor problem. To investigate how to exploit the available data, we propose a framework that distinguishes three levels on which data can generate value. Furthermore, we present a novel solution method that integrates the traditionally separate steps of demand estimation and inventory optimization into a single optimization problem. In our empirical analysis with real-world data, we find that data-driven methods outperform traditional approaches in most cases and that the benefit of improved forecasting dominates other potential benefits of data-driven methodologies. Chapter III is concerned with managing inventories for multiple products in a product category. We present a novel data-driven solution approach based on machine learning that integrates the estimation and opti- mization steps and takes complex substitution effects into account. We evaluate our approach on two real-world datasets. We find that our data-driven approach outperforms the benchmark on the first dataset and performs competitively on the second. Chapter IV focuses on dynamic inventory problems. We propose a novel solution approach that leverages auxiliary data. Our approach divides the problem into multiple single-stage problems using dynamic programming and uses machine learning methods in each stage to improve inventory decisions. In a computational study, we find that our method performs close to the optimal decision and significantly outperforms the benchmark

Online Joint Assortment-Inventory Optimization under MNL Choices

We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model, and the attraction parameters are unknown a priori. The retailer makes periodic assortment and inventory decisions to dynamically learn from the realized demands about the attraction parameters while maximizing the expected total profit over time. In this paper, we propose a novel algorithm that can effectively balance the exploration and exploitation in the online decision-making of assortment and inventory. Our algorithm builds on a new estimator for the MNL attraction parameters, a novel approach to incentivize exploration by adaptively tuning certain known and unknown parameters, and an optimization oracle to static single-cycle assortment-inventory planning problems with given parameters. We establish a regret upper bound for our algorithm and a lower bound for the online joint assortment-inventory optimization problem, suggesting that our algorithm achieves nearly optimal regret rate, provided that the static optimization oracle is exact. Then we incorporate more practical approximate static optimization oracles into our algorithm, and bound from above the impact of static optimization errors on the regret of our algorithm. At last, we perform numerical studies to demonstrate the effectiveness of our proposed algorithm

An enhanced approximation mathematical model inventorying items in a multi-echelon system under a continuous review policy with probabilistic demand and lead-time

An inventory system attempts to balance between overstock and understock to reduce the total cost and achieve customer demand in a timely manner. The inventory system is like a hidden entity in a supply chain, where a large complete network synchronizes a series of interrelated processes for a manufacturer, in order to transform raw materials into final products and distribute them to customers. The optimality of inventory and allocation policies in a supply chain for a cement industry is still unknown for many types of multi-echelon inventory systems. In multi-echelon networks, complexity exists when the inventory issues appear in multiple tiers and whose performances are significantly affected by the demand and lead-time. Hence, the objective of this research is to develop an enhanced approximation mathematical model in a multi-echelon inventory system under a continuous review policy subject to probabilistic demand and lead-time. The probability distribution function of demand during lead-time is established by developing a new Simulation Model of Demand During Lead-Time (SMDDL) using simulation procedures. The model is able to forecast future demand and demand during lead-time. The obtained demand during lead-time is used to develop a Serial Multi-echelon Inventory (SMEI) model by deriving the inventory cost function to compute performance measures of the cement inventory system. Based on the performance measures, a modified distribution multi-echelon inventory (DMEI) model with the First Come First Serve (FCFS) rule (DMEI-FCFS) is derived to determine the best expected waiting time and expected number of retailers in the system based on a mean arrival rate and a mean service rate. This research established five new distribution functions for the demand during lead-time. The distribution functions improve the performance measures, which contribute in reducing the expected waiting time in the system. Overall, the approximation model provides accurate time span to overcome shortage of cement inventory, which in turn fulfil customer satisfaction

Inventory - forecasting: mind the gap

We are concerned with the interaction and integration between demand forecasting and inventory control, in the context of supply chain operations. The majority of the literature is fragmented. Forecasting research more often than not assumes forecasting to be an end in itself, disregarding any subsequent stages of computation that are needed to transform forecasts into replenishment decisions. Conversely, most contributions in inventory theory assume that demand (and its parameters) are known, in effect disregarding any preceding stages of computation. Explicit recognition of these shortcomings is an important step towards more realistic theoretical developments, but still not particularly helpful unless they are somehow addressed. Even then, forecasts often constitute exogenous variables that serially feed into a stock control model. Finally, there is a small but growing stream of research that is explicitly built around jointly tackling the inventory forecasting question. We introduce a framework to define four levels of integration: from disregarding, to acknowledging, to partly addressing, to fully understanding the interactions. Focusing on the last two, we conduct a structured review of relevant (integrated) academic contributions in the area of forecasting and inventory control and argue for their classification with regard to integration. We show that the development from one level to another is in many cases chronological in order, but also associated with specific schools of thought. We also argue that although movement from one level to another adds realism, it also adds complexity in terms of actual implementations, and thus a trade-off exists. The article makes a contribution into an area that has always been fragmented despite the importance of bringing the forecasting and inventory communities together to solve problems of common interest. We close with an indicative agenda for further research and a call for more theoretical contributions, but also more work that would help to expand the empirical knowledge base in this area

Revenue Maximization and Learning in Products Ranking

We consider the revenue maximization problem for an online retailer who plans to display a set of products differing in their prices and qualities and rank them in order. The consumers have random attention spans and view the products sequentially before purchasing a ``satisficing'' product or leaving the platform empty-handed when the attention span gets exhausted. Our framework extends the cascade model in two directions: the consumers have random attention spans instead of fixed ones and the firm maximizes revenues instead of clicking probabilities. We show a nested structure of the optimal product ranking as a function of the attention span when the attention span is fixed and design a $1/e$-approximation algorithm accordingly for the random attention spans. When the conditional purchase probabilities are not known and may depend on consumer and product features, we devise an online learning algorithm that achieves $\tilde{\mathcal{O}}(\sqrt{T})$ regret relative to the approximation algorithm, despite of the censoring of information: the attention span of a customer who purchases an item is not observable. Numerical experiments demonstrate the outstanding performance of the approximation and online learning algorithms