The Impact of a Target on Newsvendor Decisions

Goal achieving is a commonly observed phenomenon in practice, and it plays an important role in decision making. In this paper, we investigate the impact of a target on newsvendor decisions. We take into account the risk and model the effect of a target by maximizing the satisficing measure of a newsvendor’s profit with respect to that target. We study two satisficing measures: (i) conditional value at risk (CVaR) satisficing measure that evaluates the highest confidence level of CVaR achieving the target; (ii) entropic satisficing measure that assesses the smallest risk tolerance level under which the certainty equivalent for exponential utility function achieves the target. For both satisficing measures, we find that the optimal ordering quantity increases with the target level. We determine an optimal order quantity for a target-based newsvendor and characterize its properties with respect to, for example, product’s profit margin

Managers and Students as Newsvendors - How Out-of-Task Experience Matters

We compare how freshmen business students, graduate business students and experienced procurement managers perform on a simple inventory ordering task. We find that, qualitatively, managers exhibit ordering behavior similar to students, including biased ordering towards average demand. Experience, however, affects subjects’ utilization of information. The managers’ work experience seems most valuable when there is only historical demand data to guide decision making, while students better utilize analytical information and task training. As a result, when information necessary to solve the problem to optimality is added to historical information, students catch up to the managers, and students with classroom experience in operations management outperform managers.

An Advanced Heuristic for Multiple-Option Spare Parts Procurement after End-of-Production

After-sales service is a major profit generator for more and more OEMs in industries with durable products. Successful engagement in after-sales service improves customer loyalty and allows for competitive differentiation through superior service like an extended service period after end of production during which customers are guaranteed to be provided with service parts. In order to fulfill the service guarantee in these cases, an effective and efficient spare parts management has to be implemented, which is challenging due to the high uncertainty concerning spare parts demand over such a long time horizon. The traditional way of spare parts acquisition for the service phase is to set up a huge final lot at the end of regular production of the parent product which is sufficient to fulfill demand up to the end of the service time. This strategy results in extremely high inventory levels over a long period and generates major holding costs and a high level of obsolescence risk. With increasing service time more flexible options for spare parts procurement after end of production gain more and more importance. In our paper we focus on the two most relevant ones, namely extra production and remanufacturing. Managing all three options leads to a complicated stochastic dynamic decision problem. For that problem type, however, a quite simple combined decision rule with order-up-to levels for extra production and remanufacturing turns out to be very effective. We propose a heuristic procedure for parameter determination which accounts for the main stochastic and dynamic interactions between the different order-up-to levels, but still consists of quite simple calculations so that it can be applied to problem instances of arbitrary size. In a numerical study we show that this heuristic performs extremely well under a wide range of conditions so that it can be strongly recommended as a decision support tool for the multi-option spare parts procurement problem.Spare Parts, Inventory Management, Reverse Logistics, Final Order

Behavioral analyses of retailers’ ordering decisions

The main objective I pursue in this thesis is to better understand how different factors may independently and in combination influence retailers' ordering decisions under different supply chain structures (single agent and multi agent), different demand uncertainty (deterministic and stochastic), and different interaction among retailers (no interaction, competition and cooperation). I developed three different studies where I build on different formal management model and then run multiple behavioral studies to better understand subjects’ behavior. The first study analyzes order amplification in a single-supplier single-retailer supply chain. I used a behavioral experiment to test retailers’ orders under different ordering delays and different times to build supplier’s capacity. Results provide (i) a better understanding of the endogenous dynamics leading to retailers’ ordering amplification, and (ii) a description of subjects’ biases and deviation from optimal trajectories; despite subjects have full information about the system structure. The second study analyzes how order amplification can also take place when there is fierce retailer competition and limited supplier capacity. I study how different factors (different time to build supplier capacity, different levels of competition among retailers, different magnitudes of supply shortage and different allocation mechanisms) may independently and in combination influence retailers’ order in a system with two retailers under supply competition. Results show that (i) the bullwhip effect persists even when subjects do not have incentives to deviate, (ii) subjects amplify their orders in an attempt to build an unnecessary safety stock to respond to potential deviations from the other retailers, and (iii) retailers’ underperformance varies with the allocation mechanism used by the supplier. In the last study, I analyze retailers’ orders in a system where there is uncertainty in the final customer demand. I experimentally explore the effect of transshipments among retailers in a single-supplier multi-retailer supply chain. Specifically, I explore retailers’ orders under different profit and communication conditions. In addition, I integrate analytical and behavioral models to improve supply chain performance. Results show that (i) the persistence of common biases in a newsvendor problem (pull-to-center, demand chasing, loss aversion, psychological disutility), (ii) communication could improve coordination and may reduce demand chasing behavior, (iii) supply chain performance increases with the use of behavioral strategies embedded within a traditional optimization model, and (iv) dynamic heuristics improve overall coordination, outperforming a simple Nash Equilibrium strategy

The Impact of Demand Uncertainty on Consumer Subsidies for Green Technology Adoption

This paper studies government subsidies for green technology adoption while considering the manufacturing industry’s response. Government subsidies offered directly to consumers impact the supplier’s production and pricing decisions. Our analysis expands the current understanding of the price-setting newsvendor model, incorporating the external influence from the government, who is now an additional player in the system. We quantify how demand uncertainty impacts the various players (government, industry, and consumers) when designing policies. We further show that, for convex demand functions, an increase in demand uncertainty leads to higher production quantities and lower prices, resulting in lower profits for the supplier. With this in mind, one could expect consumer surplus to increase with uncertainty. In fact, we show that this is not always the case and that the uncertainty impact on consumer surplus depends on the trade-off between lower prices and the possibility of underserving customers with high valuations. We also show that when policy makers such as governments ignore demand uncertainty when designing consumer subsidies, they can significantly miss the desired adoption target level. From a coordination perspective, we demonstrate that the decentralized decisions are also optimal for a central planner managing jointly the supplier and the government. As a result, subsidies provide a coordination mechanism

The Newsvendor Problem: Review and Directions for Future Research

In this paper, we review the contributions to date for analyzing the newsvendor problem. Our focus is on examining the specific extensions for analyzing this problem in the context of modeling customer demand, supplier costs, and the buyer risk profile. More specifically, we analyze the impact of market price, marketing effort, and stocking quantity on customer demand; how supplier prices can serve as a coordination mechanism in a supply chain setting; integrating alternative supplier pricing policies within the newsvendor framework; and how the buyer’s risk profile moderates the newsvendor order quantity decision. For each of these areas,we summarize the current literature and develop extensions. Finally, we also propose directions for future research

On-demand last-mile distribution network design with omnichannel inventory

E-commerce delivery deadlines are getting increasingly tight, driven by a growing ‘I-want-it-now’ instant gratification mindset of consumers and the desire of online and omnichannel retailers to capitalize on the growth of on-demand e-commerce. On-demand deliveries with delivery deadlines as tight as one or two hours force companies to rethink their last-mile distribution network, since tight delivery deadlines require decentralization of order picking and inventory holding to ensure close proximity to consumers. This fundamentally changes the strategic design process of last-mile distribution networks. We study the impact of incorporating inventory order-up-to level decisions into the strategic design process of last-mile distribution networks with tight delivery deadlines. We develop an approximate inventory model by including an estimate of the cost of late delivery and additional transportation due to local stock-outs in a newsvendor formulation. Such local stock-outs require an order to be delivered from a more distant facility, which may lead to late delivery and additional transportation cost. We integrate our approximate inventory model and a location-allocation mixed-integer program that determines optimal facility locations, associated order-up-to inventory levels, and fleet composition, into a metamodel simulation-based optimization approach. Our numerical analyses demonstrate that pooling the additional online inventory with brick-and-mortar (B&M) inventories leads to cannibalization by the B&M network and higher B&M service levels. However, the pooling benefits to the online network outweigh the cost of inventory cannibalization. Furthermore, we show under which circumstances omnichannel retailers may have an incentive to consolidate online inventory in specific B&M facilities

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