Establishing an Extendable Benchmarking Framework for E-Fulfillment

The growth in attended home deliveries motivates research in prescriptive analytics for e-fulfillment. Introducing new analytics solutions, for instance, for vehicle routing or revenue management, requires simulation-based benchmarking and analyses on relevant problem scenarios. Unfortunately, creating the required systems induces high overhead for analytics researchers. This paper introduces the simulation-based benchmarking framework SiLFul, which aims to support scientific rigor and practical relevance of research by reducing this overhead. It provides a toolbox of approaches, a modular and extendable architecture, and a comprehensive, application-related data model. Thereby, it facilitates controllable analyses and transparent and replicable research. Moreover, we propose a research process that leverages the framework for evaluating analytics and allows continuous development of the framework as a community effort

Modeling strategic customers using simulations - with examples from airline revenue management

AbstractA condition for airline revenue management is the possibility of identifying and differentiating customer segments (refer to Chiang et al. (2007) for a state of the art). Traditionally, customer differentiation has been realized by the time of request in days before departure as well as by restrictions connected to the tickets sold. Customer segments have been regarded to be fixed over time, based on myopic customer behavior. With the market transparency increased through the Internet as well as the rise of no-frills offers and flat-rates, customer behavior has changed during the last decades. Strategic customer behavior describes a tendency to remember previous buying experiences, adapt expectations and observe the market over longer periods of time before deciding on what (and whether) to buy. The empirical consequences of strategic customer behavior for traditional as well as state-of-the-art revenue management have been little examined. A major reason for this is that measuring the degree of strategic versus myopic tendencies of demand in real customers is difficult and expensive. In this paper, we formulate a mathematical model of strategic customer behavior including parameters defining the propensity to delay buying as well as learning and communicating. We test the empirical consequences of our model using a stochastic simulation, in which customers act as agents deciding whether and when to buy. Thereby, we provide first results on how different ways of strategic behavior affect the success of methods of revenue management, highlighting the possible weaknesses and strengths of approaches when confronted with strategic customers. Of course, strategic customer behavior is not limited to airline revenue management. Useful models of strategic behavior as implemented in the simulation can be applied to analyze a wide range of situations: conditions for this transfer as well as examples are provided as part of the outlook

SOLVING THE DATA-DRIVEN NEWSVENDOR WITH ATTENTION TO TIME

Inventory management systems support firms in planning for an uncertain future by using demand forecasts and optimization models to make restocking decisions. Recent work on the data-driven newsvendor found that incorporating machine learning (ML) can improve the success of inventory management by accounting for demand-driving information. However, ML methods are infamously hard to interpret, which may hinder their acceptance. To ameliorate this, we show how to apply an interpretable attention-based architecture, the Temporal Fusion Transformer (TFT), to the data-driven newsvendor problem. Our approach replicates and extends the original TFT time series forecasting method to the inventory management domain. We evaluate our method on two real-world retail datasets, each covering 260 perishable food items, and provide domain-specific benchmarks. The computational study illustrates TFT’s interpretable predictions and their comparatively high accuracy. Our work aims to lay the groundwork for further design science research on transparency in human-AI collaboration in this domain

When Are Deliveries Profitable? - Considering Order Value and Transport Capacity in Demand Fulfillment for Last-Mile Deliveries in Metropolitan Areas

The paper aims to optimize the final part of a firm’s value chain with regard to attended last-mile deliveries. It is assumed that to be profitable, ecommerce businesses need to maximize the overall value of fulfilled orders (rather than their number), while also limiting costs of delivery. To do so, it is essential to decide which delivery requests to accept and which time windows to offer to which consumers. This is especially relevant for attended deliveries, as delivery fees usually cannot fully compensate costs of delivery given tight delivery time windows. The literature review shows that existing order acceptance techniques often ignore either the order value or the expected costs of delivery. The paper presents an iterative solution approach: after calculating an approximate transport capacity based on forecasted expected delivery requests and a cost-minimizing routing, actual delivery requests are accepted or rejected aiming to maximize the overall value of orders given the computed transport capacity. With the final set of accepted requests, the routing solution is updated to minimize costs of delivery. The presented solution approach combines well-known methods from revenue management and time-dependent vehicle routing. In a computational study for a German metropolitan area, the potential and the limits of value-based demand fulfillment as well as its sensitivity regarding forecast accuracy and demand composition are investigated

Airline Codeshare Alliances - Marketing Boon and Revenue Management Information Systems Challenge

The paper juxtaposes the challenges that airline codeshare alliances create for analytical information systems on the one hand and their motivation from a marketing perspective on the other. The authors review the state-of-the-art literature on potential marketing benefits and analyze the impact on airline planning systems. In this regard, revenue management systems are of particular interest. Based on a simulation study, the authors infer a severe impact of decentralized codeshare controls as currently widely implemented in the industry on revenue management performance. In the scenarios examined, complementary codesharing reduces alliance-wide revenues by up to 1 %. Losses increase when a carrier experiences high local demand or a high degree of codeshare demand, and disseminate over the whole network. Virtual codeshares also cause losses of 0.3 % to 1.5 % depending on the discount level offered by the marketing carrier and on the demand structure. Finally, the authors formulate a set of managerial implications based on these findings

Data-Mining für die Analyse von Nachfrage und Angebot im Revenue Management am Beispiel von Fluggesellschaften

Nachfrageprognose und Angebotsteuerung werden im Revenue Management zunehmend auf die Betrachtung komplementäre und substituierbare Produkte ausgeweitet. Im Revenue Management bedeutet dies die Prognose und Optimierung auf der Ebene von Reisewegen, Verkaufsstandorten, Buchungsklassen und Buchungsklassengruppen. Diese detaillierte Betrachtungsweise ermöglicht eine gezielte Optimierung der Angebotssteuerung, doch sie erhöht auch die Komplexität des Modells. Dieses Paper präsentiert eine Möglichkeit zur Analyse und Klassifikation von Nachfrage und Angebot durch die Anwendung von Data-Mining. Eine entsprechende Klassifikation bildet eine Möglichkeit, das Problem kleiner Zahlen und den Aufwand komplexer Einflüsse zu reduzieren

Collaborative urban transportation : Recent advances in theory and practice

We thank the Leibniz Association for sponsoring the Dagstuhl Seminar 16091, at which the work presented here was initiated. We also thank Leena Suhl for her comments on an early version of this work. Finally, we thank the anonymous reviewers for the constructive comments.Peer reviewedPostprin

Flexible dynamic time window pricing for attended home deliveriesy

In the challenging environment of attended home deliveries, pricing of different delivery options can play a crucial role to ensure profitability and service quality of retailers. To differentiate between standard and premium delivery options, many retailers include time windows of various lengths and fees within their offer sets. Customers want short delivery time windows, but expect low delivery fees. However, longer time windows can help to maintain flexibility and profitability for the retailer. We present flexible dynamic time window pricing policies that measure the impact of short time windows on the underlying route plan during the booking process and set delivery fees accordingly. Our goal is to nudge customers to choose time windows that do not overly restrict the flexibility of route plans. To this end, we introduce three dynamic pricing policies that consider temporal and/or spatial routing and customer characteristics. We consider customer behavior through a nested logit model, which is able to mimic customer choice for time windows of multiple lengths. We perform a computational study considering realistic travel and demand data to investigate the effectiveness of flexible dynamic time window pricing. Our pricing policies are able to outperform static pricing policies that reflect current business practice

Resilient revenue management:A literature survey of recent theoretical advances

Recently, resilience has emerged as a concept that describes a system's ability to persist and adapt under uncertainty. Revenue management is a textbook example of planning under uncertainty-A ny revenue optimisation model relies on a range of assumptions, among them the accuracy of the demand forecast. Revenue management's objective is to maximise revenue given uncertain market conditions, capacity, and even fares. This contribution reviews recent advances in making revenue management more resilient. To this end, it identifies and categorises uncertainties that affect the revenue management process. In the resulting framework, we review contributions aiming to increase solutions' ability to persist or adapt, listing relevant references by their focus and character. Thereby, we contribute a comprehensive review of research accumulated in the last ten years, outline a research agenda, and thus prepare the ground for further research efforts