Out-of-stock problem: possible classification schemes

An out-of-stock (OOS) event is referred as one of the biggest supply-chain management problem concerning retailers, distributors and consumers. We present available PCG data and discuss how to determine the importance of some features (fields), their interconnections and compare them with standard data fields used in other publicly accessible studies and recommendations from Efficient Consumer Response (ECR). We propose several models and algorithms to predict and solve Out of stock problem and at the end the computational results of these models are presented

Influence of artificial intelligence on public employment and its impact on politics: A systematic literature review

Goal:Public administration is constantly changing in response to new challenges, including the implementation of new technologies such as robotics and artificial intelligence (AI). This new dynamic has caught the attention of political leaders who are finding ways to restrain or regulate AI in public services, but also of scholars who are raising legitimate concerns about its impacts on public employment. In light of the above, the aim of this research is to analyze the influence of AI on public employment and the ways politics are reacting. Design / Methodology / Approach: We have performed a systematic literature review to disclose the state-of-the-art and to find new avenues for future research. Results: The results indicate that public services require four kinds of intelligence – mechanical, analytical, intuitive, and empathetic – albeit, with much less expression than in private services. Limitations of the investigation: This systematic review provides a snapshot of the influence of AI on public employment. Thus, our research does not cover the whole body of knowledge, but it presents a holistic understanding of the phenomenon. Practical implications: As private companies are typically more advanced in the implementation of AI technologies, the for-profit sector may provide significant contributions in the way states can leverage public services through the deployment of AI technologies. Originality / Value: This article highlights the need for states to create the necessary conditions to legislate and regulate key technological advances, which, in our opinion, has been done, but at a very slow pace.info:eu-repo/semantics/publishedVersio

Avaliação de sistemáticas para detecção de rupturas de estoque aplicadas no setor varejista

Rupturas de estoque geram perdas relevantes para o varejo e para indústria, mas apesar de serem objeto de estudos desde a década de 1960, a taxa de ruptura observada no varejo não tem diminuído consideravelmente. O primeiro passo para endereçar esse problema reside na correta medição e detecção de rupturas. Dessa forma, o objetivo deste artigo é avaliar e comparar o desempenho de dois métodos de detecção de ruptura parcial em gôndola, que têm como base a análise de dados de venda diária. Foi comparado o método proposto por Hausruckinger (2006) e uma adaptação menos suscetível à ocorrência de outliers. Empregando a simulação de Monte Carlo, foram simulados três cenários que modelam o nível de estoque em gôndola de um produto de alto giro ao longo do dia. A simulação gerou os dados utilizados nos métodos e permitiu avaliar seus desempenhos. Concluiu-se que a adaptação proposta teve um maior índice de detecção de rupturas do que o método de Hausruckinger (2006). Constatou-se ainda que essa categoria de método de detecção de ruptura se mostrou aplicável apenas a produto com baixa volatilidade de vendas. Por fim, recomenda-se sua utilização de forma complementar a outros métodos de detecção de ruptura

Essays on Predictive Analytics in E-Commerce

Die Motivation für diese Dissertation ist dualer Natur: Einerseits ist die Dissertation methodologisch orientiert und entwickelt neue statistische Ansätze und Algorithmen für maschinelles Lernen. Gleichzeitig ist sie praktisch orientiert und fokussiert sich auf den konkreten Anwendungsfall von Produktretouren im Onlinehandel. Die “data explosion”, veursacht durch die Tatsache, dass die Kosten für das Speichern und Prozessieren großer Datenmengen signifikant gesunken sind (Bhimani and Willcocks, 2014), und die neuen Technologien, die daraus resultieren, stellen die größte Diskontinuität für die betriebliche Praxis und betriebswirtschaftliche Forschung seit Entwicklung des Internets dar (Agarwal and Dhar, 2014). Insbesondere die Business Intelligence (BI) wurde als wichtiges Forschungsthema für Praktiker und Akademiker im Bereich der Wirtschaftsinformatik (WI) identifiziert (Chen et al., 2012). Maschinelles Lernen wurde erfolgreich auf eine Reihe von BI-Problemen angewandt, wie zum Beispiel Absatzprognose (Choi et al., 2014; Sun et al., 2008), Prognose von Windstromerzeugung (Wan et al., 2014), Prognose des Krankheitsverlaufs von Patienten eines Krankenhauses (Liu et al., 2015), Identifikation von Betrug Abbasi et al., 2012) oder Recommender-Systeme (Sahoo et al., 2012). Allerdings gibt es nur wenig Forschung, die sich mit Fragestellungen um maschinelles Lernen mit spezifischen Bezug zu BI befasst: Obwohl existierende Algorithmen teilweise modifiziert werden, um sie auf ein bestimmtes Problem anzupassen (Abbasi et al., 2010; Sahoo et al., 2012), beschränkt sich die WI-Forschung im Allgemeinen darauf, existierende Algorithmen, die für andere Fragestellungen als BI entwickelt wurden, auf BI-Fragestellungen anzuwenden (Abbasi et al., 2010; Sahoo et al., 2012). Das erste wichtige Ziel dieser Dissertation besteht darin, einen Beitrag dazu zu leisten, diese Lücke zu schließen. Diese Dissertation fokussiert sich auf das wichtige BI-Problem von Produktretouren im Onlinehandel für eine Illustration und praktische Anwendung der vorgeschlagenen Konzepte. Viele Onlinehändler sind nicht profitabel (Rigby, 2014) und Produktretouren sind eine wichtige Ursache für dieses Problem (Grewal et al., 2004). Neben Kostenaspekten sind Produktretouren aus ökologischer Sicht problematisch. In der Logistikforschung ist es weitestgehend Konsens, dass die “letzte Meile” der Zulieferkette, nämlich dann wenn das Produkt an die Haustür des Kunden geliefert wird, am CO2-intensivsten ist (Browne et al., 2008; Halldórsson et al., 2010; Song et al., 2009). Werden Produkte retourniert, wird dieser energieintensive Schritt wiederholt, wodurch sich die Nachhaltigkeit und Umweltfreundlichkeit des Geschäftsmodells von Onlinehändlern relativ zum klassischen Vertrieb reduziert. Allerdings können Onlinehändler Produktretouren nicht einfach verbieten, da sie einen wichtigen Teil ihres Geschäftsmodells darstellen: So hat die Möglichkeit, Produkte zu retournieren positive Auswirkungen auf Kundenzufriedenheit (Cassill, 1998), Kaufverhalten (Wood, 2001), künftiges Kaufverhalten (Petersen and Kumar, 2009) und emotianale Reaktionen der Kunden (Suwelack et al., 2011). Ein vielversprechender Ansatz besteht darin, sich auf impulsives und kompulsives (LaRose, 2001) sowie betrügerisches Kaufverhalten zu fokussieren (Speights and Hilinski, 2005; Wachter et al., 2012). In gegenwärtigen akademschen Literatur zu dem Thema gibt es keine solchen Strategien. Die meisten Strategien unterscheiden nicht zwischen gewollten und ungewollten Retouren (Walsh et al., 2014). Das zweite Ziel dieser Dissertation besteht daher darin, die Basis für eine Strategie von Prognose und Intervention zu entwickeln, mit welcher Konsumverhalten mit hoher Retourenwahrscheinlichkeit im Vorfeld erkannt und rechtzeitig interveniert werden kann. In dieser Dissertation werden mehrere Prognosemodelle entwickelt, auf Basis welcher demonstriert wird, dass die Strategie, unter der Annahme moderat effektiver Interventionsstrategien, erhebliche Kosteneinsparungen mit sich bringt

New Concepts for Efficient Consumer Response in Retail Influenced by Emerging Technologies and Innovations

The retail industry is continuously confronted with new challenges and experiences a transformation from a supplier’s market to a buyer's market. It is, thus, essential for the retail industry to consequently focus on, anticipate and fulfil consumer’s demands. Technologies and innovative business solutions can help to support to establish a required customer experience and, thereby, gain a competitive advantage. A multitude of new services and products, channels as well as players can already be identified which drive the transformation. Therefore, retailers need to understand current trends and technologies and identify as well as implement relevant solutions for their transformation since otherwise, new players will dominate the market. Hence, this dissertation aims to review and analyse new technologies which are coupled with innovative business activities in order to provide customer-centric retailing. For this purpose, this dissertation consists of five articles and derives four major contributions which introduce different approaches to establishing consumer satisfaction. Firstly, a core technology for retail is artificial intelligence (AI) which can be meaningful applied along the entire value chain and improve retailers’ positions. Two focus areas have been identified in this context which are (i) the optimisation of the entire retail value chain with the help of AI with the aim to derive transparency and (ii) the improvement of consumer satisfaction and relationship. Secondly, focussing on the consumer-retailer relationship in the digital era, a concept with a data architecture is proposed based on a real use case. The outcome was that a specific customer orientation based on data can increase the brand value and sales volume. Thirdly, the work presents that new shopping concepts, named unmanned store concepts, gain continuous growth. Unmanned store concepts employ a variety of new technologies, are characterised by attributes of speed, ease, as well as comfort, and are deemed to be the new ideal of the expectations of modern buyers. Two different directions have been deeper analysed: (i) walk-in stores and (ii) automated vending machines. The critical success factors for the usage of unmanned store solutions are distance as well as high consumer affinity for innovations. In times of the COVID-19 pandemic, which has a huge impact on retail, a continuous innovation capability still needs to be established. Finally, this work introduces a tool for systematic innovation management considering the current circumstances. Taken as a whole, this dissertation with its five articles deals with significant research questions which have not been approached so far. Thereby, the literature is extended by the introduction of novel insights and the provision of a deeper understanding of how retailers can transform their business into a more consumer-oriented way

Managing out-of-stocks and over-stock occurrences in supermarket stores: a case study in Singapore

Despite over 40 years of research on out-of-stock (OOS) and over-stock (OS) occurrences, OOS rates remain at an average of 8%. Further, while the store has been found to be a major contributor to OOS situations, it continues to remain a ‘black-box’ in OOS research, especially at the operational level. This thesis examines how supermarket stores execute in-store processes to manage OOS and OS events before, during and after their occurrences. It adopted the case study approach to investigate four specific in-store operations practices – planning and ordering, receiving and checkout, storage, and shelf replenishment - of 19 stores of a major supermarket chain in Singapore. Using semi-structured interviews supplemented by unobtrusive on-site observations of live in-store processes, this study found that OOS and OS occurrences were generally attributable to mismanagement of logistical processes, especially failure to deal with trivial operational issues and minor human errors on-time. Store managers’ attitudes toward enforcement of standard operations procedures (SOPs) also played a significant role in minimizing OOS and OS occurrences in-store. Contrasting the manner in which low-OOS and high-OOS stores handled OOS and OS events, this study unearthed five specific approaches the case supermarket stores used, depending on the in-store retailing dynamics at the time and store management’s knowledge of the causes of their occurrence. From a theoretical perspective, findings from this study have provided a theoretical thread, linking the relationships between store management commitment toward OOS and OS events and OOS and OS performance. They also bring many of the well-documented OOS and OS measures from a broad strategic dimension to the detailed operational level. From a practical standpoint, these findings offer four major Despite over 40 years of research on out-of-stock (OOS) and over-stock (OS) occurrences, OOS rates remain at an average of 8%. Further, while the store has been found to be a major contributor to OOS situations, it continues to remain a ‘black-box’ in OOS research, especially at the operational level. This thesis examines how supermarket stores execute in-store processes to manage OOS and OS events before, during and after their occurrences. It adopted the case study approach to investigate four specific in-store operations practices – planning and ordering, receiving and checkout, storage, and shelf replenishment - of 19 stores of a major supermarket chain in Singapore. Using semi-structured interviews supplemented by unobtrusive on-site observations of live in-store processes, this study found that OOS and OS occurrences were generally attributable to mismanagement of logistical processes, especially failure to deal with trivial operational issues and minor human errors on-time. Store managers’ attitudes toward enforcement of standard operations procedures (SOPs) also played a significant role in minimizing OOS and OS occurrences in-store. Contrasting the manner in which low-OOS and high-OOS stores handled OOS and OS events, this study unearthed five specific approaches the case supermarket stores used, depending on the in-store retailing dynamics at the time and store management’s knowledge of the causes of their occurrence. From a theoretical perspective, findings from this study have provided a theoretical thread, linking the relationships between store management commitment toward OOS and OS events and OOS and OS performance. They also bring many of the well-documented OOS and OS measures from a broad strategic dimension to the detailed operational level. From a practical standpoint, these findings offer four major sets of best-practice guidelines on OOS and OS management that relates to the role of store managers, adherence to SOPs, supplier relationship management and effects of contextual factors