Sales forecasting of stores in shopping malls: A study based on external data and transaction data

To improve the forecast accuracy of the sales of stores in shopping malls, this paper proposes a prediction method based on deep learning that comprehensively considers the external data, such as online review data of shopping mall stores, weather data, weekday/weekend data, and historical transaction data of the stores. To begin with, the online review data of the stores are pre-trained with BERT (Bidirectional Encoder Representations from Transformers) to complete the multi-label sentiment classification and obtain the intensity index of perceived sentiment of reviews. The index, together with other external data, such as online ratings, weather, weekday/weekend differences, and historical transactions of the stores, is pre-processed. At last, the Long Short-Term Memory (LSTM) and the Attention models are used to predict the sales volume of stores in a certain shopping mall. The results show that the addition of external data – weather, weekday/weekend, online ratings and intensity index of sentiment of reviews – to the historical sales data-based model can effectively improve the forecast accuracy of store sales

Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques

This article intends to systematically identify and comparatively analyze state-of-the-art supply chain (SC) forecasting strategies and technologies. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efficiency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning). The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research

Improving Demand Forecasting: The Challenge of Forecasting Studies Comparability and a Novel Approach to Hierarchical Time Series Forecasting

Bedarfsprognosen sind in der Wirtschaft unerlässlich. Anhand des erwarteten Kundenbe-darfs bestimmen Firmen beispielsweise welche Produkte sie entwickeln, wie viele Fabri-ken sie bauen, wie viel Personal eingestellt wird oder wie viel Rohmaterial geordert wer-den muss. Fehleinschätzungen bei Bedarfsprognosen können schwerwiegende Auswir-kungen haben, zu Fehlentscheidungen führen, und im schlimmsten Fall den Bankrott einer Firma herbeiführen. Doch in vielen Fällen ist es komplex, den tatsächlichen Bedarf in der Zukunft zu antizipie-ren. Die Einflussfaktoren können vielfältig sein, beispielsweise makroökonomische Ent-wicklung, das Verhalten von Wettbewerbern oder technologische Entwicklungen. Selbst wenn alle Einflussfaktoren bekannt sind, sind die Zusammenhänge und Wechselwirkun-gen häufig nur schwer zu quantifizieren. Diese Dissertation trägt dazu bei, die Genauigkeit von Bedarfsprognosen zu verbessern. Im ersten Teil der Arbeit wird im Rahmen einer überfassenden Übersicht über das gesamte Spektrum der Anwendungsfelder von Bedarfsprognosen ein neuartiger Ansatz eingeführt, wie Studien zu Bedarfsprognosen systematisch verglichen werden können und am Bei-spiel von 116 aktuellen Studien angewandt. Die Vergleichbarkeit von Studien zu verbes-sern ist ein wesentlicher Beitrag zur aktuellen Forschung. Denn anders als bspw. in der Medizinforschung, gibt es für Bedarfsprognosen keine wesentlichen vergleichenden quan-titativen Meta-Studien. Der Grund dafür ist, dass empirische Studien für Bedarfsprognosen keine vereinheitlichte Beschreibung nutzen, um ihre Daten, Verfahren und Ergebnisse zu beschreiben. Wenn Studien hingegen durch systematische Beschreibung direkt miteinan-der verglichen werden können, ermöglicht das anderen Forschern besser zu analysieren, wie sich Variationen in Ansätzen auf die Prognosegüte auswirken – ohne die aufwändige Notwendigkeit, empirische Experimente erneut durchzuführen, die bereits in Studien beschrieben wurden. Diese Arbeit führt erstmals eine solche Systematik zur Beschreibung ein. Der weitere Teil dieser Arbeit behandelt Prognoseverfahren für intermittierende Zeitreihen, also Zeitreihen mit wesentlichem Anteil von Bedarfen gleich Null. Diese Art der Zeitreihen erfüllen die Anforderungen an Stetigkeit der meisten Prognoseverfahren nicht, weshalb gängige Verfahren häufig ungenügende Prognosegüte erreichen. Gleichwohl ist die Rele-vanz intermittierender Zeitreihen hoch – insbesondere Ersatzteile weisen dieses Bedarfs-muster typischerweise auf. Zunächst zeigt diese Arbeit in drei Studien auf, dass auch die getesteten Stand-der-Technik Machine Learning Ansätze bei einigen bekannten Datensät-zen keine generelle Verbesserung herbeiführen. Als wesentlichen Beitrag zur Forschung zeigt diese Arbeit im Weiteren ein neuartiges Verfahren auf: Der Similarity-based Time Series Forecasting (STSF) Ansatz nutzt ein Aggregation-Disaggregationsverfahren basie-rend auf einer selbst erzeugten Hierarchie statistischer Eigenschaften der Zeitreihen. In Zusammenhang mit dem STSF Ansatz können alle verfügbaren Prognosealgorithmen eingesetzt werden – durch die Aggregation wird die Stetigkeitsbedingung erfüllt. In Expe-rimenten an insgesamt sieben öffentlich bekannten Datensätzen und einem proprietären Datensatz zeigt die Arbeit auf, dass die Prognosegüte (gemessen anhand des Root Mean Square Error RMSE) statistisch signifikant um 1-5% im Schnitt gegenüber dem gleichen Verfahren ohne Einsatz von STSF verbessert werden kann. Somit führt das Verfahren eine wesentliche Verbesserung der Prognosegüte herbei. Zusammengefasst trägt diese Dissertation zum aktuellen Stand der Forschung durch die zuvor genannten Verfahren wesentlich bei. Das vorgeschlagene Verfahren zur Standardi-sierung empirischer Studien beschleunigt den Fortschritt der Forschung, da sie verglei-chende Studien ermöglicht. Und mit dem STSF Verfahren steht ein Ansatz bereit, der zuverlässig die Prognosegüte verbessert, und dabei flexibel mit verschiedenen Arten von Prognosealgorithmen einsetzbar ist. Nach dem Erkenntnisstand der umfassenden Literatur-recherche sind keine vergleichbaren Ansätze bislang beschrieben worden

Suitable demand forecasting method for stock quantity optimization in the food industry

The goal of this master's thesis is to answer the research question, which is “what is the suitable way for the food industry to make demand forecasting?”. In this research, various demand forecasting methods were compared using a dataset from the food industry. These methods included Facebook Prophet, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and 90 days average, which is the research baseline. Forecasting methods are compared using Root Mean square error (RMSE) calculation. The results showed that the 90-day average method performed the best in accuracy. However, it is essential to note that other factors, such as data collection and unusual demand changes, can impact the effectiveness of demand forecasting in the food industry. The study also discussed the importance of inventory management in the food industry and the impact of stock quantity optimization, including using demand forecasts to optimize stock quantities. Overall, this research provides insights into demand forecasting in the food industry

Designing an On-Demand Dynamic Crowdshipping Model and Evaluating its Ability to Serve Local Retail Delivery in New York City

Nowadays city mobility is challenging, mainly in populated metropolitan areas. Growing commute demands, increase in the number of for-hire vehicles, enormous escalation in several intra-city deliveries and limited infrastructure (road capacities), all contribute to mobility challenges. These challenges typically have significant impacts on residents’ quality-of-life particularly from an economic and environmental perspective. Decision-makers have to optimize transportation resources to minimize the system externalities (especially in large-scale metropolitan areas). This thesis focus on the intra-city mobility problems experienced by travelers (in the form of congestion and imbalance taxi resources) and businesses (in the form of last-mile delivery), while taking into consideration a measurement of potential adoption by citizens (in the form of a survey). To find solutions for this mobility problem this dissertation proposes three distinct and complementary methodological studies. First, taxi demand is predicted by employing a deep learning approach that leverages Long Short-Term Memory (LSTM) neural networks, trained over publicly available New York City taxi trip data. Taxi pickup data are binned based on geospatial and temporal informational tags, which are then clustered using a technique inspired by Principal Component Analysis. The spatiotemporal distribution of the taxi pickup demand is studied within short-term periods (for the next hour) as well as long-term periods (for the next 48 hours) within each data cluster. The performance and robustness of the LSTM model are evaluated through a comparison with Adaptive Boosting Regression and Decision Tree Regression models fitted to the same datasets. On the next study, an On-Demand Dynamic Crowdshipping system is designed to utilize excess transport capacity to serve parcel delivery tasks and passengers collectively. This method is general and could be expanded and used for all types of public transportation modes depending upon the availability of data. This system is evaluated for the case study of New York City and to assess the impacts of the crowdshipping system (by using taxis as carriers) on trip cost, vehicle miles traveled, and people travel behavior. Finally, a Stated Preference (SP) survey is presented, designed to collect information about people’s willingness to participate in a crowdshipping system. The survey is analyzed to determine the essential attributes and evaluate the likelihood of individuals participating in the service either as requesters or as carriers. The survey collects information on the preferences and important attributes of New York citizens, describing what segments of the population are willing to participate in a crowdshipping system. While the transportation problems are complex and approximations had to be done within the studies to achieve progress, this dissertation provides a comprehensive way to model and understand the potential impact of efficient utilization of existing resources on transportation systems. Generally, this study offer insights to decisions makers and academics about potential areas of opportunity and methodologies to optimize the transportation system of densely populated areas. This dissertation offers methods that can optimize taxi distribution based on the demand, optimize costs for retail delivery, while providing additional income for individuals. It also provides valuable insights for decision makers in terms of collecting population opinion about the service and analyzing the likelihood of participating in the service. The analysis provides an initial foundation for future modeling and assessment of crowdshipping

The Use of Recurrent Nets for the Prediction of e-Commerce Sales

The increase in e-commerce sales and profits has been a source of much anxiety over the years. Due to the advances in Internet technology, more and more people choose to shop online. Online retailers can improve customer satisfaction using sentiment analysis in comments and reviews to gain higher profits. This study used Recurrent Neural Networks (RNNs) to predict future sales from previous using the Kaggle dataset. A Bidirectional Long Short Term Memory (BLTSM) RNN was employed by tuning various hyperparameters to improve accuracy. The results showed that this BLTSM model of the RNN was quite accurate at predicting future sales performance

Visual Communication and Fashion Popularity Contagion in Social Networks

Fast fashion has emerged as a prevalent retail strategy shaping fashion popularity. However, due to the lack of historical records and the dynamics of fashion trends, existing demand prediction methods do not apply to new-season fast fashion sales forecasting. We draw on the Social Contagion Theory to conceptualize a sales prediction framework for fast fashion new releases. We posit that fashion popularity contagion comes from Source Contagion and Media Contagion, which refer to the inherent infectiousness of fashion posts and the popularity diffusion in social networks, respectively. We consider fashion posts as the contagion source that visually attracts social media users with images of fashion products. Graph Convolutional Network is developed to model the dynamic fashion contagion process in the topology structure of social networks. This theory-based deep learning method can incorporate the latest social media activities to offset the deficiency of historical fashion data in new seasons

Deep learning macroeconomics

Limited datasets and complex nonlinear relationships are among the challenges that may emerge when applying econometrics to macroeconomic problems. This research proposes deep learning as an approach to transfer learning in the former case and to map relationships between variables in the latter case. Several machine learning techniques are incorporated into the econometric framework, but deep learning remains focused on time-series forecasting. Firstly, transfer learning is proposed as an additional strategy for empirical macroeconomics. Although macroeconomists already apply transfer learning when assuming a given a priori distribution in a Bayesian context, estimating a structural VAR with signal restriction and calibrating parameters based on results observed in other models, to name a few examples, advance in a more systematic transfer learning strategy in applied macroeconomics is the innovation we are introducing. When developing economics modeling strategies, the lack of data may be an issue that transfer learning can fix. We start presenting theoretical concepts related to transfer learning and proposed a connection with a typology related to macroeconomic models. Next, we explore the proposed strategy empirically, showing that data from different but related domains, a type of transfer learning, helps identify the business cycle phases when there is no business cycle dating committee and to quick estimate an economic-based output gap. In both cases, the strategy also helps to improve the learning when data is limited. The approach integrates the idea of storing knowledge gained from one region’s economic experts and applying it to other geographic areas. The first is captured with a supervised deep neural network model, and the second by applying it to another dataset, a domain adaptation procedure. Overall, there is an improvement in the classification with transfer learning compared to baseline models. To the best of our knowledge, the combined deep and transfer learning approach is underused for application to macroeconomic problems, indicating that there is plenty of room for research development. Secondly, since deep learning methods are a way of learning representations, those that are formed by the composition of multiple non-linear transformations, to yield more abstract representations, we apply deep learning for mapping low-frequency from high-frequency variables. There are situations where we know, sometimes by construction, that there is a relationship be-tween input and output variables, but this relationship is difficult to map, a challenge in which deep learning models have shown excellent performance. The results obtained show the suitability of deep learning models applied to macroeconomic problems. Additionally, deep learning proved adequate for mapping low-frequency variables from high-frequency data to interpolate, distribute, and extrapolate time series by related series. The application of this technique to Brazilian data proved to be compatible with benchmarks based on other techniques.Conjuntos de dados limitados e complexas relações não-lineares estão entre os desafios que podem surgir ao se aplicar econometria a problemas macroeconômicos. Esta pesquisa propõe aprendizagem profunda como uma abordagem para transferir aprendizagem no primeiro caso e para mapear relações entre variáveis no último caso. Várias técnicas de aprendizado de máquina estão incorporadas à estrutura econométrica, mas o aprendizado profundo continua focado na previsão de séries temporais. Primeiramente, aprendizagem por transferência é proposta como uma estratégia adicional para a macroeconomia empírica. Embora os macroeconomistas já apliquem aprendizagem por transferência ao assumir uma dada distribuição a priori em um contexto Bayesiano, estimar um VAR estrutural com restrição de sinal e calibrar parâmetros com base em resultados observados em outros modelos, para citar alguns exemplos, avançar em uma estratégia mais sistemática de transferência de aprendizagem em macroeconomia aplicada é a inovação que estamos introduzindo. Ao desenvolver estratégias de modelagem econômica, a falta de dados pode ser um problema que aprendizagem por transferência pode corrigir. Começamos por apresentar conceitos teóricos relacionados à transferência de aprendizagem e propomos uma conexão com uma tipologia relacionada a modelos macroeconômicos. Em seguida, exploramos a estratégia proposta empiricamente, mostrando que os dados de domínios diferentes, mas relacionados, um tipo de aprendizagem por transferência, ajudam a identificar as fases do ciclo de negócios quando não há comitê de datação do ciclo de negócios e a estimar rapidamente um hiato do produto de base econômica. Em ambos os casos, a estratégia também ajuda a melhorar o aprendizado quando os dados são limitados. A abordagem integra a ideia de armazenar conhecimento obtido de especialistas em economia de uma região e aplicá-lo a outras áreas geográficas. O primeiro é capturado com um modelo de rede neural profunda supervisionado e o segundo aplicando-o a outro conjunto de dados, um procedimento de adaptação de domínio. No geral, há uma melhora na classificação com a aprendizagem por transferência em comparação com os modelos de base. Até onde sabemos, a abordagem combinada de aprendizagem profunda e transferência é subutilizada para aplicação a problemas macroeconômicos, indicando que há muito espaço para o desenvolvimento de pesquisas. Em segundo lugar, uma vez que os métodos de aprendizagem profunda são uma forma de aprender representações, aquelas que são formadas pela composição de várias transformações não lineares, para produzir representações mais abstratas, aplicamos aprendizagem profunda para mapear variáveis de baixa frequência a partir de variáveis de alta frequência. Há situações em que sabemos, às vezes por construção, que existe uma relação entre as variáveis de entrada e saída, mas essa relação é difícil de mapear, um desafio no qual os modelos de aprendizagem profunda têm apresentado excelente desempenho. Os resultados obtidos mostram a adequação de modelos de aprendizagem profunda aplicados a problemas macroeconômicos. Além disso, o aprendizado profundo se mostrou adequado para mapear variáveis de baixa frequência a partir de dados de alta frequência para interpolar, distribuir e extrapolar séries temporais por séries relacionadas. A aplicação dessa técnica em dados brasileiros mostrou-se compatível com benchmarks baseados em outras técnicas