Machine learning applications in operations management and digital marketing

In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2

Special Libraries, July 1980

Volume 71, Issue 7https://scholarworks.sjsu.edu/sla_sl_1980/1005/thumbnail.jp

Analysing and Predicting Invoice Payment in Finnish Road Freight Transportation Industry

This thesis examines the relationship between diesel prices, 12-month Euribor interest rates, and bankruptcies of road freight transportation companies and their effects on invoice payment delays in a sample of road freight transportation companies. This investigation is relevant to the road freight transportation industry, as the changes in diesel prices and interest rates can significantly impact the transportation companies’ cost structures. The sample was collected from a heavy equipment spare parts retailer and contained information on over 70 000 invoices. The work consists of a literature review and two quantitative methods, time series regression model and binary classification models. The time series regression model looks at 92 months from 2015 to 2022. The independent variables are monthly average diesel prices, 12-month Euribor interest rates and bankruptcies, and the dependent variable is the average monthly payment delay in days. Binary classification models predict whether an invoice will be paid on time or late. These models include logistic regression and k-nearest neighbors. Overall, the time series regression model is statistically significant. From the individual coefficients, diesel prices were the most significant predictor, suggesting that they impact payment delays the most. Euribor interest rates and bankruptcies were not found significant at the 0.05 level. Furthermore, the analysis revealed an unexpected relationship where a decrease in diesel prices leads to longer delays in payments. Additionally, the binary classification results show that the k-nearest neighbors’ method with a small hyperparameter k value and the logistic regression model were effective in predicting late payments

Agile Market Engineering: Bridging the gap between business concepts and running markets

The agile market engineering process model (AMEP) is built on the insight, that market design and development is a wicked problem. Electronic markets are too complex to be completely designed upfront. Instead, AMEP tries to bridge the gap between theoretic market design and practical electronic market platform development using an agile, iterative approach that relies on early customer feedback and continuous improvement. The AMEP model is complemented by several supporting software artifacts

AI, Robotics, and the Future of Jobs

This report is the latest in a sustained effort throughout 2014 by the Pew Research Center's Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-Lee (The Web at 25).The report covers experts' views about advances in artificial intelligence (AI) and robotics, and their impact on jobs and employment

Analysis of monthly payment delays using machine learning

Η πρόβλεψη της καθυστέρησης πληρωμής των μηνιαίων τιμολογίων πελατών με συμβόλαια μακροχρόνιας δέσμευσης ή μακροχρόνια συνεργασία, βρίσκει εφαρμογή στον χρηματοοικονομικό σχεδιασμό, στην πρόβλεψη της ρευστότητας, στην επιλογή στρατηγικής για μείωση των απωλειών καθώς και γενικότερα στην αναδοχή επιχειρηματικών απαιτήσεων (factoring). Ειδικά για τις μικρομεσαίες επιχειρήσεις, έχει εκτιμηθεί πως έως και τα μισά τιμολόγια εξοφλούνται με καθυστέρηση, δημιουργώντας έτσι σημαντικό πρόβλημα. Η κατηγοριοποίηση (classification) ως προς την αναμενόμενη καθυστέρηση πληρωμής σε συνδυασμό με την εκτιμώμενη πιθανότητα αυτού του γεγονότος, επιτρέπουν την κατάταξη των πελατών ως προς τον κίνδυνο απωλειών. Ο τύπος των προϊόντων ή υπηρεσιών που προσφέρονται από την επιχείρηση, συνήθως επηρεάζει τα διαθέσιμα χαρακτηριστικά, τα οποία κατ’επέκταση αποκτούν διαφορετική βαρύτητά στη διαδικασία πρόβλεψης ενώ συχνά ο όγκος των συνολικών δεδομένων που συλλέγονται για τους πελάτες είναι τεράστιος, κατανεμημένος σε διαφορετικές βάσεις και με διαβαθμιζόμενη ποιότητα. Στην παρούσα πτυχιακή, ελέγχεται η αποτελεσματικότητα πρόβλεψης τόσο της κλάσης (πληρωμή με καθυστέρηση ή χωρίς καθυστέρηση) όσο και των ημερών που μεσολαβούν από την έκδοση του λογαριασμού έως την πληρωμή του, αξιοποιώντας ελάχιστα χαρακτηριστικά από το τρέχον τιμολόγιο και το ιστορικό των πελατών. Από αυτά, παράγονται πρόσθετα χαρακτηριστικά που συνοψίζουν το προφίλ του πελάτη έως τη δεδομένη στιγμή και πρόσφατες τάσεις, χωρίς να περιλαμβάνεται οποιαδήποτε πληροφορία δεν είναι γνωστή κατά τη στιγμή έκδοσης του λογαριασμού. Έτσι, το ενδιαφέρον εστιάζεται στη συμπεριφορά των πελατών χωρίς αυστηρή χρονική συνιστώσα, όπως στις κλασικές χρονοσειρές. Αρχικά, αξιολογούνται βασικοί αλγόριθμοι μηχανικής μάθησης που συναντώνται συχνά σε σχετικές εφαρμογές στη βιβλιογραφία και στη συνέχεια ελέγχονται μέθοδοι συνολικής μάθησης (ensemble learning), αξιοποιώντας τα βασικά μοντέλα. Τέλος, η αποτελεσματικότητά τους συγκρίνεται και με μοντέλα που χρησιμοποιούν κλασικές χρονοσειρές.The prediction of the delay of monthly payments concerning long-term customers with or without contracts, is valuable to financial planning, cash-flow forecasting, making strategic choices to reduce losses and factoring in general. Especially for small and medium enterprises, it has been estimated that up to half of theιρ invoices are paid late, thus creating a significant problem. The estimation of the expected delay combined with the corresponding probability, allows the ranking of customers according to the risk of loss. Usually, the type of product or service offered by the company, affects the available features, which consequently have different importance in the prediction process. Additionally, often the volume of data collected from the customers is huge and they are distributed on different databases and have varying quality. In this thesis, both classification (late, non-late) and regression models (days until the bill is settled) are evaluated, using minimal information from the current bill and the customer’s history. Furthermore, additional features are generated that summarize the customer’s profile up to a specific date and capture recent trends, without including any information not known at the time of issuing the bill. Thus, the focus is on the customer’s behaviour without a strict time component, as in the classic time-series. Initially, basic machine learning algorithms that are often encountered in relevant applications in the literature are evaluated and then ensemble learning methods are tested, utilizing the basic models. Finally, their performance is compared to that of models that use classic time-series

Taxation in the Digital Economy

A robust and efficient tax administration in a modern tax system requires effective tax policies and legislation. Policy frameworks should cover all aspects of tax administration and include the essential processes of capturing, processing, analyzing, and responding to information provided by taxpayers and others concerning taxpayers’ affairs. By far the greatest challenges facing tax administrations in all countries are those posed by the continuing developments in the digital economy. Whereas societies are grappling to come to terms with the transitions from the third industrial or digital revolutions, revenue authorities grapple with the consequences for the sustainability of their tax bases and the efficient administration and collection of taxes. This book presents a critical review of the status of tax systems in Asia and the Pacific in the era of the digital economy. The book suggests how countries can maximize their domestic resource mobilization when confronted by the challenges that digitalization inevitably produces, as well as how they can best harness or take advantage of aspects of digitalization to serve their own needs. The full implications of the COVID-19 crisis are still too uncertain to predict, but it is clear that the crisis will accelerate the trend towards digitalization and also increase pressures on public finances. This, in turn, may shape the preference for, and the nature of, both multilateral and unilateral responses to the tax challenges posed by digitalization and the need to address them. This book will be a timely reference for those researching on taxation in digital economy and for policy makers.

Healthcare Access

Adequate healthcare access not only requires the availability of comprehensive healthcare facilities but also affordability and knowledge of the availability of these services. As an extended responsibility, healthcare providers can create mechanisms to facilitate subjective decision-making in accessing the right kind of healthcare services as well various options to support financial needs to bear healthcare-related expenses while seeking health and fulfilling the healthcare needs of the population. This volume brings together experiences and opinions from global leaders to develop affordable, sustainable, and uniformly available options to access healthcare services