Demand Estimation Under Incomplete Product Availability


Book Description

Incomplete product availability arising from stock-out events and capacity constraints is a common and important feature of many markets. Periods of unavailability censor the observed sales for the affected product, and potentially increase observed sales of available substitutes. As a result, failing to adjust for incomplete product availability can lead to biased demand estimates. Common applications of these demand estimates, such as computing welfare effects from mergers or new products, are therefore unreliable in such settings. These issues are likely to arise in many industries, from retail to sporting events to airlines. In this paper, we study a new dataset from a wireless inventory management systems, which was installed on a set of 54 vending machines in order to track product availability at high frequency (roughly every four hours). These data allow us to account for product availability when estimating demand, and introduces a valuable source of variation for identifying substitution patterns. We also develop a simple procedure that allows for changes in product availability even when we only observe inventory (and thus availability) periodically. We find significant differences in the parameter estimates in demand, and as a result, the corrected model predicts significantly larger impacts of stock-outs on profitability.




Applying Data Science


Book Description

See how data science can answer the questions your business faces! Applying Data Science: Business Case Studies Using SAS, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions. In eight entertaining and real-world case studies, Svolba combines data science and advanced analytics with business questions, illustrating them with data and SAS code. The case studies range from a variety of fields, including performing headcount survival analysis for employee retention, forecasting the demand for new projects, using Monte Carlo simulation to understand outcome distribution, among other topics. The data science methods covered include Kaplan-Meier estimates, Cox Proportional Hazard Regression, ARIMA models, Poisson regression, imputation of missing values, variable clustering, and much more! Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, Applying Data Science bridges the gap between high-level, business-focused books that skimp on the details and technical books that only show SAS code with no business context.




Operational Research in Business and Economics


Book Description

This book gathers a selection of refereed papers presented at the 4th International Symposium and 26th National Conference of the Hellenic Operational Research Society. It highlights recent scientific advances in operational research and management science (OR/MS), with a focus on linking OR/MS with other areas of quantitative methods in a multidisciplinary framework. Topics covered include areas such as business process modeling, supply chain management, organization performance and strategy planning, revenue management, financial applications, production planning, metaheuristics, logistics, inventory systems, and energy systems.




Handbook on the Economics of Retailing and Distribution


Book Description

This Handbook explores and critically examines current research in economics and marketing science on key issues in retailing and distribution. Providing a rich perspective for the discussion of public policy, contributions from several disciplines and continents range from the history of chains and the impact of multinational retailers on international trade patterns to US merger policy in the retail context, the rise of the Internet, and consumer-to-consumer sales. The chapters address methodological issues such as the structural estimation of entry games between retailers, productivity measurement when both inputs and output are not fully observable, and demand estimation with variable assortment. Policy issues explored include mergers, zoning, and the regulation of buyer power, while other chapters address some of the recent exciting developments in technology, retail formats, and data availability. The book goes on to study the changes in online retailing and ‘big data’, and to examine competition in specific retail sectors including gasoline stations, automobile dealerships, supermarkets, and ‘big box’ retail. This state-of-the-art Handbook is an essential reference for students and academics of economics and marketing science, and offers an outsider’s perspective to specialists in operations research, data analytics, geography, and sociology.




DEMANDA FORECASTING


Book Description

In this transformative book, delve deep into the world of demand forecasting enhanced by artificial intelligence and machine learning, where every decision is based on precise data and strategic insights. This essential resource is crafted for professionals seeking to master cutting-edge techniques, ensuring that your business not only adapts but thrives in a volatile and ever-evolving market. By exploring advanced forecasting methods, you will learn to identify hidden trends, optimize inventories, reduce costs, and avoid bottlenecks that often compromise operational efficiency. With practical and detailed examples, this guide offers a clear and actionable approach designed to elevate your expertise and position your company ahead of the competition. Ensure that every step you take is backed by robust analysis and accurate forecasts, transforming the way you conduct business and driving sustainable growth. This is the ultimate tool for any leader who wants to make informed decisions, mitigate risks, and maximize return on investment in an increasingly dynamic and challenging corporate environment. Keywords: demand forecasting artificial intelligence machine learning profit optimization inventory management cost minimization operational efficiency digital transformation Google AWS Microsoft IBM Oracle SAP Salesforce Tableau Power BI Python R Hadoop Spark IoT Big Data data analysis neural networks deep learning predictive algorithms technological innovation business transformation business competitiveness supply chain management trend analysis process optimization strategic decision making predictive models time series analysis random forests linear regression decision trees Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques cybersecurity skills cybersecurity industry global cybersecurity trends Kali Linux tools cybersecurity education cybersecurity innovation penetration test tools cybersecurity best practices global cybersecurity companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle cybersecurity consulting cybersecurity framework network security cybersecurity courses cybersecurity tutorials Linux security cybersecurity challenges cybersecurity landscape cloud security cybersecurity threats cybersecurity compliance cybersecurity research cybersecurity technology







Demand Forecasting and Order Planning in Supply Chains and Humanitarian Logistics


Book Description

In a decentralized supply chain, most of the supply chain agents may not share information due to confidentiality policies, quality of information, or different system incompatibilities. Every actor holds its own set of information and attempts to maximize its objective (minimizing costs/minimizing inventory holdings) based on the available settings. Therefore, the agents control their own activities with the objective of improving their own competitiveness, which leads them to make decisions that maximize their local performance by ignoring the other agents or even the final consumer. These decisions are myopic because they do not consider the performance of all the partners to satisfy the consumer. Demand Forecasting and Order Planning in Supply Chains and Humanitarian Logistics is a collection of innovative research that focuses on demand anticipation, forecasting, and order planning as well as humanitarian logistics to propose original solutions for existing problems. While highlighting topics including artificial intelligence, information sharing, and operations management, this book is ideally designed for supply chain managers, logistics personnel, business executives, management experts, operation industry professionals, academicians, researchers, and students who want to improve their understanding of supply chain coordination in order to be competitive in the new era of globalization.







QUANTITATIVE MODELS IN OPERATIONS AND SUPPLY CHAIN MANAGEMENT


Book Description

The thoroughly revised and updated book, now in its second edition, continues to present a comprehensive view of the concepts and applications of various quantitative models used in the study of operations and supply chain management. It provides a complete account of location and layout models, production planning models, production control models, cycle inventory models, safety stock models and transportation models. A separate chapter on real-life situations provides the user with the knowledge of specific areas where the models have been applied in decision-making processes. The various techniques to solve operations and supply chain management problems are also discussed. The text is supported by a large number of illustrative examples, exercises and review questions to reinforce the students’ understanding of the subject matter. Designed as a textbook for the students of mechanical and industrial engineering, the book would also be useful to postgraduate students of management. NEW TO THE SECOND EDITION • Two new chapters on ‘Production Control—Additional Approaches’ (Chapter 6) and ‘Materials Planning and Lot Sizing’ (Chapter 8) • Forecasting and Aggregate Planning are described in two separate chapters • Each chapter includes new sections, additional examples, illustrations, short questions and exercises • Provides solutions to the exercises




Retail Supply Chain Management


Book Description

In today's retail environment, characterized by product proliferation, price competition, expectations of service quality, and advances in technology, many organizations are struggling to maintain profitability. Rigorous analytical methods have emerged as the most promising solution to many of these complex problems. Indeed, the retail industry has emerged as a fascinating choice for researchers in the field of supply chain management. In Retail Supply Chain Management, leading researchers provide a detailed review of cutting-edge methodologies that address the complex array of these problems. A critical resource for researchers and practitioners in the field of retailing, chapters in this book focus on three key areas: (1) empirical studies of retail supply chain practices, (2) assortment and inventory planning, and (3) integrating price optimization into retail supply chain decisions.