Forecasting sales in the supply chain: Consumer analytics in the big data era

[1]  Nikolaos Kourentzes,et al.  Demand forecasting with user-generated online information , 2019, International Journal of Forecasting.

[2]  Tonya Boone,et al.  Can Google Trends Improve Your Sales Forecast? , 2018 .

[3]  Raymond Y. K. Lau,et al.  Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data , 2018 .

[4]  Antonio Moreno,et al.  The Operational Value of Social Media Information , 2018 .

[5]  Maxime C. Cohen Big Data and Service Operations , 2018 .

[6]  El-Houssaine Aghezzaf,et al.  Temporal Big Data for Tactical Sales Forecasting in the Tire Industry , 2018, Interfaces.

[7]  Q. Feng,et al.  How Research in Production and Operations Management May Evolve in the Era of Big Data , 2017 .

[8]  Giuseppe Nunnari,et al.  Forecasting Monthly Sales Retail Time Series: A Case Study , 2017, 2017 IEEE 19th Conference on Business Informatics (CBI).

[9]  Li Chen,et al.  Optimal Merchandise Testing with Limited Inventory , 2017, Oper. Res..

[10]  H. Hruschka Multicategory Purchase Incidence Models for Partitions of Product Categories , 2017 .

[11]  Gérard P. Cachon,et al.  The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity , 2016, Manuf. Serv. Oper. Manag..

[12]  David Simchi-Levi,et al.  Assortment Planning for Recommendations at Checkout under Inventory Constraints , 2016, Mathematics of Operations Research.

[13]  Cathy O'Neil,et al.  Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy , 2016, Vikalpa: The Journal for Decision Makers.

[14]  N. Sanders How to Use Big Data to Drive Your Supply Chain , 2016 .

[15]  Paul Smith,et al.  Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data , 2015 .

[16]  Mario Edgar Sanguinet Hashtags, tweets and movie receipts: Social media analytics in predicting box office hits , 2016 .

[17]  Roberto Rivera,et al.  A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data , 2015, 1512.08097.

[18]  Cathal Heavey,et al.  A framework for Collaborative Planning, Forecasting and Replenishment (CPFR): State of the Art , 2015, J. Enterp. Inf. Manag..

[19]  Dean Fantazzini,et al.  Forecasting German Car Sales Using Google Data and Multivariate Models , 2015 .

[20]  Prosper F. Bangwayo-Skeete,et al.  Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach , 2015 .

[21]  Jayashankar M. Swaminathan,et al.  Estimating the Impact of Understaffing on Sales and Profitability in Retail Stores , 2015 .

[22]  Garrett J. van Ryzin,et al.  A Market Discovery Algorithm to Estimate a General Class of Nonparametric Choice Models , 2015, Manag. Sci..

[23]  Tong Wang,et al.  Demand Estimation and Ordering Under Censoring: Stock-Out Timing Is (Almost) All You Need , 2015, Oper. Res..

[24]  Tonya Boone,et al.  Incorporating Google Trends Data Into Sales Forecasting , 2015 .

[25]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[26]  Ting Liu,et al.  Predicting movie Box-office revenues by exploiting large-scale social media content , 2014, Multimedia Tools and Applications.

[27]  Joseph K. Liu,et al.  Toward efficient and privacy-preserving computing in big data era , 2014, IEEE Network.

[28]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[29]  R. Ganeshan Clickstream Analysis for Forecasting Online Behavior , 2014 .

[30]  Michael J. Paul,et al.  National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic , 2013, PloS one.

[31]  E. Brynjolfsson,et al.  The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales , 2013, ICIS 2013.

[32]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[33]  Zhi Da,et al.  The Sum of All FEARS: Investor Sentiment and Asset Prices , 2013 .

[34]  V. Kumar,et al.  Practice Prize Winner - Creating a Measurable Social Media Marketing Strategy: Increasing the Value and ROI of Intangibles and Tangibles for Hokey Pokey , 2013, Mark. Sci..

[35]  Khim-Yong Goh,et al.  Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content , 2013, Inf. Syst. Res..

[36]  Yina Lu,et al.  Measuring the Effect of Queues on Customer Purchases , 2012, Manag. Sci..

[37]  Bing Pan,et al.  Forecasting hotel room demand using search engine data. , 2012 .

[38]  Ashwin Machanavajjhala,et al.  Big privacy: protecting confidentiality in big data , 2012, XRDS.

[39]  T. Rao,et al.  Analyzing Stock Market Movements Using Twitter Sentiment Analysis , 2012, ASONAM 2012.

[40]  G. Judge,et al.  Searching for the picture: forecasting UK cinema admissions using Google Trends data , 2012 .

[41]  Thomas Dimpfl,et al.  Can Internet Search Queries Help to Predict Stock Market Volatility? , 2012 .

[42]  Michael S. Drake,et al.  Investor Information Demand: Evidence from Google Searches Around Earnings Announcements , 2012 .

[43]  Jayashankar M. Swaminathan,et al.  Effect of Traffic on Sales and Conversion Rates of Retail Stores , 2012, Manuf. Serv. Oper. Manag..

[44]  Boris Otto,et al.  Organizing Data Governance: Findings from the Telecommunications Industry and Consequences for Large Service Providers , 2011, Commun. Assoc. Inf. Syst..

[45]  Kazutoshi Sumiya,et al.  Towards better TV viewing rates: exploiting crowd's media life logs over Twitter for TV rating , 2011, ICUIMC '11.

[46]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[47]  P. Gloor,et al.  Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear” , 2011 .

[48]  Nello Cristianini,et al.  Flu Detector - Tracking Epidemics on Twitter , 2010, ECML/PKDD.

[49]  Toon Calders,et al.  Three naive Bayes approaches for discrimination-free classification , 2010, Data Mining and Knowledge Discovery.

[50]  Matthias Bank,et al.  Google search volume and its influence on liquidity and returns of German stocks , 2010 .

[51]  J. Aucott,et al.  The utility of "Google Trends" for epidemiological research: Lyme disease as an example. , 2010, Geospatial health.

[52]  Eric T. Bradlow,et al.  Structural Estimation of the Effect of Out-of-Stocks , 2010, Manag. Sci..

[53]  Eleonora Bottani,et al.  The Benefits of RFID and EPC in the Supply Chain: Lessons from an Italian Pilot Study , 2010 .

[54]  Eric T. Bradlow,et al.  Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior , 2009 .

[55]  Jennifer L. Castle,et al.  Nowcasting is not Just Contemporaneous Forecasting , 2009, National Institute Economic Review.

[56]  Hyun-young Choi,et al.  Predicting Initial Claims for Unemployment Benefits , 2009 .

[57]  Philip Hans Franses,et al.  Do experts' adjustments on model-based SKU-level forecasts improve forecast quality? , 2009 .

[58]  Zhi Da,et al.  In Search of Attention , 2009 .

[59]  Peter S. Fader,et al.  Research Note - The Traveling Salesman Goes Shopping: The Systematic Deviations of Grocery Paths from TSP Optimality , 2009, Mark. Sci..

[60]  H. Varian,et al.  Predicting the Present with Google Trends , 2009 .

[61]  Barış Tan,et al.  A method for estimating stock-out-based substitution rates by using point-of-sale data , 2009 .

[62]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[63]  Mark Ferguson,et al.  A Comparison of Unconstraining Methods to Improve Revenue Management Systems , 2009 .

[64]  R. Fildes,et al.  Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning , 2009 .

[65]  Hernán A. Bruno,et al.  Research Note - Structural Demand Estimation with Varying Product Availability , 2008, Mark. Sci..

[66]  Eric T. Bradlow,et al.  The Traveling Salesman Goes Shopping: The Systematic Deviations of Grocery Paths from TSP-Optimality , 2008 .

[67]  S. Neslin,et al.  Sales Promotion Models , 2008 .

[68]  Tonya Boone,et al.  The Value of Information Sharing in the Retail Supply Chain: Two Case Studies , 2008 .

[69]  Robert Fildes,et al.  Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting , 2007, Interfaces.

[70]  S. Karabati,et al.  Can the desired service level be achieved when the demand and lost sales are unobserved? , 2004 .

[71]  V. Rao,et al.  A General Choice Model for Bundles with Multiple-Category Products: Application to Market Segmentation and Optimal Pricing for Bundles , 2003 .

[72]  Michael R. Pearce,et al.  Evaluating Promotions in Shopping Environments: Decomposing Sales Response into Attraction, Conversion, and Spending Effects , 2001 .

[73]  Gary J. Russell,et al.  Analysis of cross category dependence in market basket selection , 2000 .

[74]  Michael R. Pearce,et al.  Retail sales force scheduling based on store traffic forecasting , 1998 .

[75]  W. E. Wecker Predicting Demand from Sales Data in the Presence of Stockouts , 1978 .