Predictive Analytics Improves Sales Forecasts for a Pop-up Retailer

References

• Caro F, Gallien J, Diaz M, Garcia J, Corredoria JM, Montes M, Ramos JA, Correa J (2010) Zara uses operations research to reengineer its global distribution process. Interfaces 40(1):71–84.Link, Google Scholar
• Ching-Chin C, Ieng AIK, Ling-Ling W, Ling-Chieh K (2010) Designing a decision-support system for new product sales forecasting. Expert Systems Appl. 37(2):1654–1665.Google Scholar
• Christopher M, Lowson R, Peck H (2004) Creating agile supply chains in the fashion industry. Internat. J. Retail Distribution Management 32(8):367–376.Google Scholar
• De Toni A, Meneghetti A (2000) The production planning process for a network of firms in the textile-apparel industry. Internat. J. Production Econom. 65(1):17–32.Google Scholar
• Fildes R, Ma S, Kolassa S (2019) Retail forecasting: Research and practice. Internat. J. Forecast, ePub ahead of print December 5, https://doi.org/10.1016/j.ijforecast.2019.06.004.Google Scholar
• Fisher M, Rajaram K (2000) Accurate retail testing of fashion merchandise: Methodology and application. Marketing Sci. 19(3):266–278.Link, Google Scholar
• Friedman JH (2002) Stochastic gradient boosting. Comp. Statist. Data Anal. 38(4):367–378.Google Scholar
• Gogoi P (2007) Pop-up stores: All the rage. Business Week (February 12), 23.Google Scholar
• Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. (Springer, New York).Google Scholar
• Hesterberg T, Choi NH, Meier L, Fraley C (2008) Least angle and l1 penalized regression: A review. Statist. Surveys 2:61–93.Google Scholar
• Hossain S, Ahmed SE, Doksum KA (2015) Shrinkage, pretest, and penalty estimators in generalized linear models. Statist. Methodology 24:52–68.Google Scholar
• Pomodoro S (2013) Temporary retail in fashion system: An explorative study. J. Fashion Marketing Management 17(3):341–352.Google Scholar
• Ren S, Chan H-L, Ram P (2017) A comparative study of fashion demand forecasting models with multiple sources of uncertainty. Ann. Oper. Res. 257:335–355.Google Scholar
• Rudkowski J, Heney C, Yu H, Sedlezky S, Gunn F (2020) Here today, gone tomorrow? Mapping and modeling the pop-up retail customer journey. J. Retailing Consumer Services 54(May):1–25.Google Scholar
• Sanders NR, Manrodt KB (2003) The efficacy of using judgmental vs. quantitative forecasting methods in practice. Omega: Internat. J. Management Sci. 31(6):511–522.Google Scholar
• Sood A, James GM, Tellis GJ (2009) Functional regression: A new model for predicting market penetration of new products. Marketing Sci. 28(1):36–51.Link, Google Scholar
• Steimer S (2017) The magic of pop-up shop marketing, American Marketing Association. Accessed May 20, 2021, https://www.ama.org/marketing-news/the-magic-of-pop-up-shop-marketing/.Google Scholar
• Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psych. Methods 14(4):323–348.Google Scholar
• Sun Z, Choi T, Au K, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems 46(1):411–419.Google Scholar
• Surchi M (2011) The temporary store: A new marketing tool for fashion brands. J. Fashion Marketing Management 15(2):257–270.Google Scholar
• Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. B 58(1):267–288.Google Scholar
• Warnaby G, Shi C (2018) Pop-Up Retailing: Managerial and Strategic Perspectives (Springer, Cham, Switzerland).Google Scholar
• Watson MZ, Yan R (2013) An exploratory study of the decision processes of fast vs. slow fashion consumer. J Fashion Marketing Management 17(2):141–159.Google Scholar
• Xia M, Zhang Y, Weng L, Ye X (2012) Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs. Knowledge-Based Systems 36:253–259.Google Scholar