High-frequency forecasting for grocery point-of-sales: intervention in practice and theoretical implications for operational design
Tóm tắt
Từ khóa
Tài liệu tham khảo
Alftan A, Kaipia R, Loikkanen L, Spens K (2015) Centralised grocery supply chain planning: Improved exception management. Int J Phys Distrib Logist Manag 45(3):237–259
Ali ÖG, Sayın S, Van Woensel T, Fransoo J (2009) SKU demand forecasting in the presence of promotions. Expert Syst Appl 36(10):12340–12348
Amine A, Cadenat S (2003) Efficient retailer assortment: A consumer choice evaluation perspective. Int J Retail Distrib Manag 31(10):486–497
Anderson Consulting (1996) Where to look for incremental sales gains: The retail problem of out-of-stock merchandise. http://www.ccrrc.org/wp-content/uploads/sites/24/2014/02/Where_to_Look_for_Incremental_Sales_Gains_The_Retail_Problem_of_Out-of-Stock_Merchandise_1996.pdf. Accessed 20 October 2019
Armstrong JS (2001) Evaluating forecasting methods. In: Armstrong, J.S. (ed) Principles of forecasting: a handbook for researchers and practitioners. Springer Science & Business Media, pp 441–472
Bishop CM, Tipping ME (2003) Bayesian regression and classification. Nato Science Series sub Series III Computer and Systems Sciences 190:267–288
Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. John Wiley & Sons
Brozyna J, Mentel G, Szetela B, Strielkowski W (2018) Multi-seasonality in the TBATS model using demand for electric energy as a case study. Econ Comput Econ Cybern Stud Res 52(1)
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?– Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250
Chand S, Hsu VN, Sethi S (2002) Forecast, solution, and rolling horizons in operations management problems: A classified bibliography. Manuf Serv Oper Manag 4(1):25–43
Choi TM, Wallace SW, Wang Y (2018) Big data analytics in operations management. Prod Oper Manag 27(10):1868–1883
Christodoulos C, Michalakelis C, Varoutas D (2010) Forecasting with limited data: Combining ARIMA and diffusion models. Technol Forecast Soc Chang 77(4):558–565
Corsten D, Gruen T (2003) Desperately seeking shelf availability: an examination of the extent, the causes, and the efforts to address retail out-of-stocks. Int J Retail Distrib Manag 31(12):605–617
De Livera AM, Hyndman RJ, Snyder RD (2011) Forecasting time series with complex seasonal patterns using exponential smoothing. J Am Stat Assoc 106(496):1513–1527
Denyer D, Tranfield D, van Aken JE (2008) Developing design propositions through research synthesis. Organ Stud 29:393–413
De Toni AF, Zamolo E (2005) From a traditional replenishment system to vendor-managed inventory: A case study from the household electrical appliances sector. Int J Prod Econ 96(1):63–79
Ehrenthal JCF, Honhon D, Van Woensel T (2014) Demand seasonality in retail inventory management. Eur J Oper Res 238(2):527–539
Fernie J, Grant DB (2008) On-shelf availability: The case of a UK grocery retailer. Int J Logist Manag 19(3):293–308
Fildes R, Hibon M, Makridakis S, Meade N (1998) Generalising about univariate forecasting methods: Further empirical evidence. Int J Forecast 14(3):339–358
Fildes R, Ma S, Kolassa S (2019) Retail forecasting: Research and practice. Int J Logist Manag
Gardner ES (2006) Exponential smoothing: The state of the art, Part II. Int J Forecast 22(4):637–666
Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis. Chapman and Hall/CRC
Godfrey GA, Powell WB (2000) Adaptive estimation of daily demands with complex calendar effects for freight transportation. Transport Res B-Meth 34(6):451–469
Goodwin P (2010) The holt-winters approach to exponential smoothing: 50 years old and going strong. Foresight 19(19):30–33
Gould PG, Koehler AB, Ord JK, Snyder RD, Hyndman RJ, Vahid-Araghi F (2008) Forecasting time series with multiple seasonal patterns. Eur J Oper Res 191(1):207–222
Grmanová G, Laurinec P, Rozinajová V, Ezzeddine AB, Lucká M, Lacko P, Návrat P (2016) Incremental ensemble learning for electricity load forecasting. Acta Polytech Hung 13(2):97–117
Gruber V, Holweg C, Teller C (2016) What a waste! Exploring the human reality of food waste from the store manager’s perspective. J Public Policy Mark 35(1):3–25
Holmström J, Holweg M, Lawson B, Pil FK, Wagner SM (2019) The digitalization of operations and supply chain management: Theoretical and methodological implications. J Oper 65(8):728–734
Holmström J, Ketokivi M, Hameri AP (2009) Bridging practice and theory: A design science approach. Decis Sci 40(1):65–87
Hyndman RJ, Athanasopoulos G (n.d.) Complex seasonality. https://otexts.com/fpp2/complexseasonality.html. Accessed 1 October 2019
Hyndman RJ, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2018) forecast: Forecasting functions for time series and linear models. Software, R package
Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688
Hyndman RJ, Koehler AB, Ord JK, Snyder RD (2008) Forecasting with exponential smoothing: the state space approach. Springer Science & BusinessMedia, pp 14-27
Karabiber OA, Xydis G (2019) Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS. ANN and ARIMA Methods Energies 12(5):928
Kiil K, Dreyer HC, Hvolby HH, Chabada L (2018) Sustainable food supply chains: the impact of automatic replenishment in grocery stores. Prod Plan Control 29(2):106–116
Koehler AB, Murphree ES (1988) A comparison of results from state space forecasting with forecasts from the Makridakis competition. Int J Forecast 4(1):45–55
Kotzab H, Teller C (2003) Value-adding partnerships and co-opetition models in the grocery industry. Int J Phys Distrib Logist Manag 33(3):268–281
Koupriouchina L, van der Rest JP, Schwartz Z (2014) On revenue management and the use of occupancy forecasting error measures. Int J Hosp Manag 41:104–114
Kourentzes N, Crone SF (2008) Forecasting high-frequency time series with neural networks - an analysis of modelling challenges from increasing data frequency. The 4th International Conference on Data Mining
Kourentzes N, Petropoulos F, Trapero JR (2014) Improving forecasting by estimating time series structural components across multiple frequencies. Int J Forecast 30(2):291–302
Kourentzes N, Trapero JR, Svetunkov I (2014) Measuring the behaviour of experts on demand forecasting: a complex task http://kourentzes.com/forecasting/wp-content/uploads/2014/12/Kourentzes_Complex-bias.pdf. Accessed 1 October 2019
Lago J, De Ridder F, De Schutter B (2018) Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. Appl Energy 221:386–405
Ma S, Fildes R, Huang T (2016) Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra-and inter-category promotional information. Eur J Oper Res 249(1):245–257
Narayanan A, Sahin F, Robinson EP (2019) Demand and order-fulfillment planning: The impact of point-of-sale data, retailer orders and distribution center orders on forecast accuracy. J Oper Manag 65(5):468–486
Pereira LN (2016) An introduction to helpful forecasting methods for hotel revenue management. Int J Hosp Manag 58:13–23
Peterson H (2014) 4 Ways American Grocery Shopping Is Changing Forever https://www.businessinsider.com/trends-that-are-changing-grocery-stores-2014-4?r=US&IR=T&IR=T. Accessed 4 October 2019
Poler R, Mula J (2011) Forecasting model selection through out-of-sample rolling horizon weighted errors. Expert Syst Appl 38(12):14778–14785
Pulkka A (2020) Applying Bayesian regression to forecast retail demand in the Christmas season. M.Sc. thesis, Aalto University, Finland
Ramos P, Fildes R (2017) Characterizing retail demand with promotional effects. In International Symposium on Forecasting, International Institute of Forecasters Cairns, Australia
Segal D (2019) The world wastes tons of food. A grocery ‘happy hour’ in one answer. The New York Times. https://www.nytimes.com/2019/09/08/business/food-waste-climate-change.html. Accessed 3 February 2020
Sillanpää V, Liesiö J (2018) Forecasting replenishment orders in retail: value of modelling low and intermittent consumer demand with distributions. Int J Prod Res 56(12):4168–4185
Taylor JW (2003) Short-term electricity demand forecasting using double seasonal exponential smoothing. J Oper Res Soc 54(8):799–805
Taylor JW, De Menezes LM, McSharry PE (2006) A comparison of univariate methods for forecasting electricity demand up to a day ahead. Int J Forecast 22(1):1–16
Taylor JW (2007) Forecasting daily supermarket sales using exponentially weighted quantile regression. Eur J Oper Res 178(1):154–167
Taylor JW (2010) Triple seasonal methods for short-term electricity demand forecasting. Eur J Oper Res 204(1):139–152
Taylor JW (2011) Multi-item sales forecasting with total and split exponential smoothing. J Oper Res Soc 62(3):555–563
Van Donselaar K, van Woensel T, Broekmeulen RACM, Fransoo J (2006) Inventory control of perishables in supermarkets. Int J Prod Econ 104(2):462–472
Veit A, Goebel C, Tidke R, Doblander C, Jacobsen HA (2014) Household electricity demand forecasting: benchmarking state-of-the-art methods. In Proceedings of the 5th international conference on Future energy systems pp 233–234
Whiteoak P (2004) Rethinking efficient replenishment in the grocery sector. In: Fernie, J. & Sparks, L. (ed) Logistics and Retail Management, 2nd edn. London & Sterling, pp 138–163
Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manage Sci 6(3):324–342