Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains. (English) Zbl 1487.90050

Van Belle, Jente; Guns, Tias; Verbeke, Wouter

Eur. J. Oper. Res. 288, No. 2, 466-479 (2021).

Summary: Operational forecasting in supply chain management supports a variety of short-term planning decisions, such as production scheduling and inventory management. In this respect, improving short-term forecast accuracy is a way to build a more agile supply chain for manufacturing companies. Demand forecasting often relies on well-established univariate forecasting methods to extrapolate historical demand. Collaboration across the supply chain, including information sharing, is suggested in the literature to improve upon the forecast accuracy of such traditional methods. In this paper, we review empirical studies considering the use of downstream information in demand forecasting and investigate different modeling approaches and forecasting methods to incorporate such data. Where empirical findings on information sharing mainly focus on point-of-sale data in two-level supply chains, this research empirically investigates the added value of using sell-through data originating from intermediaries, next to historical demand figures, in a multi-echelon supply chain. In a case study concerning a US drug manufacturer, we evaluate different methods to incorporate this data and consider both time series methods and machine learning techniques to produce multi-step ahead weekly forecasts. The results show that the manufacturer can effectively improve its short-term forecast accuracy by integrating sell-through data into the forecasting process and provide useful insights as to the different modeling approaches used. The conclusion holds for all forecast horizons considered, though it is most pronounced for one-step ahead forecasts. Therefore, our research provides a clear incentive for manufacturers to assess the forecast accuracy that can be achieved by using sell-through data.

MSC:

90B05	Inventory, storage, reservoirs
62M20	Inference from stochastic processes and prediction
62P20	Applications of statistics to economics
68T05	Learning and adaptive systems in artificial intelligence

Keywords:

forecasting; bullwhip effect; information sharing; sell-through data; machine learning

Software:

R; Kernlab; smooth; forecast; Forecast; MASS (R); DYNAMO; glmnet; ranger; caret; expsmooth; ElemStatLearn

References:

[1]	Ali, Ö. G.; Sayın, S.; Van Woensel, T.; Fransoo, J., SKU demand forecasting in the presence of promotions, Expert Systems with Applications, 36, 10, 12340-12348 (2009)
[2]	Athanasopoulos, G.; Hyndman, R. J., Modelling and forecasting Australian domestic tourism, Tourism Management, 29, 1, 19-31 (2008)
[3]	Bergmeir, C.; Hyndman, R. J.; Koo, B., A note on the validity of cross-validation for evaluating autoregressive time series prediction, Computational Statistics & Data Analysis, 120, 70-83 (2018) · Zbl 1469.62021
[4]	Box, G. E.; Jenkins, G. M.; Reinsel, G. C.; Ljung, G. M., Time series analysis: forecasting and control (2015), John Wiley & Sons
[5]	Breiman, L., Random forests, Machine Learning, 45, 1, 5-32 (2001) · Zbl 1007.68152
[6]	Breiman, L.; Friedman, J.; Stone, C.; Olshen, R., Classification and regression trees (1984), Wadsworth and Brooks: Wadsworth and Brooks Monterey, CA · Zbl 0541.62042
[7]	Byrne, P.; Heavey, C., The impact of information sharing and forecasting in capacitated industrial supply chains: A case study, International Journal of Production Economics, 103, 1, 420-437 (2006)
[8]	Byrne, R. F., Beyond traditional time-series: Using demand sensing to improve forecasts in volatile times., Journal of Business Forecasting, 31, 2 (2012)
[9]	Chen, F.; Drezner, Z.; Ryan, J. K.; Simchi-Levi, D., Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information, Management Science, 46, 3, 436-443 (2000) · Zbl 1231.90019
[10]	Davydenko, A.; Fildes, R., Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts, International Journal of Forecasting, 29, 3, 510-522 (2013)
[11]	Davydenko, A.; Fildes, R., Forecast error measures: Critical review and practical recommendations, (Gilliland, M.; Tashman, L.; Sglavo, U., Business Forecasting: Practical Problems and Solutions (2016), John Wiley & Sons)
[12]	Demšar, J., Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, 7, Jan, 1-30 (2006) · Zbl 1222.68184
[13]	Di Pillo, G.; Latorre, V.; Lucidi, S.; Procacci, E., An application of support vector machines to sales forecasting under promotions, 4OR, 14, 3, 309-325 (2016) · Zbl 1349.62575
[14]	Forrester, J., Industrial dynamics (1961), M.I.T. Press: M.I.T. Press Cambridge
[15]	Fransoo, J. C.; Wouters, M. J., Measuring the bullwhip effect in the supply chain, Supply Chain Management: An International Journal, 5, 2, 78-89 (2000)
[16]	Friedman, J.; Hastie, T.; Tibshirani, R., Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software, 33, 1, 1-22 (2010)
[17]	Friedman, M., A comparison of alternative tests of significance for the problem of m rankings, The Annals of Mathematical Statistics, 11, 1, 86-92 (1940) · JFM 66.1305.08
[18]	Gartner Inc. (2019). It glossary. Accessed 5 April 2019https://www.gartner.com/it-glossary/.
[19]	Hanssens, D. M., Order forecasts, retail sales, and the marketing mix for consumer durables, Journal of Forecasting, 17, 3-4, 327-346 (1998)
[20]	Hartzel, K. S.; Wood, C. A., Factors that affect the improvement of demand forecast accuracy through point-of-sale reporting, European Journal of Operational Research, 260, 1, 171-182 (2017) · Zbl 1402.90066
[21]	Hastie, T.; Tibshirani, R.; Friedman, J., The elements of statistical learning: Data mining, inference, and prediction (2011), Springer: Springer NY
[22]	Holweg, M.; Disney, S.; Holmström, J.; Småros, J., Supply chain collaboration: Making sense of the strategy continuum, European Management Journal, 23, 2, 170-181 (2005)
[23]	Hosoda, T.; Naim, M. M.; Disney, S. M.; Potter, A., Is there a benefit to sharing market sales information? Linking theory and practice, Computers & Industrial Engineering, 54, 2, 315-326 (2008)
[24]	Huang, T.; Fildes, R.; Soopramanien, D., The value of competitive information in forecasting FMCG retail product sales and the variable selection problem, European Journal of Operational Research, 237, 2, 738-748 (2014)
[25]	Hyndman, R. J.; Khandakar, Y., Automatic time series forecasting: The forecast package for R, Journal of Statistical Software, Articles, 27, 3, 1-22 (2008)
[26]	Hyndman, R. J.; Koehler, A. B., Another look at measures of forecast accuracy, International Journal of Forecasting, 22, 4, 679-688 (2006)
[27]	Hyndman, R. J.; Koehler, A. B.; Ord, J. K.; Snyder, R. D., Forecasting with exponential smoothing: The state space approach (2008), Springer-Verlag: Springer-Verlag Berlin · Zbl 1211.62165
[28]	Karatzoglou, A.; Smola, A.; Hornik, K.; Zeileis, A., kernlab – an S4 package for kernel methods in R, Journal of Statistical Software, 11, 9, 1-20 (2004)
[29]	Kelepouris, T.; Miliotis, P.; Pramatari, K., The impact of replenishment parameters and information sharing on the bullwhip effect: A computational study, Computers & Operations Research, 35, 11, 3657-3670 (2008) · Zbl 1205.90154
[30]	Kim, K. K.; Ryoo, S. Y.; Jung, M. D., Inter-organizational information systems visibility in buyer-supplier relationships: The case of telecommunication equipment component manufacturing industry, Omega, 39, 6, 667-676 (2011)
[31]	Kourentzes, N.; Petropoulos, F., Forecasting with multivariate temporal aggregation: The case of promotional modelling, International Journal of Production Economics, 181, 145-153 (2016)
[32]	Kuhn, M., Building predictive models in R using the caret package, Journal of Statistical Software, 28, 5, 1-26 (2008)
[33]	Lee, H.; Kim, M. S.; Kim, K. K., Interorganizational information systems visibility and supply chain performance, International Journal of Information Management, 34, 2, 285-295 (2014)
[34]	Lee, H. L.; Padmanabhan, V.; Whang, S., The bullwhip effect in supply chains, SLOAN Management Review, 38, 3, 93-102 (1997)
[35]	Lee, H. L.; Padmanabhan, V.; Whang, S., Information distortion in a supply chain: The bullwhip effect, Management Science, 43, 4, 546-558 (1997) · Zbl 0888.90047
[36]	Ma, S.; Fildes, R.; Huang, T., Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information, European Journal of Operational Research, 249, 1, 245-257 (2016) · Zbl 1346.62165
[37]	Makridakis, S.; Spiliotis, E.; Assimakopoulos, V., Statistical and machine learning forecasting methods: Concerns and ways forward, PLoS ONE, 13, 3, e0194889 (2018)
[38]	Makridakis, S.; Spiliotis, E.; Assimakopoulos, V., The M4 competition: 100,000 time series and 61 forecasting methods, International Journal of Forecasting, 36, 1, 54-74 (2020)
[39]	Ord, K.; Fildes, R.; Kourentzes, N., Principles of business forecasting (2017), Wessex Press Publishing Co
[40]	Raghunathan, S., Information sharing in a supply chain: A note on its value when demand is nonstationary, Management Science, 47, 4, 605-610 (2001) · Zbl 1232.90084
[41]	Sagaert, Y. R.; Aghezzaf, E.-H.; Kourentzes, N.; Desmet, B., Tactical sales forecasting using a very large set of macroeconomic indicators, European Journal of Operational Research, 264, 2, 558-569 (2018) · Zbl 1376.62116
[42]	Sanders, N. R.; Graman, G. A., Quantifying costs of forecast errors: A case study of the warehouse environment, Omega, 37, 1, 116-125 (2009)
[43]	Smola, A. J.; Schölkopf, B., A tutorial on support vector regression, Statistics and Computing, 14, 3, 199-222 (2004)
[44]	Sugiura, N., Further analysis of the data by Akaike’s information criterion and the finite corrections, Communications in Statistics - Theory and Methods, 7, 1, 13-26 (1978) · Zbl 0382.62060
[45]	Svetunkov, I. (2020). Smooth: Forecasting Using State Space Models. R package version 2.5.6. https://CRAN.R-project.org/package=smooth.
[46]	Syntetos, A. A.; Babai, Z.; Boylan, J. E.; Kolassa, S.; Nikolopoulos, K., Supply chain forecasting: Theory, practice, their gap and the future, European Journal of Operational Research, 252, 1, 1-26 (2016) · Zbl 1346.90181
[47]	Tashman, L. J., Out-of-sample tests of forecasting accuracy: An analysis and review, International Journal of Forecasting, 16, 4, 437-450 (2000)
[48]	Trapero, J. R.; Kourentzes, N.; Fildes, R., Impact of information exchange on supplier forecasting performance, Omega, 40, 6, 738-747 (2012)
[49]	Trapero, J. R.; Kourentzes, N.; Fildes, R., On the identification of sales forecasting models in the presence of promotions, Journal of the Operational Research Society, 66, 2, 299-307 (2015)
[50]	Trapero, J. R.; Pedregal, D. J.; Fildes, R.; Kourentzes, N., Analysis of judgmental adjustments in the presence of promotions, International Journal of Forecasting, 29, 2, 234-243 (2013)
[51]	Venables, W. N.; Ripley, B. D., Modern applied statistics with S (2002), Springer: Springer New York · Zbl 1006.62003
[52]	Williams, B. D.; Waller, M. A., Creating order forecasts: Point-of-sale or order history?, Journal of Business Logistics, 31, 2, 231-251 (2010)
[53]	Williams, B. D.; Waller, M. A., Top-down versus bottom-up demand forecasts: The value of shared point-of-sale data in the retail supply chain, Journal of Business Logistics, 32, 1, 17-26 (2011)
[54]	Williams, B. D.; Waller, M. A.; Ahire, S.; Ferrier, G. D., Predicting retailer orders with POS and order data: The inventory balance effect, European Journal of Operational Research, 232, 3, 593-600 (2014) · Zbl 1305.90042
[55]	Wright, M. N.; Ziegler, A., ranger: A fast implementation of random forests for high dimensional data in C++ and R, Journal of Statistical Software, 77, 1, 1-17 (2017)
[56]	Zhang, C.; Tan, G.-W.; Robb, D. J.; Zheng, X., Sharing shipment quantity information in the supply chain, Omega, 34, 5, 427-438 (2006)
[57]	Zhang, G.; Patuwo, B. E.; Hu, M. Y., Forecasting with artificial neural networks: The state of the art, International Journal of Forecasting, 14, 1, 35-62 (1998)