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Tactical sales forecasting using a very large set of macroeconomic indicators

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Abstract
Tactical forecasting in supply chain management supports planning for inventory, scheduling production, and raw material purchase, amongst other functions. It typically refers to forecasts up to 12 months ahead. Traditional forecasting models take into account univariate information extrapolating from the past, but cannot anticipate macroeconomic events, such as steep increases or declines in national economic activity. In practice this is countered by using managerial expert judgement, which is well known to suffer from various biases, is expensive and not scalable. This paper evaluates multiple approaches to improve tactical sales forecasting using macro-economic leading indicators. The proposed statistical forecast selects automatically both the type of leading indicators, as well as the order of the lead for each of the selected indicators. However as the future values of the leading indicators are unknown an additional uncertainty is introduced. This uncertainty is controlled in our methodology by restricting inputs to an unconditional forecasting setup. We compare this with the conditional setup, where future indicator values are assumed to be known and assess the theoretical loss of forecast accuracy. We also evaluate purely statistical model building against judgement aided models, where potential leading indicators are pre-filtered by experts, quantifying the accuracy-cost trade-off. The proposed framework improves on forecasting accuracy over established time series benchmarks, while providing useful insights about the key leading indicators. We evaluate the proposed approach on a real case study and find 18.8% accuracy gains over the current forecasting process.
Keywords
Forecasting Tactical planning Leading indicators LASSO Variable selection

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Please use this url to cite or link to this publication:

Chicago
Sagaert, Yves, El-Houssaine Aghezzaf, Nikolaos Kourentzes, and Bram Desmet. 2018. “Tactical Sales Forecasting Using a Very Large Set of Macroeconomic Indicators.” European Journal of Operational Research 264 (2): 558–569.
APA
Sagaert, Y., Aghezzaf, E.-H., Kourentzes, N., & Desmet, B. (2018). Tactical sales forecasting using a very large set of macroeconomic indicators. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 264(2), 558–569.
Vancouver
1.
Sagaert Y, Aghezzaf E-H, Kourentzes N, Desmet B. Tactical sales forecasting using a very large set of macroeconomic indicators. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH. Elsevier; 2018;264(2):558–69.
MLA
Sagaert, Yves et al. “Tactical Sales Forecasting Using a Very Large Set of Macroeconomic Indicators.” EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 264.2 (2018): 558–569. Print.
@article{8528861,
  abstract     = {Tactical forecasting in supply chain management supports planning for inventory, scheduling production, and raw material purchase, amongst other functions. It typically refers to forecasts up to 12 months ahead. Traditional forecasting models take into account univariate information extrapolating from the past, but cannot anticipate macroeconomic events, such as steep increases or declines in national economic activity. In practice this is countered by using managerial expert judgement, which is well known to suffer from various biases, is expensive and not scalable. This paper evaluates multiple approaches to improve tactical sales forecasting using macro-economic leading indicators. The proposed statistical forecast selects automatically both the type of leading indicators, as well as the order of the lead for each of the selected indicators. However as the future values of the leading indicators are unknown an additional uncertainty is introduced. This uncertainty is controlled in our methodology by restricting inputs to an unconditional forecasting setup. We compare this with the conditional setup, where future indicator values are assumed to be known and assess the theoretical loss of forecast accuracy. We also evaluate purely statistical model building against judgement aided models, where potential leading indicators are pre-filtered by experts, quantifying the accuracy-cost trade-off. The proposed framework improves on forecasting accuracy over established time series benchmarks, while providing useful insights about the key leading indicators. We evaluate the proposed approach on a real case study and find 18.8% accuracy gains over the current forecasting process.},
  author       = {Sagaert, Yves and Aghezzaf, El-Houssaine and Kourentzes, Nikolaos and Desmet, Bram},
  issn         = {0377-2217},
  journal      = {EUROPEAN JOURNAL OF OPERATIONAL RESEARCH},
  keywords     = {Forecasting Tactical planning Leading indicators LASSO Variable selection},
  language     = {eng},
  number       = {2},
  pages        = {558--569},
  publisher    = {Elsevier},
  title        = {Tactical sales forecasting using a very large set of macroeconomic indicators},
  url          = {http://dx.doi.org/10.1016/j.ejor.2017.06.054},
  volume       = {264},
  year         = {2018},
}

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