Print Demand Forecasting with Machine Learning at HP Inc.

References

• Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H (2010) An empirical comparison of machine learning models for time series forecasting. Econometric Rev. 29(5–6):594–621.Google Scholar
• Armstrong JS(2001) Combining forecasts. Armstrong JS, ed. Principles of Forecasting: A Handbook for Researchers and Practitioners (Springer, Boston), 417–439.Google Scholar
• Bandara K, Bergmeir C, Smyl S (2020) Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach. Expert Systems Appl. 140:112896.Google Scholar
• Bergstra J, Yamins D, Cox D (2013) Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. Proc. 30th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 28, no. 1 (PMLR, New York), 115–123.Google Scholar
• Billington C, Callioni G, Crane B, Ruark JD, Rapp JU, White T, Willems SP (2004) Accelerating the profitability of Hewlett-Packard’s supply chains. Interfaces 34(1):59–72.Link, Google Scholar
• Cargille B, Branvold D (2000) Diffusing supply chain innovations at Hewlett-Packard company: Applications of performance technology. Performance Improvement Quart. 13(4):6–15.Google Scholar
• Chen A, Chow A, Davidson A, DCunha A, Ghodsi A, Hong SA, Konwinski A, et al. (2020) Developments in MLflow: A system to accelerate the machine learning lifecycle. Proc. Fourth Internat. Workshop Data Management End-to-End Machine Learning (ACM, New York), 1–4.Google Scholar
• Cortes C, Jackel LD, Chiang WP (1994) Limits on learning machine accuracy imposed by data quality. Tesauro G, Touretzky D, Leen T, eds. Advances in Neural Information Processing Systems, vol. 7 (MIT Press, Cambridge, MA).Google Scholar
• Curtland C, Neto P, Ghozeil A (2022) HP Inc. advanced analytics powers technology in the service of humanity. Accessed May 20, 2025, https://pubsonline.informs.org/do/10.1287/orms.2022.02.18/full/.Google Scholar
• Deng T, Zhao Y, Wang S, Yu H (2021) Sales forecasting based on LightGBM. 2021 IEEE Internat. Conf. Consumer Electronics Comput. Engrg. (ICCECE) (IEEE, Piscataway, NJ), 383–386.Google Scholar
• Deng Y, Zhang X, Wang T, Wang L, Zhang Y, Wang X, Zhao S, Qi Y, Yang G, Peng X (2023) Alibaba realizes millions in cost savings through integrated demand forecasting, inventory management, price optimization, and product recommendations. INFORMS J. Appl. Analytics 53(1):32–46.Link, Google Scholar
• Dodin P, Xiao J, Adulyasak Y, Alamdari NE, Gauthier L, Grangier P, Lemaitre P, Hamilton WL (2023) Bombardier aftermarket demand forecast with machine learning. INFORMS J. Appl. Analytics 53(6):425–445.Link, Google Scholar
• Ferreira KJ, Lee BHA, Simchi-Levi D (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.Link, Google Scholar
• Fildes R, Ma S, Kolassa S (2022) Retail forecasting: Research and practice. Internat. J. Forecast. 38(4):1283–1318.Google Scholar
• Fildes R, Goodwin P, Lawrence M, Nikolopoulos K (2009) Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. Internat. J. Forecast. 25(1):3–23.Google Scholar
• Findley DF (1983) On the use of multiple models for multi-period forecasting. Proc. Bus. Econom. Statist. Amer. Statist. Assoc., 528–531.Google Scholar
• Gardner ES (1990) Evaluating forecast performance in an inventory control system. Management Sci. 36(4):490–499.Link, Google Scholar
• Hamzaçebi C, Akay D, Kutay F (2009) Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems Appl. 36(2):3839–3844.Google Scholar
• Hartzel KS, Wood CA (2017) Factors that affect the improvement of demand forecast accuracy through point-of-sale reporting. Eur. J. Oper. Res. 260(1):171–182.Google Scholar
• Herzen J, Lässig F, Piazzetta SG, Neuer T, Tafti L, Raille G, Pottelbergh TV, et al. (2022) Darts: User-friendly modern machine learning for time series. J. Mach. Learn. Res. 23(124):1–6.Google Scholar
• Howard J (2019) Practical deep learning for coders [online course]. Fast.ai. Retrieved May 23, 2025, https://course.fast.ai/.Google Scholar
• Hyndman RJ, Athanasopoulos G (2018) Forecasting: Principles and Practice (OTexts, Melbourne, Australia).Google Scholar
• Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Internat. J. Forecast. 22(4):679–688.Google Scholar
• John MM, Olsson HH, Bosch J (2021) Towards MLOps: A framework and maturity model. 2021 47th Euromicro Conf. Software Engrg. Adv. Appl. (SEAA) (IEEE, Piscataway, NJ), 1–8.Google Scholar
• Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) LightGBM: A highly efficient gradient boosting decision tree. Adv. Neural Inform. Processing Systems 30:3146–3154.Google Scholar
• Khosrowabadi N, Hoberg K, Imdahl C (2022) Evaluating human behaviour in response to AI recommendations for judgemental forecasting. Eur. J. Oper. Res. 303(3):1151–1167.Google Scholar
• Kremer M, Siemsen E, Thomas DJ (2016) The sum and its parts: Judgmental hierarchical forecasting. Management Sci. 62(9):2745–2764.Link, Google Scholar
• Kurtuluş M, Ülkü S, Toktay BL (2012) The value of collaborative forecasting in supply chains. Manufacturing Service Oper. Management 14(1):82–98.Link, Google Scholar
• Laval C, Feyhl M, Kakouros S (2005) Hewlett-Packard combined or and expert knowledge to design its supply chains. Interfaces 35(3):238–247.Link, Google Scholar
• Lawrence MJ, Edmundson RH, O’Connor MJ (1986) The accuracy of combining judgemental and statistical forecasts. Management Sci. 32(12):1521–1532.Link, Google Scholar
• Lee HL (2002) Aligning supply chain strategies with product uncertainties. Calif. Management Rev. 44(3):105–119.Google Scholar
• Makridakis S, Hibon M (2000) The m3-competition: Results, conclusions and implications. Internat. J. Forecast. 16(4):451–476.Google Scholar
• Makridakis S, Spiliotis E, Assimakopoulos V (2018) Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS One 13(3):e0194889.Google Scholar
• Makridakis S, Spiliotis E, Assimakopoulos V (2022a) The m5 competition: Background, organization, and implementation. Internat. J. Forecast. 38(4):1325–1336.Google Scholar
• Makridakis S, Spiliotis E, Assimakopoulos V (2022b) M5 accuracy competition: Results, findings, and conclusions. Internat. J. Forecast. 38(4):1346–1364.Google Scholar
• Manary MP, Wieland B, Willems SP, Kempf KG (2019) Analytics makes inventory planning a lights-out activity at intel corporation. INFORMS J. Appl. Analytics 49(1):52–63.Link, Google Scholar
• Marcellino M, Stock JH, Watson MW (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J. Econometrics 135(1–2):499–526.Google Scholar
• McElroy T (2015) When are direct multi-step and iterative forecasts identical? J. Forecast. 34(4):315–336.Google Scholar
• Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) WaveNet: A generative model for raw audio. Preprint, submitted September 12, https://arxiv.org/abs/1609.03499.Google Scholar
• Oreshkin BN, Carpov D, Chapados N, Bengio Y (2019) N-beats: Neural basis expansion analysis for interpretable time series forecasting. Preprint, submitted May 24, https://arxiv.org/abs/1905.10437.Google Scholar
• Petropoulos F, Siemsen E (2023) Forecast selection and representativeness. Management Sci. 69(5):2672–2690.Link, Google Scholar
• Petropoulos F, Kourentzes N, Nikolopoulos K, Siemsen E (2018) Judgmental selection of forecasting models. J. Oper. Management 60(1):34–46.Google Scholar
• Qi M, Shi Y, Qi Y, Ma C, Yuan R, Wu D, Shen ZJ (2023) A practical end-to-end inventory management model with deep learning. Management Sci. 69(2):759–773.Link, Google Scholar
• Ritzman LP, King BE (1993) The relative significance of forecast errors in multistage manufacturing. J. Oper. Management 11(1):51–65.Google Scholar
• Rodriguez-Lujan I, Huerta R, Elkan C, Santa Cruz Fernández C (2010) Quadratic programming feature selection. J. Mach. Learn. Res. 11(49):1491–1516.Google Scholar
• Sagaert YR, Aghezzaf EH, Kourentzes N, Desmet B (2018) Temporal big data for tactical sales forecasting in the tire industry. Interfaces 48(2):121–129.Link, Google Scholar
• Salinas D, Flunkert V, Gasthaus J (2017) DeepAR: Probabilistic forecasting with autoregressive recurrent networks. Preprint, submitted April 13, https://arxiv.org/abs/1704.04110.Google Scholar
• Seifert M, Siemsen E, Hadida AL, Eisingerich AB (2015) Effective judgmental forecasting in the context of fashion products. J. Oper. Management 36(1):33–45.Google Scholar
• Simatupang TM, Sridharan R (2002) The collaborative supply chain. Internat. J. Logist. Management 13(1):15–30.Google Scholar
• Simatupang TM, Sridharan R (2005) An integrative framework for supply chain collaboration. Internat. J. Logist. Management 16(2):257–274.Google Scholar
• Taylor SJ, Letham B (2018) Forecasting at scale. Amer. Statist. 72(1):37–45.Google Scholar
• Taylor TA, Xiao W (2010) Does a manufacturer benefit from selling to a better-forecasting retailer? Management Sci. 56(9):1584–1598.Link, Google Scholar
• Ward J, Zhang B, Jain S, Fry C, Olavson T, Mishal H, Amaral J, et al. (2010) HP transforms product portfolio management with operations research. Interfaces 40(1):17–32.Link, Google Scholar
• Zaharia M, Chen A, Davidson A, Ghodsi A, Hong SA, Konwinski A, Murching S, et al. (2018) Accelerating the machine learning lifecycle with MLflow. IEEE Data Engrg. Bull. 41(4):39–45.Google Scholar
• Zellner M, Abbas AE, Budescu DV, Galstyan A (2021) A survey of human judgement and quantitative forecasting methods. Roy. Soc. Open Sci. 8(2):201187.Google Scholar
• Zhang Y, Zhu C, Wang Q (2020) LightGBM-based model for metro passenger volume forecasting. IET Intelligent Trans. Systems 14(13):1815–1823.Google Scholar