Profit Implications of Judgmental Adjustments to Forecast Inputs: Evidence from a Large-Scale Field Experiment

References

• Argote L, McEvily B, Reagans R (2003) Managing knowledge in organizations: An integrative framework and review of emerging themes. Management Sci. 49(4):571–582.Link, Google Scholar
• Athey S, Imbens GW (2017) The econometrics of randomized experiments. Handbook of Economic Field Experiments, vol. 1 (Elsevier, North-Holland), 73–140.Crossref, Google Scholar
• Bajari P, Chernozhukov V, Hortacsu A, Suzuki J (2019) The impact of big data on firm performance: An empirical investigation. AEA Papers Proc. 109:33–37.Google Scholar
• Beresford C, Miller C (2021) Average age of vehicles on the road rises above 12 years. Accessed July 14, 2025, https://www.caranddriver.com/news/a33457915/average-age-vehicles-on-road-12-years/.Google Scholar
• Blattberg RC, Hoch SJ (1990) Database models and managerial intuition: 50% model + 50% manager. Management Sci. 36(8):887–899.Link, Google Scholar
• Caro F, de Tejada Cuenca AS (2023) Believing in analytics: Managers adherence to price recommendations from a DSS. Manufacturing Service Oper. Management 25(2):524–542.Link, Google Scholar
• Doney PM, Cannon JP (1997) An examination of the nature of trust in buyer-seller relationships. J. Marketing 61(2):35–51.Crossref, Google Scholar
• Donohue K, Katok E, Leider S (2019) The Handbook of Behavioral Operations (John Wiley & Sons, Inc., Hoboken, NJ).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. Forecasting 25(1):3–23.Crossref, Google Scholar
• Franses PH, Legerstee R (2010) Do experts’ adjustments on model‐based SKU‐level forecasts improve forecast quality? J. Forecasting 29(3):331–340.Crossref, Google Scholar
• Fügener A, Grahl J, Gupta A, Ketter W (2022) Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Inform. Systems Res. 33(2):678–696.Link, Google Scholar
• Gal-Or E, Geylani T, Dukes AJ (2008) Information sharing in channel with partially informed retailers. Marketing Sci. 27(4):642–658.Link, Google Scholar
• Goltsos TE, Syntetos AA, Glock CH, Ioannou G (2022) Inventory–forecasting: Mind the gap. Eur. J. Oper. Res. 299(2):397–419.Crossref, Google Scholar
• Goodwin P (2009) Taking stock: Assessing the true cost of forecast errors. Internat. J. Appl. Forecasting (15):8–11.Google Scholar
• Ibanez MR, Clark JR, Huckman RS, Staats BR (2017) Discretionary task ordering: Queue management in radiological services. Management Sci. 64(9):4389–4407.Link, Google Scholar
• Ibrahim R, Kim SH, Tong J (2021) Eliciting human judgement for prediction algorithms. Management Sci. 67(4):2314–2325.Link, Google Scholar
• Jain A (2022) Sharing demand information with retailer under upstream competition. Management Sci. 68(7):4983–5001.Link, Google Scholar
• Kesavan S, Kushwaha T (2020) Field experiment on the profit implications of merchants’ discretionary power to override data-driven decision-making tools. Management Sci. 66(11):5182–5190.Link, 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
• Levitt T (1965) Exploit the product life cycle. Harvard Bus. Rev. 43(6):81–94.Google Scholar
• Marr B (2023) The 7 biggest business challenges every company is facing in 2023. Forbes (November 15), https://www.forbes.com/sites/bernardmarr/2022/11/15/the-7-biggest-business-challenges-every-company-is-facing-in-2023/.Google Scholar
• McKinsey & Company (2017) The changing aftermarket game: And how automotive suppliers can benefit from arising opportunities. Accessed July 14, 2025, https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/the-changing-aftermarket-game-and-how-automotive-suppliers-can-benefit-from-arising-opportunities.Google Scholar
• Perera HN, Hurley J, Fahimnia B, Reisi M (2019) The human factor in supply chain forecasting: A systematic review. Eur. J. Oper. Res. 274(2):574–600.Crossref, 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 60:34–46.Crossref, Google Scholar
• Schweitzer ME, Cachon GP (2000) Decision bias in newsvendor problem with a known demand distribution: Experimental evidence. Management Sci. 46(3):404–420.Link, Google Scholar
• Shin J, Sudhir K, Yoon D (2012) When to “fire” customers: Customer cost-based pricing. Management Sci. 58(5):932–947.Link, Google Scholar
• Siemsen E, Roth AV, Balasubramanian S (2008) How motivation, opportunity, and ability drive knowledge sharing: The constraining-factor model. J. Oper. Management 26(3):426–445.Crossref, Google Scholar
• Syntetos AA, Nikolopoulos K, Boylan JE, Fildes R, Goodwin P (2009) The effects of integrating management judgement into intermittent demand forecasts. Internat. J. Production Econom. 118(1):72–81.Crossref, Google Scholar
• Van der Auweraer S (2019) Forecasting spare part demand with installed base information: A review. Internat. J. Forecasting 35(1):181–196.Crossref, Google Scholar
• Van Donselaar KH, Gaur V, van Woensel T, Broekmeulen RACM (2010) Ordering behavior in retail stores and implications for automated replenishment. Management Sci. 56(5):766–784.Link, Google Scholar
• Wakelin N (2021) How many parts are in a car? Accessed July 14, 2025, https://knowhow.napaonline.com/how-many-parts-are-in-a-car/.Google Scholar