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Based on this, we construct different simulation models and compare their performance with the actual realized performance. We classify them according to the extent in which they accurately approximate the service level and average waiting times. We also obtain a theoretical understanding on how to distinguish between the model error and other aspects such as random noise. We conclude that modeling explicitly breaks and agent heterogeneity is crucial for obtaining a precise model.\n<\/jats:p>","DOI":"10.1007\/s11134-025-09935-4","type":"journal-article","created":{"date-parts":[[2025,2,9]],"date-time":"2025-02-09T18:37:56Z","timestamp":1739126276000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Call center data analysis and model validation"],"prefix":"10.1007","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1776-8369","authenticated-orcid":false,"given":"Ger","family":"Koole","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sihan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,9]]},"reference":[{"key":"9935_CR1","unstructured":"Akidas, H.: A machine learning approach to predict service level for call centers. 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