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In this study, we adopt the Shapley value (SV) to quantify the contributions of random factors and historical data points to the output variance based on the policy-augmented Bayesian network (PABN) model. To improve the efficiency of SV estimation, we exploit the sparsity inherent in SVs of random factors and data points. In particular, we first formulate a top-[Formula: see text] selection problem to identify the subset of random factors that collectively explain a prescribed proportion of the variance in output, and then develop a sequential budget allocation algorithm to solve it. Second, to mitigate the model risk with limited real-world data, we design a data valuation procedure that integrates sequential budget allocation with compressive sensing, framing the estimation of SVs as a sparse recovery problem. Numerical experiments on both synthetic and real-world PABN models demonstrate that the proposed methods substantially improve the accuracy and efficiency of selecting key random factors and informative data points with constrained simulation budgets.<\/jats:p>","DOI":"10.1142\/s0217595925400135","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T09:31:32Z","timestamp":1758879092000},"source":"Crossref","is-referenced-by-count":1,"title":["Efficient Sensitivity Analysis in Biomanufacturing with Sequential Shapley Value Estimation"],"prefix":"10.1142","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1163-8382","authenticated-orcid":false,"given":"Zixuan","family":"Bai","sequence":"first","affiliation":[{"name":"Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, P. R. 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