Supervised machine learning for theory building and testing

Yen-Chun Chou, Howard Hao-Chun Chuang, Ping Chou, and Rogelio Oliva, Supervised machine learning for theory building and testing: Opportunities in operations management, Journal of Operations Management, 2023. pp. 1–33. (Codes in R)

Machine learning's (ML's) unique power to approximate functions and identify non-obvious regularities in data have attracted considerable attention from researchers in natural and social sciences. The emergence of predictive modeling applications in OM studies notwithstanding, it remains unclear how OM scholars can effectively leverage supervised ML for theory building and theory testing, the primary goals of scientific research. We attempt to fill this gap by conducting a literature review of recent developments in supervised ML in OM to identify vacancies in the extant literature, shedding light on how ML applications can move beyond problem-solving into theory building, and formulating a procedure to help OM scholars leverage ML for exploratory theory development. Our procedure employs the random forest with well-developed properties and inference toolkits that are crucial for empirical research. We then expand the boundary of ML usage and connect supervised ML to the explanatory modeling and hypothesis testing employed by OM empiricists for decades, and discuss the use of supervised ML for causal inference from observational data. We posit that contemporary ML can facilitate pattern exploration and enhance the validity of theory testing. We conclude by discussing directions for future empirical OM studies that aim to leverage ML.

The role of optimization in some recent advances in data-driven decision-making

Baardman, L., Cristian, R., Perakis, G. et al. The role of optimization in some recent advances in data-driven decision-making. Mathematical Programming 200, 1–35 (2023). https://doi.org/10.1007/s10107-022-01874-9.

Data-driven decision-making has garnered growing interest as a result of the increasing availability of data in recent years. With that growth many opportunities and challenges have sprung up in the areas of predictive and prescriptive analytics. Often, optimization can play an important role in tackling these issues. In this paper, we review some recent advances that highlight the difference that optimization can make in data-driven decision-making. We discuss some of our contributions that aim to advance both predictive and prescriptive models. First, we describe how we can optimally estimate clustered models that result in improved predictions. Next, we consider how we can optimize over objective functions that arise from tree ensemble models in order to obtain better prescriptions. Finally, we discuss how we can learn optimal solutions directly from the data allowing for prescriptions without the need for predictions. For all these new methods, we stress the need for good performance but also the scalability to large heterogeneous datasets.

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友達 AI 數位化打造高效供應鏈

吳珍儀，友達AI數位化打造高效供應鏈 蟬聯美國製造領導獎，Yahoo 財經，2023年6月29日

友達在智慧研發環節開發色彩飽和度模擬系統，引入大數據分析演算法，進行材料選用的相關模擬，提前預測客戶喜好的顏色和材料穿透率，使設計階段效率提升83%。在面板前段製程中，建立元件標準化資料庫並結合佈局設計演算法，成功縮短50%的光罩設計時程。