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【MBA中國(guó)網(wǎng)訊】天津大學(xué)管理與經(jīng)濟(jì)學(xué)部英文講座預(yù)告：價(jià)格不確定下的商品采購(gòu)數(shù)據(jù)驅(qū)動(dòng)優(yōu)化（Data-Driven Optimization for Commodity Procurement under Price Uncertainty）

講座時(shí)間：9月23日（周一）9：00-11：00

講座地點(diǎn)：25樓3A教室

主講人：Prof. Stefan Minner

主講人介紹：Stefan Minner是慕尼黑工業(yè)大學(xué)管理學(xué)院物流與供應(yīng)鏈管理的全職教授。他在物流和運(yùn)營(yíng)研究期刊的幾個(gè)編輯委員會(huì)任職。Stefan 教授是

講座內(nèi)容：We study a practice-motivated multi-period stochastic commodity procurement problem under price uncertainty with forward and spot purchase options. Existing approaches are based on parametric price models, which inevitably involve price model misspecification and generalization error. We propose a non-parametric, data-driven approach (DDA) that is consistent with the optimal procurement policy structure but without requiring the a-priori specification and estimation of stochastic price processes. In addition to historical prices, DDA is able to leverage real-time feature data, such as economic indicators, in solving the problem. This paper provides a framework for prescriptive analytics in dynamic commodity procurement, with optimal purchase policies learned directly from data as functions of features, via mixed integer linear programming (MILP) under cost minimization objectives. Hence, DDA focuses on optimal decisions rather than optimal predictions. Furthermore, we combine optimization with regularization from machine learning (ML) to extract decision-relevant data from noise. Based on numerical experiments and empirical data, we show that there is a significant value of feature data for commodity procurement when procurement policy parameters are learned as functions of features. However, overfitting deteriorates the performance of data-driven solutions, which asks for ML extensions that improve out-of-sample generalization. In a practical application, compared to an internal best practice benchmark, DDA would have generated savings of on average 9.1 million Euros p.a. (4.33%) for ten years of backtesting and potential savings of 7.7 million Euros over 18 months after handing over the project results and DDA tools. A practical benefit of DDA is that it yields simple but optimally structured decision rules that are easy-to-interpret and easy-to-operationalize. Furthermore, DDA is generalizable and applicable to many other procurement settings.