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Suche nach „[S.] [Minner]“ hat 4 Publikationen gefunden
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    DigitalF: Angewandte Wirtschaftswissenschaften

    Zeitschriftenartikel

    Christian Mandl, S. Minner

    Data-Driven Optimization for Commodity Procurement Under Price Uncertainty

    Manufacturing and Service Operations Management, no. Published Online:18 Aug 2020

    2020

    DOI: 10.1287/msom.2020.0890

    Abstract anzeigen

    Problem definition: We study a practice-motivated multiperiod 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. Academic/practical relevance: We propose a nonparametric, 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. Methodology: 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. Results: 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 to improve out-of-sample generalization. Compared with an internal best-practice benchmark, DDA generates savings of on average 9.1 million euros per annum (4.33%) for 10 years of backtesting. Managerial implications: 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.

    DigitalF: Angewandte Wirtschaftswissenschaften

    Buch (Monographie)

    Christian Mandl, S. Nadarajah, S. Minner, S. Gavirneni

    Structured Data-Driven Operating Policies for Commodity Storage

    2019

    Abstract anzeigen

    Storage assets are critical for temporal trading of commodities under volatile prices. State-of-the-art methods for managing storage such as the reoptimization heuristic (RH), which are part of commercial software, approximate a Markov decision process (MDP) assuming full information regarding the state and the stochastic commodity price process and hence suffer from informational inconsistencies with observed price data and structural inconsistencies with the true optimal policy, which are both components of generalization error. Based on extensive backtests, we find that this error can lead to significantly suboptimal RH policies and qualitatively different performance compared to the known near-optimality and behavior of RH in the full-information setting. We develop a forward-looking data-driven approach (DDA) to learn policies and overcome generalization error. This approach extends standard (backward-looking) DDA in two ways: (i) it uses financial-market features and estimates of future prots as part of the training objective, which typically includes past prots alone; and (ii) it enforces structural properties of the optimal policy. To elaborate, DDA trains parameters of bang-bang and base-stock policies, respectively, by solving linear-and mixed-integer programs, thereby extending known DDAs that parameterize decisions as functions of features without enforcing policy structure. We backtest the performance of DDA and RH on six major commodities from 2000 to 2017 with features constructed using Thomson Reuters and Bloomberg data. DDA significantly improves RH on real data, with base-stock structure needed to realize this improvement. Our research advances the state-of-the-art for storage operations and suggests modifications to commercial software to handle generalization error.

    DigitalF: Angewandte Wirtschaftswissenschaften

    Zeitschriftenartikel

    Christian Mandl, S. Minner

    Von Predictive zu Prescriptive Analytics. Big Data in der Rohstoffbeschaffung

    Beschaffung aktuell, no. 4

    2017

    DigitalF: Angewandte Wirtschaftswissenschaften

    Buch (Monographie)

    Christian Mandl, S. Minner

    When Do Commodity Spot Price Regimes Matter for Inventory Managers?

    2017

    Abstract anzeigen

    An increasing number of firms buy commodities at spot markets characterized by price volatility. Due to different market regimes (e.g., bull and bear), spot price dynamics are non-stationary and only partially observable; neither the underlying stochastic price process, nor its parameters are known with certainty. To capture uncertainty in both price and price model, we exploit recent spot price observations to dynamically update (learning) probabilistic price regime information in the context of inventory control under stochastic demand and purchase price. By means of Bayesian dynamic programming, we prove that, if prices evolve according to doubly embedded stochastic processes described by hidden Markov regime switching (MRS) models, price(s)- and regime-belief-dependent (prior respectively posterior) base-stock policies, rather than price-dependent policies, are optimal. We distinguish between independent and Markovian price processes and demonstrate the difference concerning optimal base-stock functions and monotonicity properties. We numerically establish that ignoring regime shifts leads to suboptimal inventory decisions and we quantify the operational value of spot price models. We find that Bayesian regime belief updates (learning) can yield significant cost savings that are particularly high when demand volatility and inventory holding cost are low and regime persistence is high. However, in empirical environments, stochastic price models induce misleading speculation resulting in misspeculative inventory. Based on real spot market data, we show that price forecast accuracy and operational performance are not perfectly correlated and that it can be advantageous for inventory managers to ignore sophisticated price forecasting and instead follow a naïve strategy.