Prof. Dr. Christian Mandl

  • Beschaffung, Logistik, Supply Chain Management
  • Data Analytics, Operations Research

Professor

Ansprechpartner für den Studienschwerpunkt Beschaffung & Supply Chain Management

A 006.1

0991/3615-5531


Sprechzeiten

  • Montag, 14:30 - 15:30 (Nach vorheriger Terminvereinbarung)
  • Bei Interesse an einer Abschlussarbeit/einem Praktikum im Bereich Digitale Beschaffung, Logistik & Supply Chain Management und Data Analytics kontaktieren Sie mich jederzeit gerne


JournalArticle
  • Christian Mandl
Datengetriebene Planungsmethoden zur Steuerung der Rohstoffbeschaffung und des physischen Rohstoffhandels unter Preisrisiko , vol70
  • 2020
  • Angewandte Wirtschaftswissenschaften F_EN: Applied Economics
  • Academic
  • DIGITAL
Contribution
  • Christian Mandl
Datengetriebene Ansätze zur Optimierung der Beschaffung und Lagerhaltung von Rohstoffen in volatilen Märkten
  • 2020
Preisschwankungen stellen sowohl für rohstoffverarbeitende als auch für rohstoffhandelnde Unternehmen eine große Herausforderung dar. Die vorliegende Arbeit untersucht die Auswirkungen von Preisunsicherheit auf optimale Beschaffungs- und Lagerhaltungsstrategien. Eine zentrale Erweiterung der existierenden Literatur sind der Fokus auf Preismodellunsicherheit, sprich unvollständige Information über den zugrundeliegenden stochastischen Preisprozess, sowie auf kostenoptimale Beschaffungsentscheidungen statt optimaler Preisprognosen. Die entwickelten stochastischen und datengetriebenen Analytics-Ansätze kombinieren hierbei mathematische Optimierungsverfahren des Operations Research mit Methoden aus dem Bereich des Maschinellen Lernens und liefern als Ergebnis effektive und interpretierbare Entscheidungsregeln. Das primäre Ziel dieser Arbeit ist es, Rohstoffeinkäufern im digitalen Zeitalter Entscheidungsunterstützungstools an die Hand zu geben, mit deren Hilfe Big Data für verbesserte Beschaffungs- und Lagerhaltungsentscheidungen genutzt werden kann. Die entwickelten Algorithmen wurden dabei auf der Basis von Echtdaten für verschiedene Rohstoffklassen (Metalle, Energie, Agrar) validiert.
  • Angewandte Wirtschaftswissenschaften F_EN: Applied Economics
  • Academic
  • DIGITAL
JournalArticle
  • Christian Mandl
  • S. Minner
Data-Driven Optimization for Commodity Procurement Under Price Uncertainty
  • 2020

DOI: 10.1287/msom.2020.0890

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.
  • Angewandte Wirtschaftswissenschaften F_EN: Applied Economics
  • Academic
  • DIGITAL
Book
  • Christian Mandl
  • S. Nadarajah
  • S. Minner
  • S. Gavirneni
Structured Data-Driven Operating Policies for Commodity Storage
  • 2019
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.
  • Angewandte Wirtschaftswissenschaften F_EN: Applied Economics
  • Academic
  • DIGITAL
Book
  • Christian Mandl
  • S. Minner
When Do Commodity Spot Price Regimes Matter for Inventory Managers?
  • 2017
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.
  • Angewandte Wirtschaftswissenschaften F_EN: Applied Economics
  • Academic
  • DIGITAL
JournalArticle
  • Christian Mandl
  • S. Minner
Von Predictive zu Prescriptive Analytics. Big Data in der Rohstoffbeschaffung
  • 2017
  • Angewandte Wirtschaftswissenschaften F_EN: Applied Economics
  • Academic
  • DIGITAL
Thesis
  • Christian Mandl
Optimal Procurement and Inventory Control in Volatile Commodity Markets Advances in Stochastic and Data-Driven Optimization
  • Angewandte Wirtschaftswissenschaften F_EN: Applied Economics
  • Academic
  • DIGITAL

Vita

  • Unternehmensberater bei McKinsey & Company Inc., München
  • Gründer von prelytico, München
  • Forschungaufenthalt an der Cornell University, Ithaca/New York
  • Promotion am Lehrstuhl Logistik & Supply Chain Management, TU München
  • M.Sc. Technologie- und Managementorientierte Betriebswirtschaftlehre, TU München
  • B.Eng. Wirtschaftsingenieurwesen, Hochschule München


Sonstiges

Gutachtertätigkeit

  • International Journal of Production Economics
  • OR Spectrum

Preise & Auszeichnungen

  • Gewinner MSOM Practice-Based Research Competition (zusammen mit Prof. Stefan Minner)
  • Dissertationspreis der Gesellschaft für Operations Research (GOR)
  • Wissenschaftspreis des Bundesverbands Materialwirtschaft, Einkauf & Logistik (BME)
  • Finalist beim Best Student Paper Award der International Society for Inventory Research (ISIR)

Mitgliedschaften

  • Gesellschaft für Operations Research (GOR)
  • Bundesverband Materialwirtschaft, Einkauf & Logistik (BME)
  • TUM Management Alumni e.V.