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Prof. Dr. Christian Mandl

  • Procurement, Logistics, Supply Chain Management
  • Advanced Analytics, Operations Research, Machine Learning

Faculty of Applied Economics (School of Management)

Professors

Professor

Head of “Digital Procurement and Supply Chain Management”

A 006.1

0991/3615-5531

consulting time

  • Monday,1:00 pm - 2:00 pm (after consultation via email)
  • In presence (Office A.006.1) or via Zoom

Sortierung:
Zeitschriftenartikel

  • Christian Mandl
  • S. Minner

Data-Driven Optimization for Commodity Procurement Under Price Uncertainty

In: Manufacturing and Service Operations Management vol. 25 pg. 371-810.

  • 18 Aug 2020 (2023)

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
  • DIGITAL
Vortrag

  • Christian Mandl

Datengetriebenes Preisrisikomanagement bei Agrarrohstoffen - Inwiefern helfen KI und Data Analytics?

In: 13. BME-Forum „Rohstoffeinkauf in der Lebensmittelindustrie"

Bundesverband Materialwirtschaft, Einkauf und Logistik e.V. Online

  • 15.05.2023 (2023)
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Vortrag

  • Christian Mandl

Von Predictive zu Prescriptive Analytics – Big Data in der Rohstoffbeschaffung

In: DigiCamp der TH Deggendorf

Technische Hochschule Deggendorf Deggendorf

  • 12.04.2022 (2022)
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Vortrag

  • Christian Mandl

Risikomanagement im Rohstoffeinkauf – Data-Analytics zur Risiko-Analyse & Risiko-Minimierung einsetzen

In: Österreichisches Einkaufsforum 2022

Wien

  • 06.-07.10.2022 (2022)
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Vortrag

  • Christian Mandl

Prescriptive Analytics for Commodity Storage Applications. Invited Talk.

In: Brown Bag Seminar der Wirtschaftswissenschaftlichen Fakultät

Katholische Universität Eichstätt-Ingolstadt Eichstätt

  • 18.05.2022 (2022)
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Zeitschriftenartikel

  • Christian Mandl
  • S. Nadarajah
  • S. Minner
  • S. Gavirneni

Data‐driven storage operations: Cross‐commodity backtest and structured policies

In: Production and Operations Management pg. 1-19.

  • (2022)

DOI: 10.1111/poms.13683

Storage assets are critical for physical trading of commodities under volatile prices. State-of-the-art methods for managing storage facilities 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. Focusing on spot trades, we find via an extensive backtest that this error can lead to significantly suboptimal RH policies. We develop a forward-looking data-driven approach (DDA) to learn policies and reduce generalization error. This approach extends standard (backward-looking) DDA in two ways: (i) It represents historical and estimated future profits as functions of features in the training objective, which typically includes only past profits; and (ii) it enforces structural properties of the optimal policy. To elaborate, DDA trains parameters of bang-bang and base-stock policies, respectively, using linear- and mixed-integer programs, thereby extending known DDAs that parameterize decisions as functions of features without policy structure. We backtest the performance of RH and DDA on six major commodities, employing feature selection across data from Reuters, Bloomberg, and other public data sets. DDA can improve RH on real data, with policy structure needed to realize this improvement. Our research advances the state-of-the-art for storage operations and can be extended beyond spot trading to handle generalization error when also including forward trades.
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Christian Mandl

Prescriptive Analytics for Commodity Procurement Applications

In: Operations Research Proceedings 2021. Selected Papers of the International Conference of the Swiss, German and Austrian Operations Research Societies (SVOR/ASRO, GOR e.V., ÖGOR), University of Bern, Switzerland, August 31 – September 3, 2021. null (Lecture Notes in Operations Research) pg. 27-32.

Cham

  • (2022)

DOI: 10.1007/978-3-031-08623-6_5

In this work, we investigate the implications of commodity price uncertainty for optimal procurement and inventory control decisions. While the existing literature typically relies on the full information paradigm, i.e., optimizing procurement and inventory decisions under full information of the underlying stochastic price process, we develop and test different data-driven approaches that optimize decisions under very limited statistical model assumptions. Our results are data-driven policies and decision rules that can support commodity procurement managers, inventory managers as well as commodity merchants. We furthermore test all optimization models based on real data from different commodity classes (i.e., metals, energy and agricultural).
  • Angewandte Wirtschaftswissenschaften
Vortrag

  • Christian Mandl

Optimal Procurement and Inventory Control in Volatile Commodity Markets

In: Annual Conference of the German Society of Operations Research

Bern, Schweiz

  • 31.08.2021 (2021)
  • Angewandte Wirtschaftswissenschaften
Vortrag

  • Christian Mandl

Datengetriebene Ansätze zur Optimierung der Rohstoffbeschaffung unter Preisrisiken

Technologie Campus Grafenau Grafenau

  • 08.06.2021 (2021)
  • Angewandte Wirtschaftswissenschaften
Zeitschriftenartikel

  • Christian Mandl

Datengetriebene Planungsmethoden zur Steuerung der Rohstoffbeschaffung und des physischen Rohstoffhandels unter Preisrisiko

In: OR News (Das Magazin der Gesellschaft für Operations Research e.V.)

  • (2020)

  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Christian Mandl

Datengetriebene Ansätze zur Optimierung der Beschaffung und Lagerhaltung von Rohstoffen in volatilen Märkten

In: Supply Management Research. Aktuelle Forschungsergebnisse 2020 (Advanced Studies in Supply Management)

  • (2020)

DOI: 10.1007/978-3-658-31898-7_3

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
  • DIGITAL
Hochschulschrift

  • Christian Mandl

Optimal Procurement and Inventory Control in Volatile Commodity Markets: Advances in Stochastic and Data-Driven Optimization

Technische Universität München München Lehrstuhl für Logistik und Supply Chain Management

  • 15.05.2019 (2019)

Volatile prices constitute a challenge for both commodity-processing and commodity-trading firms. This thesis investigates the implications of price uncertainty on the optimal operating policies in multi-period procurement and inventory control. A central contribution to the existing literature that addresses the full information problem is the focus on the implications of price model uncertainty, i.e., incomplete information about the underlying price process. Based on advances in stochastic and data-driven optimization, we propose mathematical models for practical decision support and test them on real data. Hence, this thesis gives guidance to managers in the digital age on how to use real-time information and Big Data in combination with methods from statistical learning theory (Bayesian learning, machine learning) in an optimization framework in order to improve commodity procurement and inventory management decisions. The first problem considers operational hedging via inventory control. We show how a Bayesian belief structure can be used to express uncertainty about the price process, which is subject to switches in regimes. We prove the structure of the optimal storage policy and test its cost impact relative to several more practical but suboptimal control policies. We find that Bayesian learning yields significant cost savings. The second problem addresses commodity procurement via forward contracting. We propose a data-driven and machine learning-enabled mixed integer linear programming model that jointly optimizes forecasts and decisions by training optimal purchase signals as functions of features related to the price. Finally, we quantify the performance loss caused by ignoring feature information in procurement. The third problem considers optimal commodity storage from the perspective of a merchant with buying, storing and reselling opportunities. We propose several data-driven models for storage optimization. Based on empirical data of six major exchange-traded commodities, we find that optimally structured data-driven policies can outperform state-of-the-art reoptimization approaches.
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Zeitschriftenartikel

  • Christian Mandl
  • S. Minner

Von Predictive zu Prescriptive Analytics. Big Data in der Rohstoffbeschaffung

In: Beschaffung aktuell

  • (2017)

  • Angewandte Wirtschaftswissenschaften
  • DIGITAL

core competencies

Areas of application

  • Digital procurement
  • Inventory control
  • Logistics optimization
  • Supply chain management

Methods

  • Prescriptive analytics
  • Data-driven optimization
  • Machine learning

Vita

  • Management consultant at McKinsey & Company Inc., Munich
  • Visiting scientist at Cornell University, Ithaca/New York
  • PhD at the chair of Logistics & Supply Chain Management, TU Munich
  • M.Sc. Business and Technology, TU Munich
  • B.Eng. Industrial Engineering, Munich University of Applied Sciences

Other

Reviewer Service

  • Production and Operations Management
  • International Journal of Production Economics
  • OR Spectrum

Honors & Awards

  • Winner MSOM Practice-Based Research Competition (together with Prof. Stefan Minner)
  • Dissertation award of the German Society of Operations Research (GOR)
  • Science award of the Federal Association of Materials Management, Purchasing and Logistics (BME)
  • Runners-up Best Student Paper Award of the International Society for Inventory Research (ISIR)

Memberships

  • German Society of Operations Research (GOR)
  • Federal Association of Materials Management, Purchasing and Logistics (BME)
  • TUM Management Alumni e.V.