New Publication in Special Issue of Management Science

How to transform large amounts of data into better decisions in Operations and Supply Chain Management is the main question that lies at the heart of all of the research projects of the Chair of Logistics and Quantitative Methods. During the last years, our researchers have developed a variety of new algorithms that combine machine learning methods with optimization techniques and we were able to show that these new approaches lead to very substantial performance improvements compared to methods that have long been considered “best-in-class”. We partnered with numerous companies from different industries and obtained rich data sets that allow us to demonstrate the superior performance of our new techniques.

The practical and theoretical relevance and value of our work has recently been acknowledged by Management Science, the leading Journal in our discipline. The paper “Prescriptive Analytics for Flexible Capacity Management”, authored by Pascal Notz (a doctoral researcher at our chair, who recently completed his doctoral studies) and Richard Pibernik, was published in a special issue on “Data-driven Prescriptive Analytics”.

Link to the paper
https://pubsonline.informs.org/doi/10.1287/mnsc.2020.3867

Absract of the paper
Motivated by the real-world problem of a logistics company, this paper proposes a novel distribution-free prescriptive analytics approach—termed kernelized empirical risk minimization (kernelized ERM)—to solve a complex two-stage capacity planning problem with multivariate demand and vector-valued capacity decisions and compares this approach both theoretically and numerically with an extension of the well-known sample average approximation (SAA) approach termed weighted SAA. Both approaches use integrated machine learning algorithms to prescribe capacities directly from historical demand and numerous features (covariates) without having to make assumptions about the underlying multivariate demand distribution. We provide extensive analytical insights into both approaches. Most important, we prove the universal approximation property for the kernelized ERM approach when using a universal (data-independent) kernel and show how out-of-sample guarantees can be derived for various kernels. We demonstrate the applicability of both approaches to a real-world planning problem and evaluate their performance relative to traditional parametric approaches that first estimate a multivariate demand distribution and then solve a stochastic optimization problem and a nonparametric approach (SAA). Our results suggest that the two prescriptive analytics approaches can result in substantial performance improvements of up to 58% compared with traditional approaches. Additional numerical analyses shed light on the behavior and performance drivers of the various approaches and demonstrate that in our case, the prescriptive approaches are much more robust to variations of exogenous cost parameters than traditional approaches.