Ralf Seifert

Biography

Ralf W. Seifert is Professor of Technology & Operations Management (TOM) at the College of Management of Technology (CDM) at Ecole Polytechnique Fédérale de Lausanne (EPFL) since 2003. His primary research and teaching interests relate to operations management, supply chain strategy and technology network management. He is also active in industry analysis, international project work and new venture formation.

Based on his work with companies, Professor Seifert has co-authored more than 30 case studies covering different industries. These efforts have been recognized by multiple international case awards granted by EFMD in 2018, 2012, 2009 and 2003, ECCH in 2011 and 2006, as well as POMS in 2004. He continues to actively research issues of supply chain strategy, supply chain finance and technology management and has more than 70 articles and international conference presentations to his credit. In addition, he co-authored two books: one focused on strategic supply chain management and another one concerning start-up challenges of technology ventures.

In parallel to his appointment at EPFL, he continues to serve a position at IMD, were he has been appointed Professor of Operations Management in 2000. Prior to joining IMD, Professor Seifert studied and worked in Germany, Japan and the US. He earned PhD and MS degrees in Management Science at Stanford University, a Diplom Ingenieur degree in Mechanical Engineering at the Karlsruhe Institute of Technology (KIT) and a Master's degree in Integrated Manufacturing Systems Engineering from North Carolina State University. While in the US, he consulted for Hewlett-Packard and served as Teaching and Research Assistant at Stanford University. In Germany he worked for Booz & Company, McKinsey & Company and Freudenberg & Co. In addition, he spent one year as a Visiting Scholar in Operations Research at Waseda University in Tokyo.

Official source

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A supply chain, sometimes expressed as a "supply-chain", is a complex logistics system that consists of facilities that convert raw materials into finished products and distribute them to end consume

In commerce, supply chain management (SCM) deals with a system of procurement (purchasing raw materials/components), operations management (ensuring the production of high-quality products at high

Rights are legal, social, or ethical principles of freedom or entitlement; that is, rights are the fundamental normative rules about what is allowed of people or owed to people according to some legal

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Related publications (165)

A Machine Learning-based Framework for Forecasting Sales of New Products With Short Life Cycles Using Deep Neural Networks

Yara Kayyali Elalem, Ralf Seifert

Demand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales of newly launched, short-lived products that are similar to previous products when there is limited availability of historical sales data for the new product. In addition to exploiting historical data using time-series clustering, we perform data augmentation to generate sufficient sales data and consider two quantitative cluster assignment methods. We apply one traditional statistical (ARIMAX) and three machine learning methods based on deep neural networks (DNNs) – long short-term memory, gated recurrent units, and convolutional neural networks. Using two large data sets, we investigate the forecasting methods’ comparative performance and, for the larger data set, show that clustering generally results in substantially lower forecast errors. Our key empirical finding is that simple ARIMAX considerably outperforms the more advanced DNNs, with mean absolute errors up to 21%–24% lower. However, when adding Gaussian white noise in our robustness analysis, we find that ARIMAX’s performance deteriorates dramatically, whereas the considered DNNs display robust performance. Our results provide insights for practitioners on when to use advanced deep learning methods and when to use traditional methods.

2022

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Making assortment decisions is becoming an increasingly difficult task for many retailers worldwide as they implement omnichannel initiatives. Discrete choice modeling lies at the core of this challenge, yet existing models do not sufficiently account for the complex shopping behavior of customers in an om-nichannel environment. In this paper, we introduce a discrete choice model called the multichannel at-traction model (MAM). A key feature of the MAM is that it specifically accounts for both the product substitution behavior of customers within each channel and the switching behavior between channels. We formulate the corresponding assortment optimization problem as a mixed integer linear program and provide a computationally efficient heuristic method that can be readily used for obtaining high-quality solutions in large-scale omnichannel environments. We also present three different methods to estimate the MAM parameters based on aggregate sales transaction data. Finally, we describe general effects of the implementation of widely-used omnichannel initiatives on the MAM parameters, and carry out nu-merical experiments to explore the structure of optimal assortments, thereby gaining new insights into omnichannel assortment optimization. Our work provides the analytical framework for future studies to assess the impact of different omnichannel initiatives. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

ELSEVIER2022

Information Retrieval Under Network Uncertainty: Robust Internet Ranking

Ralf Seifert, Anna Timonina-Farkas

Internet ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including scientometrics and systemic risk in finance (SinkRank, DebtRank, etc.). The traditional approach to internet ranking goes back to the seminal work of Sergey Brin and Larry Page, who developed the initial method PageRank (PR) in order to rank websites in search engine results. Recent works have studied robust reformulations of the PageRank model for the case when links in the network structure may vary; that is, some links may appear or disappear influencing the transportation matrix defined by the net-work structure. We make a further step forward, allowing the network to vary not only in links, but also in the number of nodes. We focus on growing network structures and pro-pose a new robust formulation of the PageRank problem for uncertain networks with fixed growth rate. Defining the robust PageRank in terms of a nonconvex optimization problem, we bound our formulation from above by a convex but nonsmooth optimization problem. Driven by the approximation quality, we analyze the resulting optimality gap theoretically and demonstrate cases for its reduction. In the numerical part of the article, we propose some techniques which allow us to obtain the solution efficiently for middle-size networks avoiding all nonsmooth points. Furthermore, we propose a coordinate-wise descent method with near-optimal step size and address high-dimensional cases using multino-mial transition probabilities. We analyze the impact of the network growth on ranking and numerically assess the approximation quality using real-world data sets on movie reposito-ries and on journals on computational complexity.

INFORMS2022