Dr. Marjolein Buisman has been an Assistant Professor in Retail Analytics at the WHU – Otto Beisheim School of Management since 2020. Her research is primarily focused on grocery retail, with a specific emphasis on optimizing inventory management, reducing food waste and enhancing the sustainability of the food supply chain. In one of her recent studies, she investigated the environmental sustainability of packaging fresh produce.

She holds an MSc degree in Management, Economics and Consumer studies and a degree in Sustainable Food Process engineering from Wageningen University. She obtained her doctoral degree at the same institution in 2019 with her thesis on the reuse and reduction of food waste in the grocery sector. “

Dr. Marjolein Buisman

Perakende markaları , büyümek , küresel pazarlarda rekabet ederkenbilmek için yenilikçi çözümlere, dijital dönüşüme ve akıllı modellere uyum sağlamada öncü olmayı benimsiyorlar.

8 Aralık 2023 tarihinde 12.’nci KÜMPEM Perakende Konferansımızda lider markalarda yenilikçi çözümler üzerinde çalışmakta olan  seçkin konuşmacıları dinleyeceğiz. Müşteri geri bildirimlerini çoklu kanallardan alırken anlayabilme,  ürün hattını dinamik tutarken envanter yönetimini ve kısa ömürlü ürünlerdeki israfı iyileştirme ve ayrıca optimizasyon modellerinin operasyonları geniş ölçekte daha ucuz ve daha hızlı hale getirme konusunda nasıl ilerlediği hakkında görüşmelere ev sahipliği yapacağız.

8 Aralık 2023 Cuma günü online olarak gerçekleştireceğimiz konferansımıza ücretsiz erişim için lütfen aşağıdaki bağlantıyı kullanın.

Sizi aramızda görmeyi sabırsızlıkla bekliyoruz:

https://virtualmagix.zoom.us/webinar/register/WN_f8S2-V2lSCyexy2sjjBTPQ#/registration

Distinguished Professor of Marketing

Co-founder and General Director of the DATA (Digital, Analytics, Technology, Automation) Initiative

D’Amore-McKim School of Business at Northeastern University

Hayden Hall 206D

360 Huntington Avenue, Boston, MA 02115

kpauwels@northeastern.edu

Publications available at http://marketingandmetrics.com/

Currently on sabbatical at Wharton

Re-engineering Amazon’s logistics network to optimize for speed, cost and selection

Abstract. Omnichannel retail today requires fulfillment of customer orders through a transportation network that is both low-cost and fast. In addition, to provide access to the retailer’s entire selection (which may include third parties selling through the retailer’s marketplace), the network needs to connect thousands of nodes to end customers, on a continental scale, using large fleets of vehicles. Designing and executing this complex transportation network is challenging and can lead to undesired inefficiencies. We solve this problem by adding structures to the network to encourage flow concentration and consolidation. This is done by decomposing the solution space into different pieces which are loosely coupled, reducing the search space by removing decision variable choices that do not make business sense, and accepting different optimality tolerances for different business decisions.

Xiaoyan Si is a Senior Research Scientist at Amazon’s Modeling and Optimization team. The team is responsible for building models and systems that support Amazon’s customer fulfillment network design at scale. Before joining Amazon, Xiaoyan worked in the railroad industry for 10 years on problems ranging from crew scheduling, data analytics, and computer vision. Xiaoyan received her Ph.D. in Operations Research and Industrial Engineering from the University of Texas at Austin. Her research interest is solving large-scale optimization problems encountered in the industry.

Xiaoyan Si

Semih Atakan is a Senior Applied Scientist at Amazon’s Modeling and Optimization Team. He has been working on strategic network design and resource allocation problems since 2018. He received his Ph.D. degree from the University of Southern California, and M.Sc. and B.Sc. degrees from Sabanci University in Industrial Engineering. His research interest involves large-scale computational optimization, stochastic programming, mixed-integer programming, and their applications.

Semih Atakan