Shortening Delivery Times by Predicting Customers' Online Purchases: a Case Study in the Fashion Industry

Online retailers still struggle with the disadvantage of delivery times compared to traditional brick and mortar stores. With the emergence of big data analytics, it has become possible to extract meaningful knowledge from the volume of data that online retailers collect on their website. Nevertheless, limited research exists that investigates how this data can be used to optimize delivery times for customers. The goal of this paper is to develop a prediction model for anticipatory shipping, which predicts customers' online purchases with the aim of shipping products in advance, and subsequently minimize delivery times. Different forecasting methods in combination with k-means clustering are applied to test if, and how early, it is possible to predict online purchases. Results indicate that customer purchases are, to a certain extent, predictable, but anticipatory shipping comes at a high cost due to wrongly sent products. The proposed prediction model can easily be implemented and used to predict purchases, which can also be leveraged for other areas of application besides anticipatory shipping

Novel Data Analytics Meets Conventional Container Shipping: Predicting Delays by Comparing Various Machine Learning Algorithms

Supply chain disruptions are expected to significantly increase over the next decades. In particular, delay of container vessels is likely to escalate due to rising congestion from continued growth of container shipping and higher frequency of extreme weather events. Predicting these delays could result in significant cost savings from optimizing operations. Both academic research and container shipping industry, however, lack analytical solutions to predict delay. To increase transparency on delay, we develop a prediction model based on 315 explanatory variables, 10 regression models, and 7 classification models. Using machine learning algorithms, we obtain best results for neural network and support vector machine with a prediction accuracy of 77 percent compared to only 59 percent of a naive baseline model. Various shipping players including sender, carrier, terminal operator, and receiver benefit from the easy-to-use prediction model to optimize operations such as buffers in schedules and the selection of ports and routes

Shortening Delivery Times by Predicting Customers\u27 Online Purchases: a Case Study in the Fashion Industry

Online retailers still struggle with the disadvantage of delivery times compared to traditional brick and mortar stores. With the emergence of big data analytics, it has become possible to extract meaningful knowledge from the volume of data that online retailers collect on their website. Nevertheless, limited research exists that investigates how this data can be used to optimize delivery times for customers. The goal of this paper is to develop a prediction model for anticipatory shipping, which predicts customers\u27 online purchases with the aim of shipping products in advance, and subsequently minimize delivery times. Different forecasting methods in combination with k-means clustering are applied to test if, and how early, it is possible to predict online purchases. Results indicate that customer purchases are, to a certain extent, predictable, but anticipatory shipping comes at a high cost due to wrongly sent products. The proposed prediction model can easily be implemented and used to predict purchases, which can also be leveraged for other areas of application besides anticipatory shipping

Sustainable Operations Management: An Enduring Stream or a Passing Fancy?

Paul Kleindorfer was among the first to weigh in on and nurture the stream of Sustainable Operations Management. The thoughts laid out here are based on conversations we had with Paul relating to the drivers underlying sustainability as a management issue: population and per capita consumption growth, the limited nature of resources and sinks, and the responsibility and exposure of firms to ensuing ecological risks and costs. We then discuss how an operations management lens contributes to the issue and criteria to help the Sustainable Operations Management perspective endure. This article relates to a presentation delivered by Morris Cohen for Paul's Manufacturing and Service Operations Management Distinguished Fellows Award, given at Columbia University, June 18, 2012. We wrote this article at Paul's request

A study on the impact of extreme weather events on the ceramic manufacturing in Egypt

Nowadays, global economic production is organized around a complex system of highly interdependent supply chains that are currently enormously disrupted due to COVID 19. What would happen if a fast-growing risk could pose a more significant threat to our supply chains? Are our supply chains resilient to climate change? Even though governments, businesses, and climate change organizations in developed countries are forced to work together trying to mitigate and adapt to this fast-moving phenomenon, developing countries like Egypt are less concerned about this topic. This study has developed a system dynamic model based on a four-phase mixed methodology approach; we captured the complex interconnected interactions between supply chain performance and climate change physical risks. A cognitive map was first developed to capture the relationship between the climate change physical risks variables and the supply chains. Then, historical climate data and data from a ceramic manufacturing company were analyzed using the Statistical Package for the Social Sciences (SPSS). A case study of a ceramic manufacturing company located in Egypt is provided to show the applicability of our developed system dynamic model. Lastly, we simulated different scenarios to assess the ramifications and consequences of climate change extreme weather-related events on the manufacturing process of the selected case company. We have observed a negative impact; a decrease in the manufacturing inventory level and production rate, total received orders and sales. As far as our knowledge, our study is the first to investigate the impacts of climate change extreme weather events on supply chains located in Egypt. Our main contribution is to prove and establish awareness among business owners, organizations, decision-makers and the Egyptian government that climate change and related extreme weather events exist and disruptions due to this fast-moving phenomenon must be considered.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Exploring Metabolic Pathway Reconstruction and Genome-Wide Expression Profiling in Lactobacillus reuteri to Define Functional Probiotic Features

The genomes of four Lactobacillus reuteri strains isolated from human breast milk and the gastrointestinal tract have been recently sequenced as part of the Human Microbiome Project. Preliminary genome comparisons suggested that these strains belong to two different clades, previously shown to differ with respect to antimicrobial production, biofilm formation, and immunomodulation. To explain possible mechanisms of survival in the host and probiosis, we completed a detailed genomic comparison of two breast milk–derived isolates representative of each group: an established probiotic strain (L. reuteri ATCC 55730) and a strain with promising probiotic features (L. reuteri ATCC PTA 6475). Transcriptomes of L. reuteri strains in different growth phases were monitored using strain-specific microarrays, and compared using a pan-metabolic model representing all known metabolic reactions present in these strains. Both strains contained candidate genes involved in the survival and persistence in the gut such as mucus-binding proteins and enzymes scavenging reactive oxygen species. A large operon predicted to encode the synthesis of an exopolysaccharide was identified in strain 55730. Both strains were predicted to produce health-promoting factors, including antimicrobial agents and vitamins (folate, vitamin B12). Additionally, a complete pathway for thiamine biosynthesis was predicted in strain 55730 for the first time in this species. Candidate genes responsible for immunomodulatory properties of each strain were identified by transcriptomic comparisons. The production of bioactive metabolites by human-derived probiotics may be predicted using metabolic modeling and transcriptomics. Such strategies may facilitate selection and optimization of probiotics for health promotion, disease prevention and amelioration

Face-to-face communication as a tool to support second-hand fashion sales : a field experiment at Fashion Week in Berlin

We conducted a random allocation experiment at fashion week in Berlin in 2017, testing how face-to-face (f2f) communication affects sales of a fashion start-up focusing on second-hand. The experiment revealed that 11% of guests of an f2f event afterwards turned paying customers with an average basket size 11.8% higher than the overall sales event average. We add insights to research on entrepreneurial practice as well as on offline operations in the context of circular consumption in fashion, exposing the leveraging effect of f2f communication for customer acquisition and revenue of start-ups in the field of sustainable fashion

Consumer perception of online attributes in circular economy activities

Businesses like Airbnb have shown that a successful circular economy (CE) business can operate exclusively online. Although online communication and web appearance attributes have been subject to academic research given accelerated digitization, there is still a lack of knowledge about online attributes and their role in facilitating CE. We close the portrayed knowledge gap by conducting a discrete-choice experiment with best to worst scaling and focusing on the effect of CE experience on the perception of a CE website by ranking nine online attributes, grouped in three subsets. We therefore contribute by identifying online attributes that are perceived as favorable for CE businesses and detect how participation in CE activities affects the perception of these attributes. We find that third-party associated online attributes (e.g., user reviews or third-party guarantees) rank significantly higher throughout CE consumption patterns of the sample, being always amongst the top three attributes. This novel finding on online preferences opens a new direction for further research, as well as allows practitioners to optimize online operations accordingly. Furthermore, we find that users without prior touchpoints with CE have a higher need for information about the business model as compared to CE active users who are more interested in community related attributes