Abstract
Background: The coffee industry is one of the most important world supply chains, with an estimated consumption of two billion cups daily, making it the most consumed beverage worldwide. Coffee beans are primarily grown in tropical countries, with Brazil accounting for almost 50% of the production. The objective of this study is to examine the Brazilian trade between 2018 and 2022, focusing on state producers, logistical corridors, and importer countries. Methods: The methodology approach revolves around a quantitative method using Social Network Analysis measures. Results: The results reveal a massive concentration in local production (99.5%-Minas Gerais), port movements (99.9%-Santos, Itaguai, and Rio de Janeiro), and country buyers (80.9%-the United States, United Kingdon, and Japan). Conclusions: The study concludes that the Brazilian green coffee supply chain relies on a fragile and overloaded logistical network. Due to that, this study indicates that the stakeholders and decision-makers involved must consider this high concentration of production in some areas and companies. They must also address the bottlenecks in logistical corridors and the fierce competition involved in acquiring and processing Brazilian coffee production because these factors can drastically affect the revenue of the companies operating in this sector.

Abstract
Discrete choice models can forecast market shares and individual choice probabilities with different price and alternative set scenarios. This work introduces a method to personalize choice models involving causal variables, such as price, using rich observational data. The model provides interpretable customer- and context-specific preferences, and price sensitivity, with an estimation procedure that uses orthogonalization. We caution against the nalive use of regularization to deal with the high-dimensional observational data challenge. We experiment with the attended home delivery (AHD) slot choice problem using data from a European online retailer. Our results indicate that while the popular non-personalized multinomial logit (MNL) model does very well at the aggregate (day-slot) level, personalization provides significantly and substantially more accurate predictions at the individual-context level. But the nalive personalization approach using regularization without orthogonalization wrongly predicts that the choice probability will increase if the slot price increases, rendering it unfit for forecasting demand with pricing scenarios. The proposed method avoids this problem. Further, we introduce features based on potential consideration sets in the AHD slot choice context that increase accuracy and allow for more realistic substitution patterns than the proportional substitution implied by MNL.

Abstract
In the current competition environment, transportation costs continue to rise, causing a reduction in the profit margins of companies. There are several tools in the literature to support the planning of logistics activities, but individualised solutions are not yet effective. In this study, a linear programming model is proposed to jointly plan the demand fulfilment of two competing companies by encouraging the search for synergies that enhance collaboration in the use of existing resources. To demonstrate the validity of the proposed mode, a case study is carried out and the results obtained with the initiation of the collaboration are evaluated. In conclusion, the proposed model reduces the logistics costs by up to 13%, as well as decreases the carbon footprint by 37%. By focusing on optimising economic and environmental aspects, this approach serves as a guide for companies to promote collaborations and to facilitate decision making at a managerial level.

Abstract
To encourage customers to take a chance in finding the right product, retailers and marketplaces implement benevolent return policies that allow users to return items for free without a specific reason. These policies contribute to a high rate of returns, which result in high shipping costs for the retailer and a high environmental toll on the planet. This paper shows that these negative impacts can be significantly minimized if inventory is exchanged within the supplier network of marketplaces upon a return. We compare the performance of this proposal to the standard policy where items are always sent to the original supplier. Our results show that our proposal-returning to a closer supplier and using a predictive heuristic for fulfilment-can achieve a 16% cost reduction compared to the standard-returning to the original supplier and using a myopic rule for fulfilment.

Abstract
[No abstract available]

Abstract
Retailers face increasing competitive pressure to determine how best to deliver products purchased online to the end customer. Grocery retailers often require attended home delivery where the customer must be present to receive the delivery. For attended home delivery to function, the retailer and customer must agree on a delivery time slot that works for both parties. Using online data from a grocery retailer, we observe customer preferences for three delivery service attributes associated with each time slot: speed, precision, and timing. We define speed as the expected time between the placement of an order and its delivery, precision as the duration of the offered time slot, and timing as the availability of choices across times of the day and days of the week. We show that customers not only value speed as an attribute of delivery service but that precision and timing are also key drivers of the customer's time slot selection process. We also observe substantial customer heterogeneity in the willingness of customers to pay for time slots. Customers that differ in their loyalty to the retailer, basket value, basket size, and basket composition exhibit distinct differences in their willingness to pay. We show that retailers with the capability to tailor their time slot offerings to specific customer segments have the potential to generate approximately 9% more shipping revenue than those who cannot. Our findings inform practitioners seeking to design competitive fulfillment strategies and academics customer behavior in the attended home context.