Purpose: This paper aims to explore the last-mile (LM) challenges faced by on-demand food delivery (ODFD) riders during the coronavirus pandemic. This study contributes to the literature on the less-explored domain of ODFD services. Design/methodology/approach: A grounded theory methodology is used. Riders working for multiple ODFD firms in various urban and semi-urban areas of India were interviewed. Open, axial and selective coding of interview transcripts was done. Findings: A grounded model is developed consisting of riders' challenges represented broadly under four core categories: Operational, Customer-related, Organisational and Technological issues. The study indicates that while some of the challenges are inherent to the ODFD supply chain, these have been visibly exposed and intensified by COVID-19, while other challenges are specific to the pandemic. Research limitations/implications: The model is a qualitative proposition representing LM delivery issues in ODFD services faced by the riders in India's urban and semi-urban areas during the COVID-19 pandemic. Other countries may face similar problems, but further studies are necessary to confirm or refute the findings. Practical implications: ODFD companies must address the riders' issues to better adapt to the current and future disruptions and improve riders' quality of work–life to achieve operational excellence. Originality/value: This study builds on the extant ODFD literature by focusing on one of its less addressed aspects: the working conditions of the riders. This work is conducted amid the COVID-19 pandemic in the context of a developing country and aims to study the challenges in ODFD operations. © 2021, Emerald Publishing Limited.

Last-mile challenges in on-demand food delivery during COVID-19: understanding the riders' perspective using a grounded theory approach

E-commerce system has become more popular and implemented in almost all business areas. E-commerce system is a platform for marketing and promoting the products to customer through online. Customer segmentation is known as a process of dividing the customers into groups which shares similar characteristics. The purpose of customer segmentation is to determine how to deal with customers in each category in order to increase the profit of each customer to the business. Segmenting the customers assist business to identify their profitable customer to satisfy their needs by optimizing the services and products. Therefore, customer segmentation helps E-commerce system to promote the right product to the right customer with the intention to increase profits. There are few types of customer segmentation factors which are demographic psychographic, behavioral, and geographic. In this study, customer behavioral factor has been focused. Therefore users will be analyzed using clustering algorithm in determining the purchase behavior of E-commerce system. The aim of clustering is to optimize the experimental similarity within the cluster and to maximize the dissimilarity in between clusters. In this study there are relationship between three clusters: event type, products, and categories. In this research, the proposed approach analyzed the groups that share similar criteria to help vendors to identify and focus on the high profitable segment to the least profitable segment. This type of analysis can play important role in improving the business. Grouping their customer according to their similar behavioral factor to sustain their customer for long-term and increase their business profit. It also enables high exposure of the e-offer to gain attention of potential customers. In order to process the collected data and segment the customers, an learning algorithm is used which is known as K-Means clustering. K-Means clustering is implemented to solve the clustering problems.

/pdf/k-means-clustering-approach-for-intelligent-customer-n63wja26.pdf

K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data

https://doi.org/10.1016/j.jclepro.2022.131068

Data-driven optimal dynamic pricing strategy for reducing perishable food waste at retailers

Blockchain and IoT embedded sustainable virtual closed-loop supply chain in E-commerce towards the circular economy

Companies require a greater understanding of the Supply Chain (SC) benefits that can be gained from industry 4.0 (I4.0) and, more specifically, which technologies and concepts that can improve certain SC performance measures. A state-of-the-art systematic literature review (SLR) has been done on supply chain performance measurement linked with various industry 4.0 technologies. Based on the findings of the review through content analysis, this paper presents a framework for exploring the usage of I4.0 technologies to identify the potential supply chain performance measures. This framework includes the dimensions of Procurement 4.0, Manufacturing 4.0, Logistics 4.0, and Warehousing 4.0. As a scientific contribution, this study has validated the proposed framework through case studies, where the existing studies are limited. Finally, several fruitful future possible extensions have been discussed based on the proposed framework. 

Ecem Eroglu Turhanlar

Papers

Lateral inventory share-based models for IoT-enabled E-commerce sustainable food supply networks

Additive manufacturing integration in E-commerce supply chain network to improve resilience and competitiveness

Autonomous mobile robot travel under deadlock and collision prevention algorithms by agent-based modelling in warehouses