A prescriptive analytics framework for efficient E-commerce order delivery

2021 | Decision Support Systems | Citations: 0

Authors: Kandula, Shanthan; Krishnamoorthy, Srikumar; Roy, Debjit

Abstract: Achieving timely last-mile order delivery is often the most challenging part of ... Expand

Abstract: Achieving timely last-mile order delivery is often the most challenging part of an e-commerce order fulfillment. Effective management of last-mile operations can result in significant cost savings and lead to increased customer satisfaction. Currently, due to the lack of customer availability information, the schedules followed by delivery agents are optimized for the shortest tour distance. Therefore, orders are not delivered in customer-preferred time periods resulting in missed deliveries. Missed deliveries are undesirable since they incur additional costs. In this paper, we propose a decision support framework that is intended to improve delivery success rates while reducing delivery costs. Our framework generates delivery schedules by predicting the appropriate delivery time periods for order delivery. In particular, the proposed framework works in two stages. In the first stage, order delivery success for every order throughout the delivery shift is predicted using machine learning models. The predictions are used as an input for the optimization scheme, which generates delivery schedules in the second stage. The proposed framework is evaluated on two real-world datasets collected from a large e-commerce platform. The results indicate the effectiveness of the decision support framework in enabling savings of up to 10.2% in delivery costs when compared to the current industry practice. Collapse

Topics: decision support electronic commerce geographic information system logistics management decision support system

Methods: machine learning XGBoost simulation random forest classification computational algorithm

Impact of Heterogeneous Prior Contribution on Reciprocity in Online Sellers’ Community

2019 | International Conference on Information Systems | Citations: 0

Authors: Yi, Chen

Abstract: This study explores how different types of resources a seller contributes in the ... Expand

Abstract: This study explores how different types of resources a seller contributes in the online community will trigger others’ reciprocity, reflected by the responses the seller’s threads receive. Drawing on social exchange theory and using machine learning techniques, we identify two important types of resources transferred in an online sellers’ community: informational resource and instrumental resource. Our findings reveal that a seller’s provision of informational resource is positively associated with the responses the seller’s threads receive, while a seller’s provision of instrumental resource is negatively associated with the responses the seller’s threads receive. Moreover, the effect is moderated by the types of resources sought by a certain thread. Specifically, both effects are strengthened for threads that seek informational resource and undermined for threads that seek instrumental resource. The study contributes to the understanding of online reciprocity by uncovering the differing impact of different contribution and its boundary condition. Collapse

Topics: online community information exchange electronic commerce

Methods: computational algorithm qualitative coding classification method forums posting robustness check

Theories: social exchange theory

Two-stage consumer credit risk modelling using heterogeneous ensemble learning

2019 | Decision Support Systems | Citations: 0

Authors: Papouskova, Monika; Hajek, Petr

Abstract: Modelling consumer credit risk is a crucial task for banks and non-bank financia ... Expand

Abstract: Modelling consumer credit risk is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. To model the overall credit risk of a consumer loan in terms of expected loss (EL), three key credit risk parameters must be estimated: probability of default (PD), loss given default (LGD) and exposure at default (EAD). Research to date has tended to model these parameters separately. Moreover, a neglected area in the field of LGD/EAD modelling is the application of ensemble learning, which by benefitting from diverse base learners reduces the over-fitting problem and enables modelling diverse risk profiles of defaulted loans. To overcome these problems, this paper proposes a two-stage credit risk model that integrates (1) class-imbalanced ensemble learning for predicting PD (credit scoring), and (2) an EAD prediction using a regression ensemble. Furthermore, multi-objective evolutionary feature selection is used to minimize both the misclassification cost (root mean squared error) of the PD and EAD models and the number of attributes necessary for modelling. For this task, we propose a misclassification cost metric suitable for consumer loans with fixed exposure because it combines opportunity cost and LGD. We show that the proposed credit risk model is not only more effective than single-stage credit risk models but also outperforms state-of-the-art methods used to model credit risk in terms of prediction and economic performance. Collapse

Topics: peer-to-peer model enterprise information system missing data

Methods: computational algorithm ensemble learning meta learning machine learning support vector machine

Early Predictions of Movie Success: The Who, What, and When of Profitability

2016 | Journal of Management Information Systems | Citations: 21

Authors: Lash, Michael T.; Zhao, Kang

Abstract: We focus on predicting the profitability of a movie to support movie-investment ... Expand

Abstract: We focus on predicting the profitability of a movie to support movie-investment decisions at early stages of film production. By leveraging data from various sources, and using social network analysis and text mining techniques, the proposed system extracts several types of features, including “who” is in the cast, “what” a movie is about, “when” a movie will be released, as well as “hybrid” features. Experiment results showed that the system outperforms benchmark methods by a large margin. Novel features we proposed made weighty contributions to the prediction. In addition to designing a decision support system with practical utility, we also analyzed key factors of movie profitability. Furthermore, we demonstrated the prescriptive value of our system by illustrating how it can be used to recommend a set of profit-maximizing cast members. This research highlights the power of predictive and prescriptive data analytics in information systems to aid business decisions. Collapse

Topics: social network team productivity marketing management analytical information system accounting

Methods: machine learning experiment lasso regression computational algorithm random forest classification

Maximize What Matters: Predicting Customer Churn With Decision-Centric Ensemble Selection

2015 | European Conference On Information Systems | Citations: 0

Authors: Baumann, Annika; Lessmann, Stefan; Coussement, Kristof; Bock, Koen W De

Abstract: Churn modeling is important to sustain profitable customer relationships in satu ... Expand

Abstract: Churn modeling is important to sustain profitable customer relationships in saturated consumer markets. A churn model predicts the likelihood of customer defection. This is important to target retention offers to the right customers and to use marketing resources efficiently. The prevailing approach toward churn model development, supervised learning, suffers an important limitation: it does not allow the marketing analyst to account for campaign planning objectives and constraints during model building. Our key proposition is that creating a churn model in awareness of actual business requirements increases the performance of the final model for marketing decision support. To demonstrate this, we propose a decision-centric framework to create churn models. We test our modeling framework on eight real-life churn data sets and find that it performs significantly better than state-of-the-art churn models. Further analysis suggests that this improvement comes directly from incorporating business objectives into model building, which confirms the effectiveness of the proposed framework. In particular, we estimate that our approach increases the per customer profits of retention campaigns by $.47 on average. Collapse

Topics: resource allocation user requirement marketing information system communication service infrastructure logistics management

Methods: sensitivity analysis statistical hypothesis test regression analysis method ensemble learning LogitBoost

SpamHunting: An instance-based reasoning system for spam labelling and filtering

2007 | Decision Support Systems | Citations: 0

Authors: Fdez-Riverola, F.; Iglesias, E. L.; Díaz, F.; Méndez, J. R.; Corchado, J. M.

Abstract: In this paper we show an instance-based reasoning e-mail filtering model that ou ... Expand

Abstract: In this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable enhanced instance retrieval network able to accurately generalize e-mail representations. The reuse of similar messages is carried out by a simple unanimous voting mechanism to determine whether the target case is spam or not. Previous to the final response of the system, the revision stage is only performed when the assigned class is spam whereby the system employs general knowledge in the form of meta-rules. Collapse

Topics: electronic mail email spam artificial intelligence internet filter Java

Methods: experiment support vector machine computational algorithm descriptive statistic machine learning

Theories: theory of case-based reasoning