Susmitha Harinath
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Susmitha Harinath has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Sanjoy Kumar Paul
UTS Business School, University of Technology Sydney, Ultimo, NSW 2007, Australia
1 shared publication - Sunil Kumar Jauhar
Operations Management & Decision Sciences, Indian Institute of Management Kashipur, Kashipur, India
1 shared publication - Venkataraghavan Krishnaswamy
Indian Institute of Management Tiruchirappalli Department of Information Systems and Analytics Tiruchirappalli
1 shared publication
Latest Publications
- Open Access
- 26 October 2024
- Published by Springer Nature in Annals of Operations Research
- Vol. 354 (1) , 103-142
Explainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics
Optimizing costs and profits while meeting customer demand is a critical challenge in the development of perishable supply chains. Customer-centric demand forecasting addresses this challenge by considering customer characteristics when determining inventory levels. This study proposes a solution framework comprising two steps: (a) segmentation using customer characteristics and (b) demand forecasting for each segment using transparent and responsible artificial intelligence techniques. We employed k-means, hierarchical clustering, and explainable AI (XAI) to segment, model, and compare several machine-learning techniques for demand forecasting. The results showed that support vector regression outperformed the autoregressive models. The results also showed that the two-step segmentation and demand forecasting process using hierarchical clustering and LSTM outperforms (Weighted average RMSE across segments = 61.57) the conventional single-step unsegmented forecasting process (RMSE overall data = 238.18). The main implication of this study is the demonstration of XAI in enhancing transparency in machine learning and an improved method for reducing forecasting errors in practice, which can strengthen the supply chain resilience for perishable products.
- 26 October 2024
- Published by Springer Nature in Annals of Operations Research
- Vol. 354 (1) , 103-142
Open AccessExplainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics
Optimizing costs and profits while meeting customer demand is a critical challenge in the development of perishable supply chains. Customer-centric demand forecasting addresses this challenge by considering customer characteristics when determining inventory levels. This study proposes a solution framework comprising two steps: (a) segmentation using customer characteristics and (b) demand forecasting for each segment using transparent and responsible artificial intelligence techniques. We employed k-means, hierarchical clustering, and explainable AI (XAI) to segment, model, and compare several machine-learning techniques for demand forecasting. The results showed that support vector regression outperformed the autoregressive models. The results also showed that the two-step segmentation and demand forecasting process using hierarchical clustering and LSTM outperforms (Weighted average RMSE across segments = 61.57) the conventional single-step unsegmented forecasting process (RMSE overall data = 238.18). The main implication of this study is the demonstration of XAI in enhancing transparency in machine learning and an improved method for reducing forecasting errors in practice, which can strengthen the supply chain resilience for perishable products.