A model for green order quantity allocation in a collaborative supply chain. (10th April 2023)

• Record Type: Journal Article
• Authors: Di Pasquale, Valentina; Iannone, Raffaele; Nenni, Maria Elena; Riemma, Stefano
• Is Part Of: Journal of cleaner production. Volume 396(2023)
• Publication Date: 2023-04-10
• Subjects: Supply chain management -- Supplier selection -- Green order quantity allocation -- Collaboration -- Sustainable supply chain; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2023.136476
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Several models have been proposed in the literature for allocating quantity orders, but few of them consider collaboration to enhance supply chain (SC) performance sustainably. Despite the increasing attention given to sustainability and the triple bottom line (TBL) criteria, the existing models for order allocation are limited to considering TBL scores only in the phase of selecting suppliers, thus evaluating mainly environmental attributes, without integrating economic and social ones. For these reasons, based on the main research gaps in the current literature, this paper aims to investigate the green order allocation problem in an SC with high operational variability, exploiting the advantages of a collaborative and sustainable SC. The model we propose considers the uncertainty of the demand, the limited capacity of the suppliers, the minimum levels of purchase and production, the environmental, economic, and social scores of suppliers, and the total emissions related to the production, ordering, maintenance, and transport of the allocated items. Our model is based on allocation curves that are continuously updated and shared between the customer and the supplier. On the customer side, it allows to dynamically optimise order allocation, and on the supplier side, it provides clear information on the possible future requests from the customer based on the overall and real economic, environmental, and social performance. To validate the model, the authors applied it to a numerical example, which resulted in minimising both costs and the TBL impacts. Moreover, considering the TBL scores only in the selection of suppliers led to non-optimal choices in the present case study; for example, assigning the largest share of the order to the supplier with the lowest TBL score did not result in the most sustainable allocation. The continuous exchange of information and the synchronism triggered by the buyer–supplier collaboration allows managers to know in advance of any critical issues that may arise in the future and intervene promptly to reorganise the allocation. Therefore, the proposed model can help both researchers and practitioners establish a systematic approach to green order allocation and might help firms increase their sustainability degrees throughout the supply chain. Highlights: This study investigates green order allocation in a supply chain with high operational variability. Quantity allocation considers costs, suppliers' sustainability scores, and constraints. We designed a decision-making tool that can be used by supply chain managers for green order allocation. The proposed model promotes the exchange of information among all the supply chain actors.