A probabilistic approach to information management of order fulfilment reliability with the help of perfect-order analytics. (February 2023)

• Record Type: Journal Article
• Authors: Lukinskiy, Valery; Lukinskiy, Vladislav; Ivanov, Dmitry; Sokolov, Boris; Bazhina, Darya
• Is Part Of: International journal of information management. Volume 68(2023)
• Publication Date: 2023-02
• Subjects: Supply chain -- Information management -- Perfect-order analytics -- Supply reliability; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2022.102567
• Languages: English
• ISSNs: 0268-4012

Abstract

Abstract: Supplier reliability and order fulfilment performance are usually assessed using a perfect-order calculation. Information management of perfect-order estimation is frequently reduced to expert estimates and to the multiplication of probabilities of failure-free performance of some logistics operations. Moreover, perfect-order estimation is calculated without consideration of supply chain structure, possible combinations of failures, and operational policies (e.g., safety stock levels and alternative transportation routes). As a result, the existing methods frequently provide different estimates for the same statistics and cannot be consistently used in the allocation of companies' resources to improve the order fulfilment process. This paper considers different variants of probabilistic assessment of a perfect order and proposes an approach to assess the impact of changes in parameter probabilities and number of parameters on the value of a perfect order. The proposed models are based on an analytical approach using discrete distributions of random variables. We illustrate the applicability of our approach to several numerical examples to confirm the adequacy of the proposed method. Our approach can be immediately applied in practice to assess supply and order fulfilment process reliability and to evaluate the effectiveness of various operational policies (safety stock levels or modes of transportation) to achieve some planned values of a perfect order in the supply chain. Highlights: Probabilistic approach to supplier reliability and order fulfilment. Information management of perfect order analytics. Analytical approach using discrete distributions of random variables. Effectiveness of operational policies for perfect order analytics.