Original Research

Optimising inventory, procurement and production with excess demand and random parameters

Purnawan A. Wicaksono, Sutrisno Sutrisno, Solikhin Solikhin, Abdul Aziz
Journal of Transport and Supply Chain Management | Vol 17 | a894 | DOI: https://doi.org/10.4102/jtscm.v17i0.894 | © 2023 Purnawan A. Wicaksono, Sutrisno Sutrisno, Solikhin Solikhin, Abdul Aziz | This work is licensed under CC Attribution 4.0
Submitted: 23 May 2023 | Published: 20 October 2023

About the author(s)

Purnawan A. Wicaksono, Department of Industrial Engineering, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
Sutrisno Sutrisno, Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia
Solikhin Solikhin, Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia
Abdul Aziz, Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia

Abstract

Background: Manufacturing and service industries in many sectors face extraordinary situations, such as excessive demands and uncertain prices during the post-pandemic period. In this situation, ordinary decision-making support is no longer suitable.

Objectives: This study aims to propose new mathematical programming in the form of probabilistic dynamical optimisation that can be used for optimising integrated inventory, procurement and production planning. The problem contains multiperiod, multisupplier, multiraw material and multiproduct. Furthermore, several parameters, including prices and costs were assumed to be probabilistic with some known probability distributions.

Method: The expectation of the profit was maximised in the model and the uncertain programming algorithm was used to calculate the optimal decision. The laboratory scaled computational experiments were also conducted with some randomly generated data.

Results: The results showed the proposed model successfully provided the optimal decision. This included the optimal amount of each observation period and raw material parts to be sold to each supplier and stored in the inventory. It also included the optimal amount of each product brand to be produced and stored with the maximal expectation of the profit earned for the whole optimisation horizon time.

Conclusion: The proposed decision-making support can be used by the decision-makers and managers in industries.

Contribution: A novel decision-making support is provided, which can be used to solve integrated inventory, procurement and production with excess demand and random parameters.


Keywords

after pandemic; recovery time; decision-making; probabilistic programming; production planning; raw part procurement; supply chain

JEL Codes

C61: Optimization Techniques • Programming Models • Dynamic Analysis; C65: Miscellaneous Mathematical Tools; M11: Production Management

Sustainable Development Goal

Goal 9: Industry, innovation and infrastructure

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