An Inventory Decision Model When Demand Follows Innovation Diffusion Process under Effect of Technological Substitution

Author:

Aggarwal K. K.1ORCID,Jaggi Chandra K.1,Kumar Alok1

Affiliation:

1. Department of Operational Research, Faculty of Mathematical Sciences, New Academic Block, University of Delhi, Delhi 110007, India

Abstract

The concept of marketing literature, especially innovation diffusion concept, plays a pivotal role in developing EOQ models in the field of inventory management. The integration of marketing parameters, especially the idea of diffusion of new products with the inventory models, makes the models more realistic which is most essential while building the economic ordering policies of the products. Also, because of rapid technological development, the diffusion of technology can also be viewed as an evolutionary process of replacement of an old technology by a new one. Therefore, the effect of technological substitution along with the diffusion of new products must be taken into account while formulating economic ordering policies in an inventory model. In this paper, a mathematical model has been developed for obtaining the Economic Order Quantity (EOQ) in which the demand of the product is assumed to follow an innovation diffusion process as proposed by Fourt and WoodLock (1960). The idea of effect of technological substitution of products has been incorporated in the demand model to make the economic ordering policies more realistic. A numerical example with sensitivity analysis of the optimal solution with respect to different parameters of the system is performed to illustrate the effectiveness of the model.

Funder

University of Delhi

Publisher

Asia University

Subject

Applied Mathematics,Computational Mathematics,Statistics and Probability,General Decision Sciences

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A production inventory model for high-tech products involving two production runs and a product variation;Journal of Industrial and Management Optimization;2023

2. A Text Mining Approach to Covid-19 Literature;International Journal of Information Technology & Decision Making;2022-11-30

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