Manufacturing/remanufacturing based supply chain management under advertisements and carbon emissions process

¹ Department of Mathematics, Meerut College, Meerut, Uttar Pradesh, India
² Department of Mathematics, Baba Mastnath University, Rohtak, India
³ Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, South Korea
⁴ Center for Transdisciplinary Research (CFTR), Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University 162, Poonamallee High Road, Velappanchavadi, Chennai, Tamil Nadu 600077, India

^* Corresponding author: bsbiswajitsarkar@gmail.com

Received: 31 July 2021
Accepted: 15 December 2021

Abstract

One of the most successful ways to get the word out about a product’s popularity across all types of customers is through advertising. It has a valuable direct influence on increasing product demand. The supply chain model is developed for manufacturer and retailer, where advertisements are dependent on demand. The advertisement rate has been considered a function that has enhanced at a diminishing rate concerning time, although the growth rate slowed. During the manufacturing cycle, the market’s demand is a function of advertisement, and the customer’s demand is a linear function of time. The production rate exceeds the demand rate during manufacturing and remanufacturing; shortages are not faced. It involves a manufacturing/remanufacturing process that quickly delivers consumer products and less waste. To keep the environment clean, the cost of carbon emissions is incorporated into the manufacturer’s and supplier’s holding and degrading costs. The model’s primary purpose is to minimize the overall cost of manufacturing and remanufacturing. The overall cost during the manufacturing cycle is higher than that during the remanufacturing cycle. This study confirms that the increasing cost of advertising provides the continuous increasing value of the total cost. A numerical example is provided, graphical representation and sensitivity analysis determine the function’s behavior and test the model.