Abstract

In recent years, the importance of waste reduction and efficient use of resources has led companies to focus more on eco-friendly processes at different stages of the manufacturing process, such as acquiring raw materials, production, assembly, distribution and recycling. Additionally, manufacturers in many countries have been either under governmental pressure to follow eco-friendly guidelines in their production process, or have chosen to do so to benefit from governmental incentives such as tax reductions. Furthermore, the public interest in environmentally friendly goods has been on the rise as a result of growing awareness towards the negative consequences of industrial activities and practices that harm the environment. All of these factors have made it imperative for industries to adopt green practices in order to gain or maintain their competitive edge. However, staying green is not easy, as green production lines, products and practices are often more costly than their regular non-green counterparts. Therefore, in this study, we have developed a mathematical model based on supply chains and manufacturing facilities of all sizes producing various products with two distinct goals. First, our mathematical model helps companies maximize their profit and second it allows them stay green while the profit is maximized. This mathematical model is then solved for two scenarios in three different companies to (1) evaluate the effects of customer sensitivity towards eco-friendliness of the products and (2) the effects of changing CO2 emissions and transportation costs. The results of the calculations performed by the mathematical model shows the profit that the company stands to gain based on an allowed production volume that does not exceed the defined green criteria and the amount of raw materials to be purchased from suppliers that offer different degrees of greenness. In practice, this mathematical model can be expanded to include more constraints and can also be implemented in commercial software solutions to provide managers with valuable data to facilitate the decision making processes within the companies and among connected commercial entities.