Pooling Strategies in Supply Chains: Development of Simulation Models to Explore Their Effects on CO2 Emissions

Introduction

Greenhouse gas (GHG) emissions are one of the main causes of the global climate change we are currently witnessing. Almost all economic activities produce GHG, albeit to various degrees and sizes. In the area of supply chain, burning fossil fuels for logistical purposes produces CO2, one of the most prevalent GHGs (Yudi and Wei Lin, 2017, Montzka et al., 2011). This pollutant causes global warming. Indeed, the average world temperature has significantly increased because of CO2 emissions. The thermal expansion of water is one of the principal effects of this temperature rise. Growing temperatures cause sea levels to rise, posing a severe risk to civilization and the economy as melting glaciers add water to the ocean (Sulaiman and Abdul-Rahim 2018; Mohsin et al. 2019). Also, CO2 emissions have a big impact on health. Because of smog and air pollution, they lead to respiratory illnesses. Extreme weather, a disruption in the food supply, and an increase in forest fires are some additional repercussions of climate change brought on by CO2 emissions.

Many academics have looked into various strategies to reduce CO2 emissions in the supply chain. One of the key strategies to lower their CO2 emissions is a cooperation between supply chains (Duong and Chong, 2020; Ho et al., 2019). Pooling logistics resources is one of the creative supply chain collaboration strategies. This approach is mainly built on a partnership agreement to share infrastructure, vehicles, etc. It goes beyond the straightforward notion of pooling vehicles. The purpose of this sharing is to create a supply chain that is more effective and efficient. This sharing attempts to either decrease CO2 emissions specifically or boost the efficiency of supply networks (Taieb et al. 2014). Implementing pooling began with strictly economic considerations. However, supply chain managers were forced to think about pooling logistics resources for environmental goals including decreasing CO2 emissions because of new rigorous regulatory rules on eco-efficiency that were imposed at the administrative level (Ülkü 2012; Wang et al. 2011).

The study and advancement of the concepts of logistics pooling involve numerous research projects. Most scholars have studied and quantified the effects of supply chain pooling on CO2 emissions using mathematical modeling in this paradigm. Pan et al. (2013) examined how supply chain pooling affected CO2 emissions from both road and rail transportation. To assess and improve the CO2 emissions of supply chains through pooling, they created a piecewise linear optimization model. They put this approach to the test using a 12-week database of a distribution network made up of the supply chains of two French retailers. According to the findings, CO2 emissions have significantly decreased. In addition, the combined use of roads and railroads has a greater impact on this reduction. Ouhader and El kyal (2017) attempted to estimate the potential economic and environmental advantages of horizontal transport cooperation. They used a method based on bi-objective mathematical modeling to investigate the connection between CO2 emissions and transport costs. Then they attempted to combine the facility site and distribution route selections in urban freight to reduce the overall cost and environmental effects of transportation. The comparison's findings revealed a decrease in CO2 emissions, transportation expenses, and kilometers traveled as well as an improvement in vehicle load rate. Montoya-Torres et al. (2016) undertook a case study using information from three businesses in Bogota, Colombia. Each of these businesses operates a store. The authors' study was based on mathematical modeling estimating travel distance to compare a collaborative scenario to a non-collaborative one. Each company uses its stores to distribute its products in the non-collaborative scenario. In the collaborative model, distribution to clients is handled through a shared store managed by one of the three organizations, which also functions as a central hub. Based on the travel distances, the authors calculated the resulting CO2 emissions. The outcomes showed the numerical gains made possible by the collaboration of logistics activities.