# Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review
## 1 Introduction  
For decades, worldwide attention has focused on renewable energy production to mitigate greenhouse gas (GHG) emissions. Although biodiesel cannot entirely replace petroleum-based fossil fuels, it remains a good option to reduce greenhouse gas (GHG) emissions and environmental pollution problems [1]. For example, biodiesel that is made of either vegetable oils or animal fat produces only 702.96 g of CO\({}_{2}\) per liter, so it can reduce GHG emissions of petroleum diesel by 74% (i.e., 2689.27 g of CO\({}_{2}\) per liter) [2]. Moreover, compared with petroleum diesel, biodiesel has two significant advantages. First, it is non-flammable and non-explosive, with a flash point of 423 K for biodiesel, compared to 337 K for petroleum diesel [3]. Second, it is biodegradable and non-toxic, and when burned as a fuel, significantly reduces toxic and other emissions [4].  
Recently, due to the increase in petroleum prices and the implementation of financial incentives for its use [5], the cost of biodiesel use has become competitive compared to petroleum diesel. [PERSON] and [PERSON] [6] found the total production cost of canola-based biodiesel involving feedstock and processing costs to be between USD 1.46 and 1.69 per liter. On the other hand, petroleum diesel production cost involving crude oil and refining costs is USD 0.50 per liter [7]. Although the production cost of biodiesel is approximately three times that of petroleum diesel production, incentive policies, such as biodiesel production and blending tax credit, provide USD 0.26 per liter in the U.S. [8], and biodiesel blending regulations for transportation use [9] have been enacted to increase the use of biodiesel.  
However, from a long-term perspective, to make the cost of biodiesel competitive without any incentives and monetary supports given by government agencies, an efficient supply chain management of biodiesel should be considered. Traditionally, the cost of petroleum diesel consists of crude oil cost (51%), refining cost (21%), distribution and marketing cost (11%), and tax (16%) [7]. Similar to petroleum diesel, the efficient management of supply chain directly influences refining cost involving feedstock warehousing and the distribution cost of biodiesel, so that from the supply chain management perspective, there is the possibility of mitigating the sales cost of biodiesel [10]. Efficient supply chain management is one of the critical issues for the operational excellence of an organization; it can reduce operational costs while also improving the productivity of a supply chain [11]. With the growth of interest in environment-friendly and sustainable supply chain management, it becomes a more challenging issue for the long-term success of an organization [12]. This trend is not exceptional for the biofuel supply chain that produces either biodiesel or bioethanol using cellulose, hemicellulose, and lignin [13].  
The goal of this study is to investigate how machine learning technologies are utilized for the supply chain management of biodiesel. In particular, the literature of three major application areas of feedstock yield prediction, biodiesel productivity prediction, and biodiesel quality prediction is reviewed. This review article contributes to informing engineers of which machine learning technologies are appropriate for a particular application area to enable the efficient management of a biodiesel supply chain.  
The remaining sections are organized as follows: Section 2 addresses the characteristics of biodiesel supply chains, while Section 3 investigates recent studies of machine learning technologies applied to the supply chain management of biodiesel. Section 4 discusses the advantages and disadvantages of machine learning technology use in supply chain management; then, Section 5 concludes the study and findings.