برنامه ریزی مسیر موجودی اثرات کربن و انتخاب تامین کنندگان نقطه گرم

In order to achieve the data accuracy on carbon emission from the suppliers, a complete carbon footprint inventory must be compiled at each supplier's site. Generally speaking, to collect the carbon emission inventory, data from various sources must be obtained, resulting in consumption of many resources from enterprises and suppliers. Therefore, to perform the compilation efficiently, a more systematic method for visiting suppliers is required. The carbon footprint inventory routing problem, based the vehicle routing problem (VRP), explores the selection of appropriate suppliers for inventory compilation after the carbon emission reaches a certain accuracy level and determination of the efficient carbon emission inventory route. In this study, the VRP is modified for the selection of the suppliers. Furthermore, by applying the sensitivity analysis, this study discusses the replacement of primary data by secondary data and development of the decision method that can be used to evaluate the route optimization, efficiency maximization, and cost minimization for carbon footprint inventory routing planning.

In recent years, many enterprises have voluntarily disclosed the data on carbon emission during the manufacturing of their products or services. Using the data, corporate carbon footprint can be determined based on the amount of direct and indirect carbon dioxide equivalent emission during the entire life cycle of its manufacturing processes or products. Most enterprises follow the guidelines of ISO 14064-1 (2006), ISO14064-2 (2006), and PAS 2050 (BSI, 2008) for calculating the carbon footprint of a product or service. A life cycle analysis (LCA) method, based on (ISO 14040 series) (ISO 14040, 2006a and ISO 14040, 2006b), is also adopted in evaluation of the environmental impact of raw materials use, manufacturing, distribution, and disposal of their products and waste to develop suitable carbon reduction plans, which would help in creation of eco-friendly products. The life cycle concept and inventory method were first proposed in the late 1960s to evaluate the environmental impact of products, services, and treatment from an environmental perspective. The data collected from various phases of product manufacturing are used to compile a life cycle inventory (LCI), an accounting of carbon gas releases incurred throughout the life cycle of a product (SETAC, 1991). Although the life cycle analysis is the widely accepted practice, it is still regarded as a specialized research field as the analysis involves a considerable data collection and examination from many sectors, including the suppliers of raw material, manufacturers, logistics providers, and waste treatment facilities. Therefore, it has not been used extensively by enterprises. Recently, because of the increasing public awareness on environment issues, enterprises have attempted to collect information of their own carbon emission during production. Although enterprises’ suppliers are willing to contribute to the life cycle inventory based on the relevant data, it is not an easy task as the globalized modern enterprises rely on operations from their functional divisions and suppliers scattered around the world. This creates difficulties in collecting the carbon footprint data from factories throughout the entire product life cycle. Other issues interfering with an accurate data collection include: (1) the geographical separation and language barrier of global suppliers around the world; (2) qualification of employees and suppliers to collect the accurate inventory data; and (3) diverse methods applied by the suppliers to calculate their own carbon emissions for different components and at various stages of the manufacturing processes. For a complete and accurate life cycle inventory, an on-site data compilation must be conducted for each supplier to obtain the primary data. An on-site inventory activity includes collection of various data on the consumption of resources, such as labor, materials, cost, and time, by enterprises and suppliers. Traditionally, the suppliers contributing to the life cycle inventory compilation are selected arbitrarily; systematic selection of parts suppliers does not exist. In order to reduce some workloads associated with the inventory compilation, some enterprises may define the accuracy of carbon emission inventory in advance, then subjectively selects certain suppliers and replaces first-hand information collected from the supplier with secondary data source obtained from published studies. Although this kind of method reduces costs associated with carbon emission inventory compilation, the data accuracy is compromised. When selecting suppliers for direct data collection, the selection of “hot spot” suppliers—those with high level of carbon dioxide emission, the determination of the efficient route for visiting suppliers for data collection, the reduction of costs, and accuracy of life cycle inventory are critical issues for modern enterprises to consider. In the past, there is no research that is directly related to route planning for carbon emission inventory compilation. Taking a cue from the fields of transportation, distribution and logistics (Kopfer and Kopfer, 2013 and Ubeda et al., 2011), the researchers determine an efficient route for visiting suppliers in regards to compiling life cycle inventory by modeling after the vehicle routing problem (VRP). However, the main difference between the VRP and the present problem is that this problem on the LCI involves finding the appropriate suppliers for data collection after their carbon emission reaches a defined percentage of accuracy value. To conduct a life cycle inventory compilation, the company must assess the inventory cost, route, time needed for compilation, and other unanticipated problems. This study will therefore model the optimization problem after the VRP, through the application of linear programming, and develop a decision-making model for supplier selection and efficient carbon emission inventory route planning. Further, with the sensitivity analysis, this study will discuss the use of secondary data by enterprises in replacement of primary data and aim at finding a decision method that can evaluate route optimization, maximize efficiency, and minimize cost for carbon footprint inventory routing, to conduct an efficient carbon footprint compilation. Finally, a case study is implemented to illustrate the research model.

Currently, none of enterprises conduct a on-site inventory of all products in the inventory for carbon dioxide emissions because of the cost, labor, resources, and time implications involved in conducting the inventory compilation. Thus, this study solves the inventory compilation route problem modeled after VRP through dynamic and integer programming. The research also presented a case study to eliminate the uncertainty of the traditional inventory to achieve integrity, consistency, accuracy, and transparency during the inventory compilation process. Conclusions and suggestions are provided as the following: (1) The efficiency of the study model is better than that of traditional planning process. The proposed method yields an efficient route to satisfy the inventory compilation and cost minimization. (2) In the case study, an efficient model facilitates the calculation of an inventory compilation route with a minimum cost for each supplier based on constraints and given parameters. (3) This model adapts flexibly and adjusts to the changes of parameters. Furthermore, it determines the required route and lowest target value with high efficiency. (4) This study model provides the limitation of parameters for traveling costs and emissions, and generates an efficient route for suppliers based on the distance or suppliers with lowest emission. (5) Based on the sensitivity analysis, the impacts of suppliers’ constraints and parameters on the overall objective value can be calculated, and the inventory route can be adjusted quickly to changes. Through the sensitivity analysis, the route for inventory compilation can be adjusted quickly when parameters change. Moreover, various constraints or changes in parameters have a great impact on costs. This is the first study to plan an inventory compilation route modeled after VRP. However, some limitations must be addressed in future studies: (1) In this study, the time constraint is not considered. If a time constraint is considered, the object function value may change. (2) The carbon dioxide variation is smaller based on the sensitivity analysis. A control mechanism for gas emissions by suppliers needs to be implemented to produce a greater impact on costs and prevent a greater change to costs and routing. In terms of results of the hot spot analysis, the accu