A green framework for manufacturing: product, process and system levels

The term green manufacturing is coined to reflect the new manufacturing paradigm that employs various green strategies (objectives and principles) and techniques (technology and innovations) to become more eco-efficient. The greening of manufacturing industry requires a holistic view spanning throughout the product, process and system level including: less material and energy consumption, reduced waste and emission as well as recycling or reuse. The aim of this research is to employ green strategies that lead towards green manufacturing via product, process and system level. This work is divided into three segments: product base, process base and system base. In the product base segment, Life cycle inventory (LCI) is a popular measure which is computed to acquire the consumption (raw materials or energy) and emission (greenhouse gas or waste quantity) of a product system. The three main currently available methods of LCI are: Process based LCI, Input output LCI and Hybrid method method. These methods may provide different environmental impact results for the same product. In order to choose a particular method, one should know the calculation process, relative advantages and limitations for the intended purpose. These methods provide environmental impact data which are utilized in different sustainability measures. Environmental decision making is one such important LCI application. However, literatures are found where this decision making are performed on the basis of a particular impact category although a comparison based on overall environmental impact is more realistic. Different impact categories exhibit different increasing and decreasing trends simultaneously and they have different unit of measurement. In this project, a review on the LCI methods and a novel approach for using overall LCI data for environmental decision making for food products has been presented. Under process base improvement, green energy management is one of the prime concerns for any industry. For green energy management, a renewable energy source is highly required. Waste-to-energy (WtE) can be an attractive solution for renewable energy source. The objective of this work is to propose a strategy to reduce the electricity bill for the industry under variable electricity pricing. In order to reduce the electricity bill, a fuzzy Inference System (FIS) based energy management strategy to produce electricity in low pricing period and utilize it in peak period is proposed by integrating small scale WtE and storage into industry system. Though this model is built for energy management, it indirectly works as a tool for waste management as well. The performance of the proposed model is tested with the data collected from a plastic container manufacturing industry. Green supply chain network synthesis is one of the major system level improvements. This network is the combination of various stages such as; raw materials acquisition, processing, manufacturing, packaging, distribution and so on. Green supply chain network design is such an optimization act which combines the feasible pathways among the supply chain stages to serve environmental sustainability. However, modelling supply chain network is a complex task. Though mathematical modelling is a conventional approach to design this complex network, for larger size problem it becomes highly difficult. Furthermore, changing any variable like; materials, energy sources or process technologies etc. make this optimization even more time consuming. Process Network Synthesis (PNS) methodology based on P-graph (process Graph) is a new approach recently been adopted by practitioners for designing a sustainable supply chain network successfully. In this work, a green supply chain network is designed by P-graph approach for co-firing of bio mass in Rajshahi, Bangladesh.