Greenhouse Gas Emission Analysis of Integrated Production-Inventory-Transportation Supply Chain Enabled by Additive Manufacturing

2021 ◽  
pp. 1-14
Author(s):  
Lei Di ◽  
Yiran Yang
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Supply Chains ◽  
Greenhouse Gas ◽  
Manufacturing Industry ◽  
Ghg Emissions ◽  
Chain Structure ◽  
Integrated Production ◽  
Production Inventory ◽  
Supply Chain Structure

Abstract Additive manufacturing (AM), owing to its unique layer-wise production method, can offer evident advantages comparing to traditional manufacturing (TM) technologies such as faster production, lower cost, and less waste. The uses of AM in rapid tooling, prototyping, and manufacturing have been innovating the current manufacturing industry from the process level to the entire supply chain. Most existing research on AM is focused on process improvement and new materials, largely neglecting the potential economic and environmental benefits enabled by AM supply chains. This research investigates an innovative supply chain structure, i.e., the integrated production-inventory-transportation (PIT) structure that is uniquely enabled by AM because of its capability of fabricating the entire product with less or even no need for assembly and labor involvement. This paper quantifies and compares the greenhouse gas (GHG) emissions of TM and AM-enabled PIT supply chains. The manufacturing industry is a major source of GHG emissions in the U.S., which therefore needs to be studied in order to explore opportunities for reducing GHG emissions for environmental protection. Case study results suggest that a potential reduction of 26.43% of GHG emissions can be achieved by adopting the AM-enabled PIT supply chain structure. Sensitivity analysis results show that a 20% variation in GHG emission intensity (the amount of CO2eq emissions caused by generating a unit of electricity) can lead to a 6.26% change in the total GHG emissions in the AM-enabled PIT supply chain.

Greenhouse Gas Emission Analysis of Integrated Production-Inventory-Transportation Supply Chain Enabled by Additive Manufacturing

2021 ◽  
Author(s):  
Lei Di ◽  
Gaurav Manish Shah ◽  
Yiran Yang ◽  
Cuicui Wei
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Greenhouse Gas ◽  
Emission Intensity ◽  
Manufacturing Industry ◽  
Ghg Emissions ◽  
Ghg Emission ◽  
Integrated Production ◽  
Production Inventory ◽  
Traditional Manufacturing

Abstract The manufacturing industry is a major source of greenhouse gas emissions (GHG). Additive manufacturing, owing to its multiple advantages, plays a critical role in innovating the current manufacturing industry, especially from a supply chain perspective. Currently, the majority of research on GHG emissions in the manufacturing industry is focused on traditional manufacturing, either single processes in the supply chain or specific case studies, indicating the lack of models on GHG emissions in additive manufacturing-enabled supply chain structures. In this work, a mathematical model is established to estimate the GHG emissions in both traditional manufacturing and additive manufacturing-enabled supply chains. To explore the advantages of additive manufacturing in terms of fast production and reduced or even eliminated the need for assembly and labor involvement, a unique integrated production-inventory-transportation structure is investigated in additive manufacturing case studies. The results indicate that a potential reduction of 26.43% of GHG emissions can be achieved by adopting the additive manufacturing technique in the supply chain. Also, the impacts of rush order rate, emission intensity, and vehicle GHG emission constant rate on the overall GHG emissions are investigated in the sensitivity analysis. Results indicate that a 20% variation in GHG emission intensity (the amount of CO2eq emissions caused by generating a unit of electricity) can lead to a 6.26% change in the total GHG emissions in additive manufacturing.

The impact of additive manufacturing on supply chains

2017 ◽  
Vol 47 (10) ◽  
pp. 954-971 ◽  
Author(s):  
Christian F. Durach ◽  
Stefan Kurpjuweit ◽  
Stephan M. Wagner
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Manufacturing Industry ◽  
Chain Structure ◽  
Survey Study ◽  
Content Type ◽  
Multi Stage ◽  
Supply Chain Structure ◽  
The Impact ◽  
Am Processes

Purpose The purpose of this paper is to offer empirical insights on emerging additive manufacturing (AM) processes, barriers to their adoption and a timeline of expected impacts on the supply chain in the manufacturing industry. Design/methodology/approach A multi-stage survey study was conducted with a panel of 16 experts from industry and academia. Findings Only five out of today’s seven AM processes are of future importance, as are two emerging key processes. In total, 15 barriers to their adoption are identified, all of which are expected to be gone within ten years. Eight propositions are derived postulating as to whether and when supply chain impacts can be expected in terms of changes to supply chain structure, customer centricity, logistics and supply chain capability. Research limitations/implications “Soft” barriers are new to the literature, which has traditionally focused on “technical” barriers. Often-discussed barriers such as production speed and costs do not reflect the true concerns of the research panel. Furthermore, some of the supply chain implications discussed in both the academic literature and the media are found to be unlikely to materialize. Practical implications The study summarizes AM processes, technologies, barriers and supply chain implications solicited from experts in the field. Originality/value This is one of the first studies to make empirical contributions to a vastly conceptual discussion. It is also the first study to give insights on a timeline for barriers and supply chain implications.

Information Sharing Strategies in Business-to-Business E-Hubs

2012 ◽  
pp. 72-93
Author(s):  
Yifeng Zhang ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Information Sharing ◽  
Chain Structure ◽  
Agent Based ◽  
Key Factor ◽  
Supply Chain Structure ◽  
Hub Model ◽  
Business To Business ◽  
Practical Implications

Studies show that supply chain structure is a key factor affecting information sharing. Business-to-business (B2B) e-hubs have fundamentally changed many companies’ supply chain structure, from a one-to-many to a many-to-many configuration. Traditional supply chains typically center around one company, which interacts with multiple suppliers or customers, forming a one-to-many structure. B2B e-hubs, on the contrary, usually connect many buyers and sellers together, without being dominated by a single company, thus forming a many-to-many configuration. Information sharing in traditional supply chains has been studied extensively, but little attention has been paid to the same in B2B e-hubs. In this study, the authors identified and examined five information sharing strategies in B2B e-hubs. Agent performances under different information sharing strategies were measured and analyzed using an agent-based e-hub model and practical implications were discussed.

The “snowball effect” in the transmission of disruptions in supply chains

2016 ◽  
Vol 27 (3) ◽  
pp. 1002-1038 ◽  
Author(s):  
Artur Swierczek
Keyword(s):  
Risk Factors ◽  
Supply Chain ◽  
Supply Chains ◽  
Chain Structure ◽  
Supply Chain Integration ◽  
Negative Consequences ◽  
Content Type ◽  
External Risk ◽  
Supply Chain Structure ◽  
Snowball Effect

Purpose The purpose of this paper is to explore the link between interorganizational integration with respect to its intensity and span, as well as the propagation and amplification of disruptions alongside a supply chain. Design/methodology/approach The paper opted for an exploratory study using a survey of companies. In order to extract the constructs manifesting the span and intensity of integration between companies in supply chains, the principal component analysis was employed. The obtained factor scores were then used as classification criteria in the cluster analysis. It enabled to include similar organizations in terms of intensity and span of supply chain integration. In order to validate the obtained results, the analysis of variance (ANOVA) was conducted and regression models were developed. Findings The findings of the study show that there is a relationship between the intensity and span of supply chain integration and the “snowball effect” in the transmission of disruptions. The obtained findings show that the span of supply chain integration is negatively associated with the strength of the “snowball effect” in the transmission of disruptions. In addition, the results suggest that more intense supply chain integration contributes to the “snowball effect” in material flows in the forward and backward transmission of disruptions. Research limitations/implications Although the current study investigates the intensity and span of integration within the basic, extended and ultimate supply chain structure, it still lacks the broader analysis of the “snowball effect” in the transmission of disruptions. The study investigates this phenomenon only within the basic supply chain structure, constituted by the primary members. Another challenge is to examine if the effects of external risk factors (e.g. natural disasters) may also be transferred to other links in the supply chain structure, and what are the similarities and differences (if any) between the mechanism of propagation and amplification of disruptions elicited by internal and external risk factors. Another future direction of study is to define other ways of identification and measurement of the “snowball effect” in order to make cross-industrial and international comparisons of disruptions amplified in the transmission more standardized and objective. In the current study, the phenomenon of the “snowball effect” is anchored in the subjective opinions of managers who may view the problem from different angles. Consequently, the study is limited to individual perceptions of the strength of disruptions affecting the solicited company, its customers and suppliers. Practical implications In practical terms, the findings provide crucial information for the framework of supply chain risk management and therefore enable its more efficient and effective implementation. The better the managers understand the nature of the “snowball effect” in the transmission of disruptions, the easier it is for them to allocate resources and apply necessary managerial tools to mitigate the negative consequences of risk more effectively. The deliverables of the study also confirm that the interorganizational exchange of information accompanying the supply chain integration enables to mitigate the strength of the “snowball effect” in the transmission of disruptions. Another important implication is the broadening of practical expertise concerning the use of integration not only as a means of obtaining and sustaining supply chain effectiveness and efficiency, but also as the way to mitigate the “snowball effect” in the transmission of disruptions. Therefore, nowadays the supply chain managers are facing another challenging task – namely, how to balance supply chain integration in terms of span and intensity to ensure profits from integration and mitigate the negative risk consequences transmitted among the links in supply chains. Originality/value The paper elaborates on the underestimated issue of the “snowball effect” in the transmission of disruptions and its drivers. In particular, the paper attempts at filling the gap in empirical studies concerning the relationships between the “snowball effect” in the transmission of disruptions and supply chain integration.

Supply Chain Structure and Competitor Relationship

2020 ◽  
Author(s):  
Jing Wu ◽  
Yang Xu
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Theoretical Basis ◽  
Chain Structure ◽  
Shock Propagation ◽  
Supply Chain Network ◽  
Empirical Methods ◽  
Competition Intensity ◽  
Cross Border ◽  
Supply Chain Structure

This chapter discusses recent relevant empirical research using the supply chain structure observed in the actual data, including shock propagation in the supply chain network, social capital, and supply chains, and cross-border supply chains. It also introduces some commonly used empirical methods and databases, and provides the corresponding financial theoretical basis for the conclusions of these studies. Finally, the chapter suggests a new angle to fully utilize the supply chain structure to identify the competitor relationship and the competition intensity. The chapter indicates that higher supply chain overlap increases the correlation of the competitors’ economic performance, suggesting that sharing supply chains reduce competition. This conclusion is helpful for entrepreneurs to better manage firm competitions.

Layer analysis of CO2 sources in the US economic supply chains: an input output LCA study

2017 ◽  
Vol 117 (10) ◽  
pp. 2171-2193 ◽  
Author(s):  
Gokhan Egilmez ◽  
N. Muhammad Aslaam Mohamed Abdul Ghani ◽  
Ridvan Gedik
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Carbon Footprint ◽  
Sustainability Assessment ◽  
Chain Structure ◽  
Total Carbon ◽  
Input Output ◽  
Content Type ◽  
The Us ◽  
Supply Chain Structure

Purpose Carbon footprint assessment requires a holistic approach, where all possible lifecycle stages of products from raw material extraction to the end of life are considered. The purpose of this paper is to develop an analytical sustainability assessment framework to assess the carbon footprint of US economic supply chains from two perspectives: supply chain layers (tiers) and carbon footprint sources. Design/methodology/approach The methodology consists of two phases. In the first phase, the data were collected from EORA input output and environmental impact assessment database. In the second phase, 48 input-output-based lifecycle assessment models were developed (seven CO2 sources and total CO2 impact, and six supply chain tiers). In the third phase, the results are analyzed by using data visualization, data analytics, and statistical approaches in order to identify the heavy carbon emitter industries and their percentage shares in the supply chains by each layer and the CO2 source. Findings Vast majority of carbon footprint was found to be attributed to the power generation, petroleum refineries, used and secondhand goods, natural gas distribution, scrap, and truck transportation. These industries dominated the entire supply chain structure and found to be the top drivers in all six layers. Practical implications This study decomposes the sources of the total carbon footprint of US economic supply chains into six layers and assesses the percentage contribution of each sector in each layer. Thus, it paves the way for quantifying the carbon footprint of each layer in today’s complex supply chain structure and highlights the importance of handling CO2 source in each layer separately while maintaining a holistic focus on the overall carbon footprint impacts in the big picture. In practice, one size fits all type of policy making may not be as effective as it could be expected. Originality/value This paper provides a two-dimensional viewpoint for tracing/analyzing carbon footprint across a national economy. In the first dimension, the national economic system is divided into six layers. In the second dimension, carbon footprint analysis is performed considering specific CO2 sources, including energy production, solvent, cement and minerals, agricultural burning, natural decay, and waste. Thus, this paper contributes to the state-of-art sustainability assessment by providing a comprehensive overview of CO2 sources in the US economic supply chains.

Integrated Production-Inventory Model in a Multi-Echelon Closed-Loop Supply Chain with Product Returns

2012 ◽  
Vol 472-475 ◽  
pp. 3305-3311 ◽  
Author(s):  
Abu Hassan Zarina ◽  
Luong Lee ◽  
Sang Heon Lee
Keyword(s):  
Mathematical Modeling ◽  
Supply Chain ◽  
Supply Chains ◽  
Closed Loop ◽  
Inventory Model ◽  
Deterministic Approach ◽  
Product Returns ◽  
Integrated Production ◽  
Closed Loop Supply Chains ◽  
Production Inventory

This paper deals with production-inventory policy in the context of multi-echelon closed-loop supply chains. The system comprises of a number of distributors, single manufacturer, single supplier and single dismantler. The objective is to develop and formulate the mathematical modeling for deterministic approach.

Information Sharing Strategies in Business-to-Business E-Hubs

2010 ◽  
Vol 6 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Yifeng Zhang ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Information Sharing ◽  
Chain Structure ◽  
Agent Based ◽  
Key Factor ◽  
Supply Chain Structure ◽  
Hub Model ◽  
Business To Business ◽  
Practical Implications

Studies show that supply chain structure is a key factor affecting information sharing. Business-to-business (B2B) e-hubs have fundamentally changed many companies’ supply chain structure, from a one-to-many to a many-to-many configuration. Traditional supply chains typically center around one company, which interacts with multiple suppliers or customers, forming a one-to-many structure. B2B e-hubs, on the contrary, usually connect many buyers and sellers together, without being dominated by a single company, thus forming a many-to-many configuration. Information sharing in traditional supply chains has been studied extensively, but little attention has been paid to the same in B2B e-hubs. In this study, the authors identified and examined five information sharing strategies in B2B e-hubs. Agent performances under different information sharing strategies were measured and analyzed using an agent-based e-hub model and practical implications were discussed.

A Complexity Model for Assembly Supply Chains and Its Application to Configuration Design

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Hui Wang ◽  
Jeonghan Ko ◽  
Xiaowei Zhu ◽  
S. Jack Hu
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Product Variety ◽  
Chain Structure ◽  
Complexity Measure ◽  
Supply Chain Configuration ◽  
Supply Chain Structure ◽  
Definition Of ◽  
Optimal Assembly ◽  
Chain Configuration

A complexity measure for assembly supply chains has been proposed based on Shannon’s information entropy. This paper extends the definition of such a measure by incorporating the detailed information of the supply chain structure, the number of variants offered by each node in the supply chain, and the mix ratios of the variants at each node. The complexity measure is then applied to finding the optimal assembly supply chain configuration given the number of variants offered at the final assembler and the mix ratios of these variants. The optimal assembly supply chain configuration is theoretically studied in two special scenarios: (1) there is only one dominant variant among all the variants offered by the final assembler, and (2) demand shares are equal across all variants at the final assembler. It is shown that in the first scenario where one variant dominates the demand, the optimal assembly supply chain should be nonmodular; but in the scenario of equal demand shares, a modular supply chain is better than nonmodular one when the product variety is high. Finally a methodology is developed to find the optimal supply chain with/without assembly sequence constraints for general demands.

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