Supply chain optimization of large-scale continuous processes

2005 ◽  
Vol 29 (6) ◽  
pp. 1305-1316 ◽  
Author(s):  
E.P. Schulz ◽  
M.S. Diaz ◽  
J.A. Bandoni
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Supply Chain Optimization ◽  
Continuous Processes

scholarly journals Supply chain optimization with variable demand by considering financial criteria and scenarios

2017 ◽  
Vol 26 (44) ◽  
pp. 21 ◽  
Author(s):  
John Willmer Escobar
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Net Present Value ◽  
Programming Model ◽  
Supply Chain Design ◽  
Mixed Integer ◽  
Supply Network ◽  
Distribution Centers ◽  
Supply Chain Optimization ◽  
Starting Point

This paper contemplates the supply chain design problem of a large-scale company by considering the maximization of the Net Present Value. In particular, the variability of the demand for each type of product at each customer zone has been estimated. As starting point, this paper considers an established supply chain for which the main problem is to determine the decisions regarding expansion of distribution centers. The problem is solved by using a mixed-integer linear programming model, which optimizes the different demand scenarios. The proposed methodology uses a scheme of optimization based on the generation of multiple demand scenarios of the supply network. The model is based on a real case taken from a multinational food company, which supplies to the Colombian and some international markets. The obtained results were compared with the equivalent present costs minimization scheme of the supply network, and showed the importance and efficiency of the proposed approach as an alternative for the supply chain design with stochastic parameters.

Blockchains for Value Creation and Supply Chain Optimization

2019 ◽  
pp. 28-44
Author(s):  
Arun N. Nambiar
Keyword(s):  
Information Technology ◽  
Supply Chain ◽  
Value Creation ◽  
Large Scale ◽  
Market Information ◽  
Supply Chain Optimization ◽  
Point Of Sale ◽  
Planning Software ◽  
Highly Dispersed ◽  
Data Transparency

Managing today's highly dispersed and intertwined supply chain in order to maximize the overall organizational benefit by leveraging partner competencies is a herculean task and one that is of ever-growing importance in a highly competitive and truly globalized market. Information technology in the form of point-of-sale data, materials requirement planning software, and enterprise-wide systems have often been leveraged to assist with this. However, with the proliferation of data, storing, managing, and analyzing data on a large scale is a challenge. Blockchains provide numerous benefits such as data transparency, immutability, and traceability that are so critical in building a cohesive cyberinfrastructure that facilitates cooperation and collaboration among supply chain partners. This chapter examines the characteristics of blockchain that make it suitable for supply chains and explore how the benefits afforded by blockchain can be leveraged to enhance value creation while optimizing the supply chain.

scholarly journals Supply chain optimization framework for CO2 capture, utilization, and storage in Germany

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tuan B. H. Nguyen ◽  
Grazia Leonzio ◽  
Edwin Zondervan
Keyword(s):  
Supply Chain ◽  
Carbon Capture ◽  
Large Scale ◽  
Investment Strategies ◽  
The Novel ◽  
Supply Chain Optimization ◽  
Sources And Sinks ◽  
Transportation Modes ◽  
First Time ◽  
And Storage

Abstract In this study, the carbon capture, utilization, and sequestration (CCUS) supply chain network with real geographic locations of sources and sinks, and different CO2-based products for Germany is proposed here for the first time, because not yet investigated in the literature. The CCUS network is a large-scale comprehensive model which is used to meet the mandated target of CO2 emission reduction at different levels with a maximum profit. The novel CCUS infrastructure includes various stationary sources, capture processes, transportation modes, and sequestration and utilization sites. The results suggest that it is possible to reduce current CO2 emissions by 40–80% in Germany with the total annual costs ranging from 519.34 to 1372.03 billion euro while generating 681.55 to 1880.98 billion euro of revenue annually as a result of producing CO2-based chemical products including methanol, dimethyl ether, formic acid, acetic acid, urea, and polypropylene carbonate. Overall, the optimal CCUS systems achieve economic profits of 999.62–1568.17 euro per ton of CO2 captured and utilized. The CCUS model may be critical in aiding decision-makers to ascertain investment strategies for designing CCUS infrastructures.

Blockchains for Value Creation and Supply Chain Optimization

2021 ◽  
pp. 993-1009
Author(s):  
Arun N. Nambiar
Keyword(s):  
Information Technology ◽  
Supply Chain ◽  
Value Creation ◽  
Large Scale ◽  
Market Information ◽  
Supply Chain Optimization ◽  
Point Of Sale ◽  
Planning Software ◽  
Highly Dispersed ◽  
Data Transparency

Managing today's highly dispersed and intertwined supply chain in order to maximize the overall organizational benefit by leveraging partner competencies is a herculean task and one that is of ever-growing importance in a highly competitive and truly globalized market. Information technology in the form of point-of-sale data, materials requirement planning software, and enterprise-wide systems have often been leveraged to assist with this. However, with the proliferation of data, storing, managing, and analyzing data on a large scale is a challenge. Blockchains provide numerous benefits such as data transparency, immutability, and traceability that are so critical in building a cohesive cyberinfrastructure that facilitates cooperation and collaboration among supply chain partners. This chapter examines the characteristics of blockchain that make it suitable for supply chains and explore how the benefits afforded by blockchain can be leveraged to enhance value creation while optimizing the supply chain.

Biofuel Supply Chain Optimization Using Lévy-Enhanced Swarm Intelligence

2020 ◽  
pp. 169-197
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Optimization Techniques ◽  
Optimal Decision ◽  
Supply Chain Optimization ◽  
Industrial Systems ◽  
Planning Optimization ◽  
Biofuel Supply Chain ◽  
Solution Methods ◽  
Optimal Decision Making

Supply chain planning/optimization presents various challenges to decision makers globally due to its highly complicated nature as well as its large-scale structure. Over the years various state-of-the-art methods have been employed to model supply chains. Optimization techniques are then applied to such models to help with optimal decision making. However, with highly complex industrial systems such as these, conventional metaheuristics are still plagued by various drawbacks. Strategies such as hybridization and algorithmic modifications have been the focus of previous efforts to improve the performance of conventional metaheuristics. In light of these developments, this chapter presents two solution methods for tackling the biofuel supply chain problem.

Biofuel Supply Chain Optimization Using Random Matrix Generators

2020 ◽  
pp. 198-215
Keyword(s):  
Supply Chain ◽  
Random Matrix ◽  
Large Scale ◽  
Supply Chain Optimization ◽  
Heavy Atoms ◽  
Large Scale Problems ◽  
Biofuel Supply Chain

Supply chain problems are large-scale problems with complex interlinked variables. This sort of characteristic closely resembles structures often encountered in the nuclei of heavy atoms (e.g., platinum, gold or rhodium). Such structures are said to have the property of universality.

scholarly journals How does the position of firms in the supply chain affect their performance? An empirical study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Siddharth Arora ◽  
Alexandra Brintrup
Keyword(s):  
Supply Chain ◽  
Empirical Data ◽  
Large Scale ◽  
Individual Performance ◽  
Supply Network ◽  
Supply Chain Networks ◽  
Focal Firm ◽  
Cooperative Competition ◽  
Large Scale Networks ◽  
The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

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