S&T | The Complex Network Approach in Strategic Supply Chain Network Design

S&T | The Complex Network Approach in Strategic Supply Chain Network Design

By Jiangbo Zheng 
School of management, Jinan University, Guangzhou, China

S&T | The Complex Network Approach in Strategic Supply Chain Network Design

Image Attribute: MIT Supply Chain

To better understand the significance for strategic supply chain design, it is necessary for us to briefly review the background and the fundamental contents about complex network theory. A network is a set of items, which are called nodes, with connections between them, called edges. Systems taking the form of networks (also called graphs in some of the mathematical literature) abound in the world.

Typical examples of networks include the World Wide Web (www), information networks of citations between academic papers, technological networks, biological networks and social networks of acquaintance or other connections between individuals, organizations and business relations among companies, and supply chain networks for sure. The study of networks, in the form of mathematical graph theory, is one of the fundamental pillars of discrete mathematics. Networks have also been studied extensively in the social sciences and in the 1930s socialists realized the importance of the patterns of connection between people to the understanding of the function of human society.  

From then on, typical relevant researchers address issues of centrality (which individuals are best connected to others or have most influence) and connectivity (whether and how individuals are connected to one another through the network). 

Recent years there has been a new change with the research focus shifting from the analysis of the single small system and the property of individual nodes or edges within such systems to consideration of large-scale (maybe million or evermore of nodes and edges) statistics properties of systems.  

Recent work in this area is inspired particularly by a groundbreaking paper by Watts and Strogatz [1]. This new approach has been driven largely by the availability of computers and communication networks that allow us to gather and analyze data on a scale further larger than previously possible. This change of scale forces us a corresponding change in our analytic approach - strategic supply chain network is the case in point.  

For example, traditional research works about supply chain network of tens or hundreds nodes, it is a relatively straightforward matter to draw a picture of the network with actual points and lines and derive specific analysis (the human eye is also an analytic tool) about it through examining this picture. 

But this approach is not useful with a complex network of thousands or even more nodes - that is very common now with some modern multi-national companies' supply chain networks. Furthermore, the theoretical body of complex network is established to do primary three aspects: 

1) To find and highlight statistical properties, such as path lengths and degree distributions, which characterize the structure and behavior of a network, and to suggest appropriate ways measure these properties;  

2) To create models of networks that can help us to understand the meaning of these properties such as how they came to be as they are and how they interact with one another;  

3) To predict what the behavior of networks system will be on the basis of measured structural properties and the local rules governing individual nodes. In fact, the scientific field has made an excellent start on the first two of these aims by drawing on ideas from a broad variety of disciplines.  

But such achievements are not well introduced in the research field of supply chain systems; especially the point (2) (as mentioned above) is understated in the planning of strategic supply chain network.  

This new approach has been driven largely by the availability of computers and communication networks that allow us to gather and analyze data on a scale further larger than previously possible. A supply chain is defined as a network of nodes (e.g. suppliers, manufacturing plants, distribution centers, warehouses, etc.) and lines (including physical lines, e.g. transportation lines, and virtual lines, e.g. information and communication channels) that perform a set of operations ranging from the acquisition of raw materials, the transformation of these materials into intermediate and finished products, to the distribution of the finished products to the customers network.  

Generally, when we talk about a static model considering a multi-commodity, multi-facility, and single-country network, the decision variables concern the number of locations, the capacity and technology of manufacturing in plants and warehouses, selection of suppliers, selection of distribution channels, transportation modes, and material flows. In his book –“Dynamic Supply Chain”, John Gattorna rightly said, that for too long, there has been an unhealthy preoccupation with infrastructure and asset utilization, driven mainly by the obsessive desire to cut costs. Even today, many executives see logistics and supply chain management as areas for cost cutting. Yet it is impossible to grow a company by continually cutting costs. Moreover, recent  years many research works have addressed the dynamic location problem such as Daskin et al. [2] propose an  extensive review of location problems, Beamon [3]  distinguishes models with deterministic data from those  with stochastic data, Owen and Daskin [4] clearly  separate the static and dynamic models. 

The attempts to bridge the gap between the complicated dynamic strategic supply chain in practice and the sound describing models, in theory, is always going remain a challenge. So the primary and core objective for any supply chain network designer is to provide a mathematical modeling framework to assist the decision-makers in the design of their supply chains at the first place itself. It is necessary to point out that the mathematical modeling framework should firstly include the relocation of existing logistics nodes, and consequently, reflect the expansion and reduction of the nodes – which is very common in the practical supply chain operations.  

Furthermore, the general notion of production is a very wide concept which varies from one industry to other but generally it does include the classifications of services and production processes. Then, the setup or shutdown of a node, or the expansion or reduction of a node, is usually a time-and-cost-consuming process.  

Finally, in strategic supply chain circumstances, it is emphasized that resources should be made full use and be integrated for better efficiency. So the capacity should be transmitted to new nodes when some existing ones are shut down. 

Therefore, to implement the smooth transition to a new network configuration, the implemented design needs better coordination of all operational aspects involved in this process and better management of the required investment capital. Hence, to abate the financial burden put on such a comprehensive project, capital expenditures, and network design decisions should be planned over some periods. Based on that, we need to put forward a modeling framework supported with sound tactics which can generally reflect the factors taken into account in the optimizing strategic supply chain.

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References:

[1] D. J. Watts and S.H. Strogatz  "Collective dynamics of ‘small-world’ networks,” Nature, No. 393, pp. 440-442

[1] M. Daskin, L. Snyder, and R. Berger, “Facility location in supply chain design,” Logistics Systems: Design and Optimization, pp. 39–65, 2005

[2] B. M. Beamon, “Supply chain design and analysis: Models and methods,” International Journal of Production Economics, No. 55, pp.281–294, 1998

[3] S. H. Owen and M. S. Daskin, “Strategic facility location: A review,” European Journal of Operational Research, No. 111, pp. 423–447, 1998.

Copyright © 2016 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).http://creativecommons.org/licenses/by/4.0/
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