Analyzing a High-Tech Manufacturing Supply Chain with Social Network Theory
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Imec is a world-leading nano-electronics research institute working on a broad variety of topics, ranging from advanced silicon chip technology to healthcare, solar cells, wireless communications and many other applications. The Imec Innovation Services and Solutions division is the semiconductor manufacturing division of Imec which helps innovators, entrepreneurs and universities realize their ideas in hardware and software by providing design and low-cost prototyping, volume production and system integration of silicon integrated circuits and electronic assemblies.
In our first blog, we defined the wide scope of the business and technology data that we have to store, process and analyze. The raw input data is mostly in the form of tabular financial datasets which are linked and extended using unstructured technology descriptions in a wide variety of formats. The heterogeneous nature of these data sources has directly impacted the architecture of the software infrastructure and analysis principles. We have used the Elastic Stack for our data processing and business analytics framework. On the one hand, this has enabled us to make the traditional analytics process more interactive leading to easily customizable dashboards. On the other hand this has enabled us to adopt less common analytics approaches such as graph-theoretic analysis of the linked data or the application of machine learning for time-based data analysis.
The Business Network Graph
Since then, we have developed a relatively unique usage of graph analysis for business network modeling using the Graph plug-in of the Elastic Stack. It deals with modeling and analysis of Imec Innovation Services and Solutions manufacturing and customer supply chain by visualizing it as a social network graph.
Here we draw on the rich literature which discuss the modeling and evaluation of popular social networks as undirected graphs, and from which we can profit from employing a wide variety of graph-theoretic parameters. One of the most intriguing graph-based parameters is known as the degree of separation. This is a measure of the average number of “hops” across a graph to connect two vertices. This measure has received a lot of attention since Facebook showed that average degree of separation among active Facebook users is approximately 3.5 and has been decreasing as the network has grown in size: a phenomenon which has been termed the “network effect”.
While perhaps not as dramatic as the growth experienced by Facebook, our manufacturing supply chain network has also experienced rapid growth. The work described in this blog was therefore initiated to see if the network effect could be used to explain our success. In contrast to a standard social network graph model with only one type of a node, a user, our business graph model recognizes three types of nodes. The first node type corresponds to customers, mostly universities, research institutes and SMEs (Small Medium Enterprises). The second type represents suppliers of manufacturing services, such as foundry-based manufacturing of silicon chips, chip packaging, testing, etc. The third type of node is a project node, created internally, to connect the manufacturing services providers to the customers.
Connections between nodes of our graph model therefore represent financial transactions and associated flows of costs and revenues. For example, an invoice sent to a customer forms a connection between a project node and a customer node. A purchase order sent by a supplier forms connection between a project node and a supplier node. The data on which we base these connections are mined from the SAP datasets covering the financial operations in question and are kept in an internal SQL-based data warehouse. As well as connections between customers and projects and suppliers and projects, connections between individual projects are established through customers or suppliers taking part in two or more common projects, and thereby creating a rich network. Figure 1 shows examples of the above described connections between three different project examples.
Figure 1: A graph model of three project examples (P1, P2 and P3) and their possible connections. Projects P1 and P3 are connected through a common customer C1 which participates in both projects and the project P1 and P2 are connected through a common supplier S1 that provides service for both projects.
The business network is quite dynamic because the lifetime of each project is limited. For our evaluation and analysis, we generated and analyzed partner networks in consecutive one- month time windows. An instance of a network graph covers status of all projects created or already existing in a month. The lifetime of a project graph connection is bounded by the first and last financial transaction of the project. Figure 2 shows a part of monthly snapshot of our partner network graph in 2016.
Figure 2: A part of monthly snapshot of the Imec Innovation Services and Solutions partner network graph in 2016 where blue nodes represents projects, orange nodes customers and yellow nodes suppliers
Technical Solutions
As was already mentioned, the core data for our analysis are in the form of tabular datasets from our internal data warehouse. These data must be processed into a form suitable for the Elasticsearch index when the local, intermediate SQL storage is imported using Logstash. These data are augmented with large amounts of unstructured textual data, such as technical and non-technical presentations, mail communication, etc., which exist in many different formats such as *.doc, *xls(x), *.pdf, *.txt, etc. The raw text content of these documents is extracted and linked to the previously created Elasticsearch index. In this way, project information with technical and technological details and with additional info related to our customers involvement not covered by standard CRM records is incorporated. Figure 3 shows the business analytics infrastructure with the Elastic Stack at its core.
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