With the size and complexity of supply chains soaring, a daunting challenge is confronting companies: identifying the critical nodes hidden within the vast expanse of their supply networks. These are suppliers, or supplier sites, that might be in the second-tier or lower, which means the big buying companies would ordinarily have no contact and might not even know exist.
These “nexus suppliers” could be important for one or both of two reasons:
- A disruption of its operation would have a surprisingly huge impact on the original-equipment manufacturer’s production. (A much-cited example is how an explosion at a German factory owned by Evonik Industries that supplied 70% of world demand for a type of nylon resin wreaked havoc in the auto industry.)
- The supplier possesses critical market information. (For instance, LG Electronics learned by accident that the Taiwan Semiconductor Manufacturing Company (TSMC), which has connections to lots of industries, can provide early signs of changes in economic conditions and, as a result, supply and demand.)
We are the members of a research team at CAPS Research, a joint venture of Arizona State University and the Institute for Supply Management, that is on the verge of creating a way to identify these suppliers by doing three things: creatively applying network concepts, taking advantage of emerging large databases, and utilizing business analytics, the practice of methodically exploring data in an iterative fashion.
The field of operations is becoming more familiar with network concepts such as various types of node centrality measures — a gauge of how critical a node (each supplier) is to the network: e.g., its number of connections, the variety of industries from which these connections come, how quickly it can reach others in the network, and so on. If companies could get their hands on such data for all the members of their supply networks, they could compute centrality measures for all of their suppliers and identify the most critical ones based their scores. Yes, there could be thousands of these nexus suppliers, but we now have the computing power to identify how critical each is.
Currently, generating a map of supply networks is a daunting task. Many companies and research centers have attempted it and found it to be labor intensive and time consuming. See this article for a description of one that’s being tested at Ford and this one for a sample supply network involving Honda that took several years to complete.
The good news is in the last few years, companies have begun selling large databases that can make this job much easier. We are using Bloomberg Supply Chain Database (SPLC), which keeps track of about 28,000 companies worldwide. Other potential databases include Capital IQ, FactSet Revere, and LexisNexis. For each company, SPLC provides a list of suppliers and customers, based on information revealed in a variety of sources (public filings, industry reports, etc.). For a given buying firm, we can identify all its suppliers, its suppliers’ suppliers, and continue iteratively. We can then use the collected supplier-customer relationships to construct an extensive supply network for the focal firm.
Then we can integrate complicated mathematical measures of centrality to create a nexus supplier index (NSI), which is an aggregate measure of criticality of a supplier in a buying firm’s supply network.
Once we have computed initial NSI scores for all suppliers, we can use business analytics to validate the scores. We have compiled the data for the top three tiers of a particular automotive company to its third-tier and identified over 8,000 suppliers. We have computed NSI scores for second-tier suppliers and are in process of compiling fourth-tier data to compute the NSI’s for third-tier suppliers. We plan to provide a formal CAPS Research report on our effort by September of this year.
Once a buying firm has identified and scored the nodes in its supply chain, it can group its nexus suppliers into different categories according to their distinct network positions and develop and implement suitable strategies to manage each type in order to achieve such objectives as minimizing costs, mitigating risks, increasing responsiveness, and discovering and accelerating the development of potential innovations. Then it can start monitoring its nexus suppliers and make use of the knowledge they can provide.
In an era when extended, complex supply chains pose unprecedented risks and opportunities, big buying companies cannot afford to be in the dark about suppliers far down their supply chains. Thanks to advances in knowledge about network modelling, databases, and analytics, they don’t have to be.