At a first glance, the term Social Network Analysis (SNA) may sound like a way to grow a social media following or expand ones LinkedIn network; this however couldn’t be further from the reality.
SNA is a powerful analysis technique which can be used to better understand and visualise the relationships between entities in a complex network. Given the increasing focus on resilience within supply chain, more companies are looking at techniques like this as an effective way of discovering their unidentified risks.
It can help to identify who key players are, how they interact with each other, and crucially, where there may be potential bottlenecks or disruption risks.
No supply chain is free from disruption and for today’s complex supply networks, these disruptive events will at best impact an organisation’s performance or profit, and in the worst-case cause lasting damage to a company’s reputation and consumer confidence. Whether it be a family-favourite fast-food chain running out of chicken, a container ship blocking the Suez Canal, or any manner of supply shortages, many disruptions feature a common element – organisations often don’t have clear visibility over what is happening in their supply chain. SNA can help address this.
Heightened visibility should form the foundation to any supply chain risk strategy, but getting to heightened visibility is easier said than done. The impact many businesses experienced as a result of the COVID-19 pandemic is forcing the issue.
Too often focus has been placed disproportionately on response and recovery when things start to go wrong, while understanding exactly how disruption could have been avoided in the first place is left to hindsight. This doesn’t have to be the case.
Until recently, SNA was primarily used for academic studies; it’s potential application to supply chain risk and procurement resilience remains largely untapped. Organisations that use SNA techniques enjoy the benefits of early identification of potential disruptions and thus, enable greater preparedness.
Once this foundation has been achieved, network analysis can provide a user with complete flexibility on what areas to investigate – from supplier selection, sustainability propagation, supply market intelligence, or indeed network resilience.
When implemented well, an organisation will gain oversight into the criticality of suppliers from tiers two and three. Additionally they can gain a deeper appreciation of how the interconnectedness and configuration of their network directly drives strategic decision-making and futureproofing of an organisations’ strategy.
At first sight, a map of a network as pictured above, lacks real meaning, and seems overly complex. However, with some manipulation, tangible insights can start to be realised and strategic decisions validated.
To bring the potential benefits of SNA to life, recall the 2013 grounding of Boeing’s 787 Dreamliner fleet. Not long into service, several battery malfunctions resulted in a full grounding of the fleet until the source of the failure was rectified.
Unbeknownst to Boeing, the origin of this disruption lay with a tier two battery manufacturer whose lacklustre inspection processes resulted in defective battery components making their way into service.
In the years that followed, Boeing took it upon themselves to develop a deeper relationship with its critical suppliers to ensure no fault of this magnitude could resurface again and by 2019 this supplier was even awarded ‘Supplier of the Year for Innovation’ by the aerospace OEM.
Had Boeing developed a holistic SNA model of their supply network, they would have quickly uncovered the operationally critical position of this supplier. This would have afforded them the opportunity to implement more robust audit and quality control procedures, reducing or even eliminating the likelihood of a disruption of this nature from occurring.
For businesses wishing to start developing their own SNA model, the good news is that there is little barrier to entry or heavy investment required. With most leading SNA software being open source such as Gephi, PythonX etc. the potential roadblock is ensuring access to accurate and holistic supply chain data, and a robust understanding of the concepts.
With developments in data science, the time and speed it takes to get meaningful insights is also reducing, meaning new avenues for supply chain resilience will become more accessible to a wider audience than historically seen.
Authors: Chris Powell and Marc Norris