The closure of a terminal in China at the world’s third-busiest container port is one of the last examples of supply chain bottlenecks that led to disruptions propagating across the world of trade. The Greensill collapse that wreaked havoc in the financial services industry earlier this year calls for a better understanding of risk management interdependencies between manufacturing and financial services sectors.
Sometimes things must go wrong before they’ll go right
As demand for vehicles is increasing, the inventory to sales ratio (which serves as an indicator of the number of months of inventory that are on hand in relation to the sales for a month) hit rock bottom in July 2021, to levels not seen in modern history as depicted in Figure 1.Inflation is a growing risk for global supply chains as well – with price pressures coming from demand growth that outstrips supply growth, rising commodities prices and disrupted inventories. At the same time there is little evidence of quantitative models that incorporate the impact of inflation expectations on purchasing behavior from the supply chain perspective. The effects of supply chain disruptions have been observed during the pandemic with shortages of materials such as semi-conductor, toilet paper and timber to name a few.
Industries such as automotive form complex supply chains, with production outputs vulnerable to disruptions due to shortages of inputs across tiers and geographies. At the same time smaller businesses reported delays with suppliers, concentrated in other industry sectors such as chemical, construction, and labor services. To make things even more complicated customers expect services and products to be available through a combination of digital and physical channels. So, what can companies do to measure, mitigate, and hedge the risk of lost sales?
The light at the end of the tunnel
Risk management is a profession with a long history and while main advances and breakthroughs are typically attributed to risk modeling techniques developed in the financial services industry, other industry sectors have important contributions and areas of application as well. The chemical industry is a good example, with various techniques and standards developed over the last decades, including the operational loss distribution approach (Meel et al. 2007). Environmental and health safety, hazard identification techniques, risk analysis and heat maps to mention a few are well-established risk management techniques. The industry regulatory bodies led the development of various regulatory standards (such as the ISO standards) that are closely followed in practice.
Financial services firms, on the other hand, focused on the innovation in risk modeling techniques to address Basel regulatory requirements. The main regulatory requirement is the amount of capital that needs to be held by financial institutions in case of a severe economic downturn and this is the area where banks and insurance firms historically directed significant resources.
However, due to the digital transformation and data availability which has grown dramatically over the last few years major advances and convergence in risk modeling techniques across industry sectors can be seen.
The recipe for risk management success
There are several factors driving the advances of risk management techniques that can help your organization to cope with these challenges.
1. Data availability increased over the last few years
While financial services firms have built their business models predominantly using data, manufacturing firms have made enormous progress in terms of data availability and analysis. ERP systems providers form the lifeblood of any organization’s data capabilities, and most risk management modules are interconnected with the central data repository allowing for model development in real time.
2. Companies have realized that they must invest in technology to remain profitable in the digital era
With the unfolding of digitalization, companies that are willing to stay ahead of the pack are investing in technology to remain profitable. Adding new technologies to the existing technology stack has never been easier and companies are using third party vendors to enhance their capabilities.
3. Covid-19 accelerated the transformation and enabled tech driven customer/supplier management
The side effect of the pandemics is that digitalization was forced to happen at an unprecedented scale and speed. With the majority of knowledge workers completing their tasks remotely, new ways of communication and global collaboration have emerged as a result. Customer and supplier relationship models emerge driven by data that allow for predictive analytics across industry sectors.
4. Sustainability agenda is driving the visibility in supply chains
Climate change and the regulatory developments in the sustainability space are the drivers as investors expect ESG to be part of the firms’ long-term commitment. With the goal to reduce CO2 emissions in the extended value chain, there is a lot of opportunities for increased supplier visibility.
5. Risk modeling methodologies converge at the intersection of data and enterprise risk analytics.
As an example, Value at Risk, still largely unexplored by non-financial firms is gaining increased interest as the concepts of resilience and tail events drive corporate risk agendas.
Another useful measure, Time to Recovery (see Mizgier et al., 2013) is an essential concept used in scenario building and stress testing. Cross industry workshops can help to establish common data sources (similarly to ORX) that firms can use to validate their corporate risk models. Economic Capital (see Mizgier, 2018) can also help to assess the ROI and decide how to steer the business to achieve the best risk-return profile and manage supplier relationships on a risk-adjusted basis.
Conclusion – when managing risk think globally, act locally
Financial services and manufacturing firms form a complex system of suppliers, customers, capital providers and insurers that is densely interconnected on a global scale. Covid-19 brought this dependency to the forefront and the repercussions can be seen unfolding to this day and will stay with us for the unforeseeable future. Luckily, risk managers have tools and techniques at their disposal that can help them to navigate these troubled waters if used wisely across industry sectors.
It is time to revisit your risk management strategy, think globally and act locally according to your industry’s level of maturity. For this to occur, more collaboration is needed both in terms of data sharing and understanding of risk management tools. It is not going to happen in one day, but we can already see progress in that space that will materialize in the years to come.
About the Author
Kamil J. Mizgier works as Global Supplier Relationship and Risk Management Leader at Dow. Prior to this role, he gained professional experience in risk modeling at BNY Mellon, UBS and Aduno Group. He has published several academic and practitioner articles on risk management. He obtained his Master’s degree in Applied Physics from the Warsaw University of Technology and a PhD in Supply Chain Management at ETH Zurich.
- Meel, L.M. O’Neill, J.H. Levin, W.D. Seider, U. Oktem, N. Keren, Operational risk assessment of chemical industries by exploiting accident databases, Journal of Loss Prevention in the Process Industries, Volume 20, Issue 2, 2007, Pages 113-127
- Mizgier, Kamil J./Jüttner, Matthias/Wagner, Stephan M. (2013): Bottleneck Identification in Supply Chain Networks, International Journal of Production Research, Vol. 51, No. 5, March, pp. 1477-1490, doi: 1080/00207543.2012.695878
- Mizgier, Kamil J. (2018): On Economic Supply Chain Risk Capital, The European Financial Review, August/September 2018.