On Economic Supply Chain Risk Capital

Kamil Mizgier-columnist

By Kamil J. Mizgier

Economic risk capital is an established risk management framework used by firms in the financial services industry to drive their capital management decisions. Due to the recent developments in financial technology, statistical supply chain risk models are finding new applications in managing risk of the digitally enabled extended enterprise.

In May 2018, De Beers, one of the world’s leading diamond companies, announced that it had tracked 100 high-value diamonds from miner to retailer using blockchain, in the first effort of its kind to clear the supply chain of imposters and conflict minerals. This is just one example of the enormous technological developments that the world of commerce has witnessed in the last decade. The increase and availability of almost unlimited computer power, combined with the abundance of all types of data, has led to the widespread adoption of quantitative algorithms by businesses. Digitalisation has helped to create new business models, develop new products that are available to the masses and disrupted (or is about to disrupt) industries that were considered too big to be disrupted. Meanwhile, the boundaries between industries are wearing off which enables the cross sharing of risk management practices that originated in different industry sectors. For example, selected risk management methods established in the financial services industry can be successfully transferred to the manufacturing industry (while avoiding pitfalls faced in the past) and vice versa.

Until recently, economic risk capital was used by insurance firms and banks to calculate their capital requirements, based on the internal view of risk that the organisation is taking. With the adoption of Basel II, it became a standard methodology to calculate the capital needed to sustain extreme losses such as those incurred by banks during the financial crisis of 2007–2009. Due to flexibility and sophistication of the underlying risk models over time, it became the methodology of choice for most financial organisations. Non-financial firms were left behind, as risk capital has not been on the radar of their regulators. But this is changing, as the responsibility to quantify risks in a sustainable extended enterprise is becoming one of the top concerns for those in charge of making strategic supply chain decisions.

Measuring supply chain risk in a digital enterprise

The central idea presented in this article is that supply chain risk models based on Value at Risk1,2,3,4 are now mature enough to be put to work. This is mainly due to the automation of data exchange among supply chain members, which can be achieved through the distributed ledger technology (DLT). It has often been criticised that the bespoke supply chain network-based risk models are not fit for purpose as (I) they do not capture the real flows of products observed in supply chain networks, (II) that the upstream supply chain transparency is limited to the first tier of suppliers and (III) that there is no data to calibrate the business interruption risk parameters. I argue that while it was true a decade ago when I began my research on supply chain risk, with the advances in DLT, big data and analytics, embedding these models in the firms’ real operating environment is now possible. The enablers of this innovation are as follows:

Thanks to DLT and sensor data all products’ physical positions and states can be tracked across the digital supply chain network.

A complete supply chain network topology can be mapped by using firms’ proprietary transactional data as well as relying on external parties (think of Palantir).

Insurance firms maintain databases4,5 that can be used by firms to calibrate the business interruption risk parameters in addition to their internal data.

Economic Supply Chain Risk Capital (ESCRC) converts supply chain risk to an amount of capital that is needed to support it.

As the example of De Beers shows, blockchain technology creates visibility in the supply chain – lack thereof, used to be one of the major challenges for the manufacturing industry. Augmented with Internet of Things, it helps to record every transaction and marks each product location on the distributed ledger in the extended enterprise. The ledger is shared among all supply chain members in a secure and irrevocable environment, and keeps the risk management systems updated in real time. Firms like Maersk or UPS are already using it to map and track their supply chains, while SAP is adding blockchain technology to its supply chain traceability platform. Once supply chain transparency is achieved and risk parameters are derived from the underlying data, supply chain risk can be calculated by using statistical methods (similarly to credit or market risk), thereby producing an economic estimate of the risk faced by the extended enterprise.

Strategic importance of economic supply chain risk capital

What is the value added of using Value at Risk-based supply chain risk models and why supply chain managers should consider implementing them?

The answer is: Economic Supply Chain Risk Capital (ESCRC).

I define ESCRC as the amount of capital that a firm needs to cover supply chain risk losses materialising at some future date with a certain probability. In other words, ESCRC converts supply chain risk to an amount of capital that is needed to support it. While Economic Risk Capital is an established methodology used in the financial services industry, the notion of Economic Supply Chain Risk Capital is new. The concept of Supply Chain Capital is known to supply chain researchers and is defined as “the value of a firm’s supply chain network, derived from both the structural configuration and the nature of direct and indirect relationships present within the supply chain.”6 However, this definition needs to be enhanced to incorporate supply chain risk and this is where ESCRC shows its full power.

Using ESCRC helps to answer several strategic management questions pertaining to risk management, capital and performance management.

For instance, if you want to know how much capital is needed to withstand the materialising supply chain risk losses, ESCRC quantifies it by taking into account all economic risks that the extended enterprise is exposed to.

The aim of any organisation is to optimally manage its performance. One of the key differentiators is the allocation of capital to the suppliers that provide the largest value added to the enterprise. Once the total ESCRC is computed, it can be allocated down to the first tier of the supply chain network and to the next tiers downstream. By running this exercise on a frequent basis, managers know how much capital is assigned to which suppliers and can proactively risk manage their suppliers.

With the evolution of DLT, big data and analytics, achieving an end-to-end supply chain visibility is now possible. 

Furthermore, ESCRC equips supply chain managers with tools that provide useful insights about their most critical suppliers. By calculating loss contributions, managers can quickly identify their most risky suppliers both on a standalone and integrated basis (which captures the interconnectedness of the suppliers).

Another important strategic consideration is which suppliers to develop and which supplier dependencies to reduce. Supplier development programs are risky and involve a trade-off between risk and cost of the investments in the enhancement of supplier capabilities7. Depending on the supplier riskiness and profitability of the relationship with a given supplier, the supplier dependencies can be re-evaluated and ESCRC can help executives to drive their strategic supplier relationship decisions. Defining return on ESCRC as a risk-adjusted profitability measure helps to make strategic decisions about the supply chain configuration. Suppliers with higher return on capital will get allocated more capital than those with a lower return.

Companies need to make a step forward from a purely operational model of the extended enterprise to a risk-adjusted model of the digital extended enterprise by integrating risk-adjusted profitability measures in their operations, governance and business decisions. Long-term capital planning on an economic supply chain risk-adjusted basis is one example where manufacturing firms have an ample room for improvement. As opposed to the financial services industry, capital-intensive industries such as mining or automotive can increase their return on investment if economic supply chain risk is taken into account at the planning stage of the investment process.

Prerequisites to adoption of economic supply chain risk capital

While technology has evolved and created boundaries for the application of ESCRC, it is important to note that organisations can only implement ESCRC successfully if it is fully supported by senior management. This can only happen if staff is properly trained and appropriate talent is available, whilst the board is supportive of the inevitable change programme. Statistical supply chain risk models are only as good as the data that is used to calibrate them. Collecting and preparing these data, and moving to a digital supply chain require investment in technology as well as in people’s skills. Simply put, with the evolution of DLT, big data and analytics, achieving an end-to-end supply chain visibility is now possible. With this capability in store, executives can embrace ESCRC as a new strategic supply chain management tool to make better-informed decisions. 

This article was originally published in The European Financial Review on 21 August 2018. It can be accessed here: https://www.europeanfinancialreview.com/on-economic-supply-chain-risk-capital/

About the Author

Kamil J. Mizgier is Senior Quantitative Analyst in the financial services industry. He received his MSc in Applied Physics from the Warsaw University of Technology and a Ph.D from the ETH Zurich’s Department of Management, Technology and Economics, where he also worked as a Senior Researcher until 2016. He does research in Supply Chain Management, Risk Management and Insurance and Manufacturing. His most recent publication is “Zurich Insurance Uses Data Analytics to Leverage the BI Insurance Proposition.”



1. 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

2. Mizgier, Kamil J./Thakur-Weigold, Bublu/Wagner, Stephan M. (2014): Pragmatic Risk Management in a Tightly-Coupled World, Ivey Business Journal, March/April 2014

3. Mizgier, Kamil J./Wagner, Stephan M./Jüttner, Matthias (2015): Disentangling Diversification in Supply Chain Networks, International Journal of Production Economics, Vol. 162, April, pp. 115-124

4. Mizgier, Kamil J. (2017): Global Sensitivity Analysis and Aggregation of Risk in Multi-Product Supply Chain Networks, International Journal of Production Research, Vol. 55 , No. 1, January, pp. 130-144

5. Mizgier, Kamil J./Kocsis, Otto/Wagner, Stephan M. (2018): Zurich Insurance Uses Data Analytics to Leverage the BI Insurance Proposition, Interfaces, Vol. 48, No. 2, March-April, pp. 94-107

6. Autry, C. W./Griffis, S. E. (2008), Supply Chain Capital: The Impact Of Structural And Relational Linkages On Firm Execution And Innovation. Journal of Business Logistics, 29: 157-173.

7. Mizgier, Kamil J./Pasia, Joseph/Talluri, Srinivas (2017):  Multiobjective Capital Allocation for Supplier Development Under Risk. International Journal of Production Research, Vol. 55, No. 18, 5243-5258


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