Mission Grey's latest geopolitical briefing is available here. It especially focuses on the situation between Armenia and Azerbaijan, and how that situation is linked to global geopolitical networks.
We encountered similar global networks, when Hamas attacked Israel. And for businesses the big issue is Taiwan.
Mission Grey new country Stability Index is available soon. It combines Liberal Democracy Index with economical metrics. We have also developed an interdependency index for it, i.e. indicating how countries are trading with stable versus unstable nations.
Companies have also started minimizing their geopolitical risks by preferring some sources, using restrictions, or avoiding others altogether. The US is worried about buying and investing in China. There are sanctions on buying from and selling to Russia. Many countries want to promote friend-shoring to minimize dependencies on countries outside their own allies. However, it is not as simple as that. If we don’t understand the trading networks and global supply channels, these kinds of activities are meaningless and misleading.
Do direct restrictions work?
The Economist just wrote that Joe Biden’s China strategy is not working. The US has introduced new policies for trading with China; this started already during Trump’s term. America’s policies include tariffs, export and investment control and especially special measures for sensitive technologies, like AI and microchips. The target is to stop China from developing advanced military technology and also otherwise getting an advantage in the technology area, and on the other hand, limit dependences on Chinese products and production.
The Economist also continues how the US recommends that its friends (including Vietnam and India) and allies avoid trading and doing business with China and prefer friend-shoring. This policy can also look like a success at first glance. The US import of low-cost items from China has dropped significantly, and Chinese firms’ investment in the US has dropped from $48 billion to $3.1 billion in six years. Furthermore, when China was the #1 investment target for Americans in Asia, now it is behind India and Vietnam.
The reality is much more complex
Yet, the reality is much more complex than this, based on The Economist article. For example, the US supply chains have moved to other places, such as India, Mexico and South East Asia. Those places are now more dependent on China than ever. This has actually increased those countries’ economic links to China significantly when they import components from China and then sell products to the US. Many countries are happy to take investments from China and, at the same time, develop exports to the US. This can increase China’s global influence, which is quite the opposite of what American officials think to do.
Same issues with Russian sanctions
We have also seen similar things with Russian sanctions. The export to Russian allies has increased, and also import from countries that are quite linked to Russia. Here is one example: together with its Western allies, Finland introduced strong sanctions against Russia in 2022.
At the same time, its export to Kazakhstan increased by 143%, and 68% of exported goods were items that are on the Russian sanction lists. Is it realistic to think that Finnish companies suddenly had so many more items to export there? Or does this export continue elsewhere? We can find many similar examples in the global trading data.
Supply chains are networks
We wrote earlier that friend-shoring is not such a simple solution. There are three important things to remember when we think of supply chain strategies:
30 years of globalization
When we have lived 30 years through the globalization phase, but it looks like this kind of basic understanding has been forgotten. Strangely, the Cold War situation was simpler to understand and manage. The communist and Western blocks were more isolated, and many third-party countries (e.g. countries with some important natural resources) were usually associated with one of the blocks.
Global supply chains
However, nowadays, we have a reality with global supply chains related not only to certain physical components but also to competencies, investments and needed services. When managing risks, restricting investments, and exporting to certain countries, we need much more data, understanding, and sophisticated methodology.
Mission Grey has published reports that illustrate some examples of dependencies between countries and also how some changes in the trading relationship and political liberties seem to correlate. For example, the links between Australia and China are interesting to analyze further. Australia is a country that has been politically very free but regularly trades with more authoritarian countries.
Connecting the geopolitical dots
Understanding global networks and managing their associated risks is essential for businesses. In addition, governmental actors and the third sector have much to benefit from network analysis and portfolio management, for example, when planning effective sanctions, regulations, and trade policies.
All this just highlights that all parties need better data and tools to analyze global trade, dependencies and risks. And it is not enough to see risks associated with individual countries but really understand networks and analyze the risk portfolios. You can never minimize risk to zero, but it is important to understand what risks you have, where they are and how to build a portfolio so that you can diversify your risk and recover quickly when some risks are realized.
Companies need to evaluate and monitor many external factors all the time. They need this data to make decisions and evaluate possible changes in the current business and its environment. Some information is based on internal discussions or ad hoc consultant reports, and some information is from publicly available metrics, reports and indexes. But ad hoc reports are not enough for many purposes, and they also have transparency issues.
Credit ratings, stock market indexes, real estate bubble indexes, ESG (Environmental, Social, and Governance) reports, political freedom indexes, stock analyst reports, consultant reports to evaluate different markets and suppliers and many other reports basically serve the same purpose: how to better understand countries, businesses, and external factors to make better business decisions. However, there are huge differences in the degree of transparency among different reports and indexes, especially in terms of how systematically they are updated and how they track trends.
On the other hand, the fact that they are public metrics, indexes and intelligence also means that it’s more difficult for management to simply ignore or discount them in their decision-making processes. If metrics and intelligence are publicly available, the company can’t claim afterward they didn’t know something.
Credit ratings, ESG and geopolitical data are good examples of how different sources and models to get information can give very different outcomes.
External metrics produce different outcomes
Credit ratings are well respected and quite transparent tools to evaluate the risk of lending money to a country, business or individual. No professional lender can ignore them; no one can say they didn’t know a credit rating before making a lending decision. The ratings also help define each loan’s risk and interest rate. There are, of course, many things that could be improved, e.g. having more up-to-date data and how different loans can be bundled to manage risks. But credit ratings are commonly accepted ‘currency’ for the lending market.
ESG has become an important metric for many companies and investors, but ESG reports and metrics have many issues. Especially problematic is that ESG has many qualitative components that lead different ESG reports to produce very different results. This also creates opportunities for greenwashing; you just need to find or buy a report that supports your decisions.
Geopolitical data. Here, the situation is even worse than with ESG reports. In the last year following the Russian invasion of Ukraine and resulting sanctions, many companies realized they hadn’t evaluated geopolitical risks properly. There is a lot of data and evidence that companies had a lot of information on risks with Russian businesses, but it was often ignored.
This illustrates a fundamental difference from credit ratings: many companies and executives claimed they couldn’t have known it was a risk to buy oil and gas from Russia, or to set up operations in Russia. But many of these companies also likely did internal evaluations and bought expensive consulting reports about Russian risks – and as long as those reports remain confidential, it is impossible for us to know how well management was informed about the risks, and whether they just decided to ignore them.
Open decision making
There are many areas to evaluate in any business, and of course there are many more information sources, indexes and reports out there. But the examples above illustrate the point – some information sources and ratings are more transparent than others, and some give a much clearer basis for decision-making, while others can be misused or hidden. This is a very important aspect of risk management, corporate governance and accountability.
Obviously, no company wants to open all its decision-making processes to the public and reveal all the information they have. It is normal for any company to try to get more and better information than its competitors. Executives and management teams also have their own considerations, and some businesses want or need to take more risk than others.
But at the same time, it is of value to all parties to have enough respected and sovereign information sources. We can easily understand that the lending market would be very complicated and riskier if there were no credit ratings. This is especially true when we start talking about financial inclusion for emerging markets and unbanked people – you need a credit rating system of some kind before you can develop lending and finance services that will work for such markets.
Especially for listed companies, it is very important for the shareholders to have unchallenged information and data that has been used in the decision-making process. Or, if the management wants to challenge this type of information, they must clearly communicate why. Now, it is just too easy to select a consultant or ESG report that is suitable for them, or choose geopolitical facts that support their decisions.
ESG and geopolitics need better metrics and tools
It has been suggested in many places that ESG must be updated or even split into several metrics. At the same time geopolitical metrics such as political risks, dependencies on certain countries and governments, human rights and freedoms also need their own sovereign reports and indexes. That information is now too fragmented, with too many individual data points and dependences not being evaluated properly.
The current state of the world – the rising tension between China and the USA (with many countries caught in the middle), local wars and conflicts and disruptions in global supply chains – make it mandatory to understand these risks better.
There is a lot of data and information available in the world nowadays, and businesses must use it. But it requires proper tools and metrics to be able to systematically use data in decision-making. And it is not enough to take a snapshot – it is also fundamental to use data sources that give up-to-date information that can be tracked and followed systematically.
This will not only help companies to make better decisions but also give much more visibility of the decisions to all stakeholders, and hold the decision-makers accountable.
Mission Grey has published a new report: Measuring, Modelling, and Mitigating Risks in Global Business Networks. You can download it here.
In this report, we present a theoretical model of such analysis and how it can be operationalized in practical business solutions by relying on the Modern Portfolio Theory (MPT). Our goal is to help companies, investors, and public sector decision-makers to 1) avoid disasters, 2) save money, and 3) make profit.
We also offer practical examples how we use Liberal Development Index (LIDI) and Liberal Interdependence Index (IDI) to evaluate risks in trade. We also highlight some cases, for example, Ukraine, Australia, China and Delicate countries.
To fully instrumentalize the tools already available in the stock market and traditional business intelligence, one must move beyond the division between “geopolitical analysis”, supply chain management, and investment diversification. In the end, it is all the same – ignoring one of the three factors may result in grave disasters or at least significant loss of profit.
The US aims to limit China’s development of advanced semiconductors. Huawei and ZTE can no longer sell their equipment in the US. TikTok is under pressure to be banned in the US, and some US states already limit its use. But the reality of high tech, national security, and data is much more complex than stopping individual companies or products. Do these individual activities make sense, or do they divert attention away from more important things?
It is easy to look at an individual company or product and decide to impose restrictions on it. However, the picture becomes quite murky when we think about the development of advanced technologies, global Internet platforms, and ownership of logistic chains. What seems like a simple decision may turn out to be not so effective in practice, and can also be very expensive for the countries involved.
Let’s look at some factors that make risk and restriction evaluations complex:
Individual companies in a broader ecosystem
To take microchips as a topical example, it is very complex and expensive to develop new microchips. No single country can make them alone. So, if the world is divided into two (or more) blocs that produce microchips separately, this will impact development. Competition can improve microchip development, but when the microchip manufacturing process depends on a network of many players, the impact of blocs will likely be negative.
It’s noteworthy that the US restrictions on microchips targeting China don’t only impact the already existing production capability in China – they also have an impact on the foreign companies that have production in China. This is not necessarily accidental, as the US and some other governments would naturally like to see production move elsewhere. Then we have cases like Foxconn’s strikes in China that might be good for the US government, but not so good for Apple.
The factors mentioned above, coupled with already high inflation, lead to an increase in prices. The reality is that nowadays, almost all electronic products have microchips. The ultimate question is whether the costs of restricting microchip development are higher than the costs associated with the risks. These are very difficult things to evaluate.
TikTok has been a headache for western governments for some years now. It is also expanding its business to e-commerce. But it is doing this with partner companies, such as TalkShopLive and ChannelEngine. This is just one example, but it raises a more general question: how do you evaluate partnerships if governments want to restrict some services or products?
Even more complexity
Several western companies are getting better at evaluating which Chinese companies they are willing to do business with. They can assess a company’s ownership, management, and relationship with the Chinese government. But how much of this reveals the real risks, when Chinese companies are required under a 2017 law to cooperate with Beijing’s intelligence apparatus? We have also seen what happened in Russia. It didn’t matter with whom you made business in Russia – it was over after February 24.
How about countries where China has a lot of ownership and influence? How do we evaluate the risk of operations in those countries, or cooperation with companies from those countries? Then we have countries like Hungary and Turkey that are EU and NATO members respectively, but their position in the blocs is not so stable. Maybe they even want to utilize the situation by picking cherries from cakes of all blocs.
And more generally, if, say, a populist party wins power somewhere and decides to ignore western commitments in favor of quick economic wins by making deals with Beijing or Moscow, can western countries do much? What if this populist win happens in the US in 2024?
Then we have acquisitions. The UK is looking at the acquisition case of Britain’s biggest semiconductor plant, Newport Wafer Fab, by a Dutch company owned by China’s Wingtech. China’s COSCO is willing to buy a stake in Hamburg port, one of the key logistics hubs in Northern Europe. One can say a majority stake is grounds for blocking it, but what about a minority investment? Is the risk the same?
We need proper evaluations, not political posturing
Let’s face reality: there are no simple and clear answers to these questions. Each government and business must make its evaluations and decide what they want to do, what risks they want to take, and the costs associated with the risks. Besides, there are issues related to national security, personal freedom, and ethics that are much more important than any normal risks and business calculations.
And it is not only about what the US or EU make, but also what China, India, Russia, Brazil, and other important countries are doing. But there is a difference – for the real global powers (e.g. the US, China, and EU), it is hard for anyone to ignore them, and everyone needs to have a solution to work with them somehow.
It would be fundamental that each government and actor makes proper analyses and evaluations of those issues and understand the consequences. Simple decisions to ban individual elements can send political signals, and sometimes are an easy way to score political points at home. But do they really bring value? Are they enough? Could they also cause damage?
Alternatively, do those individual cases take up too much attention, when we could (and perhaps should) be considering other actions that would actually be much better in the context of the bigger picture?
The global business nowadays is a complex network where most nodes are somehow directly or indirectly linked to all other nodes. Therefore, it requires proper analyses and a lot of data to really understand impacts and consequences.
Many companies are approaching risk management for their production and supply chains by embracing nearshoring and friend-shoring to move some operations to countries with smaller risks. However, this is not as straightforward a move as it may look. Basic theories about risk management tell us that it is especially important to diversify, and that mathematics matters more than how we perceive risks.
Years ago, while I was studying for my MBA at the University of Texas in Austin, there was a portfolio management course professor who had extensive practical experience with investments in different situations worldwide. He always reminded us: “Don’t try to see which companies are great or bad. Just remember mathematics. You must diversify your portfolio and construct it properly – i.e. the values of the assets you invest should not correlate.”
At the time, GE was a very successful company and its CEO Jack Welch was a business celebrity. But our professor warned us that even the best companies fail one day. And many business heroes eventually lose their reputation if they don’t know when to stop. He predicted one day that even GE would have problems. Of course he turned out to be right about GE – and sooner than we had expected.
He also said that luck is important in business. Sometimes companies and leaders are lucky; another day their luck can turn sour. The reason he told us this was to make it clear: forget about trying to guess who is doing well, who is lucky, or who has the right timing. Do the math and diversify your investments.
Nearshoring vs friend-shoring How is this relevant for supply chains and production locations? As with stock markets, we can of course make some estimates about which locations are safer, where the price level is stable and which ones are reliable. But it is still very hard to really know these things for sure. Things can change. Just think about software companies in Ukraine, businesses caught up in lockdowns in China, or the future risks for Taiwan or the US after the 2024 presidential elections. How much can we really know?
Yes, we can make estimates and risk analyses, but that’s not the same as knowing for certain. That’s why it is important to take a systematic approach, not just think about how things look now.
Nearshoring and friend-shoring are becoming more popular trends. If these terms are new to you, nearshoring is where a business moves its operations to a nearby country from a greater distance. For example, a US company may do its sourcing or have production in Mexico, while a German company would have them in Poland or Romania.
Friend-shoring is where a group of countries with shared values deploy policies that encourage companies to expand manufacturing within that group. The goal is to prevent less-like-minded nations from unfairly leveraging their market position in key raw materials, technologies, or products to disrupt a country’s economy, or the economy of its allies. This is quite a natural development in the current situation where the world is again becoming more divided, and we start to see an emerging ‘cold war’ involving technology businesses.
Moving from one risk to another
But while friend-shoring sounds pragmatic and safer on paper, it’s important to understand that it doesn’t automatically remove your risks. Friendly countries can still be linked to other countries outside of your friendly network – you must understand those links too.
Also, it may be the case that you will need things that your friendly network members cannot supply – e.g. certain materials, energy, or competence. So you cannot just work with friends. Just as no country can produce all things by itself, neither can a friendly network. And of course, history is full of examples where your ally today may be your enemy tomorrow.
Friend-shoring is useful and helps to handle several things, but it’s important to see its limits. As an analogy, if you’re looking at your investment portfolio and you see that tech shares are now risky, but banks make better money with higher interest rates, you might think, “Okay, let’s move all the money from tech stocks to bank stocks.” That might be a good move for a time, but it is not really professional risk management or portfolio management. We tend to make educated guesses. But they’re just guesses.
The future of supply chain management
Supply chains and production locations are only a couple of examples of global dependencies. But similar principles can be applied to other things too – for example, which market you want to sell your products to. Businesses must understand their networks and be able to do professional risk management for them.
Professional risk management requires several competencies to understand global risks. A business should understand politics, economics, business, and also mathematics to model all those things systematically.
This matters because systematic modeling is often not done properly. Enterprises gather reports, consultants and local experts, and evaluate all that information. But compared to investment portfolio management models, this is a very simplified – even amateurish – approach.
COVID, the Ukraine war, and tension between the US and China have reminded all businesses to think about their global risks properly. But many businesses are still in an early phase of doing it systematically and professionally. Consultants and workshops are a good start, but much more systematic models are needed.
This also means we need more data analytics, clear metrics, and systematic updates when businesses and situations change (which they do, all the time).
I wrote earlier about how network analytics could be used to better understand geopolitics and global supply chain networks. Network analytics can see how risks spread in networks – even when the root cause is further away in the network, yet still impacts you. This is a good idea, but we must also remember that technology alone won’t solve the problem or make risk assessment more effective.
The good news is that none of this is unexplored territory. There are plenty of good examples of portfolio management, social network analysis and risk evaluation tools. It is now the time to put them to systematic use.
Businesses need to change the way they evaluate risk to their global supply networks. Social network analytics provide the data and tools they they need.
The Ukraine war and COVID have badly damaged many supply chains. We’re encountering an energy and food crisis with much higher prices. This has caused serious problems for many companies. This is especially true for companies that have done business with Russia and have supply chains there.
Many CEOs and executive team members claim that it was impossible to anticipate this kind of risk. That is quite a naïve claim. But it illustrates how poorly many companies have evaluated their risks and global supply chain networks.
With proper big data and network analyses, they could do it much better. In fact, they could learn a lot from social network analytics.
Geopolitical risks matter
Many enterprises have been lazy to evaluate geopolitical risks. Also, as I wrote earlier, since the Cold War era many executive teams have lived under an illusion that politics have no serious impact on their business, and therefore they can ignore it. We have also seen large companies – partly state-owned or having no strong shareholders – where the management has been willing to take high risks when the main upside is their bonuses and options if things go well. If they don’t go well, the shareholders pay the consequences.
COVID-19 was a more surprising disruption than Russia’s invasion. Of course, there were warnings and scenarios about a future global pandemic, but the timing and impact were obviously hard to predict.
But it is intellectually unsustainable to claim that it was impossible to see risks in Russia. The invasion of Georgia in 2008, and then Eastern Ukraine and Crimea in 2014, to say nothing of the Russian state taking over companies owned by oligarchs (e.g. Mikhail Khodorkovsky) who criticized Putin, foreshadowed what is happening in Ukraine now.
Maybe investors, businesses, and executives have now learned a valuable lesson. They cannot ignore geopolitical and global disruption risks. We are back in the time of realpolitik. So, what can they do in practice?
Reports are just snapshots
Traditionally, investors and businesses follow the reports prepared by governments, consulting firms, and think tanks to understand geopolitics and international risks. Larger companies have in-house analysts and also buy projects from the leading consulting firms when they make an investment decision in a new country.
These types of reports and consulting projects can help to make better decisions. But they have some fundamental problems:
Social networks also matter
Over 15 years ago, I was a founder of a startup business to analyze social networks between people. We focused mostly on marketing and advertising, but in some cases we also looked at risk management (e.g., criminal or terrorist networks). The idea was to analyze individual people, how they are connected to each other and how they have an impact on each other.
We all know that word-of-mouth and the influence of other people are important. At the same time, we also know one person can influence us in one thing (say, choosing a car) and another person can influence us in something else (going to a doctor). This model can be applied to countries, industries and businesses; they are linked to each other, they have dependencies on each other and links in different matters.
It is surprising that we are in a very fledgling phase when it comes to better analyzing those networks and links, and making proper risks analysis and scenarios based on them, when a lot of data is already available. There are probably many more analytics tools to analyze networks between people for advertising purposes than real life big data analyses of global business networks. Also, social network analytics of people is important to analyze the spreading of diseases too, as we saw with Covid.
Lessons from social networks
Some important lessons from social network analytics include the following:
Network analytics underutilized
The last two years should have been a wake-up call for businesses and executives that they must better understand and evaluate their global political environment. Most businesses are now a part of global networks. They should be able to evaluate their networks and understand how events outside their immediate network can have an impact on them.
It is not enough to make snapshot analyses when making an investment or selecting a new supplier. It is necessary to analyze the situation all the time, and also pick up on weaker signals that can become significant.
Basically, we need better big data network analytics for businesses and economies, and we need better scenario tools to make fast decisions when something significant in our network happens.
Open source has played an important role in software development over the last thirty years. It also matters in some other areas, such as intelligence. Open source intelligence has become increasingly important – especially since 9/11, but recent wars in Syria and Ukraine have made it more well known. Can open source intelligence expand to become much more systematic in tracking and predicting world events?
Bellingcat is an investigative journalism organization become the best-known user of open source intelligence. Bellingcat started in 2014 by investigating weapons used in the Syrian war. It analyzed photos from the war, trying to not only identify weapons and items in them, but also confirm the photo’s location.
Later Bellingcat became especially famous after it discovered who was guilty of the downing of Malaysia Airlines Flight 17, the Skripal poisoning, and the poisoning of Alexei Navalny. In those cases, Bellingcat combined information from many sources – not just pure open source intelligence, but also information from Russian passport and travel databases.
On the whole, open source intelligence has been the most important data source for Bellingcat. This open source intelligence consists of data from many sources, including social media updates, satellite photos, photos and information people are willing to share.
Most intelligence data is already publicBut of course, open source intelligence is much more than Bellingcat. It even has its own acronym: OSINT. Some countries have laws and regulations for OSINT. For example, in the US, the law defines OSINT as “intelligence derived from publicly available information, as well as other unclassified information that has limited public distribution or access.” After 9/11, the CIA launched an open-source directorate. Now US spy agencies have a foundation for this kind of activity.
Collecting intelligence from public sources is nothing new – it has been said that during the Cold War era, 80% of information collected by the intelligence services came from public sources, like newspapers, media, public documents and public speeches. What has changed during the last twenty years is that technology has developed significantly to enable collection of a lot of data that was not available earlier.
Publicly available satellite photos and videos, radar information, social media content, web cameras, public government data, academic databases and many other sources have made a lot of new data available. Nowadays, basically anyone can use powerful tools to search and combine data from many sources. This is what makes open source intelligence such a significant development.
Is the world safer or more dangerous with open source intelligence?Has open source intelligence made the world safer or more dangerous? Opinions are sharply divided. Some people say open source intelligence can hamper secret diplomatic negotiations that have sometimes been important to solve conflicts. When all parties can see the other’s actions, they must make countermoves rapidly, which can escalate quickly.
However, another opinion is that open source intelligence can prevent parties from taking action – or at least enable the public to see what they’re doing them early, which consequently makes it harder for them to prepare for something secretly. For example, last winter we saw public information that Russia had amassed a lot of troops and weapons at the Ukraine border. Nonetheless, many parties didn’t want to believe Russia would (or could) actually start a large-scale invasion.
The Ukraine war is also an example of where military personnel become sources of open source intelligence when they publish information on social media. There are even examples of how some Russian soldiers published photos of the entire route from their military base to a battlefield. As a result, such information could be helpful to anyone who seeks to determine which troops are used and how their logistics work. It also looks like some soldiers have published photos that could be used against them as evidence in war crimes cases.
Opportunities for more systematic models
Clearly, open source intelligence is already very important for investigative journalism and for intelligence services. But it can be much more in the future. Nowadays, a lot of this information is still analyzed partly manually.
When there is so much data available all the time, it is also possible to automate many analyses, and we’re starting to detect unusual events automatically and making various predictions based on data. This in turn could also expand the use of data and data analyses. For example, companies could better evaluate risks to their supply chains. Also, investment funds could evaluate risks for their funds, and companies could take into account the latest information in their investment decisions.
This requires complex data models, as well as the ability to combine information from many sources and understand the dependency between different events and objects. But at least for certain purposes, this is already very feasible. It is more important to make sure that some parties can start developing this systematically and find good business models for it. It could be something like Palantir, but based more on open source software and intelligence, and more transparent.
Open source software has changed the software industry. Open source intelligence has become an important tool for investigative journalism and intelligence agencies. But when the use of data is automated better, open source intelligence can be applied to many other use cases, including business. There is so much information available in the world nowadays. The question really is: who can make better models and tools to utilize it systematically?