Supply chains have been upended. Here’s how to make them more resilient

  • Supply chain visibility is crucial to understanding the impact of disruption.
  • Digitizing records will make supply chains more resilient to future shocks.
  • Block-chain will help ensure data privacy for suppliers.

In the wake of supply chain disruptions due to coronavirus, several experts have reiterated the need to obtain more visibility across the chain. Companies who sell finished goods generally know production and shipment schedules for their Tier 1 suppliers, but they usually have little to no knowledge of suppliers further up the chain.

Obtaining this visibility is considered key to optimizing supply chain efficiency and agility during normal production. When critical supply chain disruptions hit, this visibility becomes crucial to understanding the impact of the disruption on the rest of the chain so that others in the ecosystem can plan and take action, such as developing routes to alternative suppliers.

Because COVID-19 has led to lockdowns, suppliers in the chain are temporarily ceasing production, and logistics providers can no longer transport goods as seamlessly, particularly across borders.

Fiat Chrysler Automobiles announced in mid-February that it was temporarily halting production at a car factory in Serbia because it could not get parts from China. Hyundai has made a similar announcement for factories in Korea. International air travel transported a significant amount of trade cargo prior to COVID-19 but has seen its flights decrease by 55% since the beginning of the pandemic. China plays a central role in global supply chains.

This is not a new problem: companies have been trying to uncover this data for decades, but suppliers are not forthcoming with the information. Here is how we can actually achieve visibility across the entire value chain:

1. Move away from paper to digitization

Trade is notoriously reliant on paper-based processes: the “Bill of Lading” – a detailed list of a ship’s cargo; notices filled out by hand; paper copies of packing lists from each logistics carrier. In some cases, such as with the Bill of Lading, physical paper copies are required by law. For the most part, however, companies have yet to digitize their supply chain processes because they have determined the cost of doing so does not bring enough efficiency or security to justify the endeavour.

Protective measures for COVID-19 have made clear that operations dependent on physical assets, such as paper, can face serious disruption when physical presence is not a possibility. Wet signatures and paper printouts are usually handled by operations personnel who must come to the office, or another place of work, and coordinate with others. In addition, value chains that rely on information in these paper documents lose access to that visibility very quickly and cannot react to changing conditions.

Digitizing, then, is not simply a matter of cost, but primarily of visibility and managing supply chain risk. To limit the impact of points of failure in the value chain, it is important to make data available through digital means. In the current COVID-19 pandemic, governments and businesses with strong digital infrastructure and enabling regulations such e-signature and e-transactions laws, are dealing with the supply chain disruptions much better than those without.

2. Ensure data privacy for suppliers

The reason why upstream suppliers will not reveal information to end customers, even if it’s easy for them to do so, is that they fear losing commercial advantage if their customers know even more about their operations, pricing and sourcing. Suppliers have to be able to control exactly who receives what data from them, and independently verify such controls.

Most digital communication in the supply chain happens via Electronic Data Interchange (EDI) and Excel spreadsheets. When passed back and forth only between two parties in the supply chain ecosystem, data privacy is easily controllable and not a concern. When the data in these communications needs to be distributed to more parties, however, traditional supply chain systems, which are centralized, cannot grant independent and auditable access controls to each individual party. A decentralized system that is nevertheless owned by a large buyer is the best way to give suppliers the privacy they need and buyers the visibility they want.

A blockchain with either private or public permissions meets this criteria. When created properly, suppliers can audit their data-sharing permissions directly on their own blockchain node. At the same time, their data can be securely distributed to others in the blockchain network without requiring the point-to-point integration that centralized systems do. We’ve therefore solved a key technology problem in getting suppliers to participate in supply chain visibility initiatives.

3. Give suppliers an incentive to share their data

For buyers who value data highly, they may consider paying their suppliers for the data itself, in addition to the physical goods they’re sourcing. A more cost-efficient and profitable method is to institute supply chain finance programmes that offer the buyer’s own competitive interest rates.

Many buyers already offer such programmes to their Tier 1 suppliers. However, the lack of visibility is a problem that stems from other suppliers who are Tier 2 or even further up the supply chain. The financing needs to reach these suppliers as well. Blockchain is the ideal technology to ensure that data on performance and risk, which underpin all supply chain finance transactions, can be shared in an authenticated manner with financiers and other parties to a transaction, even when there is no direct relationship between them.

Using blockchain, buyers can, for example, use payment commitments on the blockchain as alternatives to a Letter of Credit, pay suppliers later, reduce cost of goods sold, and insulate themselves from supplier bankruptcy. Suppliers, in turn, recognize revenue sooner and replace their current supply chain finance arrangements with much lower financing terms. These benefits multiply as the network grows. The result is a financing ecosystem that makes data sharing pay for itself.

4. Start early – don’t assume the current disruptions will never happen again

Supply chain initiatives take time to roll out. The most effective move to take now is implement supply chain finance programmes to support suppliers in financial straits and make the value chain more capital efficient. If companies begin to institute data sharing in their supply chains at the same time, they will be in a much better position to deal with a future shock.

Global trade and supply chains are going through an unusual and massive shock, which strikes from both ends – the supply and the demand side. Companies, whether buyers or suppliers, are facing tremendous challenges in keeping the goods and services flow at a time of global lockdowns. Countries, especially developing countries, are carrying the direct consequences of supply chain breakdowns aggravated by trade restrictions. As the COVID-19 situation changes daily, it’s crucial for all parties to have visibility into the supply chain, to share data, and communicate effectively. Technologies accompanied by enabling policies can play a significant role in rebuilding the trade and supply chain system, and making the supply chain more shock-proof in the decades to come.

The World Economic Forum’s Platform for the Future of Trade and Global Economic Interdependence works closely with public and private stakeholders to address acute shocks and continuing tensions, and lead the effort to rebuild a resilient and responsive global system.

What is the World Economic Forum doing about the coronavirus outbreak?

A new strain of Coronavirus, COVID 19, is spreading around the world, causing deaths and major disruption to the global economy.

Responding to this crisis requires global cooperation among governments, international organizations and the business community, which is at the center of the World Economic Forum’s mission as the International Organization for Public-Private Cooperation.

The Forum has created the COVID Action Platform, a global platform to convene the business community for collective action, protect people’s livelihoods and facilitate business continuity, and mobilize support for the COVID-19 response. The platform is created with the support of the World Health Organization and is open to all businesses and industry groups, as well as other stakeholders, aiming to integrate and inform joint action.

As an organization, the Forum has a track record of supporting efforts to contain epidemics. In 2017, at our Annual Meeting, the Coalition for Epidemic Preparedness Innovations (CEPI) was launched – bringing together experts from government, business, health, academia and civil society to accelerate the development of vaccines. CEPI is currently supporting the race to develop a vaccine against this strand of the corona-virus.

How to protect yourself from cyber-attacks when working from home during COVID-19

  • As many companies adopt work-from-home policies in response to the COVID-19 pandemic, cybersecurity is a growing issue.
  • Cybercriminals are seeking to exploit coronavirus to target companies and individuals.
  • Here’s how businesses and employees can protect themselves online.

As we navigate the challenges posed by COVID-19 and the need to halt the spread of this deadly pandemic, many of us are settling into a routine of working from home. This can pose many difficulties, including how to maintain focus, how to balance other priorities, such as childcare, and how to be productive without requisite tools or dedicated office space – not to mention the struggle to avoid raiding the whole snack cupboard in one day.

There are compromises to be found for many of these challenges in what we hope will be a relatively short-term arrangement. What we must not compromise on is security.

What is the World Economic Forum doing about the coronavirus outbreak?

A new strain of Coronavirus, COVID 19, is spreading around the world, causing deaths and major disruption to the global economy.

Responding to this crisis requires global cooperation among governments, international organizations and the business community, which is at the centre of the World Economic Forum’s mission as the International Organization for Public-Private Cooperation.

As an organization, the Forum has a track record of supporting efforts to contain epidemics. In 2017, at our Annual Meeting, the Coalition for Epidemic Preparedness Innovations (CEPI) was launched – bringing together experts from government, business, health, academia and civil society to accelerate the development of vaccines. CEPI is currently supporting the race to develop a vaccine against this strand of the corona-virus.

Many cybercriminals are seeking to exploit our thirst for information as a vector for attack. Most commonly, as with other high-profile events, attackers are using COVID-19-themed phishing e-mails, which purport to deliver official information on the virus, to lure individuals to click malicious links that download Remote Administration Tools (RATs) on their devices.

In addition, there have been multiple reported cases of malicious COVID-19-related Android applications that give attackers access to smartphone data or encrypt devices for ransom. The global pandemic has also led to the creation of more than 100,000 new COVID-19 web domains, which should be treated with suspicion, even though not all of them are malicious. (Palo Alto Networks is continually updating the latest COVID-19 related cyber threats here.)

Attackers are also taking advantage of the fact that many people who are working from home have not applied the same security on their networks that would be in place in a corporate environment, or that enterprises haven’t deployed the right technologies or corporate security policies to ensure that all corporate-owned or corporate-managed devices have the exact same security protections, regardless of whether they’re connected to an enterprise network or an open home WiFi network.

Both business leaders and individual employees have critical roles and responsibilities in securing their organization and in ensuring that cyber attacks do not further compound the already disrupted work environment.

How businesses can respond

In this critical time, business leaders have a heightened responsibility to set clear expectations about how their organizations are managing security risk in the new work environments, leveraging new policies and technologies and empowering their employees. It’s important that messages on security come from the very top of an organization, and that good examples are set from the start. Here are three recommendations for business leaders.

Understand the threats to your organization. Business leaders should work with their security teams to identify likely attack vectors as a result of more employees working from home and prioritize the protection of their most sensitive information and business-critical applications.

Provide clear guidance and encourage communication. They must ensure that home-working policies are clear and include easy-to-follow steps that empower employees to make their home-working environment secure. This should include instructing employees to communicate with internal security teams about any suspicious activities.

Provide the right security capabilities. Leaders should ensure all corporately owned or managed devices are equipped with essential security capabilities, extending the same network security best practices that exist within the enterprise to all remote environments. These critical capabilities include:

  • An ability to securely connect users to their business-critical cloud and on-premise applications, such as video teleconferencing applications increasingly relevant for remote work environments
  • Endpoint protection on all laptops and mobile devices, including VPN tools with encryption
  • An ability to enforce multi-factor authentication (MFA)
  • An ability to block exploits, malware and command-and-control (C2) traffic using real-time, automated threat intelligence
  • An ability to filter malicious domain URLs and perform DNS sink holing to thwart common phishing attacks

How individuals can respond

Individual users must be empowered to follow the guidance provided to them by organizations and take preventative measures.

Maintain good password hygiene. Employees should use complex passwords and multifactor authentication where possible and change these passwords frequently.

Update systems and software. Individuals should install updates and patches in a timely manner, including on mobile devicesand any other non-corporate devices they might use for work.

Secure your WiFi access point. People should change their default settings and passwords in order to reduce the potential impact on their work of an attack via other connected devices.

Use a virtual private network (VPN). VPNs can help create a trusted connection between employees and their organizations and ensure ongoing access to corporate tools. Corporate VPNs provide additional protection against phishing and malware attacks, the same way corporate firewalls do in the office.

Be wary of COVID-19 scams. We’ve seen phishing e-mails, malicious domains and fake apps out in the wild already. Threat actors love to exploit real-world tragedies, and COVID-19 is no different.

Don’t mix personal and work. Employees should use their work devices to do work and their personal devices for personal matters. If you wouldn’t install or use a service while you’re at the office, don’t do it while at home on your work device.

Taking these relatively straightforward steps at both an enterprise and individual level should help address some of the most common security risks facing our home-working environments. We should also recognize that our threat environment is not static, which means it’s important to keep a close eye on evolving threats to avoid unnecessary additional costs and disruptions in a time when we can least afford them.

False positives/negatives and Bayes rule for COVID-19 testing

Why both false positives and false negatives are bad for the COVID-19 tests. Why Bayes rule is important for these situations.

What is a false positive/negative anyway?

A disease-screening medical test, like the one used to detect whether you are infected with the dreaded COVID-19 virus, essentially gives you a YES/NO answer. But, here are some questions to think about.

  • Would you trust the answer unequivocally?
  • Is the probability of a wrong answer higher for a positive result than a negative result?
  • What is the cost of a mistake for a wrong answer? Are the costs the same for a ‘YES’ answer vs. a ‘NO’ answer?
  • Is it better to get multiple tests done to increase the probability of getting a correct diagnosis? Does it make more sense for a ‘YES’ answer vs. a ‘NO’ answer?

In fact, no test is 100% accurate. You may have seen on the news that there is a wide variation of accuracy in the tests that are being rapidly developed and deployed for COVID-19. But, it turns out even the term ‘accuracy’ means a very specific thing when it comes to medical tests.

If you think about it, there are four distinct scenarios, for a particular test outcome, with respect to a specific person.

  • You may be really infected, and the test says ‘YES’. This is called a TRUE POSITIVE (TP).
  • You may not be infected, but still, the test says ‘YES’. This is called a FALSE POSITIVE (FP).
  • You may not be infected, and the test says ‘NO’. This is called a TRUE NEGATIVE (TN).
  • You may be really infected, but the test says ‘NO’. This is called a FALSE NEGATIVE (FN).

Now, from a personal point of view, I would be happy with the performance of the test, if it can just detect the ‘right condition’ for me. That means if it has high TP and high TN, it does the job for me, personally. It is not only about detecting a positive COVID-19 patient with a ‘YES’ verdict, but it is also about correctly saying ‘NO’ for a COVID-19 negative patient.

…it turns out even the simple term ‘accuracy’ means a very specific thing when it comes to medical tests.

The exact terminology can vary a little bit, but, in almost all cases, the ‘accuracy’ measure will denote how well the test is doing with respect to the sum of TP and TN as a percentage of the total tests administered.

But a high accuracy is not the only metric by which a test should be judged. Equally important are the other measures like FP and FN numbers.

Why?

Because out of the four situations, described above, only one leads to non-action with no consequence i.e. the TN case. In this situation, you, after being tested, will go back home, without taxing the healthcare system and any long-term health repercussions.

All of the other three situations have a varying degree of costs (societal, medical, economic, whatever you want to call them) associated with them. And the total cost to the state or nation may well depend on how the test is performing on those metrics.

It is not only about detecting a positive COVID-19 patient with a ‘YES’ verdict, but it is also about correctly saying ‘NO’ for a COVID-19 negative patient.

Case of TRUE NEGATIVE (TN)

Let us cover the least expensive one first — the case of TN. As stated above, in this situation, you, after being tested, will go back home, without taxing the healthcare system and any long-term health repercussions. The only cost is the emotional toll on you while you wait for the test to be administered and for the result to come out.

Case of TRUE POSITIVE (TP)

This is a personally dreaded scenario (but not the worst one!). You have been detected as a COVID-19 positive patient and now the ordeal starts. Depending on your exact health situation, and the criticality of the symptoms, you may be advised to self-quarantine or check into a hospital. The costs are of course different in these two alternative situations. One taxes you and your immediate family more, whereas another one taxes the healthcare system significantly.

But, at least, you got a correct assessment! There is a worse outcome, which is the next case.

Case of FALSE NEGATIVE (FN)

In the case of COVID-19, this is definitely the worst-case situation. A person, with the pathogen in his/her lungs, will go untreated. Depending on the underlying health conditions, and many other physiological parameters, the outcome is not necessarily a fatality, but surely this has higher personal and societal cost than the TP case. If this happens for someone in the high-risk cohort, then a tragic (and possibly avoidable) loss of life can ensue with a high enough possibility.

Case of FALSE POSITIVE (FP)

This is the most dreaded scenario for the medical system, patient, who, in reality, does not have the virus, is declared positive. The outcome can be of varying nature here. The person may be temporarily admitted into the healthcare system, thereby overloading the system and, more importantly, occupying extremely limited resources, which could have served a truly positive patient. If the person is sent back home, he/she goes through enormous emotional upheaval — for nothing — as he/she is really not infected.

But a high accuracy is not the only metric by which a test should be judged. Equally important are the other measures like FP and FN numbers.

Statisticians have been doing these for a long time

Essentially, this kind of YES/NO test falls under the so-called binary classification systems. Statisticians have been dealing with these systems for a long time and they call the same metrics by a different set of names — Type-I and Type-II errors.

They even have a fancy name for a tabular representation of all the scenarios we discussed, it is called ‘Confusion Matrix’ and it looks like following,

Image source: Wikipedia (Creative Commons license)

The recent resurgence of machine learning systems and algorithms, many of which use some form of binary (or multi-class) classifiers (e.g. logistic regression, decision tree, support vector machines, and neural networks) at their core, have made this confusion matrix popular. It is one of the most widely used metrics for judging the performance of an ML system.

The great feature of this matrix is that once it is produced, we can calculate a number of useful metrics from just the four numbers,

Image source: Wikipedia (Creative Commons license)
Image source: Wikipedia (Creative Commons license)

Characteristics and variations of the specific biomedical test (or of the software algorithm in case of an ML system) will result in different numbers for these metrics. You can simply assign different costs to each of these metrics and tune the test/algorithm to minimize the overall cost.

In the specific case of COVID-19, however, we would not venture into such an exercise. Cost-benefit analyses of such a life-altering, global pandemic should be left to experts and policy-makers at the highest level. As data science practitioners, you will be empowered to know that the same tools, that you use in your ML algorithms or statistical modeling, are utilized for measuring the success of mission-critical medical testing and public health systems.

We will, however, further discuss the utility of these measures for more advanced analysis of the test results using Bayesian probability inference.

Bayes’ rule for COVID-19 tests?

A statistical method of seeking a second opinion

Bayes’ theorem (alternatively Bayes’ law or Bayes’ rule) has been called the most powerful rule of probability and statistics. It describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

Bayes’ rule formula

It is a powerful law of probability that brings in the concept of ‘subjectivity’ or ‘the degree of belief’ into the cold, hard statistical modeling. It lets us begin with a hypothesis and a certain degree of belief in that hypothesis, based on domain expertise or prior knowledge. Thereafter, we gather data and update our initial beliefs. If the data support the hypothesis then the probability goes up, if it does not match, then probability goes down.

In the domain of medical testing, this continuous update methodology means, we are never satisfied with one set of tests. We can calculate the probability of a person being infected from the test data, repeat the test again, feed the result from the previous test to the same formula again, and update our probability.

This is just like seeing a second (or a third) opinion from a doctor about the diagnosis of a disease.

Cost-benefit analyses of such a life-altering, global pandemic should be left to experts and policy-makers at the highest level. As data science practitioners, you will be empowered to know that the same tools, that you use in your ML algorithms or statistical modeling, are utilized for measuring the success of mission-critical public health systems.

The knowledge of false positives/negatives are directly applicable

How do you declare a person COVID-19 positive? After you get a positive result from the test.

But, as we discussed, every test result is uncertain to some extent. So, we cannot actually say with 100% certainty that a person is COVID-19 positive, we can only say with high enough probability. Now, if we cast the testing process in terms of probability, here are a few quantities we can write,

P(COVID-19 positive| test = positive): This denotes the probability that the person is really COVID-19 positive given that the test result is positive. It is called a conditional probability expression. We want to calculate this. Now, if you look at the Bayes’ rule formula above, you will recognize it to be equivalent to the posterior expression P(A|B).

P(test = positive|COVID-19 positive): This is the prior P(B|A) in the Bayes’ rule. This is nothing but sensitivity i.e. how many true positives (test results) are there among all the positive cases (in reality).

P(COVID-19 positive): This is the probability of a random person having been infected by the COVID-19 virus. In the domain of medical testing, this is called the ‘prevalence rate’. This is the piece of the information that is not test-specific but needs domain knowledge or broader statistical measure. For COVID-19, experts may say, after pouring over a lot of data from all over the world that the general prevalence rate is 0.1% i.e. 1 out of 1000 people may be infected with the virus. Of course, this number can change based on the country, health system, active social distancing measure, etc. This term appears in the numerator of the Bayes’ rule ( P(A) in the Bayes’ rule).

P(test=positive): This is the denominator in the Bayes’ rule equation i.e. P(B). This can be calculated as,

P(test=positive) = P(test=positive|COVID-19 positive)*P(COVID-19 positive)+P(test=positive|COVID-19 negative)*P(COVID-19 negative)

Clearly, this calculation takes into account the fact that we can get a positive test result both for a truly infected person or a FALSE POSITIVE for a non-infected person. The term P(test=positive|COVID-19 negative) is simply the FALSE POSITIVE rate calculated from the confusion matrix. The term P(test=positive|COVID-19 positive) is the sensitivity as appearing in the numerator (discussed above).

Therefore, we can see that all the characteristics of a medical test can be readily utilized in a Bayesian calculation.

But there is more to the Bayesian statistics than this!

It lets us begin with a hypothesis and a certain degree of belief in that hypothesis, based on domain expertise or prior knowledge. Thereafter, we gather data and update our initial beliefs. If the data support the hypothesis then the probability goes up, if it does not match, then probability goes down.

Chaining Bayes’ rule

The best thing about Bayesian inference is the ability to use prior knowledge in the form of a Prior probability term in the numerator of the Bayes’ theorem.

In this setting of COVID-19 testing, the prior knowledge is nothing but the computed probability of a test which is then fed back to the next test.

That means, for these cases, where the prevalence rate in the general population is low, one way to increase confidence in the test result is to prescribe subsequent test, if the first test result is positive, and apply chained Bayes computation.

A step-by-step example

Look at the following article to understand the same process in the context of a drug screening, which is exactly equivalent to the COVID-19 testing. This article goes through a numerical example and plots and charts to make the calculations clear and shows clearly how the characteristics of a particular test can impact the overall confidence in the test result.Bayes’ rule with a simple and practical exampleWe demonstrate simple yet practical examples of the application of the Bayes’ rule with Python code.towardsdatascience.com

That means, for these cases, where the prevalence rate in the general population is low, one way to increase confidence in the test result is to prescribe subsequent test, if the first test result is positive, and apply chained Bayes computation.

Summary

The greatest global crisis since World War II and the largest global pandemic since the 1918–19 Spanish Flu is upon us today. Everybody is looking at the daily rise of the death toll and the rapid, exponential spread of this novel strain of the virus.

Data scientists, like so many people from all other walks of life, may also be feeling anxious. It may be somewhat reassuring to know that the familiar tools of data science and statistical modeling are very much relevant for analyzing the critical testing and disease-related data.

The goal of this article was to give an overview of some of the basic concepts in this regard. When you see a discussion about COVID-19 testing and its accuracy, you should be asking these questions and judge the result in light of data-driven rationality.

Medical professionals and epidemiologists work with this kind of analysis all the time. It is time that we also share this knowledge and understanding as much as we can and apply it rightly for discussion or decision-making.

Stay safe, everybody!

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