Let’s Not Waste a Crisis!

The ongoing COVID-19 related suppression of economic activity will impact incomes across the board. Irrespective of how the income is generated (e.g. business, employment, self-employment) the impact can be either positive, negative or uncertain.

  • Positive for those whose incomes are not disrupted or are increased due to demand (e.g. PPE manufacturers, health-care staff, delivery drivers).
  • Negative for those whose incomes have been disrupted without any relief in sight (e.g. restaurants, people who have been laid off with bad prospects for getting another job).
  • Uncertain for those who have been furloughed or laid off but with good prospects for getting a job.

With anything between 6-11% contraction predicted, the majority of the cases should fall in the ‘Uncertain’ category (I predict 4-7%) who will move to either Positive or Negative category over the next year or so.

Why do I say that?

I say it because there will be different responses to the challenges, from restructuring, process improvements to failing fast and even retraining/reskilling (both at individual level and at an organisational level). Depending on how effective a business is at transforming itself to survive, a lot of the people in the ‘Uncertain’ category will quickly transition to the ‘Negative’ category.

One of the main transformation patterns is to carry out process improvements/restructuring with increased automation so that costs decrease and production/service elasticity increases as incomes fall initially but then recover over the medium and long term.

This group of people who jump from Uncertain to Negative is the BIG problem as this can trigger a long term contraction in consumption. How can we help these people reskill and retrain so that they can re-enter the job market? What can we do to support people as the pressure to automate increases as business income contracts?

Universal Basic Income

One possible answer to many of these questions is Universal Basic Income. If we provide people guaranteed support with basics (e.g. food, rent) then we are not only cutting them some slack but also decoupling ‘survival’ with ‘growth’.

Universal Basic Income (UBI) is a simple concept to understand: all citizens get a basic income every month irrespective of how much they earn. This is guaranteed from the day they turn 18 till the day they die. They may also get a smaller percentage from the day they are born to help their parents with their upkeep.

See this TED Talk by Rutger Bregman for more on this: https://www.youtube.com/watch?v=aIL_Y9g7Tg0

With UBI a recession will not impact the basics of any household. It will provide a safety net for families and individuals. It will also allow people to develop their skills and innovate.

There are a few wrinkles in this. Firstly, how should we prevent inflation as ‘free money’ is handed out to people? One proposed mechanism is to use a different class of money from the currency of the country. This UBI money cannot be used as a store of value (i.e. can’t be lent for interest), just for limited exchange (e.g. food, rent). This is similar to the US Supplemental Nutrition Assistance Program (SNAP) – also known as ‘food stamps’ (https://en.wikipedia.org/wiki/Supplemental_Nutrition_Assistance_Program)which can be exchanged for certain types of food. Many other countries have tried this experiment (such as Finland, USA, Canada etc.). This form of money should also ‘expire’ periodically so that people don’t start using them in a ‘money-like’ way.

Another challenge is how do you convert the ‘temporary’ UBI money into ‘permanent’ currency. This is required for the businesses accepting UBI money to be able to pass it down the supply chain (both locally and internationally). For example if you buy all your groceries with UBI money and it is not convertible to currency then how will the grocery shop pay it’s staff and suppliers. What if the suppliers were importing groceries from other countries – how would they convert UBI money to any international currency. In SNAP, the stamps are equivalent to money. It doesn’t have the same impact as UBI as its cost is a fraction of the total US GDP (0.5%).

Still, one should never let a good crisis go to waste! Time to think differently.

The Advantage of Covid-19

Covid-19 has been wreaking havoc across the globe. But this was also expected given the fact that we have not been the best of tenants for Mother Earth.

All the doom and gloom aside, Covid-19 and the mass lockdowns are teaching us a very important lesson about the future of automation and technology.

In a single line:

A secure future requires smart people working on smart devices using smart infrastructure!

Figure 1: Relation between Smart People, Things and Infrastructure.

Figure 1 shows the interactions between Smart People, Things and Infrastructure.

The Covid-19 crisis, which has brought life to a standstill, has exposed the weakness of our automation maturity. Services from haircutting to garbage collection have been trimmed back, mostly as a proactive step. Whatever automation we do have, has helped tremendously (e.g. online grocery shopping) even as people’s behaviour changed overnight as panic set in.

So what is the panic about? What are the basics that we need? The panic is about running out of resources like food due to a collapse of supply chains which have been optimised to reduce warehousing costs.

Supply chains (Logistics) are heavily dependent on people. From farmers growing crops, workers building stuff to drivers transporting the product to the shops (or directly to your home).

This is not the only critical system to break down if large number of people fall ill at the same time.

Healthcare is another area that has been impacted because of the lockdown. Care has to maintained to protect vulnerable people which means minimising contact. This increases the vulnerability due to isolation.

Education has also been impacted with schools closed and exams postponed or cancelled. This might not seem like a big problem but consider the impact in future results.

Another area of concern are the utility networks. Can we truly survive with disruptions to our electricity or water networks?

If the automation is improved in the above areas then we would become more resilient (but not immune) to such events in the future which is as difficult to achieve as it sounds!

Bottom-up Automation

Before a drone can be piloted remotely for hundreds of miles or a truck driven under human supervision from a port to a local warehouse we need robust telecom infrastructure to provide reliable, medium-high bandwidth, low-latency, temporary data connections.

This magic network has three basic ingredients:

  1. Programmable network – devices that can be treated like ‘software’ and provide the same agility > significant progress has already been made in this area.
  2. Network slicing – to efficiently provide the right resource to the requesting service > lot of work ongoing in context of 5G networks
  3. Closed-loop, light touch orchestration – to help people look after a complex network and help make changes quickly and safely when required (e.g. providing a reliable mobile data link to a drone carrying a shipment of food from a wholesaler to a shop, for remote piloting use-case) > significant progress has been made and lot of ongoing work

Using such a network we can build other parts of the puzzle such as smart roads, smart rails and then smart cities. All of these help improve automation and support increasingly light touch automation use-cases.

Smart Things

Once we have the Smart Infrastructure we need Smart Things to use them.

For Logistics and maintaining a robust supply chain during a pandemic we need a fleet of autonomous/remotely supervised/remotely piloted vehicles such as heavy-lift drones, self-driving trains/cars/ships/trucks. We also need similar assistance inside warehouses and factories with robots carrying out the operations with human supervision (so called Industry 4.0 / Lights-out factory use-case).

Healthcare – requires logistics as well as the development of autonomous personal health monitoring kits that augment the doctor by allowing them to virtually examine a patient. These kits need to become as common as a thermometer and should fulfil multiple functions.

For scenario related to caring for vulnerable people, semi-autonomous robots are required that can do lot of the work (e.g. serve dinner).

In case of a lockdown, a teacher should be able to create virtual classrooms with similar level of interactivity (e.g. via AR/VR) as in a real classroom.

To maintain water, electricity and other utilities we need sensors that provide a snapshot of the network as well as actuators, remote inspection and repair platforms etc.

For all of this to be done remotely (e.g. in a lockdown scenario) we need a robust telecoms network. Clearly, without a data connection people would no longer be able to deal with the economic, mental, physical and emotional shock caused by a lockdown.

Smart People

So who will be these people who can pilot/supervise a drone, carrying a crate of toilet rolls from a warehouse in Bristol to a shop in Bath from a remote location? Well trained people of course!

This requires two important things:

  1. Second Job: Everyone should be encouraged to take up a second discipline (of their interest) in a semi-professional capacity. This helps increase redundancy in a system. For example, if you are a taxi driver and have an interest in radio – maybe your second job can be of a maintenance technician.
  2. Thinking beyond data-science and AI: Tech is everywhere and AI is not the final word in hi-tech. People should receive everyday technology training and if possible advanced technology training in at least one topic. E.g. everyone should be taught how to operate a computer but they should also be allowed to choose a topic for deeper study, like security, software development, IT administration etc.

Augmentation technologies should be made more accessible, including providing basic-training in Augmented and Virtual Reality systems so that in case of a lockdown, human presence can be projected via a mobile platform such as a drone or integrated platform within say a forklift or a truck.

Adaptation: This is perhaps the most important. This means not leaving anyone behind in the tech race. Ensuring all technologies allow broad access. This will ensure that in times of trouble technology can be accessed not only by those who are most able to deal with the issues but also those who are the most vulnerable.

All of the above require the presence of smart things!

Conclusion

Thus we have four themes of Logistics, Healthcare, Education and Utilities running across three layers: Smart People -> Smart Things -> Smart Infrastructure. That is what Covid-19 has taught us. A very important lesson indeed, so that the next time around (and there WILL be a next time), we are better prepared!

Digitisation of Services and Automation of Labour

Digitisation of services is all around us. Where we used to call for food, taxi, hotels and flights we now have apps. This ‘app’ based economy has resulted in a large number of highly specialised jobs (e.g. app developers, web designers). It also impacts unskilled or lower skilled jobs as gaps in the digitisation are filled in with human labour (e.g. physical delivery of food, someone to drive the taxi).

The other side of digitisation is automation. Where manual steps are digitised, the data processing steps can involve human labour (e.g. you fill a form online, a human processes it, a response letter is generated and a human puts it in an envelope for posting it). 

In case of a fully automated and digitised service, processing your data would involve ’machine labour’ (with different levels of automation [see http://fisheyefocus.com/fisheyeview/?p=863]) and any communication would also be electronic (e.g. email, SMS). One very good example of this is motor insurance, where you enter your details via a website or app, risk models calculate the premium on the fly and once payment is made all insurance documents are emailed to you. Only involvement of human labour is in the processing of claims and physical validation of documents. This is called an ‘e-insurer’.

Machine Labour

Automation involves replacing or augmenting human labour with machine labour. Machines can work 24×7 and are not paid salaries – thus the cost savings. However, machines need electricity and infrastructure to work and they cannot self-assemble, self-program or self-maintain (so called Judgement Day scenario from the Terminator series). Human labour is still required to develop and maintain an increasingly large number of (complex) automated systems. Human labour is also required to develop and maintain the infrastructure (e.g. power grids, telecom networks, logistic supply chains) that works alongside the automated systems.

So humans earn indirectly from machine labour but in the end automation and digitisation help save large amounts of money for companies by reducing operational costs (in terms of salaries, office space rentals etc.). Another side-effect is that certain types of  jobs are no longer required as automation and digitisation pick up pace.

Impact on Consumption

Now we know from basic economics that all consumption results in someone earning an income. 

For a company, the income is the difference between the value of what they sell and their total costs (fixed + variable) in making and selling it.

A company will increase digitisation and automation with a view to increase their total income. This can happen by targeting automating processes that increase sales or decrease costs. A company will also automate to keep levels of service so as not to lose customers to competition but there will always be some element of income increase involved here as well.

If costs are reduced by digitisation (e.g. less requirement for a physical ‘front office’) and/or automation (e.g. less number of people for the same level of service), it can lead to loss or reduction of income as people are downsized or move to suboptimal roles (e.g. a bank teller working in a supermarket). This also contributes to the ‘gig’ economy where apps provide more ‘on-demand’ access to labour (e.g. Uber).

People consume either from what they earn (income) or from borrowing (e.g. credit cards and loans). If the incomes go down then it can either impact consumption or in the short term lead to increased borrowing. This decrease in consumption can impact the same companies that sought an increase in income by automation and digitisation.

To Summarise:

  1. Automation and Digitisation leads to cost savings by introducing electronic systems in place of a manual process. 
  2. If less people are required to do the same job/maintain a given level of output then employers are likely to hire fewer new workers and/or reduce the size of the workforce over time. 
  3. This will reduce the income of people who are impacted by redundancies and change of job roles. 
  4. This in turn will reduce the consumption of those people which may hit the very same companies that are introducing automation and digitisation
  5. This in turn will further push the margins and thereby force further reduction in costs or increase in consumption from some quarter…. 
  6. And we seem to be trapped in a vicious circle!

This Sounds Like Bad News!

So looking at the circular nature of flows in an economy, as described in the previous section, we can predict some sort of impact on consumption when large scale digitisation and automation takes place. 

As an aside, this is a major reason why ‘basic income’ or universal income is a very popular topic around the world (read more: https://en.wikipedia.org/wiki/Basic_income). With basic income we can guarantee everyone a minimum lifestyle and thereby promise a minimum level of consumption.

The actual manifestation of this issue is not as straightforward as our circular reasoning, from the previous section, would indicate. This is because the income of a company depends upon several factors:

  1. External Consumption (exports)
  2. Amount consumed by those whose income increases due to automation and digitisation
  3. Amount consumed by those whose income decreases due to automation and digitisation
  4. Labour costs attributed to those who implement and support automation and digitisation
  5. Labour costs attributed to those who are at risk of being made redundant due to automation and digitisation (a reducing value)
  6. Variable costs (e.g. resource costs)
  7. Fixed costs

Exports can help provide a net boost to income – this external consumption may not be directly impacted by automation and digitisation (A&D). It may be indirectly boosted if the A&D activities lead to imports from the same countries.

The two critical factors are (2) and (3): namely how much of the output (or service) is sold to people who benefit from A&D and how much is sold to those who do not benefit from A&D. 

If a company employs a large number of people who can be made redundant via A&D activities and a large portion of their consumers are those whose incomes will be impacted by A&D then we have a very tight feedback loop – which can lead to serious loss of income for the employer, especially if it ties in with an external shock (e.g. increase of a variable cost like petroleum).

On the other hand if a company caters to people whose incomes increase with A&D (e.g. software developers) then the impact to its income will be a lot less pronounced and it may even increase significantly.

What works best is when a company can sell to both and has enough space for both A&D activities and manual labour. This means they can make money from both sides of the market. A good example of this are companies like Amazon, McDonalds and Uber who have human components integrated with A&D which then acts as a force multiplier. 

Using this framework we can analyse any given company and figure out how automation will impact them. We can also understand that in the short term A&D can have a positive effect as it acts as a force multiplier, opening new avenues of work and creating demand for different skills.

Breaking Point

Real issues can arise if automation is stretched further to complex tasks such as driving, parcel delivery and cooking food. Or digitisation is taken to an extreme (e.g. e-banks where you have no physical branches). This will have a large scale impact on incomes leading to a direct reduction in demand.

One way to force a minimum level of consumption is for the government to levy special taxes and transfer that income as it is to those who need it. This will make sure those who are unskilled or have basic skills are not left behind. This is a ‘means tested’ version of basic income similar to a benefits system.

The next step will be to re-skill people to allow them to re-enter the job market or start their own business.

Decoding Complex Systems

I recently read a book called ‘Metldown’ by Chris Clearsfield and Andras Tilcsik (Link: Meltdown).

The book provides a framework to reason about complex systems that can be found all around us (from the cars we drive to processes in a factory). The word ‘system’ is used in the generic sense where it means a set of components interacting with each other. Each component expects some sort of input and provides some sort of output.

The decomposition of a system into components can be done at different levels of detail. The closer we get to the ‘real’ representation more complex can the interaction between components (or sub-systems) get. Imagine the most detailed representation of a computer chip which incorporates within it a quantum model of the transistor!

Let us look at some important points to consider when trying to understand a complex system. These allow us to classify and select appropriate lines of attack to unravel the complexity.

1. Complexity of Interaction

Complexity arises when we have non-linear interactions between systems. Linear interactions are always easier to reason about and therefore to fix in case of issues. With non-linear interactions (e.g. feedback loops) it becomes difficult to predict effects of changing inputs on the output. Feedback loops if unbounded (i.e. not convergent) can lead to catastrophic system failures (e.g. incorrect sensor data leading to wrong automated response – which worsens the situation).

Solution: Break feedback loops with linear interactions. Add circuit breakers or delay in reaction where not possible to break feedback loops.

2. Tight Coupling

When two or more systems are tightly coupled then it is quite easy to bring down all by taking down just one. Slack in the interaction between systems requires a system to be able to deal with imprecise, inaccurate and missing inputs while preserving some sort of functional state.

Solution: Allow clear statement of inputs, outputs and acceptable ranges. Provide internal checks to ensure errors do not cross component boundaries. Provide clear indication of the health of a component.

3. Monitoring

Any system (or group of systems) requires monitoring to provide control decisions. For example, when operating a car we monitor speed, fuel and the dashboard (for warning lights). Any system made up of multiple components/sub-systems should ideally have a monitoring feed from each of the components. But many times we cannot directly get a feed from a component, or it can lead to information overload and we rely on observer components (i.e. sensors) to help us. This adds a layer of obfuscation around a component. If the sensor fails then the operator/controller has no idea what is going on or worse has the wrong idea without knowing it and therefore takes the wrong steps. This is a common theme with complex systems such as nuclear reactors, aeroplanes and stock markets where indirect measurements are all that is available.

The other issue is that when a system is made up of different components from different providers, each component may not have a standard way of providing status. For example in modern ‘cloud enabled’ software we have no way of knowing if a cloud component which is part of our system has failed and restarted. It may or may not impact us depending on how tightly coupled our components are to the cloud component and if we need to respond to any restarts (e.g. by flushing cached information).

Anomalising

While it is difficult to map any system approaching day-to-day complexity to figure out where it can fail or degrade we can use techniques such as Anomalising to make sure cases of failures are recorded and action taken to prevent future occurrences. The process is straight forward:

  1. Gather data – collect information from different monitoring feeds etc. about the failure (this is why monitoring is critical)
  2. Fix raised issues – replace failing/failed components, change processes, re-train operators
  3. Address Root Cause – monitor replaced components, new procedures while making sure root cause is identified (e.g. was the component at fault or is it a deeper design issue? Are we just treating the symptom and not the cause?)
  4. Ensure solution is publicised so that it becomes part of ‘best practice’
  5. Audit – make sure audit is done to measure solution effectiveness

Human Element

As most interesting systems involve a human:

  • operator (e.g. pilot)
  • controller (e.g. traffic controller)
  • supervisor (e.g. in a factory)
  • beneficiary (e.g. patient wearing a medical device)
  • dependent (e.g. passenger in a car)

Then the big question is how can we humans improve how we work with complex systems? Or the other way around: How can complex systems be improved to allow humans to work with them more effectively?

There is a deceptively simple process that can be used to peel back some of the complexity. We can describe this as a ‘check-plan-and-proceed’ mechanism.

  1. Gather how the interaction with a given system has been in the previous time frame (week/month/quarter) [Check]
  2. Create a list of changes to be tried in the next time frame [Plan]
  3. Figure out what can be improved in the next time frame [Proceed]

This allows the human component of a complex system to learn in bite-sized chunks.

This also helps in dealing with dynamic systems (such as stock markets) where (as per the book) it is the weather prediction equivalent of ‘predicting a tornado rather than simply rainfall’. When the check-plan-and-proceed mechanism is abandoned we get systems running amok towards a ‘meltdown’ – be it a nuclear meltdown, stock market crash, plane crash or collapse of a company.

International Recycle Card

It is encouraging to see availability of recyclable packaging such as plastic wrappers, cans and food containers. But we see the problem of incorrect disposal, littering and lack of waste segregation everywhere (here I believe developed and developing countries are alike).

What incentive can the public be given to not only correctly dispose off their litter but also to pick up after others?

One common method has been the use of bottle/can bank where you return empty bottles and/or cans and you get some money in return.

My idea is to extend this and making it streamlined. 

Concept is simple.

Prerequisites:

  1. All packaging to be uniquely identified using RFID/barcode/QR code etc. – this should identify the source of the packaging and the unique package itself. Something like a bar-code
  2. Everyone buying packaged items has a Recycle Card (app or physical)
  3. (Optional) People buy items using electronic cash (e.g. credit cards) – to attach personal details

Process:

  1. Person scans the item (and the package code is also scanned alongside) – over time these could be the same code.
  2. Alongside the bill, a full list (electronic) is provided on the app for Recycle Card of all the packaging you have purchased (when you purchased the product).
  3. The ‘value’ of that packaging in terms of the local currency will also be shown.
  4. Upon successfully recycling the packaging, a part of that ‘value’ will be credited to the person. This can be a monthly or weekly process.
  5. Any litter found is scanned. The full ‘value’ along with a small fine is debited from the associated Recycle Card. The Recycle Card of the person who found the litter and correctly disposed it gets a small credit applied to it.

This means we recognise the value (in terms of money) of the packaging and not just the contents. This I believe is partially happening where ‘green’ products with innovative packaging attract a premium prices.

Furthermore we should attach a loss (again in terms of money) with improper disposal of the packaging. That is done only through fines but without direct accountability.

Key Factors:

There are two important steps here:

  1. Detecting successful disposal: This should be automated probably at the recycling centre some sort of machine which can scan and tally the packaging and indicate which Recycle Card should be credited. Packaging is unlikely to arrive intact at the Recycling centre. Therefore multiple markers need to be provided. RFIDs are a good solution but may be too expensive for regular use. One option is a dye that exhibits florescence under certain light. This would give a code that can be detected using machine vision. This is similar to the Automatic Number Plate Recognition software that has become very popular at parking lots, toll plazas and petrol pumps. 
  2. Registration of the Recycle Card: This should be a global system. Mainly because the problem of plastics and other packaging materials will impact everyone. Especially if these end up in our Oceans. People should be obligated to correctly dispose packaging where-ever they are in the world. Those who do so should be rewarded and those who don’t penalised. To ensure this – every pieces of packaging must be uniquely identified. This is a big task and I am sure there will be manufacturers (perhaps small/medium sized ones or from the informal sector) who will no follow this system (at least in the beginning due to cost etc.). But the idea is to target the 80% before we target the 20%. In the sense that big companies like Unilever, Nestle, etc. and fast-food joints like McDonalds have the capacity to upgrade their packaging. These are also mass-consumption products. So it would have a noticeable impact.

Do let me know what you think about this idea!

Somewhere in there there are few good machine learning and big-data use-cases. 🙂