Thursday, December 07, 2023

How to organise yourself - the dangerous path to Explorer, Villager and Town Planners

Explorers, villagers and town planners (EVTP) is a system for organising a company that is having to deal with constant change due to an evolving environment. It's over 18 years old, it's a small part of the concept of mapping and it has been used successfully in several companies, BUT it is a difficult path to follow. I used to call this system - pioneers, settlers and town planners - unfortunately the colonialist overtone of those terms made it problematic for many and with good reason.

At the heart of EVTP is the idea that as things evolve then the characteristics of such things also changes e.g. the first ever computer is not the same as a highly industrialised cloud instance of a virtual server. Because of this, the manner in which we manage and deal with those things must also change as it evolves e.g. the techniques by which we manage computing infrastructure at a hyperscale today are not the same as the techniques we used in the past. To effectively cope with an evolving space, therefore, requires different aptitudes (as in skillsets such as engineering or finance) and different attitudes (as in mindsets). The three essential mindsets that we need are explorers, villagers and town planners and they can all be brilliant people.

Explorers are brilliant people. They are able to explore never-before-discovered concepts, the uncharted land. They show you wonder but they fail a lot. Half the time the thing doesn't work properly. You wouldn't trust what they build. They create 'crazy' ideas. Their type of innovation is what we call core research. They make future success possible. Most of the time we look at them and go "what?", "I don't understand?" and "is that magic?". In the past, we often burnt them at the stake. They built the first ever electric source (the Parthian Battery, 400AD) and the first ever digital computer (Z3, 1943).

Villagers are brilliant people. They can turn the half baked thing into something useful for a larger audience. They build trust. They build understanding. They make the possible future actually happen. They turn the prototype into a product, make it manufacturable, listen to customers and turn it profitable. Their innovation is what we tend to think of as applied research and differentiation. They built the first ever computer products (e.g. IBM 650 and onwards), the first generators (Hippolyte Pixii, Siemens Generators). 

Town Planners are brilliant people. They are able to take something and industrialise it taking advantage of economies of scale. This requires immense skill. You trust what they build. They find ways to make things faster, better, smaller, more efficient, more economic and good enough. They build the services that explorers build upon. Their type of innovation is industrial research. They take something that exists and turn it into a commodity or a utility (e.g. with Electricity, then Edison, Tesla and Westinghouse). They are the industrial giants we depend upon.

Now we have the basic idea: why should we implement this, and how do we do that? Well, the answer to the first question is - don't implement it. The process is not easy and you will fail if you don't follow every step. The only reason you would consider implementing the structure is if your organisation is so messed up with infighting, inertia, and inability to change that when compared to your competitors then you are seriously worrying about the organisation's future. In all other circumstances, keep whatever organisational structure you have "as is" because nobody enjoys a re-organisation except leaders who want to feel they are doing something and management consultants who get paid for it. Leave the org alone.

If you really must change the organisation, then these are the steps you have to take.

Step 1 - Principle
Take the following principles listed in the doctrine table (figure 1), gather a diverse group of people in your organisation and ask them, "How good are we at these?". Get them to vote on the table, add "red" or "green" or "blue" circles or squares or stars for things that you are good or poor at. Just make sure you create a label for what the symbols mean (see figure 2)

Figure 1 - The doctrine table.


Figure 2 - A hypothetical filled in table (I'm not sharing actual client's information).









The purpose of filling out the table is just to give you an idea of what state you are in and where you need to focus. In practice, 30-40 people spending 30 minutes filling out the table is enough to give you an idea. It's also worth spending an hour discussing the results with the group. The conversation is usually the most enlightening part. However that means you could have wasted as a group a total of 60 hours work on doing this, which is a lot but it's better to do this first and decide that you're ok and don't need to go further rather than pursuing the rest of the steps.
 
Step 2 - Improving Awareness.
This is the most time-consuming part. It's where we start at the bottom of the doctrine table, fixing every principle and working your way up. Let us assume that you're hopeless at every one of those principles which is quite common with organisations today. One fast track method of introducing principles is to use Wardley Maps.

Whenever you build a Wardley map, the first thing you need to do is identify the users. This is good for the map and it's also one of our basic principles. The users - whether it's public or the business or government regulators or all three or something else need to be marked on the map (see figure 3) as they are the anchor around which the map is built. Rather unimaginatively, I've added one user to our map called users.

Figure 3 - Know your users.











Now you have your users, you need to work out what their needs are. These you add to the map (figure 4) and fortunately, that's another principle ticked off. I know I'm a bit blase about this and actually getting to understand your user needs involves tasks like talking to them (you'd be gobsmacked at the number of companies who fail at this). I know it takes work.

Figure 4 - Understand your user needs











Of course, meeting those needs requires many components, i.e., an entire supply chain (of physical, technological, data and practice components) is necessary to make it happen. Since those components are all evolving we need to do several things. We need to understand what the components are, the relationships between components, how this connects to use needs, and how evolved those components are. How you manage, purchase, finance and build a totally novel and new concept that has never been done before is radically different from how you manage, purchase, finance and build a highly industrialised commodity. If someone says to you that "we should use agile everywhere" or "we should use six sigma everywhere" then they are either inexperienced or a management consultant trying to flog you their latest fad. Either train them or show them the door.

To recap, we need to understand the details and what is being considered (how evolved this). I've added that to figure 5. This is a Wardley Map.

Figure 5 - Understand the details and know what is being considered.





 




 

All maps are imperfect representations of a space but they enable people to expose their assumptions in a manner which allows others to safely challenge. I say safely because most organisations run on stories whilst telling everyone that great leaders are great story tellers. When you challenge someone's story, you're actually telling them that they are "not a great leader". People tend to get defensive at this. By putting the story into an imperfect map, I can challenge the map without challenging the person. I can say, I think this map is wrong. It might be missing a component, missing a relationship or having a component which is considered less evolved than it really is.

In figure 6, I'm challenging where we've placed app certification. You probably won't believe this, but in 2023, we still have organisations custom-building their own private clouds in a world where finally, most people have accepted that the public cloud is a utility.

Figure 6 - Challenge assumptions









When it comes to challenge, the cheat sheet for mapping can be helpful. Simply look down the list of properties, marking off each one for the component and that should give you an idea of how evolved it is (see figure 7)

Figure 7 - Cheat Sheet











Building a map is relatively simple process that gets easier with experience - understand the users, their needs, the components involved (SBOMs of Software Bill of Materials will help), how evolved the components are and then challenge the map. You can even get ChatGPT4v to help.

However, with any large enterprise then you're going to have a lot of maps and those maps are dynamic because they exist in a constantly changing and evolving landscape. If you try to map out the entire landscape before you do anything else - you will fail. It's like painting the Forth Bridge, a never-ending task. Which means we need to take an iterative approach to mapping.

The best way to do this is to create an Intelligence Function (or what I called Spend Control in UK Gov - bad name, my fault, don't repeat that). Let us start by asking a question. If you can map a system in a few hours, then what scale of project do you think it's worth spending those few hours understanding the users, their needs, the components involved, how evolved the components are before starting on the project? Pick a figure - a £1M project, a £100K project, a £10K project or every project? I suggest starting at £100K to begin with, you can change it later. Whatever figure you decide is your limit.

You now need a bit of policy. What you're going to do is create an Intelligence Function in your organisation and introduce the policy that any project over your limit must be mapped by the group building it and that map must be taken to the Intelligence Function for challenging before building of the project starts. People will complain. They will ask why do they need to do this. You just simply need to say "If you're spending £100K on a project, I just would like us to understand who the users are, what their needs are, what components are involved and have someone challenge it before we begin. It's worth the few hours". Many will still grumble. Be firm.

I will warn you now that a lot of politics will be involved. People will try to find exemptions; they don't want to be challenged in any way, and they certainly don't want to expose that they are spending £100M on a project that they have no idea what their users or user needs are. To help reduce the political pressure, make it clear that this is only for new projects, new re-compete, and new upgrades and doesn't affect the existing pipeline. Eventually all that stuff will come through your Intelligence Function but let time work its magic.

The second problem is your Intelligence Function is going to be overwhelmed. That's okay; let them challenge what they can and allow other projects to pass through with a "no comment". Over time, you'll need to staff it up appropriately.

Lastly, it's an Intelligence Function. It will give advice on the project by challenging the map. The group building the project should have the autonomy to ignore the advice if it wishes. But make it clear that this is pre-mortem challenge and all projects will go through a post-mortem learning. If the project fails because the advice was ignored, questions will be asked.

Getting this up and running is not a simple task. Give yourself a year before it is smoothly running. All the time, you'll be improving the situational awareness within the organisation of the landscape you are operating in.

Step 3 - Advice
As more maps are created, and there is less fighting over mapping and the challenge introduced, then you can extend what the Intelligence Function does. With many maps, you can start to identify duplication in projects - don't underestimate how much large organisations duplicate the same thing or the same process, often custom-building it in different ways. The record, to date, is one financial institution that has managed to rebuild risk management over a thousand times in the organisation. We don't quite know how many times because we stopped counting. Duplication of effort on the order of a hundred times or more is quite common. Be prepared to be shocked, that's the horror of looking.

With many maps, you should be able to start to spot duplication. Often you'll find the same component doing the same thing (which can be spotted by relationships being the same on different maps) but called a different name. We love our acronyms and technobabble. Finance tends to be the worst for this btw. A bit of duplication is not a bad thing (for reasons of avoiding systemic failure) but it should a conscious choice. Anyway, the Intelligence Function should be able to start not only challenging on the map itself but also suggesting that one group goes to talk to another group because they are building similar components (see figure 8).

Figure 8 - Spotting duplication.









With enough maps, you can even start to build profile diagrams and spot candidates for common services as opposed to the old-fashioned way of some executive sticking their finger up in the air and declaring we need this as a common service. The maps become both a common language and a source of data for identifying opportunities. That also happens to be two principles but for now, you can leave those until a bit later.

The other quick win you can introduce refers to methods such as project management and contracts. Since all components are evolving and we can actually visualise the components then we can apply appropriate methods to map (see figure 9 and 10). 

Figure 9 - Different Methods works in different contexts










Figure 10  - Applying appropriate methods to a map










This should stop all the management consultant led "lets agile everything" or "lets six sigma everything" or "lets [add some term] everything" fads that they love to create. If you haven't already fired all the management consultants by now, this will be a good time. If anyone grumbles just say use ChatGPT because its cheaper than ChatPPT which is what these management consultants mostly are.

This principle of appropriate methods doesn't solely apply to project management; you'll find you also need different methods for purchasing, finance, operations and even marketing.

The other low-hanging fruit is contract structure. Big outsourcing projects tend to fail because we try to cover an entire project with a single contract structure whilst that project contains multiple components at different stages of evolution. The problem is you can define the industrialised components well and hence use a very structured / rigid form of contract for those components. Unfortunately, the components in the uncharted space (e.g. the genesis of the novel and new to the custom-built) can't be defined. If you attempt to, you will inevitably incur massive change control costs. This is NOT a fault of lack of specification but instead trying to specify what is still evolving and changing. If you look at figure 10, by mapping it and applying appropriate methods, you've already neatly broken it into ten different contracts with defined interfaces for which appropriate contract structures can be applied.

Give yourself another year to get the advice aspects of the Intelligence Function up and running. OK, you might be thinking - "hang on, that's two years" - well, it is. But two years to go from a lack of understanding of users, user needs, components involved, lack of challenge combined with inappropriate methods, massive duplication and flawed contract structures to something which is reasonably sane or at least is on the path to being sane is very fast for a large organisation. Especially one that is used to executives pondering for a year or so on what to do before publishing a fairly meaningless "strategy" with the help of management consultants based on no situational awareness whatsoever.

At this point, you should already have seen visible improvements in the organisation. Just to check, it's worth re-running that doctrine table test (step 1). Given you also now have some level of visibility on the landscape and hence you can observe the environment, it's now worth talking about measurements. There are many measurements you can add such as price per transaction or delivery times or success rates. You might already be using some. Don't overload them, pick a handful and start using that to monitor progress from now on.

Step 4 - Improvement.
Two years in, you should now be pretty good at all the phase I principles in the doctrine table and have at least caught up with those lucky few organisations (and there are few) who were good at this in the first place. The bottom of the table should be looking rosy. Now, you've got the long climb up the table (figure 11),


Figure 11 - Climbing the Doctrine Table













You want to start introducing all these principles into the organisation. You want to recruit against them, reward behaviour that demonstrates them and build up slowly. Complete phase I first (if not already) and then move into phase II and then up.

During Phase II, you'll hit one principle which is Think small teams. At this point, you want to start introducing the concept of small teams built around the maps (see figure 12). 

Figure 12 - Think Small Teams









There's already a lot of good work out there on how to do this including Amazon's Two Pizza model. As a general rule of thumb, teams in the uncharted space of genesis / custom built should be 3 to 5 people but by the time the project becomes more industrialised (i.e. late product, more commodity) then Two Pizza (12 people) or slightly larger is fine. If the team is too large, use the maps to help break it down into smaller components. Use the relationships in the map (lines between nodes) to determine the fitness function (i.e. service delivery, dependencies and metrics) of each team. Do remember, that all the components are evolving. 

After about a year, you should start floating the idea of their being different attitudes (i.e mindsets) and not just aptitudes needed in each team. Don't however, do anything yet.


Step 5 - Explorers, Villagers and Town Planners.
You should be three years in, those bad old days of unknown duplication, no-one focusing on user needs, regular out-sourcing failures, management consultant fads that never worked out and the general all round strife should be past memories. Even issues like tech debt should be slowly clearing out of the organisation. When you run that doctrine test, most principles should look positive and you should be feeling pretty happy about things. If you're not, keep going with Step 4, you're not ready yet.

At this point, we will take the most dangerous and experimental step. We will re-organise the entire organisation to cope with constant evolution and change. Before you do this, consider stopping. If the organisation is doing well against competitors then take a break. Don't inflict any more change.

If you take the path, this means two things.

1) When we populate teams, we not only include the right aptitudes (engineering, finance, marketing, operations etc) but also the right attitudes (i.e. explorers, villagers, town planners)

2) We introduce a mechanism of theft.

The basic characteristics of explorers, villagers and town planners are outlined in figure 13

Figure 13 - Explorers, Villagers and Town Planners (EVTP)















The way it works is as follows. You create a parallel structure to the company which consists of pools of explorers, villagers and town planners. You allow people to select their own attitude and to change if they wish to. Then, when new projects appear, you populate the cells with not only the right aptitude but also attitude (all explorers together or all town planners together, etc). Let that bed down for a little time.

Then you introduce a system of theft. You allow teams of villagers to declare they are taking over some project run by explorers in order to turn it into a better product. You allow teams of town planners to declare they are taking over some product run by villagers in order to turn it into a utility-like service. As this starts to grow, you dismantle the rest of the organisational structure into the pools and introduce guilds across all the pools, i.e. guilds of engineers and guilds of finance (See Figure 14).

Figures 14 - Pools and cells.


There will be a period of readjustment and chaos as you re-organise. This is not for the faint hearted, and if you have any doubts, keep focusing on step 4.  This leap into step 5 should never be done unless you have almost all the principles in place and operating well. If you don't have those principles in place, I can guarantee you will fail.

What you're aiming for can be seen on the map. It's a constant process of team stealing from team, which replicates the evolution that occurs outside the organisation (see figure 15).

Figure 15 - Dealing with constant evolution.
 




 









Of the few who have gone down this path, the results have been remarkable and as one exec told me "the organisation found itself naturally falling into the structure". I cannot emphasise enough that there are too few examples to be confident of the structure and there is a litany of failure of those who have tried without principles in place. One of those failures was an organisation that got suckered into Gartner's bimodal nonsense and thought they could get themselves out of the mess by adding another bit. I did warn them. Don't do this. Fix those principles first.

This structure was right on the edge of what was possible in 2005 (when it was first used) and remains at the edge today.

Step 6
There's bound to be something beyond this. I don't know what it is. I've never been beyond EVTP. You will have to experiment to find it, and most of those interesting experiments are occurring in Chinese organisations. So keep an eye on organisations like Haier.

Thursday, November 09, 2023

Why the fuss about conversational programming - Part III

In the last post, I explored the idea of code as maps. In this post, we will dig a bit deeper and try to understand the essential role of open source.

To begin with, let us understand what code (or software) is. Code is a set of symbolic instructions. They are symbols that alter the behaviour of the system. 

The following symbols ...

#include <stdio.h>
int main() {
    printf("Hello world");
    return 0;
}

... alter the system's behaviour, making it print the words "Hello world" to the screen.

If you think about where we are heading with conversational programming, we are moving from a world where code is just text and into a world where other forms of code can exist, such as maps. The reason for this is that both enable different forms of discussion to occur around the problem space. In the world of text, the conversation tends towards the rules, style and syntax. In the world of maps, the conversation tends towards objects, relationships and context.

You've probably seen this for yourself. Walk into any engineering department, anywhere and you'll find whiteboards. A very different conversation is happening on the whiteboard compared to what is happening when programming the text on a screen. Both conversations relate to the problem space.

In figure 1, you have the text (code) that creates a map. I've provided an example for you to look at. The conversation we have around the map is very different from the conversation we have around the text.


With earlier versions of ChatGPT (a LLM or large language model), I could provide the code and discuss things such as syntax. With ChatGPT4v (a LMM or large multi-modal model), I can provide the map itself and start a conversation about its context. I'm not saying that we will only use maps to code but instead that the entire set of symbolic instructions that we use to program something will include text and maps. In fact, it already does.


ChatGPT is a system which has been trained on a lot of data. That data consists of symbols and those symbols have changed the behaviour of the system (the model) through training. As a good friend of mine, Adam Bouhenguel, points out, the data is your code and the compiled version is your model weights.

With multi-modal systems (such as ChatGPT4v), the "model" can be trained with images, with sounds, and with a variety of other sources. Maps are just images, and if ChatGPT4v wasn't programmed (i.e. trained) on images, then it wouldn't be able to interpret, analyse and have a discussion with me around the map.

Our world is increasingly one where the symbolic instructions (the code) that change the system's behaviour (i.e. the effect of programming with code) include text, data, images and a vast variety of other things. Text, data and images are all "code" in this world. They create the software that we use. In reality, they've always been "code" (ask any modder), we've just become a little preoccupied with coding in text.

There is a danger here. Over many decades we have fought the open source battle to wrestle control from the hands of the few and give to the many. In our new world, the few have once again brought their proprietary ideas to the space and demanded subservience. Even worse, they've dressed themselves up as being open. They will often open source "code" by which they mean a subset of the symbolic instructions used to program the system. They don't open the training data (which is also part of the symbolic instructions used to program the system), they might not even open the model weights. It's not open source.

Basic freedoms of open source include free redistribution, access to source code, freedom to modify, freedom to create derivative works and non discrimination. Without all the symbolic instructions used to program the system then you can't freely modify it. What if I want to reprogram my system by changing some of the training data ... I don't have access to that. It's not open source even when you hand me the model weights because that's the compiled version. I want the actual code, the training data used. The joke of course is that even the "enlightened" who share model weights can often add clauses on their use. There is nothing remotely open about most of the AI projects out there claiming to be open. It's all openwashing.

To confuse things more, the OSI is looking to redefine what open source means. Why? There is nothing wrong with what open source means. The problem is a whole bunch of people trying to claim that code (a set of symbolic instructions) doesn't include data or images (a set of symbolic instructions) and that somehow software (a collection of symbolic instructions) only includes the text bit. The OSI just needs to make it clear that this is not open source.

This is the world we're going into. An entire new era of high level conversational programming which is tied down with proprietary foundations dressed up as open. We are handing technological sovereignty (from individual to national) to a small group of players that we have no reason to suspect have your benevolence in mind. I did warn about Feudal Lords. I did warn why nurturing open source is so important especially in multi-modal space.

Laughably, the UK led an AI safety summit last week. I say laugh but if you're in the UK then you might want to cry especially given so many voices were ignored. You would have thought that handing over national sovereignty in the landscape of technology to a few would be the major safety issue. Apparently not. When it did get mentioned it was often along the lines of the hazards of open source, guardrails and control which is exactly where the lobbyists would take it. A ray of sunshine however came in the form of Oliver Dowden who provides some backing to open source AI.  Alas, not all countries are as strategically inept as the UK. The summit was barely underway before China Gov signalled its intended role in open source AI and a number of Chinese enterprises have reinforced that message since. 

Fabulous. We get to sit and watch China accelerate, take over the AI space and have the benefits of AI shared amongst its population as we enter this new world of conversational programming whilst we hand over sovereignty to a few as the "great and good" chatter about safety and how they've saved us from some future mythical frontier AI because of a signed note whilst missing the iceberg shaped hole that is sinking the boat. Top job. Trebles all round.

If anyone is laughing, it has to be China.

IN THIS SERIES on "Why the fuss about conversational programming" …
[May 2023] Code as maps, PART II
[Nov 2023] Why open source matters, PART III

Wednesday, October 04, 2023

THE FOURTH INDUSTRIAL REVOLUTION HAS A NEW PATH.

1.0 INTRODUCTION

The Fourth Industrial Revolution (Industry 4.0) has been described as revolutionising[1] the global business landscape. It is the "next phase in the digitization of manufacturing"[2]. It represents an era of connectivity, advanced analytics, automation, robotics, and new manufacturing approaches from additive techniques to chemputation[3] to IoT, digital twins, AI, blockchain, and augmented reality.

It is so exciting, that some enthusiasts have already moved beyond its simple vision of efficiency and productivity[4] and into a new transformative vision of Industry 5.0 with an “orientation to the worker”[5]. This leads to the question of what path should we be following. 4.0? 5.0? Or should we just wait a couple of months until Industry 6.0 appears, if it hasn't already?

In 2022, DXC Leading Edge, brought together a group of 17 individuals with experience and expertise in manufacturing, mapping and business transformation to map out the sector. From this exercise, a set of critical issues facing the manufacturing industry emerged. These are: - supply chain matters, management doctrine matters, and sustainability needs useful metrics. Whilst Industry 4.0 or 5.0 or I assume the soon to be appearing 6.0 barely got a mention, the research was more aligned to the values set out in the EU Industry 5.0 vision.

Supply chain matters
“Political interference, mandated lockdowns, arbitrary trade sanctions, quarantined ports, natural disasters”[6] there is a vast range of disruption going on in today's supply chains. However, according to a 2021 McKinsey Survey, whilst 48% of senior supply chain leaders could describe risks to their first tier of suppliers, that figure plummeted to less than 2%[7] for the third tier and beyond. There are collaborative efforts such as IMDS[8] in the automotive industry, CDX in the airline industry[9], regional efforts such as CONNEX Kentucky [10] and commercial entities promoting this space such as Snowflake[11]. However, supply chain information remains siloed and fragmented. 

One of the most illuminating efforts the research group encountered was the CSH (Complexity Science Hub Vienna) mapping of the entire Hungarian production economy through transaction-level VAT records [12]. Popular corporate supply chain techniques such as lean production, just-in-time delivery, supply base reduction, decreasing buffers and supplier integration may have increased profits but led to highly vulnerable systems. By graphing out the Hungarian economy through transaction records (see figure 1.1), CSH demonstrated that the entire economy can be viewed as a collection of tightly connected directed networks rather than separate supply chains. Using this graph, they noted that 45% of the entire systemic risk of the Hungarian economy can be attributed to 32 companies out of 91,595. 

Figure 1.1 – Graphing the entire Hungarian Economy 









Management doctrine matters
Management doctrine contains the set of principles by which we operate. One of the earliest discussions within the research group was on the use of agile techniques within manufacturing often citing the example of Tesla’s transformation of the automotive sector with extreme manufacturing [13], the use of robot swarms and the introduction of factory mode which is a software mechanism for determining compliance. The net effect of this should not be underestimated, with Tesla making hardware changes every 3 hours compared to Toyota with fast-track changes of approximately 2.5 years. To be clear, Tesla is operating at a speed of change of around 14,600 times faster than its competitors [14]. By comparison, Ford’s CEO in a frank interview described how legacy car manufacturers could not even compete with Tesla in software because of the techniques they use and the supply chains they have built [15].

However, whilst agile methodologies in software can produce outstanding results, they are not suitable for all contexts. This is not a new concept and was noted by the work done in designing HS2 (high-speed rail, UK) in a virtual world [16]. Agile techniques tend to far outperform other techniques when dealing with spaces that are rapidly changing due to their iterative nature. However, not all spaces are changing so rapidly and, in such cases, more prescriptive approaches can be useful. This was highlighted in The Evolution of Project Management in 2022 [17]. When considering the entire system rather than a component, a mix of techniques is often required. The “use of appropriate methods” is a fundamentally different principle from the “use of agile”.

In the case of Tesla, the methods for the manufacture of the entire car (a system which is changing in output) may not be the same as the methods used to manufacture components within that system. Just because Tesla uses agile techniques does not mean that the manufacturer of nuts and bolts within the car would be best served by agile. Our understanding of the supply chain, the components involved, how evolved they are, the use of appropriate methods and our ability to respond to feedback matters. This last point can clearly be seen in the Joe Justice video on Tesla DCM (digital self-management) [18]. The replacement of management itself by a principle of fast feedback directed to staff.

Sustainability needs useful metrics.
Manufacturing sustainability data is mostly estimation relying on consultancy services, carbon accounting engines [19] or models such as USEEIO v2 [20]. Estimation will lead to inaccuracies in measuring the environmental impact of supply chains [21]. Supply chain visibility is crucial to address this issue, but according to Deloitte, 71% of respondents to their joint survey with the Chartered Institute of Procurement and Supply have limited or no visibility beyond tier 2.[22]

The findings from the research group are summarised in Figure 1.2.

Figure 1.2 – The six boxes. 












2. ANALYSIS: A MINORITY REPORT?

2.1 PRIORITY

The process the research follows is detailed in section 3. One of the critical steps in this research is the creation of a priority list for investment. This priority list is shown in Figure 2.1 with a comparison to aggregated analyst reports and ordering of the list by ChatGPT and BARD. 

Figure 2.1 – Priority list and comparison to Analysts
















2.2 DISCUSSION

The research group focused on supply chain awareness, management doctrine, customer dynamics, sustainability and data analytics as their main priorities. The analyst reports were focused on process improvement, additive manufacturing, AI, supply chain awareness and sustainability.

The first thing that should be noted is the very high degrees of cohesion between the Analysts, ChatGPT and BARD results. The latter are being used to pick up signals from general literature; hence we can hypothesise that the general literature supports the analyst view.

The large differences mostly centred on the use of technology including AI and automation favoured by analysts versus the research group’s focus on management doctrine e.g. principles such as “use appropriate methods”, “understand your supply chain”, “focus on user needs”, “fast feedback to staff”.

That analysts favour technological (and machine-centred solutions) whilst the research group centered on more people (and system-orientated solutions) echoes the differences between Industry 4.0 (with pillars of connection of machines, making processes more automated, productivity and profit) and Industry 5.0 (with pillars of human factors, sustainability, adaptability and customer focus) [23]


2.3 WHAT DOES THIS MEAN FOR ME?

The process used has surfaced a pronounced view towards people rather than technology. Whether that view is more accurate than simply listening to analysts remains unanswered. Any investment will also be highly contextual i.e. a steel manufacturer is not the same as an automotive company. Those caveats said, the priority list given in figure 2.1 can be used as a guide to asking questions about your context. 

For example, if you are looking to invest further in process automation, you probably want to start by asking how well do you understand your supply chains? If your organisation contains significant amounts of manufacturing data, you should be asking how you are making this more open and exploring opportunities to collaborate? If you are currently looking at investing in agile manufacturing, you should be asking questions about whether the context is suitable and what tasks are suited to more prescriptive approaches? If you are looking at automation, you should question carefully whether you are looking at the machine or the system? If you are looking at sustainability, you should be asking how much is estimated and what can be done to reduce that estimation?


2.4 NOTEWORTHY

During the research, a few noteworthy examples and papers were highlighted by the research team. These include: -

International Material Database System (IMDS)[24]. A collaborative effort in the automotive industry for the management of hazardous materials that provides insights into the supply chain.

Complexity Science Hub (CSH) Vienna[25]. For the application of complexity science to trade networks including the analysis of the Hungarian economy and the more recent map of Austria’s pig trade network.

Future International Trade (FIT) alliance[26]. A supranational body focused on creating awareness about the importance of common and interoperable standards for digital bills of lading. 

From mechanistic to system thinking[27]. A seminal lecture by Russell Ackoff which helps remind us of our tendency towards reductionism with machines rather than looking at the entire system. 

Avery Dennison SmartTrac and the AD Maxdura tire tag[28]. Embedded UHF RFID tags for the tire industry enabling the tracking of the full lifecycle.

Harvard Atlas of Economic Complexity[29]. Though not cited elsewhere in this report, the tool provided a general high-level view into trade between nations and connections between component industries. 

Carbon aware computing[30]. Though not cited elsewhere in this report, the idea of time shifting and location shifting was considered applicable to computing and manufacturing in general.  

The Climate Game[31]. Though not cited elsewhere in this report, the concept of using gaming to motivate appropriate behaviours within a manufacturing organisation was discussed and influenced the final six box actions. 


3 PROCESS

3.1 THE RESEARCH PROCESS

The complete process of determining the six box (figure 1.2), starting with the collection of words to categorisation to mapping to analysis to consolidation and finally synthesis is shown in Figure 3.1

Figure 3.1 – The Research Process






Whilst the method enabled us to determine a different view for manufacturing, it is likely affected by the number of perspectives used. In this case, three were selected – Agile Manufacturing, Automation and Supply Chains. Hence the result can only be considered relevant to those three perspectives.

The process is also relatively time-consuming: -

* Collection of words: 1 hour
* Categorisation of words and selection of perspectives: 1 hour
* Mapping of perspectives: 6 – 14 hours per map.
* Analysis of map and selection of priority areas: 2-3 hours.
* Consolidation and comparison: 2-3 hours
* Synthesis: 3-4 hours.

Should the reader wish to repeat this effort, then the entire process can take 15-26 hours for a single industry or topic, assuming any mapping work is done in parallel. For each map, a diverse group of people with a wide range of experience for the chosen topic are ideal. You should aim for at least 8 people per map.

3.2 COLLECTION

The first step of the process is the group’s collection of words that matter for the future of transportation. This can be simply achieved by post-it notes on a miro or whiteboard (Figure 3.2).

Figure 3.2 – the cloud of words related to the future of manufacturing.














Notes
As an observer, I will note that in the collection of words, the group placed significant emphasis on technological advancements including IoT, chemputation, additive manufacturing, digital twins, predictive maintenance and cobots. I consider it reasonable to say that the group initially started with relatively high degrees of alignment with the analysts' focus on process improvement through technology including additive manufacturing, automation and use of AI. The concept of management doctrine was not initially raised.

3.3. CATEGORISATION

The next step of the research process is to categorise the words into themes (highlighted in grey) and then, through a process of group voting select three themes as perspectives to map (highlighted in purple). This categorisation is shown in figure 3.3 

Figure 3.3 – the categorisation of words into themes.











Notes
As an observer, I will note that at this early stage, the concept of management doctrine (principles) was not raised in any meaningful sense beyond discussion on how to focus on agile manufacturing and what this meant for other manufacturers. The discussion within the group focused on assembly lines, process types, material types and jobs to be done.

3.4. MAPPING AND ANALYSIS

Each perspective was then mapped by the group until a consensus was achieved that the map was a useful representation of the space. Onto the maps were added areas of importance for investment. These were then subdivided into areas of highest priority.

The maps are provided below, but one map on the perspective of agile methods is highlighted (figure 3.4.1)

Figure 3.4.1 – Manufacturing map from the perspective of agile methods.









Notes
This was probably the most fraught map that the group created. It started with a conversation on not just the use of agile but what we were looking for and what the manufacturers were attempting to achieve. In that discussion, the idea of implementing change and the speed of change took hold as the dominant factors. This led to the subsequent exploration of methodology, supply chain, observation, simulation and even the ontology of the models used.

During the exploration of investment areas (see figure 3.4.2 below), the idea was expressed that agile methods were focused on the flow of material in a changing system and that speed was related to adaptability. Whereas traditional methods and process improvement were focused more on the speed of movement of stocks in a defined system. This led to a realisation that from an entire system approach there could be many different methods operating at the same time and hence the concept of right tooling through management doctrine appeared.

Figure 3.4.2 – Investment map from the perspective of agile methods












The use of "right tooling" in software is not a new concept, it has been established in the mapping community for over 18 years. An example of this would be the use of appropriate methods in the building of HS2 (high speed rail) in a virtual world in 2012 which was delivered ahead of budget and schedule [32]. A map illustrating how appropriate methods can be applied based upon the context of the system that is being built is provided in figure 3.4.2.1  

Figure 3.4.2.1 – Using appropriate methods on a map
















The other maps created are also provided for reference.


Figure 3.4.3 – Manufacturing map from the perspective of automation

Figure 3.4.4 – Investment map from the perspective of automation












Figure 3.4.5 – Manufacturing map from the perspective of supply chains











Figure 3.4.6 – Investment map from the perspective of supply chains














3.5 FINAL NOTES

The above maps were then consolidated to create the priority list in figure 2.1

The priority list and the maps formed the basis of the discussion which led to the creation of the six box (figure 1.2)

All the work is licensed creative commons share alike. 

The raw code for the maps is stored in github [33].

4.0 REFERENCES
[1] How Industry 4.0 technologies are changing manufacturing, IBM, (RETRIEVED JULY 2023), https://www.ibm.com/topics/industry-4-0
[2] What are Industry 4.0, the Fourth Industrial Revolution, and 4IR? McKinsey, August 2022 (RETRIEVED JULY 2023)  https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-are-industry-4-0-the-fourth-industrial-revolution-and-4ir
[3] Model to Synthesis, Andrew White, Jun 2023 (RETRIEVED JULY 2023) - https://twitter.com/andrewwhite01/status/1670794000398184451
[4] Industry 5.0, European Commission, January 2022, (RETRIEVED JULY 2023), https://research-and-innovation.ec.europa.eu/research-area/industrial-research-and-innovation/industry-50_en
[5] From Industry 4.0 towards Industry 5.0: A Review and Analysis of Paradigm Shift for the People, Organization and Technology, July 2022, (RETRIEVED JULY 2023),  https://www.mdpi.com/1996-1073/15/14/5221
[6] The Triumph of the Supply Chain, James Amoah, Sept 2022 (RETRIEVED JULY 2023), https://www.linkedin.com/pulse/triumph-supply-chain-part-2-power-awareness-james-amoah
[7] How COVID-19 is reshaping supply chains, McKinsey, Nov 2021 (RETRIEVED JULY 2023) https://www.mckinsey.com/capabilities/operations/our-insights/how-covid-19-is-reshaping-supply-chains
[8] International Material Database Systems (RETRIEVED JULY 2023) https://www.mdsystem.com/imdsnt/startpage/index.jsp
[9] Compliance Data Exchange (CDX), DXC, (RETRIEVED JULY 2023) https://public.cdxsystem.com/en/web/cdx/home
[10] Connex Kentucky. https://kam.us.com/connexkentucky/ (RETRIEVED JULY 2023)
[11] Evolving supply chain data, Snowflake, (RETRIEVED JULY 2023) https://www.snowflake.com/guides/evolving-supply-chain-management-data
[12] Quantifying firm-level economic systemic risk from nation-wide supply networks, MAY 2022, (RETRIEVED JULY 2023), https://www.nature.com/articles/s41598-022-11522-z
[13] Scrum: Disrupting the Automotive Industry, Keynote REConf 2017 Joe Justice  https://www.youtube.com/watch?v=7gqBQv4onhU
[14] The Tesla Principle – Speed and Agility in the Automotive Industry, FullyCharged Show (RETRIEVED JULY 2023) https://fullycharged.show/blog/the-tesla-principle-speed-and-agility-in-the-automotive-industry/
[15] ASX Investor, (RETRIEVED JULY 2023) https://www.tiktok.com/@asxinvestortiktok/video/7246697318626495745 
[16] Digitizing Government: Understanding and Implementing New Digital Business Models, Fig 10.4, pp190, 2014, Brown, Thompson Fishenden, https://www.amazon.co.uk/Digitizing-Government-Understanding-Implementing-Business/dp/1137443626
[17] The evolution of Project Management (PM): How Agile, Lean and Six Sigma are changing project management Vittorio Cesarotti, Silvia Gubinelli and Vito Introna, Department of Enterprise Engineering, University of Rome, 2022, (RETRIEVED JULY 2023), https://journalmodernpm.com/manuscript/index.php/jmpm/article/view/JMPM02108
[18] Tesla Digital Self Management (DSM), JoeJustice, 2022, (RETRIEVED JULY 2023), https://www.youtube.com/watch?v=JOAI1uzZMU4
[19] How manufacturers can reduce carbon emissions, (RETRIEVED JULY 2023), https://normative.io/insight/reduce-manufacturer-carbon-emissions/
[20] USEEIO v2.0, The US Environmentally-Extended Input-Output Model v2.0, Wesley W. Ingwersen, Mo Li, Ben Young, Jorge Vendries & Catherine Birney, 2022 (RETRIEVED JULY 2023) https://www.nature.com/articles/s41597-022-01293-7
[21] Estimating and Reporting the Comparative Emissions Impacts of Products, WRI, MAR 2019 (RETRIEVED JULY 2023), https://www.wri.org/research/estimating-and-reporting-comparative-emissions-impacts-products
[22] Procurement and supply chain resilience in the face of global disruption. Survey by Deloitte and CIPS, Oct 2022 (RETRIEVED JULY 2023) https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/consultancy/deloitte-uk-procurement-and-supply-chain-resilience.pdf
[23] What are the differences between Industry 4.0 and Industry 5.0? (RETRIEVED JULY 2023) https://industriall.ai/blog/what-are-the-differences-between-industry-4-0-and-industry-5-0
[24] International Material Data System (RETRIEVED JULY 2023), https://en.wikipedia.org/wiki/International_Material_Data_System
[25] Complexity Science Hub Vienna, (RETRIEVED JULY 2023), https://www.csh.ac.at/
[26] FIT Alliance, International Federation of Freight Forwarders Associations, https://fiata.org/fit-alliance/
[27] From mechanistic to system thinking, Russell Ackoff, Nov 1993 (RETRIEVED JULY 2023), https://www.organism.earth/library/document/mechanistic-to-systemic-thinking
[28] AD Maxdura tire tag, Sept 2021, (RETRIEVED AUGUST 2023) https://www.labelsandlabeling.com/news/new-products/avery-dennison-smartrac-launches-ad-maxdura-tire-tag
[29] Harvard Atlas of Economic Complexity (RETRIEVED AUGUST  2023), https://atlas.cid.harvard.edu/explore
[30] Carbon Aware Computing, Episode 2 (RETRIEVED AUGUST  2023), https://podcasts.bcast.fm/e/28x5713n-carbon-aware-computing
[31] Can you reach net zero by 2050, FT, (RETRIEVED AUGUST  2023) https://ig.ft.com/climate-game/
[32] HS2 CIO James Findlay interview – Boats, trains and CIO reveals, March 2015, https://www.cio.com/article/196143/hs2-cio-james-findlay-interview-boats-trains-and-cio-reveals.html (RETRIEVED AUGUST 2023)
[33] Research 2022, https://github.com/swardley/Research2022

Tuesday, September 19, 2023

1. ARE VIRTUAL ROADS THE FUTURE?

Autonomous vehicles, digital twins, last-mile delivery, ridesharing, micro-mobility, transport hubs, hyperloop, decarbonisation, flying taxis and informatics - the field of transportation is as electrified as the batteries that are supposed to power tomorrow's vehicles. Or maybe they won't? Maybe the future is hydrogen?

Given all the excitement and areas competing for your attention, where should you invest in this space? In 2022, as part of my DXC Leading Edge research project, I brought together a group of 22 volunteers with experience and expertise in transportation to map out the sector. For their time given over the year, as promised, this initial report is all creative commons share alike.

From the research exercise, a set of critical issues facing the transport industry emerged. These are: - increasing our understanding of transport supply chain, virtual as a transport system and public infrastructure matters.

Increase awareness of the transport supply chain.
As was made clear in the UK GOV, Transport Data Strategy [1], transportation data is often highly siloed for reasons of legal and contractual barriers, lack of incentives for open data, lack of standardised format and lack of leadership. The transport supply chain is fragmented and poorly understood, which has impacts from the resilience of the system to consumer access. As highlighted by Deloitte in 2017, there also exists an opportunity in opening up transport data[2]. As of 2022, the key challenge remains[3] the lack of data sharing between different transport operators.

Virtual as a transport system.
When we talk about city functions (police, restaurants, banks, shops) then we often talk about the ways of getting there (road, rail, paths) and the mode of transport (car, train, cycle). However, this is constrained to a physical space. More of today’s city functions can now be provided within virtual space, from building inspections[4] to council meetings[5]. The ways of this virtual space are cable and air, the mode of transport is internet access. Virtual is a transport system and has a material impact on other transport systems. This includes a demand for resources to an impact on congestion (figure 1.1). City planning that ignores the impact of this virtual transport system, would be akin to a digital twin that ignores roads.

Public infrastructure matters.
Whilst the 2023 UWE Bristol report[6] stated that “Road are the arteries of economic and social prosperity” and we are at a critical time for public investment, we should be mindful that virtual is a transport system. Public infrastructure is not just paths, roads and waterways but also cables and spectrum frequency. Iceland, Latvia and Estonia have all nationalised telecommunications infrastructure in order to improve quality of service and make them more accessible to consumers. Iceland regularly claims to have the world’s fastest and best-value internet[7] in Europe. Hence, whilst the group would agree with the UWE findings, it would also expand the idea of what should be public infrastructure.

Figure 1.1 – The impact of virtual on congestion.















The findings from the mapping research group are summarised in the following six box (Figure 1.2)

Figure 1.2 – The six boxes.





 









2. ANALYSIS: A MINORITY REPORT?

2.1 Priority
The process the research follows is detailed in section 3. One of the critical steps in this research is the creation of a priority list for investment. This priority list is shown in Figure 2.1 with a comparison to aggregated analyst reports, ordering of the list by ChatGPT and order of the list by BARD.

Figure 2.1 – Priority list and comparison to Analysts, ChatGPT-4 and BARD.




 
















2.2 Discussion
The mapping group focused on the adoption of virtual spaces as its single highest priority item when it came to transport planning. This item did not feature strongly in the twelve analyst reports that were examined. Those were primarily concerned with the role of autonomous vehicles. This disparity continued down the priority list, with the mapping group focused on supply chain awareness, public infrastructure, decision support systems and government policy while the analyst focused on decarbonisation, emerging tech and the use of big data. The one overlap is the area of charging infrastructure which bizarrely the mapping group rated of higher priority (due to the connection with public infrastructure) than the analyst reports (despite their focus on autonomous vehicles). As an observer, it seems reasonable to conclude that the mapping group’s results represent a minority report which is against the consensus of analyst opinion.
 
2.3 What does this mean for me?
The process used has surfaced a different view on what we should be investing in, whether that view is more accurate than simply listening to analysts remains unanswered. Any investment will be highly contextual, as in there many types of organisations within the transportation field and Government is not the same as a commercial logistics company. Those caveats said, the priority list given in figure 2.1 can be used as a guide to asking questions of your context.
For example, if you are investing in building a digital twin of a city, you should be asking how the virtual world itself is modelled within the digital twin? If your organisation contains significant amounts of transport data, you should be asking how you are making this more open and exploring opportunities in that space? If you create policy that impacts the transport system, you should be asking how well do we understand the system? If you currently run a spoke and hub model of logistics, you should be asking whether you can distribute more to the edge?
 
2.4 Noteworthy
During the research, several noteworthy examples and papers were highlighted by the research team. These include: -

GITLAB, What is remote work / working from home[8]. A how-to guide for remote work.

House of Lords. Public transport in towns and cities[9]. Considered exemplary in its discussion of access and funding, including greater awareness of the transport supply chain and the use of digital advancements to allow consumers to plan journeys. However, in equal measure, the report fails to discuss ways of avoiding journeys altogether and the impact of virtual as a transport system.

Portugal, Digital Nomad Visas[10]. The first digital nomad visa in Europe which allows people to live in Portugal but work remotely. Seen by the group as a clear sign of Portugal embracing the changes and removing obstacles to virtual.

Isttelkom AS, common infrastructure is a must for savings in fiber optics[11]. Fibre / Cable are as much of a way of transport as roads and paths and hence require co-ordinated effort for the common good.

ZenobÄ“ Energy and TransGrid, Australia’s biggest electric bus depot offers solar and battery blueprint for future[12]. There are numerous complications in charging infrastructure including the capability of national grids built on AGC models. This use of solar, regenerative breaking and batteries was highlighted by group as a model to be copied.

France, the banning of short-haul flights[13]. Highlighted by the group as an example of active Government policy in encouraging alternative transport and change of consumer behaviour.

UK GOV, Transport Data Strategy[14]. Highlighted by the group as exemplary in its focus on poor awareness of the transport supply chain and the introduction of metrics to examine this sharing.

 
3 PROCESS

3.1 The Research Process
The complete process of determining the six box (figure 1.2), starting with the collection of words to categorisation to mapping to analysis to consolidation and finally synthesis is shown in Figure 3.1

Figure 3.1 – The Research Process








Whilst the method enabled us to determine a different view for transportation, it is likely affected by the number of perspectives used. In this case, three were used – Changing Consumer Behaviour, Coherent City Transport and Logistics. Hence the result can only be considered relevant to those three perspectives.

The process is also relatively time-consuming: -
* Collection of words: 1 hour
* Categorisation of words and selection of perspectives: 1 hour
* Mapping of perspectives: 6 – 14 hours per map.
* Analysis of map and selection of priority areas: 2-3 hours.
* Consolidation and comparison: 2-3 hours
* Synthesis: 3-4 hours.

Should the reader wish to repeat this effort, then the entire process can take 15-26 hours for a single industry or topic, assuming any mapping work is done in parallel. For each map, a diverse group of people with a wide range of experience for the chosen topic are ideal. You should aim for at least 8 people per map.
 
3.2 COLLECTION
The first step of the process is the group’s collection of words that matter for the future of transportation. This can be simply achieved by post-it notes on a miro or whiteboard (Figure 3.2).

Figure 3.2 – the cloud of words related to the future of transportation.

















Notes
It should be noted that in the collection, the group placed significant emphasis on common words discussing impacts on traditional transport systems – smart infrastructure, road safety, connected and autonomous vehicles, EVs and charging and sharing schemes. As an observer, it would be reasonable to say that the group initially started with relatively high degrees of alignment to the analyst reports with a focus on autonomous vehicles, ESG, emerging tech and data analytics.
 
3.3. CATEGORISATION
The next step of the research process is to categorise the words into themes (highlighted in grey) and then, through a process of group voting select three themes as perspectives to map (highlighted in purple). This categorisation is shown in figure 3.3

Figure 3.3 – the categorisation of words into themes.

















Notes
As an observer, I will note that at this stage, the only appearance of virtual was in terms of the metaverse (in relation to alternative transport) and remote working (in terms of changing consumer behaviours and demand). Virtual was not considered a major function of city planning but instead an outside technological impact with the focus remaining on concepts such as mobility hubs, sharing schemes and autonomous vehicles.
 
3.4. MAPPING AND ANALYSIS
Each perspective was then mapped by the group until a consensus was achieved that the map was a useful representation of the space. Onto the maps were added areas of importance for investment. These were then subdivided into areas of highest priority.

Figure 3.4.1 – Transport Map from the perspective of coherent city transport.
















Notes
As an observer, I want to highlight the significance of the map above. It was during the creation of this map that the group described how city functions had spaces they existed within, ways of getting to those spaces and modes of transportation. During the subsequent conversation, it was suggested that since city functions could exist not only in physical but virtual space, then there must be equivalent ways and modes of transport to those virtual spaces. The idea that virtual was itself a transport system was then raised. This was an entirely novel concept for the city planners who were part of the group and quickly led to the realisation that as a transport system then virtual has an impact on all other transport systems from resource usage to congestion (Figure 1.2).
Even though we don’t discuss virtual miles travelled, it has an impact. This realisation directly influenced the investment choices the group made, concentrating on the importance of virtual city function. This is shown in the investment map (Figure 3.4.2)

Figure 3.4.2 – Investment Map from the perspective of coherent city transport.


The other maps created are also provided for reference.

Figure 3.4.3 – Transport map from the perspective of logistics


Figure 3.4.4 – Investment Map from the perspective of logistics
 

Figure 3.4.5 – Transport map from the perspective of consumer behaviour
















Figure 3.4.6 – Investment map from the perspective of consumer behaviour


 













3.5 FINAL NOTES
The above maps were then consolidated to create the priority list in figure 2.1
The priority list and the maps formed the basis of the discussion which led to the creation of the six box (figure 1.2)
All the work is licensed creative commons share alike.
The raw code for the maps is stored in github[15].

REFERENCES
[1] UK GOV, TRANSPORT DATA STRATEGY, MARCH 2023, (RETRIEVED JULY 2023) https://www.gov.uk/government/publications/transport-data-strategy-innovation-through-data
[2] DELOITTE, OPEN DATA, JUNE 2012 (RETRIEVED JULY 2023) https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/deloitte-analytics/open-data-driving-growth-ingenuity-and-innovation.pdf
[3] TRANSPORT CATAPULT, INNOVATING FOR A PANDEMIC RESILIENT PUBLIC TRANSPORT SYSTEM, NOV 2022 (RETRIEVED JULY 2023) https://cp.catapult.org.uk/report/innovating-for-a-pandemic-resilient-public-transport-system/
[4] OPEN GOV, VIRTUAL INSPECTION 101, 2023 (RETRIEVED JULY 2023) https://opengov.com/virtual-inspection/
[5] LGA, VIRTUAL COUNCIL MEETING SURVEY, 2023, (RETRIEVED JULY 2023) https://www.local.gov.uk/publications/virtual-council-meeting-survey-2023
[6] UWE BRISTOL, KEY QUESTIONS FOR ROAD INVESTMENT, JANUARY 2023,(RETRIEVED JULY 2023) https://uwe-repository.worktribe.com/output/10295773
[7]DIGITAL TV, ICELAND TOPS EUROPEAN INTERNET VALUE SURVEY, JULY 2023 (RETRIEVED JULY 2023) https://www.digitaltveurope.com/2023/07/17/iceland-tops-european-internet-value-survey/
[8] GITLAB, WHAT IS REMOTE WORK, (RETRIEVED JULY 2023) https://about.gitlab.com/company/culture/all-remote/remote-work-starter-guide/
[9] HOUSE OF LORDS, UK, PUBLIC TRANSPORT IN TOWN AND CITIES, NOV 2022, (RETRIEVED JULY 2023) https://publications.parliament.uk/pa/ld5803/ldselect/ldbuiltenv/89/89.pdf
[10] PORTUGAL DIGITAL NOMAD VISAS, 2023, (RETRIEVED JULY 2023) https://www.portugal.com/travel/portugal-digital-nomad-visa-2023/
[11] ISTTELKOM, COMMON INFRASTRUCTURE IS A MUST FOR SAVINGS IN FIBER OPTICS, 2021, (RETRIEVED JULY 2023)   https://isttelkom.istanbul/en/common-infrastructure-is-a-must-for-savings-in-fiber-optics/
[12] THE DRIVEN, BUS DEPOT OFFERS SOLAR AND BATTERY BLUEPRINT,  JANUARY 2023, (RETRIEVED JULY 2023) https://thedriven.io/2023/01/10/australias-biggest-electric-bus-depot-offers-solar-and-battery-blueprint-for-future/
[13] SAVE A TRAIN, HOW RAIL OUSTED SHORT HAUL FLIGHTS, FEB 2023 (RETRIEVED JULY 2023) https://www.saveatrain.com/blog/how-rail-ousted-short-haul-flights-in-europe/
[14] UK GOV, TRANSPORT DATA STRATEGY, MARCH 2023, (RETRIEVED JULY 2023) https://www.gov.uk/government/publications/transport-data-strategy-innovation-through-data
[15] Research 2022, https://github.com/swardley/Research2022