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" …

[Jan 2023] What is conversational programming, PART I

[May 2023] Code as maps, PART II
[Nov 2023] Why open source matters, PART III

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

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.

Zenobē 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

INTRODUCTION

Over the last 15 months, I've been working on a research project with many volunteers to map different industries. This has been an exercise in exploration.

Most of us are familiar with using maps to apply appropriate methods, whether project management o finance. We're equally familiar with how to challenge what is being done or to find areas where we need to concentrate on efficiency or innovation or customer focus. But how do you map an industry? Which maps right? How do you find what is or maybe important?

The research has covered twelve industries and two technology sectors so far with over 300 volunteers (including 220+ involved in the primary research). Those volunteers have been from across the globe all working openly using zoom, miro and maps. I'm incredibly grateful for the effort that has been put in, and I thank you for all that was done.

In the next series of posts, I'm going to share the raw reports that we've written, including the maps and the process used. If you use online wardley maps then you'll also find the code in my github - https://github.com/swardley/Research2022

A more polished version of these reports will be available on the DXC Leading Edge site and I'll provide a link to that when they are completed.

The industries covered include retail, manufacturing, construction, defence, transportation, healthcare, government, education, finance, agriculture, energy and telecoms.

The technology sectors include AI and Cybersecurity. Though we have new sessions coming up on sustainability, gaming, the future of workplace and even quantum computing. This project is very much ongoing.

REPORT -  RETAIL

1. THE FUTURE OF RETAIL IS AT A CROSSROADS

On one hand, retail faces several challenges including the rise of e-commerce, the need to improve sustainability, and the increasing demands of consumers for transparency. On the other hand, there are also several opportunities for retailers to thrive, such as the growth of omnichannel retailing and the increasing use of data analytics to refine customer journeys. Given this, where should we focus our investment?

In this evolving landscape, one set of emerging and critical issues facing the retail industry are integrity and transparency. In recent years, there have been several high-profile scandals involving retailers, such as the use of child or slave labour and the sale of counterfeit goods[1]. These scandals have eroded consumer trust[2] and made it clear that retailers need to do more to ensure that their supply chains and their practices are ethical and sustainable. Unfortunately, we have future potential scandals heading our way, from low pay to misuse of BNPL[3] (buy-now-pay-later) and EWA[4] (earned-wages-access). One area we should focus on is the need to act with integrity and transparency, avoiding spin and admitting mistakes when they happen.

Another issue facing the retail industry concerns collaboration. To meet the demands of consumers and stay ahead of the competition, retailers need to share information and work together. However, despite early efforts such as Open Supply Hub[5], there is often a reluctance to share information, as retailers fear their competitors will benefit from it. Our second area of focus is the need to dare to share and as RILA[6] states “collaboration makes retailers better competitors”.

Finally, the industry requires metrics that drive sustainable choices. That’s not to say we don’t have metrics; we have lots of them. Unfortunately, due to the complexity of supply chains and a lack of transparency, there is a heavy reliance on estimation. The industry is “woefully behind on scope three targets”[7] which can account for 90% of emissions. Our third area of focus for retailers is the need to make use of meaningful metrics that reflect the true impact of their operations on the environment and society, including using more scientific and data-driven metrics that can accurately measure things like carbon emissions, water use, and waste production.  Unfortunately, according to a 2022 CDP study[8], less than 35% of companies’ emission reduction targets are credible.

Whilst integrity, sharing and meaningful metrics might sound a bit soft in the rough and tumble of commerce, a 2021 study[9] by Avery Dennison suggests that consumers want transparency in their supply chain, and a 2019[10] study by the World Economic Forum highlighted that leaders are often operating blind and less capable of managing shocks to the system. These “soft” issues have material impacts.  

The three key messages, challenges and actions are summarised in the following 6-box. The rest of this report will discuss how they were determined and why they are considered priorities.

Figure 1 – The six boxes.

2. EXPLORING THE WORLD OF RETAIL

In 2022, DXC Leading Edge brought together a team of 226 volunteers to explore 12 different industries. Thirteen of the group chose to explore what mattered for the future of retail. This “retail” team had a wide variety of experience in the retail sector, from senior leadership of large global conglomerates to startup founders. Our initial problem, as with all the groups, is that we started with no agreed method to explore the future. The exploration process emerged during the research project through the interactions of all the teams. That process involved the following steps.

2.1 Collect words.

To begin with, the group created a cloud of words that they felt were important to the future of retail. This varied from loyalty programs to last-mile delivery, from virtualisation of goods to influencers (see Figure 2.1). There was no initial consensus.

Figure 2.1 – the cloud of words related to the future of retail.

2.2. Categorise.

The words the group created were often connected with each other. Consumer transportation, driverless cars and electric scooters have a relationship around mobility. The team explored those relationships and created a graph of the space connecting each word to a theme (see Figure 2.2).

Figure 2.2 – the categorisation of words into themes.

The team then chose three themes they wished to explore: sustainability, shopping (an expansion on virtual shopping) and logistics. The chosen themes would become our perspectives from which to map retail.

2.3 – Mapping

The group mapped[11] each of the three chosen perspectives of retail, starting with concepts of user need, the components involved, and how evolved the components were. The maps were continually refined until the group considered the map was good enough to describe the space. On average this would take 6-14 hours per map. For illustrative purposes, one map on the perspective of shopping is given in Figure 2.3. The remaining maps on sustainability and logistics are provided in the appendix.

Figure 2.3 – Retail Map from the Perspective of Shopping.

Several aspects of the map were highlighted, including:-

1) how virtual stores allowed for a much greater industrialisation than physical stores.

2) how channels depend upon the choice of spaces (physical, virtual or both) and the experience that we create with them.

3) how space is connected to social practices, i.e., we’ve learned how to operate and interact with others in different physical spaces, but many are just learning the techniques required in the virtual space.

2.4 Analysis

For each of the three maps, areas where an organisation may choose to invest were highlighted. Once the list was considered reasonably exhaustive, the group identified for each map a few high-priority areas (see Figure 2.4). In the case of shopping, the high-priority areas were data analytics (specifically associated with the customer journey), emerging virtual practices (the social practices by which we interact with others) and integrity.

As one of the retail team commented, “mapping out the space helped us [the retail team] to think deeply about the chosen perspective and what needed to change in ways we hadn’t considered before”.

Figure 2.4 – Retail Map from the perspective of shopping, including investment areas.

The reason for using multiple perspectives is that it would enable us to aggregate importance across various maps. This is the real magic of the technique. For the reader, imagine a world where no previous maps exist. You’ve just received the first-ever maps of Paris created by three different groups. How do you know which map is correct? How do you know what landmarks are important? You look at a single map and it highlights the Eiffel Tower and Pierre’s Pizza Parlour but which is more important? You’ve never been to Paris.

By asking multiple cartographers to highlight essential features on their maps and then aggregating across them then you are more likely to find the important landmarks regardless of perspective. In the Paris example, you’ll quickly find that the Eiffel Tower is a familiar landmark across many maps compared to Pierre’s Pizza Parlour which may only appear on one.

This is precisely what we are doing: creating three perspectives and consolidating across them in an attempt to find the landmarks that matter.

2.5    Consolidation to important landmarks.

First, we aggregate across the maps , creating a priority order based on the frequency at which an area (for example “virtual goods”) is highlighted and any priority given. In the case of retail, those areas of highest priority across all maps included: - awareness of the supply chain (high), integrity (high), data analytics (focused on customer journeys), sustainability, measurement, emerging virtual practices and distribution (of storage and inventory). This order was then compared against the aggregated results of 14 analyst reports as well as comparison to ChatGPT-4 and BARD. See figure 2.5

Figure 2.5 – Consolidation and comparison to Analysts, ChatGPT-4 and BARD.

2.6 Discussion

By examination of Figure 2.5, a number of observations are made.

1) The difference between the views of analysts and those of the retail team are so wide that you can say that retail is standing at a crossroad. On one hand the analysts would have you follow a path focused on personalisation (high) and data analytics (high). The retail team would have you follow a path of awareness of the supply chain (high) and integrity (high).

2) ChatGPT and BARD had high levels of agreement between themselves along with a tendency to agree with the analysts. As large language models trained on relatively recent text, the priority order created by ChatGPT-4 and BARD should statistically reflect how common the terms appeared in common text i.e. whether analysts and others were writing about it. Hence, you would expect some level of consensus. Volatility between ChatGPT-4, BARD and the analysts would imply high levels of uncertainty in the field. Care must be taken to use prompts that forced both LLMs (large language models) to stick to sorting the given list rather than answering the question of what matters. One notable variation is the LLMS tended to agree more with the retail team on the issue of supply chain awareness.

3) The retail team’s priority order tended to have words that were implied from (rather than being identical) to those that the team originally collected (figure 2.1.) For example, the phrases “carbon footprint of global supply chains”, “resilience versus robustness of supply chains” and “Who in the supply chain is held to account” implies an “awareness of supply chain” which emerged during the mapping process as being the highest priority focus.  

4) The priority order represents a general consensus between the team. Obviously, the actual priority order for a retail company will depend upon its context. Two areas with strong consensus for the retail team were how poorly understood supply chains were and integrity. Poor awareness of the supply chain has an impact on other topics from resilience to crisis management to sustainability. A lack of integrity in the industry creates a history of scandals, from the use of slave labour to fraud to poor wages. Two current trends that were highlighted and felt likely to cause future scandals were BNPL (Buy Now Pay Later) and EWA (Earned Wages Access). The former, which allows retail to arrange bank loans for purchases seems to be a very positive change on the surface, with retailers increasing revenue (any loan interest paid as the product discount), banks receiving interest (the loans are packaged into collateralised obligations which are sold to other markets) and consumers receiving products they want (with interest-free loans). Unfortunately, such schemes have been used to buy staple goods, and the concern is the creation of a new debt bubble with retail at heart. The latter, EWA, again seems optimistic, allowing employees to gain access to earned wages and avoid using payday loans. In effect, however, this is an interest-free payday loan, and the group highlighted how this was not solving the fundamental problem of low wages and could instead create entrapment.

Based upon the maps created, the areas highlighted for investment, the consolidation of these areas to form a priority list and comparison to analysts, and the subsequent discussion, the following six boxes were created (a repetition of the previous Figure 1)

Figure 2.6 – Six Box.

2.7 Leading practice

During the research process, several companies were highlighted for what the group considered leading practice. A selection is provided as a guide for the reader.

• Deforestation-free supply chain by Unilver[12]. In particular the focus on using crowdsourcing

• B Corp certification[13]. For completeness of the entire process.
  
  
• 100% slave free the norm in chocolate by Chocolonely[14]. For clarity of purpose and focus on awareness.
  
  
• A coalition for collective action by Sustainable Apparel Coalition[15]. For collective action and sharing
  
  
• Explore global supply chain data by Open Supply Hub[16]. For collective action and sharing
  
  
• Supply chain: going beyond compliance by Levis Strauss & Co[17]. For exceptional honesty and transparency in the complexity of supply chains
  
  
• AWS Supply Chain Features by AWS[18]. For breadth of vision and provision of core components.
  
  
• Provenance[19]. For provision of mechanisms to clarify claims made by retailers.
  
  
• Retail Net Zero action plan by WRAP and WWF[20]. For collective action and focus on standardising measures.
  
  
• Activism by Patagonia[21]. For direct action and integrity.

3 PROCESS

The complete process of determining the six box, starting with the collection of words to categorisation to mapping to analysis to consolidation and finally synthesis is shown in Figure 3.

Figure 3 – the complete method.

Whilst the method enabled us to determine a different view for retail, it is likely affected by the number of perspectives used. In this case, three were used and hence and the result can only be considered relevant to those three perspectives. As more perspectives are added, a more accurate picture of what matters in an industry should be generated.

The process is 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.

The entire process can take 15-26 hours, assuming any mapping work is done in parallel.

Further studies intend to examine the validity of this process. Whilst it enables us to find a different view on what we should be investing in, whether that view is more accurate than simply listening to analysts remained unanswered.

4 NOTES

The research was completed with the aid of over 226 volunteers and hence all maps and analysis are provided as creative commons share alike. This includes the research process itself.

5 APPENDIX

Figure 5.1 – Retail Map from the perspective of logistics

Figure 5.2 – Retail Map from the perspective of logistics, including investment areas.

Figure 5.3 – Retail Map from the perspective of sustainability

 

Figure 5.4 – Retail Map from the perspective of sustainability, including investment areas.

6 REFERENCES

  [1] "We have lost a lot of control", Guardian UK, 16/04/03, https://www.theguardian.com/world/2023/apr/16/uk-at-risk-of-food-safety-expert-warns [RETRIEVED 29/06/23]

  [2] Retail has a customer trust problem. RetailWire, 22/02/03, https://retailwire.com/discussion/retail-has-a-customer-trust-problem/ [RETRIEVED 29/06/23]

  [3] Buy now, pay later schemes increasingly an avenue for financial abuse, Guardian UK, 15/11/22, https://www.theguardian.com/business/2022/nov/15/buy-now-pay-later-bnpl-schemes-financial-abuse-report [RETRIEVED 29/06/23]

  [4] EARNED WAGE ACCESS AND THE END OF PAYDAY LENDING, Boston University Law Review, Vol 101:705, https://www.bu.edu/bulawreview/files/2021/04/HAWKINS.pdf [RETRIEVED  29/06/23]

  [5] Explore global supply chain data, https://opensupplyhub.org/ [RETRIEVED 29/06/23]

  [6] HOW ONE TRADE ASSOCIATION IS HELPING RETAILERS COLLABORATE, https://www.rila.org/focus-areas/public-policy/how-one-trade-association-is-helping-retailers-col [RETRIEVED 29/06/23]

  [7] 'We have to do something', RetailDive, 18/07/22, https://www.retaildive.com/news/retail-scope-3-supply-chain-carbon-emissions/626973/ [RETRIEVED 29/06/23]

  [8] Climate Transition Plans, CDP, https://www.cdp.net/en/guidance/guidance-for-companies/climate-transition-plans [RETRIEVED 29/06/23]

  [9] Shoppers want transparency in the supply chain, Supply Chain, 13/12/21, https://supplychaindigital.com/sustainability/report-shows-shoppers-want-transparency-supply-chain [RETRIEVED 29/06/23]

  [10] How supply chain transparency can help businesses make the right calls, WEF, 19/06/20, https://www.weforum.org/agenda/2020/06/supply-chain-transparency-can-pre-risk/ [RETRIEVED 29/06/23]

  [11] Topographical intelligence in business, https://medium.com/wardleymaps [RETRIEVED 29/06/23]

  [12] Deforestation-free supply chain, Unilever, https://www.unilever.com/planet-and-society/protect-and-regenerate-nature/deforestation-free-supply-chain/ [RETRIEVED 29/06/23]

  [13] Measuring a company’s entire social and environmental impact, B CORP cetification,  https://www.bcorporation.net/en-us/certification/ [RETRIEVED 29/06/23]

  [14] 100% slave free the norm in chocolate , TonysChocolonely, https://tonyschocolonely.com/uk/en/our-mission [RETRIEVED 29/06/23]

  [15] A Coalition for collective action, Sustainable Apparel Coalition, https://apparelcoalition.org/ [RETRIEVED 29/06/23]

  [16] Explore global supply chain data, OpenSupplyHub, https://opensupplyhub.org/ [RETRIEVED 29/06/23]

  [17] Supply Chain: Going Beyond Compliance, LEVI STRAUSS & CO, 2021 https://www.levistrauss.com/sustainability-report/community/supply-chain/ [RETRIEVED 29/06/23]

  [18] AWS Supply Chain Features, AWS, https://aws.amazon.com/aws-supply-chain/features/#Supply_chain_data_lake [RETRIEVED 29/06/23]

  [19]Shop your Values, Provenance, https://www.provenance.org/directory [RETRIEVED 29/06/23]

  [20] Retailer Net Zero Collaboration Action Programme, WRAP, https://wrap.org.uk/taking-action/food-drink/initiatives/courtauld-commitment/scope-3-GHG-Emissions/retailer-net-zero-collaboration-action-programme [RETRIEVED 29/06/23]

  [21] Take Action, Patagonia, https://eu.patagonia.com/gb/en/activism/ [RETRIEVED 29/06/23]