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]

What do I use maps for?

It's an interesting question. 

There are some that use maps to challenge what they're doing i.e. "Why are those racks custom-built?"

There are others who use maps to apply the right methods - whether project management or purchasing or finance.

Then, there are those who use maps to anticipate change for reasons of investment or managing inertia or both

Whilst others use maps to convey complicated spaces, either to focus on user needs or innovation or efficiency or even just to discover that one space that people are missing (i.e. virtual is a transport system)
 

We also have people using maps for strategic play e.g. thinking about ecosystems and where to build to them (one of over 100 different forms of gameplay)

At the same time, others use maps to think about issues of sovereignty beyond territorial and into all the landscapes we compete in (economic, technological, political and cultural) i.e. where should our borders be in a technolocial / economic space, where do we colloborate and where to conflict?

Whilst others use those principles developed from those maps to look at their organisation or those of competitors.

And the list goes on and on. We haven't even talked about the use of maps in contracts or organisational structure or risk assessment or financial flow or removal of duplication or analysis of culture or finding weakness in supply chains or ... well, fortunately the question wasn't "Where can maps be used" but "What do I use maps for"?

Fundamentally, I use them as a means of communication. A way of escaping the tyranny of stories, story tellers, syntax, rules and styles and into a world of objects, relationships, patterns, consensus and context. There isn't a right use of maps, there are many uses and a lot depends upon what a group of people need at the time.

Why the fuss about conversational programming - Part II

In my last post on conversational programming I asked the reader to "get yourself ready for a world of conversational programming" but I wasn't willing to call it for ChatGPT or the existing crop of LLMs. My advice was one of preparation and not nailing any colours to a mast. There is a reason.

I think GPT4 and systems like Github CoPilot are admirable but probably in the wrong space. I think the breathless horde of consultants prognasticating the replacement of programmers with LLMs have fallen into a trap, not disimillar to their claims in 2011 of cloud saving you money (it doesn't, you end up doing more stuff - see Jevons Paradox) or cloud needing less engineers (see above) or cloud just being for startups. 

In this case, I think the problem is to do with the medium iself.

Don't get me wrong, we're on a path to conversational programming as highlighted by Nicholas Negroponte and his paper "Architecture by yourself". As the paper noted, design is the process of a conversation between two or more pespectives in the minds of one or more designers. In today's case, one of the designers is becoming the machine. That conversation is the heart of conversational programming. But, what we're missing is the graphical conversational part of what is needed. This is the bit that Yona Friedman explored in "Towards a Scientific Architecture".

To explain, I'm going to use a map of coherent city transport. It was created through a discussion between a group of people (from Turkey to Germany to UK to the US) all involved in the transport industry. I've provided the "map" and the code use to create the map in figure 1.

Figure 1 - Coherent City Transport.

The discussion around the map highlighted that in transport planning we often failed to consider one of the major transportation systems effecting the world - that of virtual transport. Every virtual conference, every zoom chat involves a number of "virtual" miles travelled which has an impact on other transportation systems. Unfortunately, we often don't recognise this or even count the virtual miles. We've even built digital twins of cities that have ignored virtual as a transport system. It's a bit like ignoring roads.

If the idea of virtual as a transport system seems odd to you then consider the impact of different modes of transport on road congestion. Figure 2 should bring the message home.

Figure 2 - Congestion on roads by mode of transport.

This realisation of the importance of virtual as a transport system itself occurred through discussion as we explored the things on the map, their relationships with each other and the context they existed in. Now take a look at the code in figure 1. It would be difficult to make that realisation in a medium that is dominated by syntax, style and rules. In reality, the code was only ever the means to make the map. The conversation happened around the map.

Of course, both the text and the map are ways of "coding" the problem space of coherent city travel. One is simply code as text where syntax, styles and rules dominate. The other is simply code as a map where things, relationships and context dominate.

With this in mind, think about where we are with conversational programming today. We're mostly trapped in syntax, style and rules around code as text. We're not really having that conversation with the machine but instead giving it instructions - write this piece of code for me, check my code, improve my code - and being amazed at what it gives back.

A fabulous examples of this was in the NewStack article on "Developers Put AI Bots to the Test of Writing Code". The conversation often veers towards completeness or accuracy of written code but Villegas hits the nail on the head with a verdict that "AI-generated code lacks context-awareness".

That's not a problem with AI but instead the medium via which we are having the conversation. It doesn't matter if it's written or spoken, the medium is still the word, it is text. The power of conversational programming will only be truly unleashed if we can escape from the confines of text (where syntax, styles and rules dominate) and into a world of maps (where things, relationships and context matters).

Before you say "but we can create maps with words" - well, that's exactly what we used to do for navigation. Early forms of navigation were based upon epic sagas that people would learn. These were the written and spoken words for navigation. I probably sound no different from the Viking standing there pointing at squiggles on a parchment and a sun-stone to another group of bemused Vikings who had spent years learning epic sagas word for word. They probably thought that Viking was “off his rocker” and this map thing will never take off. But it did and it will again.

Words themselves are a poor medium to transmit quickly the key information needed in any significant journey. This is why we have used maps in military history as methods of communication and learning. The code for the map in figure 1 is a simplified and constrained language provided through online wardley maps. However, the code for that map contains over 1,700 different words (some hidden as digits, others hidden as links). That is pages of text in which it is hard to see the relationships between things. When we're coding our maps, the only thing that matters is the syntax and completeness of the code to produce the map. The real discussion happens over the map.

That's the world we need to get to, code as maps. At which we point we can have conversations over things, relationships and context (the real substance of a discussion) with the machines as a designer. That's when conversational programming will truly explode onto the scene. Today is just the foretaste of what is to happen.

Yes, I understand that LLMs might replace a lot of writing code itself but if you think that programming was just about writing code then you're missing the bigger picture. The world of writing code as text might diminish but the world of programming has yet to reach its golden age.

I strongly believe that the world of serverless which has already become about stitching components (or gluing things) together and thinking about the context will lend itself more naturally to the world of conversational programming. I suspect the techniques for conversational programming which were first hinted at by Aleksander Simovic and Slobodan Stojanovic in the AWS 2018 presentation on "Ask Jarvis to Create a Serverless App for Me" will come out of the rapidly developing open source space around multi-modal systems. 

Don't get me wrong, I admire the work of Bard, GPT4 and Github's Copilot but as engineers we are still stuck in a model of code as text. Our early entrants might have the foresight to declare "We have no moats" but to make things worse, they will already have inertia created by commercial interests.

So, keep exploring. Just remember, this battle for the future of AI through industrialisation of fundamental components is only at the beginning and we maybe in the "Sun Cloud" moment and barking up the wrong tree. That's where I suspect we are and there is much more to come. 

For interest, this is not a unique situation in business or IT. This is no different to the current situation with military organisations around the world slowly realising there are at least five landscapes of sovereignty which matter for the defence of the nation - territorial, economic, technological, political and cultural - and most of us only have maps for one of those landscapes.

Four of the other landscapes are still dominated by text - whether written or spoken. As we've learned in the territorial landscape in the American Civil war, topographical intelligence and situational awareness is critical. For that, we need conversations around maps not better text.

Same with code, same with conversational programming.

ADDITIONAL QUESTIONS

Q. Does that mean all code will be maps?
Of course not. It simply means that much our programming is likely to head in that direction to enable conversations. There will always be a need for the novel and new, the need for optimisations and the deeper you go the more likely you are to meet code as text i.e. maps might be high level but as you dig into a concept you’ll increasingly find graphs and then text alone. This should not surprise you as a graph normally contains some elements of text and a map contains a graph (of the value chain) and associated text. Each level inherits from what it is built upon.

Q. Why not just graphs?
Graphs are a fine tool but remember we're talking about conversions with context which implies some form of landscape. The difference between a graph and a map is that in a map, space has meaning. Which is why they are good for representing landscapes. See figure 3.

Figure 3 - Graphs vs Maps. In a map, space has meaning.

Q. Do you mean your form of maps?

All maps are imperfect representations of a space, they are also models and hence wrong. We use them because they are useful tools of communication but they represent a lossy trade-off between being able to discuss the context and fidelity of detail. It took our geographical brethren thousands of years to get that balance right, we've only be doing this for 18 years. We're more at the Babylonian Clay Tablet stage rather than ordinance survey maps. So, no ... I'm hoping we can create better maps than we have. I simply use my maps as an example — think odd looking Viking muttering about scribbles on parchments and sun-stones

Q. If your maps are wrong and imperfect, why use them?
Our geographical brethren didn't magically create 1:10,000 scale representations of the landscapes with notable features overnight. These are a starting point for conversation. 

Q. Where would you look for inspiration of this change?
Two places — the open source world and the gaming world, especially the large and still vibrant community around Skyrim SE where both seem to happen. Hence keep an eye on reddit and discord groups. There’s an awful lot of work going into bringing LLMs into Skyrim SE. It’ll be interesting to see what tools they develop.

Q. Is it possible to map this landscape in any meaningful way? It’s a complex adaptive system.
Just because something is a complex adaptive system doesn’t mean everything is unpredictable and that meaningful representations can’t be created. It used to be said that you couldn’t meaningfully graph out an economy until a group in Hungary went and did that using sales tax data (figure 4) and discovered numerous chokepoints in the economy. It’s often a question of finding the right perspective and right data.

Figure 4 — Graph of the Hungarian economy. Source : https://phys.org/news/2022-05-country-entire-economy-predictand-forthe.html

Q. We are moving from “describe the code” to “describe the app”?
That’s a nice idea but it’s slightly more than that. Describing the app still evokes concepts of commands and instructions i.e. build me this thing, I’d like buttons in cornflour blue. The future of conversational programming is more likely to start with the question of “describe your need”. In many cases, conversational programming may never need a single line of code written.