Does the fourth industrial revolution have a new path?

Published in
14 min readOct 4, 2023


The Fourth Industrial Revolution ( Industry 4.0[1]) has been described as revolutionising 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 onto 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 0.03% of the companies (i.e. 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, while agile software methodologies can produce outstanding results, they are unsuitable 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 rapidly changing spacesdue 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]. A mix of techniques is often required when considering the entire system rather than a component. 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.


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, ChatGPT and Bard

A side-by-side comparison of the mapping group’s investment focus and that derived from analyst reports is provided (see figure 2.2). This is not to say that one is more correct, merely that a difference exists. The mapping group was seen as a proxy for investment for the benefit of manufacturing, whereas the aggregated analysts’ reports were seen as a proxy for return on capital i.e. where we are most likely to see growth in selling tools and solutions.

Figure 2.2 Side-by-side comparison of mapping group and analysts

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].

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.

During the research, the team highlighted a few noteworthy examples and papers. 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.


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.

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.

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.

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.

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.

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.

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 building HS2 (high-speed rail) in a virtual world in 2012, 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

Figure — 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

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].

[1] How Industry 4.0 technologies are changing manufacturing, IBM, (RETRIEVED JULY 2023),
[2] What are Industry 4.0, the Fourth Industrial Revolution, and 4IR? McKinsey, August 2022 (RETRIEVED JULY 2023)
[3] Model to Synthesis, Andrew White, Jun 2023 (RETRIEVED JULY 2023) —
[4] Industry 5.0, European Commission, January 2022, (RETRIEVED JULY 2023),
[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),
[6] The Triumph of the Supply Chain, James Amoah, Sept 2022 (RETRIEVED JULY 2023),
[7] How COVID-19 is reshaping supply chains, McKinsey, Nov 2021 (RETRIEVED JULY 2023)
[8] International Material Database Systems (RETRIEVED JULY 2023)
[9] Compliance Data Exchange (CDX), DXC, (RETRIEVED JULY 2023)
[10] Connex Kentucky. (RETRIEVED JULY 2023)
[11] Evolving supply chain data, Snowflake, (RETRIEVED JULY 2023)
[12] Quantifying firm-level economic systemic risk from nation-wide supply networks, MAY 2022, (RETRIEVED JULY 2023),
[13] Scrum: Disrupting the Automotive Industry, Keynote REConf 2017 Joe Justice
[14] The Tesla Principle — Speed and Agility in the Automotive Industry, FullyCharged Show (RETRIEVED JULY 2023)
[15] ASX Investor, (RETRIEVED JULY 2023)
[16] Digitizing Government: Understanding and Implementing New Digital Business Models, Fig 10.4, pp190, 2014, Brown, Thompson Fishenden,
[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),
[18] Tesla Digital Self Management (DSM), JoeJustice, 2022, (RETRIEVED JULY 2023),
[19] How manufacturers can reduce carbon emissions, (RETRIEVED JULY 2023),
[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)
[21] Estimating and Reporting the Comparative Emissions Impacts of Products, WRI, MAR 2019 (RETRIEVED JULY 2023),
[22] Procurement and supply chain resilience in the face of global disruption. Survey by Deloitte and CIPS, Oct 2022 (RETRIEVED JULY 2023)
[23] What are the differences between Industry 4.0 and Industry 5.0? (RETRIEVED JULY 2023)
[24] International Material Data System (RETRIEVED JULY 2023),
[25] Complexity Science Hub Vienna, (RETRIEVED JULY 2023),
[26] FIT Alliance, International Federation of Freight Forwarders Associations,
[27] From mechanistic to system thinking, Russell Ackoff, Nov 1993 (RETRIEVED JULY 2023),
[28] AD Maxdura tire tag, Sept 2021, (RETRIEVED AUGUST 2023)
[29] Harvard Atlas of Economic Complexity (RETRIEVED AUGUST 2023),
[30] Carbon Aware Computing, Episode 2 (RETRIEVED AUGUST 2023),
[31] Can you reach net zero by 2050, FT, (RETRIEVED AUGUST 2023)
[32] HS2 CIO James Findlay interview — Boats, trains and CIO reveals, March 2015, (RETRIEVED AUGUST 2023)
[33] Research 2022,

In this mapping industry series …
[June 2023] RETAIL

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