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Analysis: Striving to make better Industry 4.0 investment decisions?


External factors can ‘make or break’ any major digitization effort – but sizing them can be very tricky. A new, data-driven five-factor model can help keep things simple and objective according to Aidan Quilligan, Industry X.0 global lead, Accenture, and Raghav Narsalay, Industry X.0 Research lead, Accenture.

Most executives tasked with building business cases around brand-new digital technologies know that merely checking how well a specific solution might fit their business isn’t enough – they also need to know what’s happening in the solution’s broader business environment. A new, data-driven model five-factor model can help.

Thinking about using AI in your business? The Blockchain? Or Mixed Reality? Whatever the digital technology you are pursuing, chances are that you must figure out how to best bring them into your business. And that means that you will have to get an idea about what might help – or hinder – your implementation plans.

Well, here is the catch: while many of the factors that will come into play here exist within your company and are therefore relatively easy to review (think: strategy, organisation, resources and stakeholders etc.), some exist on the outside. And these can be tricky to wrap your head around. Because firstly which factors should you consider? And secondly once you’ve decided on some, how should you seize them?

There are answers to both questions, of course. Our research suggests that there are five sets of external factors that you should scrutinise, each of which is made up of several measures. And we have built a model that enables us to quantify each of these for a given digital technology. Here is how this works:

The five key measures you should be tracking

The five sets of external factors, which we refer to as value triggers, help executives to understand the wider business ecosystem around digital technologies. They have been derived from frameworks which Accenture experts use during major digitisation or solution implementation engagements and leveraged with research data around the business use of big data, artificial intelligence, blockchain, augmented or virtual reality, robotics, and other technology.

The value triggers are:
 Value potential. Focuses on the potential costs savings and gains in market cap value that the technology can deliver.

Talent readiness. Looks at both the existing workforce – in terms of the availability of talent and skills required for development, integration, and maintenance of the technology – as well as the current demand and supply for talent with the specific technical skill set.

Capital Adequacy. Considers the growth in venture capital investment, as well as the number of mergers and acquisitions related to the technology over the past three to five years.

Ecosystem Maturity. Analyses the availability of widely accepted standards and protocols for the technology; efforts made to address interoperability challenges; the number of consortiums (academic and industry-specific) formed to advance the technology; and the number of start-ups focused on advancing the technology.

Adoption intensity. Considers a variety of sub-elements: the number of use case applications built using the technology; the number of use cases that have made it to commercial deployment; the estimated growth in technology spend; the number of companies investing in and/or developing the technology or related offerings; and C-suite perception of the technology’s ability to improve efficiencies and deliver new experiences.

Grading technologies

The research data enabled us to grade each of the technologies on each of the value-triggers’ sub-factors on a scale of one to five; the smaller the number, the lower the maturity of a technology in the context of the particular sub-element of the value-trigger, and the higher the necessity to carefully manage the risk that might come from said trigger.

The first version of our research report – entitled ‘Delivering Digital Dividends’ – gives readers cross-industry trend scores for each of the mentioned technologies. However, the model also allows for both industry-specific analysis and the review of other digital tech, effectively giving business executives a diagnostic for reviewing and measuring external factors which could make or break their digitization efforts.

Key insights for your business case and roadmap

That, of course, is precisely what the research is for: it’s supposed to highlight which risks or opportunities might come with a specific technology. How executives will use these insights is, apparently, utterly up to them.

If, for example, blockchain scores somewhat meagre on talent availability and enterprise adoption in your industry, then a company could interpret this as a signal to push ahead while making plans for how to manage both challenges, to secure a competitive edge; e.g. by engaging the ecosystem for some help. But it might be an equally viable strategy to postpone investments in the new technology and to wait until the in-industry market becomes more mature.

Here is where this new piece of research and model can – and should – be complemented with other, similar frameworks that have been built to map out internal factors such as digital maturity, digitisation readiness, and the like. One example: A manufacturing company could go through any road mapping framework for Industry 4.0-readiness first, then through the model behind delivering digital dividends second and put the results from both together to prepare a more comprehensive business case or roadmap.

The power of value-trigger management

So, just how valuable is it to use the diagnostic in this way? Early results – again from our research – suggest it is significant. We found that companies which managed the ecosystem value-trigger particularly well saved an additional 2.4 per cent per employee and had additional costs savings of 5.2 per cent compared to those who didn’t. For the three years between 2013 and 2016, this translates to cost savings of $844 million for the ecosystem engagers, on average.

If the results for other value triggers are even remotely like this one, then one could say that disregarding even a single value-trigger could incur huge costs. Which is why it seems safe to say that systematically analysing all five value-triggers really is key when it comes to maximizing the return-on-investment of digitization efforts.

Look outside-in and inside-out
It should go without saying that, to make a truly viable business case around a digital technology, executives do have to look outside-in, and gauge the digital maturity and change readiness of their organisation, as well as the benefits it stands to gain from a specific technology and solution.

But that alone is not enough. With so much of the usability, performance, and, in the end, use value of any technology being dependent on other, external factors as well, those same executives also have to have a clear understanding around the broader business environment around anything they are trying to buy and use.

That does not mean that they need to know all the ins and outs of, say, the Blockchain technology and the markets emerging around it. But they should be able to objectively answer some key questions around it: Who’s investing in it? How much skilled talent it is out there? And Who’s managed to use it at scale?

All of this could be done from experience, brief in-house service of experts from the field, or mere guesswork. But now, it can also be done objectively, with a tried and tested, easy-to understand model which has already worked for many, many other companies.

We hope you know which way to take.

From big data to robotics

Big data analytics: high maturity, low risk: As a mature technology, Big Data analytics benefits from a very robust ecosystem. It tops the charts in terms of governing standards, number of start-ups as well as levels of interoperability. Moreover, its maturity also explains why it scores low on VC investments and growth in enterprise spends

Artificial intelligence: quickly becoming mainstream, risks vary by industry: AI has tremendous latent potential, considerably leading the pack both in terms of start-up and VC investment. What’s more, thanks to its widespread industrial applicability and cross-sector adoption, C-suite executives see significant opportunities for value-creation, especially to drive new-to-market products and experiences. However: the scoring for factors like talent availability vary from industry to industry, and might be significantly lower-than-average in some.

Blockchain: huge potential, but not everywhere. Blockchain can also deliver significant cost savings and top-line growth. Yet, it currently suffers from a very low adoption rate and a poor C-suite perception regarding the returns on investment in the technology. That said, things may well look up for blockchain soon as growth in enterprise spend outpaces that of most other technologies – especially in certain industries and functions like freight and shipping or logistics.
Immersive experiences (AR/VR/MR): better than its current reputation. Immersive experience technologies such as augmented reality (AR), virtual reality (VR) and mixed reality (MR) may be the most underrated among the bunch. Even though they boast the highest potential value, they fall far behind regarding investments, skilled talent, governing standards and interoperability. Plus, the C-suite perception of immersive experience technologies remains low.

Robotics: very mature and still improving! Leading the pack in terms of capital adequacy, robotics is a highly mature technology with proven industrial applications. While robotics is primarily regarded as a technology that can only deliver efficiency gains, it still enjoys high levels of enterprise adoption and commercial deployment, especially in combination with other technologies, like AI. fffffff

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