Capgemini had the pleasure of sponsoring The Manufacturer’s Industrial Data Summit in London recently. It was a fantastic event with over one hundred manufacturers present to discuss how data can provide “insight at scale” both in their own organisations and within the ecosystem of partners, that they must continue to build to be relevant during the digital revolution.
I was delighted to kick-off the event by giving a keynote on how data is the foundation stone for digital continuity.
Digital continuity is the unique, authoritative, and consistent stream of information running across the value chain. In the case of manufacturers’, this is typically across engineering, supply chain, manufacturing, sales and marketing, and service operations – incorporating core business functions such as finance and HR.
It is critical that manufacturers work towards achieving their digital continuity goals if they are to be successful in an industry which is being disrupted by smart connected products, hyper-personalisation and the emergence of significant new business models.
The shift from selling product, to product plus service, and even to selling services alone can only be achieved if organisations fully understand both their own offer (in real time, across the entire value chain) and their customers and how the offer is used (again, in real time and across the entire value chain). This requires mastery of data as a core business enabler, making digital continuity critical to:
- Enabling new business models
- Optimising innovation across the value chain
- Minimising time to market
- Moving from “linear processes” to a “network of capabilities” across an ecosystem of partners.
According to our research report ‘Digital Engineering: The new growth engine for discrete manufacturers’, around 60% of manufacturers are struggling to ensure digital continuity. We found that organisations are unable to:
- Synchronise different functions’ activities early in the design and development stage
- Create, access, and reuse information on how a product was designed, manufactured, and serviced
- Use data to deliver actionable insights for product innovation
- Using AI technologies to analyse customer data
- Create analytics capabilities to take advantage of the data generated from IoT sources
- Get a view of product utilisation when in service.
Further, almost two-thirds of organisations are grappling with cyber risks that pose significant reputational and financial consequences.
My top 6 insights from the Industrial Data Summit
In addition to giving a keynote presentation, I also hosted a roundtable discussion on IIoT and Big Data, with Marco Del Seta from BOC
The format for this was multiple tables covering different data related topics. There were five 30-minute discussions with delegates moving from one table to another in line with their professional interests in the topic.
IIoT is a specific interest area of mine as I believe that it can enable significant transformation for manufacturers, including the ability to create new business models using the wealth of data that the technology can generate, if done in the right way. My personal findings from the day can be categorised into six areas:
- State of technology adoption
- Use cases that organisations are starting with
- Barriers to success
- Additional value that manufacturers should look to achieve.
Summarising each of these in turn:
1. State of technology adoption
I found that most of the organisations I spoke to are at a very early stage of adoption and are building an understanding of the technology and the benefits it can deliver. This is in line with Capgemini’s research report ‘Unlocking the business value of IoT in operation’ and was expected. Most of the discussion related to the art-of-the-possible for this technology, and the enterprise architecture required to support its deployment. Fewer than five of the organisations I spoke to had deployed the technology “at scale”, either across their business or to more than one plant. These deployments were for very specific use cases such as temperature of quash tanks, emissions monitoring, and product quality.
2. Use cases that organisations are starting with
The majority of organisations spoke about considering IIoT technology to enable predictive maintenance of production assets. This is by far the most commonly discussed topic in the wider industry, however, Capgemini’s research shows that it does not normally provide a particularly strong ROI although the technology can be deployed relatively quickly. Using IIoT to support improvements in product quality offers a great ROI although the payback period tends to be longer. A relatively small number of organisations were looking at product quality initiatives, either to improve quality on the production line or to understand product quality in service.
Finally, our research shows that supply chain use cases offer the
greatest business value and ROI combination, however, almost no manufacturers were considering these at present. This might have been due to the delegates focus business areas in their own organisations which tended to be shop floor focused.
3. Barriers to success
There were numerous barriers to success, with no specific centre of gravity. They included:
- Getting data from old(er) machinery that is unsensored
Most manufacturers spoke of having old but effective production lines which lacked sensor data and connectivity. IFM sensors were discussed multiple times as a possible way to address this, but not the panacea for all problems.
- Where to put the data once it was collected
Most organisations either had not considered, or were currently considering, the implementation of a scalable IoT platform to allow data to be stored once it had been generated. There was significant discussion as to whether the enterprise resource planning (ERP) system was the right place, but a general recognition that a dedicated platform is needed.
- Showing the business case to scale from initial proof of concepts (PoCs)
This was likely due to the use cases chosen and the scale of the initial deployment. Generally, smaller-scale PoCs prove only the technical deployment and not a wider business case for change.
- A general view that the cost of implementation is very high
This is directly related to the ability to develop a compelling business case. For me, it’s clear that our industry needs to develop a better approach to showing the benefits that can be achieved which marries more appropriately to the cost to achieve.
There was discussion as to the two phases of sponsorship – the initial PoC tending to be sponsored from within the technology group as part of a funded innovation agenda, and deployment programmes needing senior business sponsorship to be successful. This is a typical model that we would expect to see around new technology.
The challenge that most organisations were facing was that business sponsorship is not forthcoming if the business case is not compelling (significant, achievable, and believable). To gain business sponsorship, technology teams must consider that PoCs need
to be non-trivial from a business perspective, that proving the technology works can be quite far removed from showing that IIoT is a high priority business investment area.
On one roundtable there was no technology talk and the discussion considered only culture, which was fascinating. The key takeaway for me was that to be successful, programmes need to consider the behaviours and incentivisation of their user community. For example, deploying technology which enables manufacturing productivity may only be successful if the key performance indicators (KPIs) of the workforce are updated.
More than one plant leader shared their experience of seeing workers “down-tools” once their shift quota had been achieved and start to clean their machines and work towards shift hand over. Another described the differences in productivity between two plants, one where the production team also handled maintenance activities (higher productivity) and another where the production and maintenance teams were separate (lower productivity).
6. Additional value
Manufacturers with B2C business models (and some with B2B models) are considering how they can create, collect, and monetise data either to create new revenue streams or to reduce in-service costs during warranty periods. In B2B scenarios, the blocker to success appeared to be the understanding of the application of the product. In B2C scenarios, it was the complexity of the challenge and the need to bring together stakeholders from across the business.
One organisation spoke about their use of millions of containers, each of which had small but non-negligible value, and how the ability to tag, track, and trace these could reduce container loss and open new revenue streams. It was the perception that relevant tags could be expensive in relation to the cost of the containers which was holding back progress.
Overall, I found my time at the Industrial Data Summit to be very valuable. The wide-ranging discussions were fascinating and insightful. Many thanks to Henry Anson and his team from The Manufacturer, and to all those who attended.
For more on the subject of data and digital continuity, read my earlier blog ‘Data as the foundation stone for digital continuity’ and learn about Capgemini’s digital manufacturing services here.