For us, the basic argument is that while physical products will continue to exist, they will be superseded by software which extends or enhances the value they're delivering.
That’s a worldview that has some serious implications for the work of Florian Bankoley, Executive Vice President for Corporate IT at manufacturer Bosch:
Applied to us, this means the way we design, manufacture and sell our products and services is changing. The way we interact with our customers, partners and suppliers is changing as well. How do we react to that?
The answer to that is by shifting gears with the intent of becoming a leading AIoT - Artificial Intelligence of Things - company, leveraging greater use of data alongside AI. Bankoley explains:
The traditional approach - some call it fire and forget or being left in the dark - what this means is, once a product is designed and manufactured, it leaves our circle of knowledge, our circle of influence, and the only time we get some feedback is when there is a problem. A problem means there's a warranty issue, there's a customer complaint, so we get some feedback. Other things we get are more of a descriptive observational nature, such as market research.
But there are ways to close the feedback loop tighter using the AIoT cycle, he argues:
Using software and connectivity, we can close the feedback cycle and we can get information back more in real time during the lifetime [of the product] and we can act on this information. For example, we can enhance the value of the product or the service to the customer; we can extend the value; we can introduce new features; we can adapt the way we design and develop. So using data and AI, we can move towards a new value creation paradigm and that is central in our strategy.
We're not only looking at that from an external point of view, but also internally. The same principles are also applied to our functional domains on the internal side, for our internal AI strategy, using the same loop. For example, in the engineering area, we are leveraging digital twins to improve the way we engineer products while they are out there, using the feedback we get, using AI, using the data we get, to improve the engineering. Another aspect where we can improve is the logistics area, where we are using automation and available data to improve the efficiency or even speed of the things we are doing.
Tapping into Tableau
Driving all this is the ‘fuel’ of data, with Bosch tapping into insight from Tableau data analytics. Bankoley says:
Having access to data is only part of the equation and only one part of the challenge. Extracting knowledge out of this data, making it tangible and also actionable, is another key aspect of unlocking the value that data can create…Using the available functionalities we get from Tableau, we have been able to move towards more data-driven decision-making, enabling teams in several areas, such as supply chain, sales, finance.
The aim is to move Bosch’s 50,000 employees in 43 countries to a "fully data-driven culture", he says. What does that mean in practice? Bankoley cites two examples, the first centering on data-driven forecasting:
In order to improve our forecasting accuracy, covering more than €2 billion in inventory stock, the team has combined five different planning tools that were in use before and plugged together very complicated manner. They've combined this in one Tableau project. Using this, they can enable more than 2000 employees working with this tool and they can move towards data-driven decision-making, moving away from gut feeling towards data-based decision-making.
Doing that, we can improve our forecasting accuracy, which in turn improves our operational efficiency and that is driving down costs. This has been scaled to more than 100 sites globally, and we will also extend it into other areas where we can leverage Tableau, leverage our data, leverage our AI, data-driven decision-making, data-driven culture, and again, closing the AIoT cycle, leveraging or improving on our overall strategy.
The second exemplar he cites relates to manufacturing:
We have a Data-as-a-Service approach and an out-of-the-box tool set we can offer to the team in manufacturing locations. This approach enables them to start quickly, without having first to set up a project and defining what type of dashboards they use and what type of data they want to access. Everything is pre-configured, so they have a quick start. This critically enables them to access the relevant data, to have access to the important dashboards, and then integrate these into their daily work and into the Continuous Improvement Process and leverage, again, Tableau, data, and AI in order to improve the manufacturing process.
We also talk about Operational Equipment Efficiency, the OEE, so increasing our efficiency in the whole production process, reducing our cost, improving our efficiency in the manufacturing process. It's an out-of-the-box approach which the teams can use on their smartphones, tablets and laptops or even at the point of interest. In the manufacturing line on their screens, they can see all the relevant data and drive towards the habit of making data-driven decisions and incorporating data into their daily work.
The big learning to come out of all of this is one that translates across business sectors:
Our AIoT transformation is a journey towards closing the feedback cycle, leveraging data, leveraging AI to close the feedback loop. Data is a key to identify hidden value and generate new growth. But it's not only a technical problem; it’s a human challenge, a challenge of mindset, culture and habits. So while technology is one part of it, we also are addressing the mindset and culture part of it, driving towards a data-driven culture. Tableau has been a key partner in this journey for us, not only with technology, but also with mindset, with best practice and use cases.