How can the use of data in the factory be generalised?
Industrial companies still fail to make widespread use of data in their plant. It is 2022 and this statement is still true.
Why is this an issue?
Data is essential to get insights to drive and optimise production process, but also to support troubleshooting and continuous improvement activities. The absence of a data-driven plant operation means that every member of the operational teams will not be able to deliver the full value they are capable of. If you multiply this potential lost value by the number of employees in your plant, the financial impact is enormous.
In an environment where business experts are increasingly difficult to recruit, getting the best of them by relieving them of tedious and worthless tasks is a major challenge.
Why industrial companies are failing to make widespread use of data in the plant?
Industrial companies already generate a lot of data in the course of their activities, but only a small part of it is used to improve their operational performance. This is due to a number of factors, but is often linked to a problem of accessibility and complexity.
Based on our experience with many manufacturers in the process industry sector, we have identified 3 main obstacles they face:
- Collecting data
One of the difficulties is that industry has data scattered in many different IT (information technology) and OT(operational technology) systems. This data is available in heterogeneous formats. Furthermore, the use of data in daily plant operations requires fresh data, i.e. near real-time data streams.
- Giving business context to data
The data as it is available cannot be exploited simply: it is often not identified in a way that is intelligible to the end-user, and its volume is too large to be handled easily. Finally, it must be transformed and aggregated into relevant information. From a technological point of view, it is complex to organise and store data in a way that provides the appropriate performance in terms of UX (user experience) and facilitates the transformation of data into information.
- Engaging a cultural shift toward a data-driven organisation
The data-driven organisation is not just about tools and technologies. It requires a good adoption of the approach by the teams in the field. Currently, one of the main problems is that operational teams do not have the direct ability to create their own tools around data to support their daily activities. They often rely on IT/OT departments to develop the tools they need. This lack of agility is a major barrier to the scalability of many digitalisation projects. In addition, many industrial data tools still lack a modern user experience (UX) and user interface (UI).
What are the key drivers to scale-up data use in your plant?
Focus on the uses and needs of your teams
- Look at where they are wasting time with data and why. Make sure you solve these problems with your project ;
- Identify the value-creating uses they have identified but are not yet implementing due to overly complex access to the right information ;
- Provide them with applications with a nice user interface and efficient user experience.
Look for data architecture (collection, storage, processing) designed for the technical constraint of industrial application:
- With near real-time data collection and processing ;
- Adapted to the type of data queries generated by the uses linked to investigation and analysis (large amounts of data, especially time series over long periods of time, cross-referencing of heterogeneous data…).
Consider data governance and its impact on your organisation
- Find the right balance between differentiated access to data and easy sharing of data between different teams to avoid silos ;
- Give ownership and responsibility for the data and information repository to the teams that are most concerned to gain agility.
Do not try to invent what already exists. Solution providers have already thought about the constraints, needs and specificities of your sector. In particular, you can benefit from their experience and solutions in:
- Predefined data models for the business context of your industry ;
- An optimised and proven data architecture to handle the kind of data and processing you need ;
- No-code configurable business applications tailored to the challenges of your industry.