1. Real-time and predictive engagement is becoming much more of a ‘must-have’, rather than a ‘nice to do’ as part of any manufacturer’s customer experience.
2. Customers expect a more personalised service. But this makes a huge difference to the analytics platform and capabilities for manufacturers and their services journey.
REQUIREMENTS
1. For a start, manufacturers need to be able to integrate data from as many sources as possible. This is not trivial, not least because of the sheer volume of data involved.
2. The Internet of Things (IoT) is expected to generate so much data that it can be likened to a tsunami.
3. There are also a number of different types of data, from input to usage, as well as streaming data, and this complexity will only grow.
4. Versioning and APIs, to mention just two issues, can create difficulties within enterprise IT structures, particularly in the manufacturing sector.
5. Manufacturers also need to be able to understand the signals from devices and particularly to detect patterns as events occur. The responses required could be extremely simple, such as an alert, or much more complex and/or automated. This means being able to run complex models, and rapidly.
FUNDAMENTALS
1. One question is whether manufacturers are actually seeing this requirement in terms of a platform, rather than a loose connection of existing tools and capabilities. A platform is likely to be important for long-term capabilities, even if tools are enough in the short-term.
2. Opening up the platform to everyone within the company, and not just trained analysts or data scientists — democratising analytics — will increase the value obtained from investment in analytics.
3. Resolving issues of data privacy and protection are fundamental to succeeding in this market. Failure to do so could result in a loss of the market altogether, not least because governments could step in with regulations, such as GDPR in Europe.
4. Manufacturers also need to consider storage and use of data. Analysing streaming data close to source means that the data do not need to be stored in the longer term, which saves on capacity and bandwith.
5. However, there is a real question about whether real-time is always the right time for analysis, or whether cleaning data for later use can give even more insights into different areas. It may be that storage is essential, at least for a while, to obtain full value later on.
POTENTIAL
1. Once a suitable analytics platform is in place, it can support much more than simple improvement of customer experience
2. real-time engagement and analysis has huge security breach prevention potential. This is a major change of pace from the previous approach that either put barriers in place to stop likely fraudulent access, or managed the results after the event.
3. Streaming data can also allow manufacturers to follow devices throughout their lifecycle. Data from this process can be fed back to improve manufacturing, but it is also an important part of ongoing quality assurance, and even predictive maintenance. This, too, helps better understanding customer voice.
CHALLENGES
1. Industry 4.0 big data comes from many and diverse sources:
- Product and/or machine design data such as threshold specifications
- Machine-operation data from control systems
- Product- and process-quality data
- Records of manual operations carried out by staff
- Manufacturing execution systems
- Information on manufacturing and operational costs
- Fault-detection and other system-monitoring deployments
- Logistics information including third-party logistics
- Customer information on product usage, feedback, and more
2. Some of these data sources are structured (such as sensor signals), some are semi-structured (such as records of manual operations), and some are completely unstructured (such as image files).
3. In all cases, however, most of the data is either unused or used only for very specific, tactical purposes. One key factor as to why Industry 4.0 big data is generally not leveraged strategically is poor interoperability across incompatible technologies, systems, and data types.
4. A second key factor is the inability of conventional IT systems to store, manipulate, and govern such huge volumes of diverse data being generated at high velocity.
INDUSTRY 4.0 BIG DATA EXAMPLE
1. Merging quality and production data to improve production quality: A semiconductor manufacturer began correlating single-chip data captured in the testing phase at the end of the production process with process data collected earlier in the process. The manufacturer could then identify faulty chips early on and greatly improve the quality of the production process.
2. Empowered customers: The automotive industry is enthusiastically embracing Industry 4.0 in order to cost-effectively meet consumer expectations for more affordable and digitally connected cars. Among the many use cases of the big data that will be generated by connected cars is the seamless exchange of data with the manufacturer. In addition to improving after-sale service for the individual car-owner, the aggregated information on car performance can be used to improve quality processes and future designs.
3. Reduced downtime: Applicable to many industrial sectors, Industry 4.0 big data analytics can uncover patterns that predict machine or process failures before they occur. Machine supervisors will be able to assess process or machine performance in real time and, in many cases, prevent unplanned downtime.
https://blogs.sas.com/content/hiddeninsights/2018/01/31/industry-4-0-needs-analytics-platform-approach/
https://www.datanami.com/2019/04/25/big-data-challenges-of-industry-4-0/
