Advanced big data analytics is a hot topic for the manufacturing industry. Manufacturers are generating vast amounts of data through their systems, but are they using it to optimise overall operations?
First, let’s answer a basic question: What’s the added value of data analysis? It’s all about uncovering critical information to enable smart operations and drive the business. Whether you look at your shop floor, your supply chain or procurement, advanced analytics helps you identify patterns and dependencies within your systems. By doing that you can make right decisions or optimise the whole process. Typical use cases for manufacturing are:
- Predictive maintenance. Knowing when a part is going to break reduces downtime and waste. By analysing factors that drive the wear of your devices, you gain transparency on the real lifetime of your products.
- Automatic quality testing. Automating this task saves time and helps avoid human errors. Instead of using manual checks, quality can be tested incorporating data from special test devices, X-ray scans, photography, etc.
- Product optimisation. Understanding what drives the quality of your production avoids waste and improves the overall equipment effectiveness (OEE). Advanced analytics identifies parameters that cause variable levels of quality or efficiency.
- Supply chain optimisation. Anticipating the right time to produce orders or plan shipping dates enables on-time delivery and resolves storage issues. Analysing the duration of individual processes and the complex interdependencies among them provides information about transportation times and the impact of disruptions.
Many more use cases are out there. How manufacturers will benefit from data analysis really depends on their capabilities, the available data and their ideas.
Enabling enterprise-wide benefits with central data access
The data from separate processes is valuable on its own, but it’s even more valuable when it’s combined. Take an obvious example: By using predictive maintenance, you know in advance when a part will break down, which helps you manage your production and maintenance workloads. Combining this knowledge with advance information from your supply chain enables you to determine the right time to order spare parts. You don’t have to store them for a long time, nor do you risk downtime due to late delivery. Traditionally, data needed for such operations has been managed within separated business units. To unleash the full potential of data analytics, it is critical to overcome organisational barriers and share the data between different units with central data access.
Essential steps for data analytics implementation
Data analytics, machine learning and artificial intelligence (AI) in manufacturing aren’t just hype. If done properly, they enable cost savings and process optimisation. Using them requires a professional approach.Many analytics projects fail because stakeholders underestimate the degree of complexity involved. To avoid such situations, manufacturers should address these areas:
- Analytics strategy. This is the DNA of your system and the main orientation point for the following areas. A clear overall roadmap on where you are with all your different efforts will help you define your goals and govern all the necessary steps.
- Gradual and agile approach. The following two areas go hand in hand. Perform them in multiple, step-by-step cycles based on an agile minimum viable product (MVP) approach. Gradual investments thus show tangible results and gradually overcome larger barriers:
- Utilities and pipelines. Infrastructure is key. You need the platform and hardware to process the data, as well as smart pipelines for gathering and storing the data centrally.
- AI experiments. Rapid prototyping and agile AI experimentation provide insights and determine the right approach for optimising your system and operations. Many organizations expect that there are ready solutions for their every need, but plug-and-play solutions are rare for most industries and applied AI solutions and applications are still under heavy research. That’s why it’s essential to have an experimental mind-set. The experimental stage is all about quickly identifying the most effective analytics approach for your company, whether it’s an existing solution, a completely self-built system or something in between.
- Operationalised AI. Finally, you need to operationalise the successful experiments. The entire workload needs to be transferred from an experiment to a stable, maintained and enterprise-wide solution. This could mean integrating it into a business application, or have it running as a micro-service in a modern architecture.
European manufacturers: Let’s get started
Although many European manufacturers are aware of the principal benefits of data analytics, they hesitate to take action. Some manufacturers don’t feel ready for advanced analytics because they lack the right infrastructure. Systems may be outdated and inappropriate for data collection and analysis. Such barriers can be overcome gradually by applying an MVP approach before turning to large investments.
Another factor that may be impeding the adoption of data analytics is that key employees at some factories may be too busy with everyday activities. In such cases, external professionals can help dramatically reduce the internal workload, modernise the infrastructure and help with data analytics’ use.
Advanced analytics can bring big benefits, and it’s high time manufacturers do a step-change to see business improvements. Define the strategy, gradually experiment to modernise the infrastructure, and let the data become your new ally.
Start your data analytics journey today and learn more about how to set up a strong AI and data strategy.