Business Intelligence in Industry 4.0
The technology that enables data preparation, data mining, data management, and data visualisation is referred to as business intelligence (BI). End users can identify actionable information from raw data using business intelligence tools and methods, allowing for data-driven decision-making in a variety of industries.
In the market, there are a variety of BI tools that assist business users in analysing performance metrics and extracting insights in real time. These technologies emphasise self-service capabilities, decreasing IT dependency and allowing decision-makers to spot performance gaps, market trends, and new income prospects faster. BI apps are frequently utilised to make informed business decisions and improve a company’s market position.
Industry 4.0 is an absolute requirement for mid-sized companies in the uncertain global economy. One of the primary advantages of Industry 4.0 can be the capability to change quickly and eliminate the uncertainty of making decisions making use of precise manufacturing data. Some companies have started using Business Intelligence (BI) platforms to serve as the basis for their industry 4.0 implementation. From a superficial perspective, this seems to be logical. BI platforms are built to help analyse data, and this is what manufacturers want when they develop their Industrial 4.0 solution. But there are a lot of problems with this method.
- In this blog , we'll discuss three reasons why it's not a good idea to exclusively depend on BI platforms for the transition into Industry 4.0.
- Problem 1: BI Tools Can't Move Data Effectively
- Problem 2: BI Can't Make Predictions
- Problem 3: You isn't able to feed insights back into your Systems
- Problem 4: BI Uses One Type of Data Structure
- Problem 5: Limited Machine Learning and Anomaly Detection Capabilities
- Business Intelligence in Industry 4.0 Survey
- Get The Ultimate Guide to Choosing an Industry 4.0 Solution
In this blog , we’ll discuss three reasons why it’s not a good idea to exclusively depend on BI platforms for the transition into Industry 4.0.
Problem 1: BI Tools Can’t Move Data Effectively
The most frequent issue with making use of BI tools as part of an Industry 4.0 solution is that many BI platforms aren’t capable of moving correct information from one platform to the next. For instance, if using SAP or Oracle ERP, connecting them directly to your database isn’t feasible without substantial development. It is necessary to utilize the API that is specifically designed to connect your ERP system, and unless you’ve got internal experts who can create connections for your company, it may be a lengthy process.
In many cases you’re not getting the most from your BI platform simply because it’s difficult to link the various systems you have in place.
Problem 2: BI Can’t Make Predictions
One of the major benefits of an effective Industry 4.0 solution is that it can take the existing business and factory floor information and provide an actionable, predictive insight. This is accomplished through taking snapshots of the data at any point in time after which it is compared with prior periods of time. However, BI platforms are limited in their ability to use data to anticipate changes. They may be great in collecting data, but to gain more value from the data, you require some advanced analytical tools. They’re not equipped to anticipate the next event and try to provide an additional perspective on the data beyond just visualizing the data at hand. If you’re looking for the Industry 4.0 system to be able to provide pre-planned maintenance, predictive analytics or predictive delivery times BI isn’t going to suffice.
Problem 3: You isn’t able to feed insights back into your Systems
Let’s say that you’ve conducted an analysis of last month’s production. You may want to record the findings of this analysis into your ERP or CRM system. If you’re using a BI system, you’re out of luck. The majority of BI software is read-only. This isn’t a feature that BI can provide.
However the correct Industry 4.0 solution can actually automate updates using data that otherwise would be stored in an Excel spreadsheet. For instance, if you run a manufacturing facility that has 10,000 SKUs, no one will be able to manually change your lead times for each item. Industry 4.0 solutions will make your manufacturing facility much more efficient.
Problem 4: BI Uses One Type of Data Structure
The majority of BI platforms store and collect data in the same structure as the data in the system that is used to create it. However, what happens if you need to conduct an analysis using multiple types or data sources? Or , if you need to track the performance in your ERP to a particular moment in time, and evaluate it against projections? This is not possible with the BI systems.
However, a robust Industry 4.0 solution can change the way data is structured stored within the system and offer the ability to analyze data that is impossible with a standard BI tool.
Problem 5: Limited Machine Learning and Anomaly Detection Capabilities
BI systems can detect anomalies, that’s the truth but there’s one major issue: you have to identify anomalies ahead of time. It isn’t able to discern by looking at information what constitutes an outlier, and what’s not. It is, in essence, lacking fundamental Machine Learning (ML) capabilities.
But an effective Industry 4.0 solution, will detect an increase in a particular customer’s order , even in the event that it’s not a significant anomaly- and then flag the issue to an account manager’s focus. This can be of huge significance for manufacturers as you can contact the customer and make sure there was no error.
Business Intelligence in Industry 4.0 Survey
- From conversation to action and implementation: Industry 4.0
- Quantum leaps in performance are driven by digitization.
- Develop stronger digital ties with customers that are more knowledgeable.
- To promote transformation, focus on people and culture.
- The foundations of transformation are data analytics and digital trust.
- Significant transformation is required to build robust, enterprise-wide data analytics capabilities.
- Globalization is accelerating thanks to Industry 4.0, but with a particular regional flavour.
- Large-scale investments with a big payoff: It’s time to make a decision.
Sensors, communication protocols, cloud computing, cyber-physical systems, additive manufacturing, business intelligence, big data, and other developing technologies are among the key technologies of Industry 4.0. The majority of these technologies are not new.
Industry 4.0, on the other hand, is distinguished by its unique blend of technology, business processes, and data processing. highlighted the importance of understanding how the Fourth Industrial Revolution will alter the “economic, social, cultural, and human milieu in which we live”. Innovative products and services, increased competitiveness, and improved operational processes will all contribute to the generation of value for enterprises.
In Industry 4.0, BI research has mostly focused on operational capabilities and has predominantly measured the development of operational value. The most commonly used measurements of operational value in the cited articles are quality rates and cost savings. Better operational capabilities can influence a company’s strategic value, but we must show the project’s link to the company’s objectives.
Get The Ultimate Guide to Choosing an Industry 4.0 Solution
You may think that the BI platform won’t be the right way to go for Industry 4.0 enterprises that are a bit more forward-thinking. But , the reality is that Industry 4.0 encompasses a broad range of technology options, From Artificial Intelligence to Industrial Internet of Things (IIoT). It’s all too easy for manufacturers’ decision makers to get distracted by the plethora of options.
In our most recent, we go into the basic principles of the most important and widely used Industry 4.0 platforms driving digital change in manufacturing. We also look at some of the key differences between choices for IIoT, BI, and I4.0 solutions.