The Lantek MES (Manufacturing Execution System) is still the foundational backbone of the manufacturing process. It is the shop-floor, the manufacturing and the enterprise-wide processes, and the continuous improvement, that really drives efficiency. Data Analytics will make these dramatically more effective than before, but will definitely not replace them. An extended-MES enriched with data insights is the actual answer to the improvement of the manufacturing processes in the new Industry 4.0 paradigm. This is provided by Lantek Analytics.
It is not what you know, it is what you do with what you know. Data Analytics helps to know much more and much faster. Data Analytics combines powerful, fully automated discovery and analysis technologies that let manufacturers use enterprise data to improve all facets of their operations, such as: ordering parts and components, setting production schedules, predictingmachine maintenance, forecasting inventory needs, discovering processes bottlenecks, maintaining product quality, and at the end of the day, satisfying customers.
METHODS
In a typical Data Analytics project the workflow can be summarized by the CRISP-DM process:
Business Understanding. Ask first the questions, then look for the data (not the other way around). You need to have a deep understanding of business. Many consulting companies fail to understand the complex customer business. Lantek knows the way!
Data Preparation: Data quality is critical. Most of the time invested (around 60% to 80%) in a Data Analytics project deals with data collection, preparation, profiling, cleaning, and wrangling to prepare a table (dataset) used later with Machine Learning. If the data quality is low, then you get -- garbage in and garbage out!
Modeling: Machine learning (ML) is the motor that drives data science. Each ML method takes in data, turns it into a Data Model, and spits out an answer. ML methods do the part of data science that is the trickiest to explain. That is where the mathematical magic happens.
Data Models assume that the future will behave in a similar way as the past. Unforeseen events, like a world financial crisis or a natural disaster, cannot be integrated in the data model.
It is worth mentioning that the data models always make a prediction with a probability associated to it. We are all used to weather forecasting and there the associated probability is always present.
Evaluation: At this stage, you’ll assess the value of your models for meeting the business goals and consider the risks when the model probability is low or it classifies with False-Positives and False-Negatives.
Deployment: When your model is ready to use, you will need a strategy for putting it to work in your business. An often-overseen issue is that this deployment is only possible with a high company maturity and workers willingness to adopt the new data-driven processes.
APPLICATIONS
Data Analytics Types and its use in Manufacturing Analytics:
With descriptive analytics, also well known as Business Intelligence, the historical data is visualized with powerful and interactive software tools. Decision makers can identify radically more about their businesses and translate that knowledge into improved decision making. Dashboards such as the OEE are created in real time as are found in Lantek Manufacturing Analytics solutions.
Identifies the cause(s) of a manufacturing problem, such as why a process has a bottleneck or why the quality is so low.
With predictive analytics, there is a look into the future. Predicted Machine Maintenance, Inventory prediction, Demand forecasting are typical applications.
Automated manufacturing systems need to make real-time decisions based on data and take actions, fully automated, creating an autonomous cyber-physical system.
.png)
.png)