Data management is one of the greatest challenges in the metal and sheet metal industry in the current 4.0 ecosystem. Gathering data by identifying it correctly via a network with an internet connection to the Internet of Things (IoT) and the sensorization of machines and processes is vital, along with storing data in the Cloud so that it can be processed using data analysis algorithms.
The implementation of Artificial Intelligence, Machine Learning and Big Data software in the model is a fundamental step for this analysis. On the one hand, to create autonomous decision-making processes in the value chain, freeing people of repetitive tasks and/or tedious decisions that require long-term analysis and vision; on the other, to predict behaviors, foresee strategic decisions, identify problems with product quality, avoid critical downtimes and reduce costs. This way, more intelligent decisions are made that improve product quality and deliver efficiencies in the productive system in terms of time and costs.
Let’s take a detailed look at five advantages of automating a factory’s data collection.
1. Obtaining information in real time from any location and device allows you, for example, to find out about a fault as soon as it occurs or automatically increase stock. Thanks to an alerts system, the operator instantly receives information on their device, wherever they are.
2. Working with reliable information that is not based on assumptions helps to detect inefficiencies caused by micro-stops, the slowing of machines, malfunction, activity overloads...
3. Focus on more productive tasks. Technicians, operators and managers can focus on tasks that contribute more value to the plant, tasks that automation and data analysis are unable to assist with.
4. Centralize information. These systems allow data to flow throughout the whole factory, without needing to request data from the corresponding department. Production is aware of the order status; maintenance, the machine breakdowns; finances, the production cost at all times; and management, the efficiency of the plant.
5. Saving on times and costs and increasing productivity results in an improvement in the factory’s profitability and competitiveness.
The personalized focus that Lantek uses to guide companies through their digital transformation is fundamentally based on data analysis, as well as an evaluation of the data’s journey through the processes.
Even the best mathematical model is of little use if the company that is supposed to implement it doesn’t understand it. In order for key data to be used for decision-making and the automation of companies, the company must use a processing model that allows it to utilize, extract and analyze its existing data to obtain more specialized information to subsequently control its processes in a more optimized manner. This self-tested cycle includes establishing the commercial objectives, data appreciation, data preparation, modeling, evaluation and implementation.
Data preparation is key for this. For this reason, Lantek has created the Data-Quality-Report to evaluate data quality bearing in mind its validity, consistency and representation, as well as the uniqueness and integrity of the data gathered. There are several tools for this and for making proper use of data in production and management. However, the human factor plays an essential role. For example, the role of Chief Data Officer (CDO) has become a fixed, executive-level position in many companies that are embarking on the digital transformation and optimization of their processes based on data intelligence.
For the metal and sheet metal industry, at Lantek, we have the most advanced software for data processing and delivering intelligent responses that help professionals to make better manufacturing decisions. That program is Lantek Analytics, an intelligent manufacturing module that gathers, filters, groups and connects all of the machines’ data to subsequently back decisions with reliable information in real time.
Lantek Analytics integrates with sheet metal design and cutting, CAD/CAM, manufacturing management, MES, and enterprise resource planning, ERP (existing or external) programs, offering a comprehensive overview of everything that happens at the plant. This way, we can find out the productivity of the manufacturing process through the OEE indicator, which measures it using three parameters. First, it offers a machine’s available running time; second, the performance of the machine; and, third, the manufacturing quality of the pieces from each machine. At the same time, it displays the current status of the machine according to three aspects: operation, programmed downtime and accidental downtime.
The program also has a module that sends alerts, both in cases of possible faults and stock requirements, allowing you to adjust the inventory to the real demand at the time, which results in improved efficiency and cost savings.
But the program doesn’t only monitor manufacturing itself, it also monitors the evolution of orders integrating sales and quoting software. Clients’ historical and current data allows us to find out more about them which facilitates the detection of consumer patterns from demand behavior (order frequencies, volume, materials type of cut...). Algorithms allow us to anticipate and improve order planning and suggest new orders that increase business volume. At the same time, customizing quotes even allowing us to offer client discounts.
All of this offers speed, flexibility, objectivity and segmentation in quoting which benefits both the factory and the client, subsequently building loyalty.
Returning to the initial point, Covid-19 forces us to be agile, to make remote decisions, for one thing is key: having reliable data in real time.
