当前的4.0生态系统中，数据管理是金属和钣金行业的最大挑战之一。 通过互联网与物联网（IoT）形成的网络正确地识别并采集数据，以及机器和流程的感测化是至关重要的，同时还包括在云端存储数据，通过数据分析算法对其进行处理。
在模型中实现人工智能、机器学习和大数据软件是进行该分析的基本步骤。 模型中实现人工智能、机器学习和大数据软件是进行该分析的基本步骤。一方面，在价值链中创建自主的决策过程，使人们从需要长期分析和观察的重复任务和/或乏味的决策中解放出来；另一方面，预测行为，预见战略决策，识别产品质量问题，避免重大停工并降低成本。 这样，就可以做出更明智的决策，以提升产品质量，并在时间和成本方面提高生产系统的效率。
让我们来仔细看一看工厂数据收集自动化的五个优点。
1. 您可以从任何位置和设备获取实时信息，例如在故障发生时立即发现或自动增加库存。 凭借警报系统，操作员无论身在何处，都能立即接收到设备上的信息。
2. 使用并非基于假设的可靠信息有助于检测由微调制动、机器减速、故障、作业超载引起的低效率……
3. 专注于更有成效的任务。 技术人员、操作人员和管理人员可以专注于为工厂带来更多价值的任务，而这些任务是自动化和数据分析无法协助完成的。
4. 信息集中化。 这些系统允许数据在整个工厂中流动，而无需向相关部门请求数据。 生产部门随时了解订单状态；维护，机器故障；财务，生产成本；管理，工厂效率。
5. 节约时间、成本并提高生产力可以提高工厂的盈利能力和竞争力。
Lantek用于指导公司数字化转型的个性化焦点本质上是基于数据分析，以及对整个过程中数据流程的评估。
如果本应使用数学模型的公司不了解该模型，那么最好的数学模型也没有太大用处。 为了将关键数据用于决策和公司自动化，公司必须使用一种处理模型，使其能够利用、提取和分析现有数据，以获得更专业的信息，从而以更优化的方式控制其流程。 这种自测循环包括建立商业目标、数据评估、数据准备、建模、评估和实施。
数据准备是关键。 因此，Lantek创建了数据质量报告，以评估数据质量，兼顾数据的有效性、一致性和代表性，以及所收集数据的唯一性和完整性。 有几种工具可用于此目的，并用于生产和管理中数据的正确使用。 然而，人的因素起着至关重要的作用。 例如，在许多正在根据数据智能对流程进行数字化改造和优化的公司，首席数据官（CDO）的角色已成为一个固定的、执行级别的职位。
对于金属和钣金行业，Lantek拥有最先进的数据处理和智能响应软件，帮助专业人士做出更好的制造决策。 该程序就是Lantek Analytics，这是一个收集、过滤、分组和连接所有机器的数据的智能制造模块，然后以可靠的实时信息支持决策。
Lantek Analytics集成了钣金设计和切割、CAD/CAM、制造管理、MES、企业资源规划、ERP（现有或外部）程序，全面概述了工厂发生的一切。 这样，我们就可以使用OEE指标通过三个参数来计算制造过程的生产力。 第一，它提供了一台机器的可用运行时间；第二，机器的性能；第三，每台机器部件的制造质量。 同时，根据操作、计划停工时间和意外停工时间三方面显示机器的当前状态。
该程序还有一个模块，在可能出现故障和库存需求的情况下发送警报，允许您根据当时的实际需求调整库存，从而提高效率并节省成本。
但该程序不仅监控制造本身，还监控订单演变，整合销售和报价软件。 客户的历史和当前数据使我们能够了解更多关于他们的信息，这有助于从需求行为（订单频率、数量、切割材料类型等）中发现消费者模式。 算法允许我们预测和改进订单计划，并提出增加业务量的新订单。 同时，定制报价甚至允许我们为客户提供折扣。
所有这些都在报价的过程中提供了速度、灵活性、客观性和细分，这对工厂和客户都有好处，进而建立忠诚度。
回到起点，新型冠状病毒肺炎迫使我们变得敏捷，进行远程决策，其中的一个关键点在于：拥有可靠的实时数据。

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.