Automotive Component Manufacturer Improves with Datonis IoT Solution

• 分析与建模 - 大数据分析
• 自动化与控制 - 人机界面 (HMI)
• 自动化与控制 - 可编程逻辑控制器 (PLC)
• 基础设施即服务 (IaaS) - 云存储服务
• 平台即服务 (PaaS) - 应用开发平台
• 平台即服务 (PaaS) - 数据管理平台
• 传感器 - 温度传感器
• 传感器 - 电表

• 汽车

• 离散制造

• 建筑能源管理
• 工厂可见化与智能化
• 机器状态监测

作为其工业 4.0 计划的一部分，客户主要希望利用物联网来最大限度地提高运营效率和生产力，减少能源足迹并最大限度地提高产能利用率。但一开始有几个挑战：

首先，客户拥有多条装配线，配备不同的机器、系统和传感器，它们都使用不同的协议进行通信。因此，他们主要需要一个能够将不同资产集连接到一个平台并利用未充分利用的“暗”数据的合作伙伴。

其次，数据量、数据类型及其应用程序非常庞大，处理它的平台需要具有可扩展性和灵活性。

最后，Varroc 面临着在“棕地”市场进行创新的典型挑战——其中真正的瓶颈在于将物联网与新旧设备同时集成，而无需任何进一步的资本支出来进行资产替代。

Altizon 确定了他们的物联网集成要求，并使用 Altizon 的基于云的可扩展工业物联网平台 Datonis® 填补了他们的技术空白。将 OPC 作为事实上的连接标准，数据从资产的异构组合和不同的 OT 数据源（PLC、传感器、CNC 控制器、继电器、HMI、CSV 等）安全、无缝地推送到不同的位置和不同的数据，使用不同协议的记录器到 OPC Server 客户端。 Datonis® 然后将使用 OPC 客户端（代理）实时处理这些数据，该客户端读取以标签格式存储在 OPC 服务器客户端中的数据。在部署的几周内，机器和系统开始将实时数据流式传输到 Datonis® 作为一个集中式平台。来自 OPC UA 的这些已配置标签的输出以近乎实时或按计划的方式使用，以提供对整体设备效率 (OEE) 和基于状态的监控 (CBM) 的洞察。除了这些现有的机器数据外，HMI 屏幕还使操作员能够记录补充数据（例如停机代码），以进一步跟踪停机时间，提高正常运行时间并计算“平均解决时间”。 Datonis® 使用一套全面的 REST API 提供与现代 Web 和移动应用程序的完全集成。这意味着客户可以将生产数据注入他们的 ERP 系统（以 SAP 为例），并构建应用程序，在任何设备上以最合适的方式呈现这些数据。因此，需要进行最少的变更管理，并且不会干扰操作员的现有工作流程。此外，Datonis® 提供灵活的定价模式，这意味着客户只需为连接的机器付费。这提供了很大的灵活性，无需为平台进行前期投资。 Datonis® 还提供正常运行时间 SLA，因此始终处理数据并且系统始终可用。

• [Data Management - Real Time Data]
  Centralized Machine Data – By connecting and pushing the data from heterogeneous machines (like Honing, Press, Injection molding, Hobbing, Pick & Place, Testing machines etc.) at shop floor, Altizon provided for a centralized platform – a single source of truth for all Operational metrics of Machine Utilization, Performance, Quality, Condition Monitoring and Energy Consumption in real time. Machine data analysis reports are now available anywhere and at any time. And with a connected shop floor, Varroc could use different monitoring tools it needs to immediately see inefficiencies and identify bottlenecks in real time.
• [Efficiency Improvement - Energy]
  Energy Monitoring – Energy meters were connected to consumption monitoring at machine, cell and plant level. It served to manage energy requirements for current operations and future scale up.
• [Data Management - Data Integration]
  SAP Integration – Integration with their existing ERP systems like SAP led to advanced insights on Inventory Planning and Lean Manufacturing programs.

• Monitoring & Maximizing Line Efficiency – OEE (Overall Equipment Effectiveness), OPE (Overall Process Effectiveness), Production Variance, Accurate Idle time pockets & Reasoning could now be monitored at the cell, machine and/or plant levels in near real time. Providing Production Visibility and Machine Efficiency across assembly lines allowed decision makers to monitor progress at a plant level and drill down into specific machines whenever needed. Due to the reduction in shifts, OEE has seen a considerable improvement which has led to an almost 20% increase in Operational Efficiency and 10% reduction in direct running costs of the machine (including labor and energy) Insights into real-time OEE and downtime, have also reduced the number of people employed in data entry of these metrics. The indirect manpower reduction has helped to the tune of 20% increase in Manpower Efficiency.

• Condition Monitoring – Systematic processes (alerts & notifications) could be created to identify the Idle time pockets that can now never be missed. Alerts triggered as per escalation plan based on business process requirements. Further Condition monitoring and a tool health dashboard ensured that the tool consumption and inventory were reduced by 2% leading to a direct reduction in TOOLS and SPARES cost.