MachineMetrics
充满信心地制造
概述
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总部
美国
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成立年份
2009
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公司类型
私营公司
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收入
< $10m
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员工人数
11 - 50
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网站
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推特句柄
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公司介绍
MachineMetrics 是实时制造分析的权威。当我们开始 MachineMetrics 时，我们发现了当前制造流程中不可否认的差距：缺乏实时生产可见性以及机器操作员与其经理之间缺乏沟通。大约 50% 的美国公司承认没有针对数字制造解决方案或自动化的系统路线图，超过 90% 的公司迄今尚未尝试整合解决方案。许多公司试图为这个问题开发解决方案，但尚未真正解决。我们与 100 多家制造商进行了交谈，以收集他们对这些解决方案为何不符合他们的需求以及如何设计得更好的反馈。
这样就创建了 MachineMetrics；一个 IIoT 制造分析和机器监控平台，可帮助公司可视化他们现在的位置，并克服数字化转型过程中的挑战，从反应性、主动性和预测性向前发展。我们的全自动机器监控解决方案提供实时制造生产数据的可视化、即时通知以及历史分析，使工厂工人能够根据实时数据做出更快、更智能、更自信的决策。
我们已经为车间简化了物联网，并让制造商能够通过市场上最容易使用和最简单的软件集成来制定实现卓越制造的路线图。 MachineMetrics 为我们的客户平均提高了 20% 以上的制造效率，我们很自豪能与他们一起开发制造的未来。
物联网解决方案
我们从整个生产车间的机器控制和机器操作员那里收集数据，并使用这些数据来监控机器状况（故障、状态、工具利用率）、生产（OEE 和机器利用率）、工作订单状态、质量跟踪和停机原因可以通过安装在机床上的触摸屏平板界面由操作员自动分类或指示。实时仪表板也安装在生产车间，以一目了然地指示工作是否达到或低于预期（相对于零件目标或 OEE 指标）。
MachineMetrics 提供强大而卓越的报告功能，包括 OEE 报告、作业报告、停机时间和质量帕累托报告，可以按班次、机器和查看单个作业/零件报告时进行比较。利用率、TEEP 和 OOE 也在历史报告中进行测量和显示。收集的实时和历史数据使管理人员不仅可以跟踪效率并快速识别与特定加工操作相关的生产瓶颈，还可以帮助衡量流程改进的效果。
MachineMetrics 的移动友好、安全的云应用程序允许您从任何地方访问这些数据，并且不需要服务器、自动更新和最小的 IT 费用。所需要的只是机器联网并且可以从我们的网关设备访问。我们的高度适应性系统可以定制，以满足任何制造商的特定生产需求。
主要客户
Fastenal, Carolina Precision Manufacturing, Carlson Products, Praxair, Benchmade
物联网应用简介
MachineMetrics 是平台即服务 (paas), 应用基础设施与中间件, 分析与建模, 和 功能应用等工业物联网科技方面的供应商。同时致力于航天, 汽车, 建筑物, 建筑与基础设施, 设备与机械, 医疗保健和医院, 和 矿业等行业。
技术栈
MachineMetrics的技术栈描绘了MachineMetrics在平台即服务 (paas), 应用基础设施与中间件, 分析与建模, 和 功能应用等物联网技术方面的实践。
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设备层
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边缘层
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云层
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应用层
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配套技术
技术能力:
无
弱
中等
强
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实例探究.
Case Study
Fastenal Builds the Future of Manufacturing with MachineMetrics
Fastenal's objective was to better understand their machine downtime, utilization, quality issues, and to embrace cutting-edge manufacturing technology/process improvement capabilities to bring their team to the next level. However, there was a lack of real-time data, visualization, and actionable insights made this transition impossible.
Case Study
MachineMetrics helped Carolina Precision saved over $1.5M on machine monitoring
Gary Bruner, the president of Carolina Precision Manufacturing, a contract manufacturer that specializes in small-diameter, close-tolerance CNC Swiss turned parts, had a problem. Over breakfast that morning, Gary had logged onto the MachineMetrics monitoring system on his laptop at home to check the status of his machines assigned to a lights-out operation, but saw immediately that two of his machines were not in production. “What’s wrong with machines 35 and 36?” he thought to himself. Upon arrival to the shop, Gary learned from his operator on duty that this type of hold up was nothing out of the ordinary, and was in fact a product of inefficient startup procedures that had simply never been analyzed or augmented previously. In an industry with razor thin margins, Gary understood that the keys to growth and success were in efficiency and quality. He understood the importance of keeping tabs on production stats, job status, uptime, and setup. However, there was no way to know how well machines were doing in real time. What was causing this additional downtime? Furthermore, CPM’s current methods of measurement and data collection were not only time consuming, but had quickly becoming outdated. Historically, CPM had an employee dedicated to the collection of utilization data. This employee would walk around to each of the machines, collect scrap tickets post-production, talk to operators, and record yesterday’s data into their current ERP system; not to mention that this manual data collection was prone to errors, and would take upwards of 2 hours per day. Without the ability to visualize their results, the recorded data was not very actionable.
Case Study
MachineMetrics Helped Carlson Products to Increase Efficiency by over 20%
There’s always a sort of lag in the information we get from the ERP. It’s not real time, its not updated every second. We were able to see what happened yesterday, what happened last week but not what was happening right now, and we would always have to somehow fill that gap. Also, to get data into the ERP we were relying on a human being to accurately define and manually input the information to enter it into our database. All of the real-time monitoring systems that Carlson Products had seen were only dedicated to machining. They had chats with some of their equipment providers and the solutions they attempted to provide them with were not visually appealing, not web-based and felt quite rudimentary. They did attempt to do manual tracking before MachineMetrics, and they got so much junk data that they just stopped trying. They kept a Microsoft access database where employees would track this information themselves but with all the huge outliers, data it went to hell and a handbasket. The most important thing Carlson Products wanted was to look at was historical labor data information: whether or not the jobs were on target. Tracking down the supervisors and operators to understand why they weren’t on target was quite time consuming, not to mention most of their employees had difficulty remembering why problems had occurred when reviewing the issues reactively. There was no ability to identify systematic issues vs singular issues. Also, the lack of historical data made it difficult for them to calculate their utilization as well as their potential capacity.
Case Study
Optimizing Equipment Utilization: A Case Study of Wiscon Products
Wiscon Products, a precision parts manufacturer, faced a significant challenge in data accuracy. The company's manual data collection methods were outdated and inefficient, often resulting in data that was several days to a week old by the time it was reviewed. This lack of real-time data made it difficult for the company to make informed decisions and implement improvements. Wiscon also struggled with gaining insight into production downtime, cycle time, and equipment utilization. The company had questions about how often spindles were turning and how many parts were being produced. Despite investing over $16 million on equipment, Wiscon was always running at capacity, and there were gaps and delays in the data, leading to a lack of visibility and an inability to respond to issues quickly.
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合作伙伴.
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