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从制造业数据中实现价值最大化的6个步骤

制造商有海量的数据,但往往没有正确的工具来开发它。这里有极大的潜力可挖。但如果你没有这些工具,你该从哪里开始?遵循这六个步骤,开始从你的数据中获得尽可能多的价值。

1. 数据整合

在制造业中,新传感器采集的可用数据激增,而传统的数据系统在处理和整合这些信息与现有来源方面存在困难。你的业务流程依赖于清楚、可靠的数据,从而带来你在运营效率、客户满意度、财务业绩等方面所期望的结果。
建立合适的基础设施来协调和集中来自任何数量或源类型的数据,以确保在整个组织中使用通用定义,同时节省大量开发时间。

2. 数据治理

数据治理是成功的数据管理的一个主要组成部分。这是一个持续的过程,用于确定哪些数据对你的业务至关重要,并确保它保持正确的质量水平。关键是要为你的企业确定正确类型的治理框架,并定义员工需要遵循的流程。

生产、运营和业务对成功的看法都略有不同。你需要调整和管理你的数据,以确保他们目标一致。

3. 分析

数据可视化使你能够以视觉上吸引人的格式浏览数据,并得出对企业成功至关重要的结论。通过从完全不同的来源获取数据,对其进行转换,并将其显示在最终用户可以看到和理解的仪表板中,你可以深入分析重要的KPI和指标。借助易于访问的高级分析,找出差距和根本原因,并揭示趋势。

4. 利益相关者权利

利益相关者的认同和持续支持对于数据项目的成功至关重要。确保自动化并在整个组织内分享见解,让每个人随时随地都能看到事情的进展。

5. 变革管理

几乎任何重大的技术或组织创新都需要对人们的工作方式做出同样重大的改变。为了使项目成功并产生预期的价值,需要积极地管理组织变更。培训、启用和支持您的团队,以确保你拥有合适角色的合适用户,从而确保成功部署。

6. 演进

随着你的不断成长而发展!基于从第一步到第五步学到的知识进行迭代。

成果

你能期望从这样的数据倡议中看到什么样的结果?这里有几个例子。
 
       · 结合生产力和财务数据,为生产经理显示每条生产线的近乎实时的利润产出,以帮助确定任何维护问题的优先级

· 将需求预测与生产计划联系起来,以确保供应得到优化,并确保正确的生产计划到位,以限制低速SKU的过度生产

· 利用物联网数据报告现场机器的健康状况,主动降低维护成本,从而更好地分配现场技术人员

一旦你通过这些基本步骤建立了基础,你就可以继续探索高级分析和人工智能的可能性。

 

作者:Raz Nistor,Keyrus公司数据科学和CPG主任

 

文章原文:

6 Steps to Maximizing Value from Manufacturing Data

Manufacturers have tons of data but often don't have the right tools to explore it. There's a wealth of potential that's just waiting to be unleashed. But if you don’t have those tools in place, where do you start? Follow these six steps to start getting the most value possible from your data.

1. Data integration

In manufacturing, there’s an explosion of available data from new sensor sources, and legacy data systems struggle to process and combine this information with existing sources. Your business processes depend on clean, reliable data to produce the results you expect in terms of operational efficiency, customer satisfaction, financial performance, and more.

Set up the right infrastructure to harmonize and centralize your data from any number or type of sources to ensure that common definitions are used throughout the organization while saving significant development time.

2. Data governance 

Data governance is a major component of successful data management. It’s a continuous process for identifying which data is critical to your business and ensuring it stays at the right level of quality. The key is to identify the right type of governance framework for your enterprise and to define the processes employees need to follow.

Production, operations, and the business all look at success slightly differently. You’ll need to align and govern your data to make sure they’re all looking at the same picture.

3. Analytics

Data visualizations allow you to explore your data in a visually appealing format and draw conclusions that are critical to the success of your business. By taking data from disparate sources, transforming it, and displaying it in dashboards where end users can see and understand it, you can drill in and analyze important KPIs and metrics. Find gaps and root causes, and uncover trends with easily accessible advanced analytics.

4. Stakeholder access

Stakeholder buy-in and continuous support are critical for data projects to succeed. Make sure to automate and share insights across the organization and allow everyone to see where things stand, any day, at all times.

5. Change management

Almost any significant technical or organizational initiative requires equally significant changes to the way people work. That organizational change needs to be actively managed in order for the project to be successful and generate the expected value. Train, enable, and support your team to ensure you have the right users in the right roles to ensure successful deployment.

6. Evolution

Evolve as you continue to grow! Iterate based on learnings from steps one through five.

Results

What kind of results can you expect to see from a data initiative like this? Here are a few examples.

Combined productivity and finance data to display the near real-time profit output of each line on the floor for production managers to help prioritize any maintenance issues
Connected demand forecasts with production schedules to ensure supply was optimized and that the right manufacturing schedules were in place to limit the overproduction of low-velocity SKUs
Proactively reduced maintenance costs using IoT data to report health of machines in the field, which leads to better allocation of field techs

Once you’ve laid the foundation with these basic steps, you can move on to exploring the art of the possible with advanced analytics and artificial intelligence.

About The Author
Raz Nistor is director of Data Science & CPG at Keyrus.

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