2022-04-27 17:11 来源：翻译
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.
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.
Evolve as you continue to grow! Iterate based on learnings from steps one through five.
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.