How can you track manufacturing KPIs using data analysis?

If you run a manufacturing business, you know how important it is to measure and improve your performance. But how can you track manufacturing key performance indicators (KPIs) using data analysis? In this article, you will learn how to define, collect, visualize, and analyze your manufacturing KPIs using data analysis tools and techniques.

1 What are manufacturing KPIs?

Manufacturing KPIs are quantifiable metrics that reflect the efficiency, quality, productivity, and profitability of your manufacturing processes. They can be used to monitor and evaluate progress towards strategic and operational goals. Some of the most common manufacturing KPIs include overall equipment effectiveness (OEE), yield, cycle time, downtime, and inventory turnover. OEE measures the ratio of actual output to optimal output of your machines. Yield is the percentage of products that meet your quality standards. Cycle time is the time it takes to complete one unit of production. Downtime is when your machines are not producing due to breakdowns, maintenance, or other issues. Lastly, inventory turnover is the number of times you sell and replace your inventory in a given period.

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Vikas Chandra Vaishya

AGM♦ Technical & Process Control lead ♦Outotec Flash smelter ♦PS Convertor♦Anode furnace♦Copper refinery( ISA Glencore)♦Aluminium smelter-AP 360 KA♦Operation management♦ Digitalisation and Data Analysis♦Quality
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Dive into the core of Manufacturing KPIs, where precision meets performance. Explore how key area-specific metrics redefine excellence, tailoring key indicators to each plant's unique production and process parameter. It should be drill down and linked to respective Operation and area.

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2 How to collect data for manufacturing KPIs?

Tracking your manufacturing KPIs requires collecting data from various sources, such as machines, sensors, production records, quality reports, and customer feedback. Data can be manually entered into spreadsheets or databases, but this can be time-consuming and prone to errors. Automated data capture with software or hardware devices can save time and ensure accuracy and consistency. Additionally, data integration tools or platforms can connect and combine data from different sources and formats into a common structure and language, enabling access and analysis of data from multiple systems and processes.

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Vikas Chandra Vaishya

AGM♦ Technical & Process Control lead ♦Outotec Flash smelter ♦PS Convertor♦Anode furnace♦Copper refinery( ISA Glencore)♦Aluminium smelter-AP 360 KA♦Operation management♦ Digitalisation and Data Analysis♦Quality
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In the quest for operational excellence, the foundation lies in effective data collection for Manufacturing KPIs. Begin by identifying the critical metrics aligned with your production goals. Implement robust data acquisition systems, leveraging sensors, automation, and real-time monitoring tools. Ensure seamless integration with your existing processes to capture accurate and timely information. Embrace technology for data validation and quality control. Regularly audit and refine your data collection methods to stay agile and responsive to evolving manufacturing dynamics. Elevate your decision-making with a robust data collection strategy that fuels the success of your manufacturing KPIs.

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3 How to visualize data for manufacturing KPIs?

To track your manufacturing KPIs, you need to visualize data in a meaningful way. Dashboards are useful for displaying KPIs in real-time, with charts, graphs, gauges, or other visual elements. Reports can help you summarize and present your results to stakeholders. Additionally, alerts can be set up to notify you when KPIs deviate from targets or thresholds, allowing you to identify and address issues or opportunities quickly. All these tools and techniques can help you monitor and compare your performance across different dimensions.

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4 How to analyze data for manufacturing KPIs?

To track your manufacturing KPIs, you need to analyze data to understand the causes and effects of your performance. Descriptive analysis can help you summarize and describe your KPIs, using measures such as mean, median, or standard deviation. Diagnostic analysis can explore and explain your KPIs, using correlation or regression. Predictive analysis can forecast and estimate your KPIs, using trend analysis or machine learning. And prescriptive analysis can optimize and recommend your KPIs, using simulation or optimization. By employing these tools and techniques, you can gain valuable insights to improve your performance.

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Swapnil Sahu

Process Specialist @ Sohar Aluminium | MBA, Process Engineering
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You need to first sort, clean and evaluate your data. Is it continuous, discrete or binary data? Or all ? Once you are sure with your measurement system, practices, frequency, data precision and accuracy, you need to build KPI in relation with your deliverables. Use of statistical process control tools like power BI will help you to see average, median, range, variation and outlayers.

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Vikas Chandra Vaishya

AGM♦ Technical & Process Control lead ♦Outotec Flash smelter ♦PS Convertor♦Anode furnace♦Copper refinery( ISA Glencore)♦Aluminium smelter-AP 360 KA♦Operation management♦ Digitalisation and Data Analysis♦Quality
Report contribution
To effectively monitor and enhance your manufacturing KPIs, a nuanced analysis of data is essential. Descriptive analysis serves as the bedrock, summarizing KPIs through measures like mean, median, or standard deviation, offering a snapshot of performance. Diagnostic analysis takes a deeper dive, unraveling the cause-and-effect relationships with tools such as correlation or regression. Looking forward, predictive analysis employs trend analysis or machine learning to forecast KPI trends. Finally, prescriptive analysis steps in to optimize and recommend improvements, utilizing simulation or optimization techniques.

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5 How to improve your manufacturing KPIs using data analysis?

To track your manufacturing KPIs, data analysis can be used to improve performance and decision making. Setting SMART goals, benchmarking best practices, and implementing continuous improvement cycles such as Plan-Do-Check-Act (PDCA) are all strategies that can be used to improve your manufacturing KPIs using data analysis. Data analysis can also be used to track and evaluate progress, identify and close gaps and opportunities, plan, execute, monitor, and adjust actions and results.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you track manufacturing KPIs using data analysis?

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1 What are manufacturing KPIs?

2 How to collect data for manufacturing KPIs?

3 How to visualize data for manufacturing KPIs?

4 How to analyze data for manufacturing KPIs?

5 How to improve your manufacturing KPIs using data analysis?

6 Here’s what else to consider

Business Operations

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How can you track manufacturing KPIs using data analysis?

1

2

3

4

5

6

1 What are manufacturing KPIs?

2 How to collect data for manufacturing KPIs?

3 How to visualize data for manufacturing KPIs?

4 How to analyze data for manufacturing KPIs?

5 How to improve your manufacturing KPIs using data analysis?

6 Here’s what else to consider

Business Operations

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