How can you compare industrial system performance with data analysis?

1 Define your objectives

The first step is to define your objectives and criteria for comparing industrial system performance. What are the key performance indicators (KPIs) that you want to measure and optimize? How do you define success and failure? How do you weigh the trade-offs between different aspects of performance, such as cost, quality, efficiency, or environmental impact? By clarifying your objectives, you will be able to focus your data analysis on the most relevant and meaningful aspects of performance.

2 Collect your data

The second step is to collect your data from the industrial systems that you want to compare. Depending on your objectives and KPIs, you may need to collect data from different sources, such as sensors, machines, databases, or surveys. You may also need to collect data at different levels of granularity, such as hourly, daily, or monthly. You should ensure that your data is accurate, reliable, and consistent across the systems that you are comparing.

3 Clean and prepare your data

The third step is to clean and prepare your data for analysis. This involves checking and correcting any errors, missing values, outliers, or inconsistencies in your data. You may also need to transform, aggregate, or normalize your data to make it more suitable for comparison. For example, you may need to convert units, adjust for inflation, or scale your data to a common range. You should also document your data cleaning and preparation steps for transparency and reproducibility.

4 Analyze your data

The fourth step is to analyze your data using appropriate methods and tools. Depending on your objectives and KPIs, you may use different types of data analysis, such as descriptive, inferential, predictive, or prescriptive. You may also use different techniques, such as statistics, machine learning, optimization, or simulation. You should choose the methods and tools that best suit your data and your questions. For example, you may use a t-test to compare the mean values of two systems, or a regression model to predict the effect of a change in one system on another.

5 Visualize your results

The fifth step is to visualize your results using charts, graphs, tables, or dashboards. Visualization can help you communicate your findings, highlight patterns, trends, or anomalies, and compare the performance of different systems. You should use the appropriate type of visualization for your data and your message. For example, you may use a bar chart to show the distribution of a categorical variable, or a scatter plot to show the relationship between two continuous variables. You should also make sure that your visualization is clear, accurate, and easy to understand.

6 Interpret and act on your results

The final step is to interpret and act on your results. This involves drawing conclusions, making recommendations, and implementing changes based on your data analysis. You should also evaluate the impact of your actions on the performance of the industrial systems that you are comparing. You may need to repeat the data analysis cycle to monitor and improve your results over time. You should also document and share your results and actions with your stakeholders and colleagues.

7 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?