Data quality
The amount of data we have access to is overwhelming; but what data is interesting? What data is important to reach your targets? Is data quality adequate? Is data consistent with the data source? An important point when it comes to data acquisition is to use resources to ensure the quality of the data.
Companies that do not spend enough resources to assure the quality of the data might not rely on data that do not meet their expectations. It is necessary with sufficient quality control of the data to ensure compliance with the data source. If not, the data is useless. If you cannot trust the data, you are not going to use it.
Data quality tests can be done through monitoring production over a given time; log manually all relevant events and verify that the data collection is correct. A challenge when performing data quality tests is to reveal problems that occur occasionally.
Lean Tech has experience with quality assurance of data, including a company that logged up to half a million data per hour from different machines. There were different suppliers involved, and extensive root cause analysis was conducted to reveal the causes of unstable data collection and missing data. In such cases it is important to obtain as much information as possible about the situation that caused the instability.
Data acquisition
The amount of data we have access to is overwhelming; but what data is interesting? What data is important to reach your targets? When implementing new production lines with data acquisition, do not practice "nice to have". Spend resources to collect only the data you need.
Are you not sure what data you need? Use resources to decide. Here are some questions that might be of help;
- What are the relevant process indicators or KPI's (Key performance indicators) for manufacturing?
- What data is necessary to follow up these targets?
- What other measures will be relevant in the future? Why?
Companies implementing comprehensive data acquisition can risk production downtime due to overloaded system or failure. The rule "Make it simple" applies here as well. Lean Tech can help you define goals and measure to collect the right measures, and to ensure good data quality.
Data visualization
Visualization of data helps communication, understanding, evaluation and planning. If "A picture is worth a thousand words"; what is a graph worth to you?
You can use excel to visualize data, or you can use more sophisticated tools like JMP, Minitab, Modde, Sigma XL or others. Lean Tech use JMP, Minitab or SigmaXL (dependent of the customers preference) for Lean Six Sigma training.
When visualizing the data it is easier to see connections and trends between data sets. Visualization of data also makes it easier to identify outliers in your data set and investigate these data. This is useful when controlling the quality of your data.
Multivariable analysis shows the correlation between various factors. Are there a correlation between factors? In what way? If you have data set you would like to analyze for trends and correlation you can contact Lean Tech.