There are many ways that data from business processes and places can be accessed, including operating databases, manufacturing intelligence tools, sensors and even spreadsheets. But translating data into insights is where the rubber hits the road.
Understand how you can improve manufacturing analysis with these five tips.
Collect as much data as possible from all business processes
As a general rule, you want to collect as much data as possible from all business processes. Bonus points for collecting across multiple points in each single process. But don’t collect this process data in a vacuum. Insights must be identified from each process. If you have no insights or measurements worth tracking, you might want to reconsider the process itself.
Collect as much data as possible directly from the manufacturing floor
Every manufacturing facility contains its own unique data on factors ranging from throughput and productivity to safety and maintenance. Monitoring data from the floor will not only help identify the problems preventing optimal performance, but also inform the business about the areas where efficiency is being increased or where improvement may be required.
It is essential to make sure that the business has access to real-time information about the performance of all manufacturing process. In addition, analytical tools can capture all of the data on the manufacturing floor — including the performance of each and every line in the manufacturing process — and analyse this data in real time. You can also streamline the manufacturing process by better managing orders and reducing cycle times.
Invest in multidisciplinary teams
If you use operational data, you need a multidisciplinary team to handle these types of data. Your analytics team will need to be both data scientists and users, and they should be engaged in the challenge to collect, analyse and understand the data. If you only have a small team that can effectively deal with this type of analysis, you’ll be making critical decisions on the basis of limited information. This is not ideal — after all why would you limit your insights? The better solution is to ensure that you have enough analytics capacity, whether in-house or through partnership with external firms.
Engage business owners, not just operations
Manufacturing a product or process requires a unique knowledge of the business. Blindly handing off data to an analyst with no context behind financial and sales data will always result in sub-optimal results.
As a business owner you have a personal responsibility to know everything about your production processes and needs to be the one to identify problems and use your analytics to improve the process.
Maintain flexibility
Standardisation of processes and data may be the first step in improved manufacturing processes, but it can be challenging to be flexible with the underlying resources. Monitoring data from different data sources often involves a need to query, customise, and reconfigure datasets for different workloads and processes. A best practice approach to engineering and analysis includes the ability to adjust the data (change the resolution, add sources, etc.) for different scenarios in line with your specific needs.
Recognising that many processes, locations and data sources require different level of data, analytics tools must be designed with flexibility in mind. Analytics tools need to analyse data collected from multiple data sources, in a way that simplifies the collection of all data and the organisation of its analytics, so you can analyse it in real time.
Wrapping it up
Like almost all initiatives worth undertaking, data analytics requires a commitment from the entire organization in order to deliver meaningful results. Remembering this will dramatically increase the chance of success for next data project.
//
Thanks for making it this far. If you enjoyed the article and want to see more please let us know with a few claps👏👏👏
For more insights, please visit the 3AG blog.
//
