Getting MES Data to Inform Analytics
Manufacturing Execution Systems are a rich source of data for manufacturing analytics. They often provide information and context that are valuable for optimizing manufacturing and not available in any other systems. However, extracting analytics data sets from these systems is challenging. Data generated by an MES is not structured and labeled for retrieval, but rather is organized in the context of manufacturing recipes which vary from site to site, product to product, batch to batch, and over time. The result is that many companies are not realizing the full value of their MES data to support analytics.
One way to attack this challenge is to hire a highly trained MES engineer to add code to export MES data and context in the format needed for analytics. While straightforward to implement, this approach is often time-consuming and expensive. It also requires constant upkeep and can result in MES and reporting performance issues. This approach can also add considerably more load on recipe authoring and testing, both initially and during lifecycle change management.
Another tactic is for the analytics tools to query the MES recipe directly. This approach can be faster to implement as it requires no recipe changes and can be tailored to the unique needs of the specific analytics platform. Unfortunately, because of the variability of recipe execution, changes to recipes over time, and limitations of the analytics software, these queries are often complex, brittle, and difficult to maintain. This solution can also result in duplicative efforts as custom queries must be crafted for each analytics tool.
A third solution is to create a data presentation or abstraction layer, for example a Unified Namespace (UNS), as multiple systems want similar data and context from an MES. This third approach takes time and thought and may bring with it new systems and data flows—you will likely want to start with a pilot project. Done properly, the result can be a comprehensive and scalable solution that is robust and efficient to maintain. Depending on your situation, any of the above methods might be best.
At Continua, our focus is to understand what data you need and how you want to use it. That understanding then informs the solutions we recommend, whether it be a targeted point solution that addresses an immediate need or a broader, holistic solution to provide a foundation for a wide range of analytics goals.
