Choosing Between Historian and MES Integration

March 30, 2016 MAVERICK SCADA, MES, S88, HMI, Data Historian, S95, OPC, SQL

While historians and manufacturing execution systems (MES) have been around for a long time, knowing which one to use and how to maximize the benefit of each for particular situations is key.

While historians and manufacturing execution systems (MES) have been around for a long time, there are still a lot of questions on how to best integrate both in order to maximize the benefit of each.

In the past, historians have been used to store process data and allow comparison of current to past process conditions. In the last decade this has changed, and historians have now grown into powerful, real-time calculation engines that allow context specific analysis of a large number of assets (> 1 million). This allows real time analysis of large and complex systems, such as wind farms, data centers, or turbines.

In MES systems, the historian is mostly used as a data source similar to structured query language (SQL), open platform communications (OPC), laboratory information management system (LIMS), supervisory control and data acquisition (SCADA) or other similar sources. This shallow integration only uses the data storage capabilities of the historian without benefitting from the real time calculations, data conditioning and abstraction. The following flow chart show both MES and historians in the ISA-95 functional hierarchy.

In general, there are two types of information flows in a manufacturing enterprise:

Transactional/relational data
Real-time data

Transactional data are found in order processing, resource management, quality, labor, maintenance etc., while real time data mostly originates from the plant floor. Real time data bubble up from production level (Level 0, 1 and 2) to the site and enterprise level while changing their characteristics:

Level 0, 1, 2: High frequency data: milliseconds to seconds, source specific, noisy
Level 3: Medium frequency data: seconds to minutes, abstract, aggregates
Level 4: Low frequency data: minutes to hours, days or weeks, abstract, aggregates.

It is important to note that the main data transformation occurs at the historian level, where data are compressed, aggregated (min, max, total, sum, …) and most importantly abstracted. Mapping performs the abstraction, for example, a controller tag TIC01234.PV to the temperature property of a reactor (e.g. reactor/temperature). A data scientist will now be able build a reactor model or predictive maintenance calculation based on the abstraction layer, instead of searching through a vast amount of uncategorized data.

For similar reasons, the MES system should not consume raw production data. Its primary function is order management, production performance calculations, forecast, quality and resource planning, etc. But performing real time transformations of process data on the MES system itself will often lead to a loss in both performance and accuracy.

Therefore, interfacing the historian with the MES should be performed on the abstraction layer as the following diagram shows:

To successfully connect the historian to the MES system requires both adherence to common standards such as S95 for the equipment model and/or S88 for the batch model. The interface will replicate the data structure between systems and validate the structural integrity.

The benefit of the above architecture is a deep integration of historian and MES that maximizes the utility of both systems. It separates the manufacturing data flows and creates common interfaces to exchange data and structures.

Historians play a central role in the manufacturing data flow of real time information. The main historian operations such as data compression, de-noising, aggregation and abstraction can benefit the enterprise data analytic as well as MES operations. This requires a deep integration of the historian by abstracting the data layer using common standards such as S95 and S88 and interfacing with the analog MES data structures. The result is an architecture that utilize both systems to the full extent of their capabilities while acknowledging the differences in the data properties and requirements.

This post was written by Dr. Holger Amort. Holger is a senior consultant at MAVERICK Technologies, a leading automation solutions provider offering industrial automation, strategic manufacturing, and enterprise integration services for the process industries. MAVERICK delivers expertise and consulting in a wide variety of areas including industrial automation controls, distributed control systems, manufacturing execution systems, operational strategy, business process optimization and more.