Tuesday, January 5, 2016

The Missing Link in Global MES/MOM Implementations

There is an old MES saying, “MES is not a project event; it is a process. It is 80% cultural and 20% technical.”  Many manufacturers’ corporate IT departments are incorrectly viewing MES as another extension of ERP and thereby incorrectly believing that each plant, each line and each work cell operate under a common set of business and operations models. Individual plants often reject MES implementations because the global application does not have functionality to address their specific set of operations’ pains and needs. Instead, individual plants end up developing their “shadow” IT in custom Excel applications, paper forms, or database applications to help the plant characterize and fix the current problem set.

Two barriers that many manufacturers face when considering an update to their system architecture are:
    The lack of vertical industry instance or templates of standards
    The cost of replacing established legacy systems using disparate data models and metrics supported by point-to-point interfaces

To overcome these issues, automate processes and optimize operations, Global MES Implementation teams must understand that each plant has its own manufacturing form of work processes and personnel culture based on “What it Makes, How it Makes it, Where it Makes it, and Who it is Made for.” Operations process and data standards must be engineered to enable effective communication between systems in plants and their supply network.

Like most technology evolutions, this change in IT architecture usually requires a substantial investment of time and dollars. However, implementing the use of best practices from The Open Group Architecture Framework (TOGAF), Software Engineering Institute’s model, Zackman’s Framework, ISA-88/95 or other similar models can help manufacturers cut down on those costs. If properly implemented, a return on investment can be realized quickly through increased efficiencies and decreased waste, the result of better analysis of a more complete data set.

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