it’s outside the range, we can check to see if it’s being sent up correctly,” says Bob Lenich, director of data management at vendor Emerson Process Management, in Austin, Texas. “The control system and MES will take appropriate action because they are smart enough to determine if the data is wrong.”

Control data is often passed through an OPC server (using the OPC open connectivity standard) on its way to delivering information to an ERP system. In most instances, the data is first processed to eliminate inaccuracies. “More and more of the data has already been verified before it gets to OPC,” says Jim Luth, technical director at the OPC Foundation, in Scottsdale, Ariz. “The devices are more and more accurate. And once the quality of the data has been determined, it can pass through as many systems as it needs.”

Filtering data for inaccuracies is not new, but it is moving into new areas. For one, more data is getting shared with more interested parties. Secondly, data sharing is moving down industry tiers to smaller companies. “People who have been doing real-time integration have been filtering the data for accuracy for a long time,” says Joanne Salazar, director of data management services at Emerson. “The big oil and gas plants make sure the data that comes into their models is correct. Now, the smaller and mid-tier chemical and petrochemical plants are starting to do it.”

ERP help?

Now that data is being shared between the plant and the business side, companies are setting up fail-safe programs between the control system and the ERP to make sure data failure in either system will not affect the other system. “If you’re having trouble at the ERP level, you want to make sure the plant can still operate,” says Paul Rauch, senior director at vendor Siemens IT Solutions and Services Inc., in Norwalk, Conn. “And when you’re having difficulties on the plant level, you want to make sure you are able to use the ERP.”

In many instances, the ERP is not configured to scout for data problems and simply accepts data as it arrives. “As you amalgamate data and put it into the ERP, the ERP doesn’t have any idea if it’s good,” says Wonderware’s Jones. “Typically, the ERP believes everything it’s told. If you tell the ERP you’ve made 1,000 pounds of a product and you’ve only consumed 800 pounds of raw materials, the ERP won’t know the difference.”

In other instances, the ERP is actually programmed to take a role in detecting problems with data. “We offer a key functionality to ensure the data collection through exception steps,” says Simo Said, director of global marketing at Germany-based ERP vendor SAP AG. “You can use these models in the ERP system and control the data exchange between the plant system and the ERP system.” The models include a series of eight prediction steps that identify errors in the data.

Some of the ERP data cleansing occurs within the ERP system. “We’ve created a tool so if there’s a data error, it gets passed off to the configurative SAP inbox,” says Siemens’ Rauch. “Here, people can figure out what do to with the errors.”

At other times, the data is reviewed for inaccuracies by ERP tools outside the ERP system itself. “We have a second context outside the ERP for customers in a multi-plant environment,” says SAP’s Said. “You can use SAP manufacturing integration intelligence to capture data around orders, batches and quality, and check to make sure the master data is accurate. It ensures process integration.”

The overriding strategy to assure the accuracy of plant data is to first assume that it’s inaccurate. Once you assume the data is corrupted, you can apply a number of programs, applications and models that can detect and fix data inaccuracies.

Sidebar Article - ERP System Ensures Accurate Data
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