Making better business decisions with better information is what The Connected Enterprise is all about. But the reality is far more complex than simply connecting disparate systems. Within a connected enterprise, manufacturing intelligence is the strategy for turning automation control system-level data into insightful information that's visible and useful to people at any level of the organization.
Many of today's manufacturing plants contain a myriad of disparate, information-generating systems to help accomplish operational tasks. While these systems are crucial to the operational areas they serve, they are often data silos that isolate information from others in the company.
Each employee is focused on the metrics or key performance indicators (KPIs) for his or her individual function. Machine operators monitor throughput and cycle time to keep machines running continuously. Meanwhile, the maintenance team monitors machine performance and tries to predict―and help prevent―downtime. Off the plant floor, managers focus on profitability and utilization.
Because it's largely siloed―and often manually inputted―people across the plant can't readily access the information they need to address issues such as quality lapses, inventory losses, equipment availability and supply chain coordination. Furthermore, they don't have access to information that leads to process-improving insights.
Manufacturing intelligence for smarter manufacturing
Effective manufacturing intelligence software applications seamlessly share related data from maintenance and quality systems in a single, coherent environment. Using industry standards and a unified production model (UPM), these applications can provide a cohesive view of seemingly disparate manufacturing data and give context for relationships among equipment, product, materials and people. The UPM can automatically issue alerts about exception conditions, missed targets and plan deviations.
For example, if a valve fails on a machine, the operator often knows this immediately because he can see and hear the failure. Once the operator fixes the problem, the system records the information and the process stops there. But by applying a manufacturing intelligence strategy, others have access to that data point. Maintenance, for example, could use that alert to drill down, finding out why the equipment failed and how to help prevent it from happening in the future.
Compared to custom code―which is prone to human error and puts the control of manufacturing intelligence largely in the hands of one or two IT specialists―off-the-shelf solutions based on open standards are easier to integrate into the existing network backbone. In addition, all elements of the off-the-shelf model are reusable, saving enormous development cycles and reducing the total cost of ownership to a fraction of what it would be otherwise.
