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Palmer Foundry in Massachusetts designs and manufactures high-end, precision aluminum castings for numerous industries, including semiconductor, automotive, energy, aerospace, robotics and many more.  Each project requires demanding specifications and diligent control of raw materials and processes.

Making data work for Dow

Data islands, or data silos, are common in many industries, but perhaps more so in manufacturing. The complexity of products and production lines requires multiple types of processes and equipment, each often with their own database and data storage system. While data can be extracted, it does not become information until it can be analyzed. And without an aggregated system to analyze and deliver relevant information, the value of the data is lost to the organization.

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The goal of process management is to optimize production and manage it as effectively as possible. Hitting the mark requires dependable performance measures that everyone involved can use to make good decisions.

Taking the time to carefully plan the implementation of an EMI real time monitoring system can save weeks of frustration, while creating a tool that quickly achieves results. The goals should be clearly identified in advance, and the planning should carefully incorporate the following steps to ensure goals are met. This document is designed to provide the best practices that other companies have discovered as they developed successful implementations.

The Challenge

EMI has become one of the top requirements for high-performance manufacturing. ARC Advisory Group detailed the top 10 Best Practices for EMI success in their Strategic Report, “Enterprise Manufacturing Intelligence: Integrating and Extracting Value from “Big Data”. NWA Focus EMI® meets or exceeds them all.

NWA Focus EMI® addresses all of the top challenges related to enterprise manufacturing intelligence identified by Gartner/AMR to deliver complete answers for immediate results and long-term solutions.

Overview

Predictive maintenance (PdM) methods actively monitor process and equipment parameters to determine optimal timing for maintenance. Control charts are used to monitor performance, trigger maintenance activity and improve Overall Equipment Effectiveness (OEE) for more productive manufacturing systems. Using process based analytics enables event-driven maintenance which produces better product at lower cost with less downtime.

To compete in today’s global markets, manufacturers need to actively use modern manufacturing operations management systems. These systems, in turn, depend on enterprise manufacturing intelligence (EMI) which extends process based analytics throughout company operations.

In today’s global markets, the pressure to perform is non-stop and the need for information is real-time. To maximize competiveness and profitability, the supply chain needs top-notch data management. But, that by itself is not enough. The data must be aggregated and analyzed to deliver complete manufacturing intelligence (MI) that, in turn, enables sound decision making for effective supply chain management.

The Model for Successful Supply Chain Management