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Figure 1. A plant manager might be intertested in an overview of how each line is doing (top), while a line manager may need an overview of each machine in the line.
Figure 2. The Pareto is one of the most useful tools for attribute data.
Businesses are buried in data. To begin with, each has a financial reporting system—a federal requirement. Many also have sophisticated ERP systems to drive production and manage inventories. Some collect manufacturing and quality data electronically for compliance or control issues. A few may have a Computer Maintenance Management System (CMMS), managing spare parts and dispatching resources. PCs throughout the company are full of homegrown spreadsheets and databases, tracking metrics related to a department or an individuals responsibility.
Since data sources in the business are not integrated, the data often have discrepancies. Each data source may have limited access for political, compliance or other security reasons. Most of the data is only available in raw-number form, distributed on several reports and screens. Is it any wonder it is difficult to get the management team to agree on potential investments or process-improvement actions?
The equipment used in modern metalworking and fabricating plants incorporates advanced CNCs, PLCs and PCs capable of collecting data on every part of the process. Collecting the data is only the first step in producing insightful, actionable information. Integrating business systems, quality systems, production systems and maintenance systems with powerful analytics is a game changer for most businesses.
What a business needs is one insightful version of the truth, unbiased by manual data entry or political concerns. Data elements become information that drives action and justifies and prioritizes investments.
Too Much Data, Too Little Information
Businesses collect an overwhelming amount of data, both in corporate-supported enterprise systems and in individual files stored on local PCs. The data are stored in a variety of formats including enterprise databases, proprietary application databases, data warehouses and any of the several spreadsheet or personal database formats.