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Avoiding the Ripple Effect of Bad Data: Systems and Data

June 17, 2020 by Terrance Holbrook

This is Part 2 of a series on maximizing efficiency and improving data analysis. Read Part 1: Why Going Paperless is Essential and Part 3: Using Process Automation to Reduce Human Error.This is Part 2 of a series on maximizing efficiency and improving data analysis. Read Part 1: Why Going Paperless is Essential and Part 3: Using Process Automation to Reduce Human Error.

Although medical device manufacturers have increased their investment in enterprise data automation, particularly enterprise resource planning (ERP) and manufacturing execution systems (MES), digital and automation processes have lagged, leaving sluggish systems in place and lost data on the rise. Those responsible for device history records (DHRs) have been saddled with piles of paper, inefficient processes, spreadsheets, and other standalone systems to monitor, collect, and aggregate data surrounding production and quality processes.

When executives from all life sciences industries were asked about their top quality management challenges, LNS Research found that 18.4% emphasized disparate information systems and data sources and 20% of executives in manufacturing operations management cite disparate systems and data sources among key challenges in addressing top manufacturing objectives for life sciences companies. Yet, a number of manufacturers continue to rely on partially digitized processes. Any paper-based DHRs still in use are not automatically integrated with electronic systems and quality processes, which, in turn, impedes the ability for information to be entirely transferred. This lack of complete connectivity between solutions represents the biggest functional gap and data-related weakness in the manufacturing software on the market today. Despite there being a number of electronic systems available, if siloed, many of these systems insufficiently track data and documents from different areas throughout the production life cycle, resulting in information gaps, blind spots, inefficient data tracking, and other preventable errors.

Poor, little, or manual communication between disconnected information systems like ERP, MES, learning management systems (LMS) and quality management systems (QMS) severely limits the throughput between manufacturing, quality, and other critical business areas. This impedes physical operations and generally undermines stakeholder collaboration. All disconnects, bad data, and the lack of visibility and collaboration across the enterprise, result in a host of business inefficiencies. According to IBM, poor data quality in the U.S. alone costs $3.1 trillion a year. Additionally, disconnected information systems can cause issues within inventory management, time-logging systems, and personnel training, increasing overall costs that affect an organization’s bottom line.

Read the full article here: Avoiding the Ripple Effect of Bad Data: Systems and Data