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The healthcare industry lags significantly behind the electronic, chemical, and automotive industries in process quality. Process sigma in healthcare is around 2.0 (achieving success about 85% of the time), while other industries' process sigma is as high as 6 or 7 (achieving success more than 99.9999% of the time). Healthcare companies have relied on inspecting quality into the products at enormous cost, yet defective product still finds its way into the marketplace too frequently. Because newer devices and pharmaceuticals are becoming more complex, it is a mathematical certainty that 2-sigma manufacturing processes will fail.

The healthcare industry argues that process-improvement efforts are hampered by regulations and that they must spend as much as 50% of each improvement program on validation. These claims are true, but new regulations allow manufacturing processes to be validated according to a science-based mechanistic understanding of process control. Once this validation is accomplished, the requirement for ongoing validation efforts is greatly reduced. Ongoing process-improvement efforts can proceed with minimal revalidation.

The US Food and Drug Administration is actively encouraging manufacturers to adopt process analytical technology (PAT) and quality by design (QbD). Both strategies require a process-control management method and a technical infrastructure for ongoing operations.

Enterprise process control and management (EPCAM) is a strategy for achieving this increase in process understanding. EPCAM is a macromanagement process executed through an integrated information solution and supported by associated systems and data, including machine-process controls through programmable logic controls (PLCs), supervisory control and data-acquisition systems (SCADA), manufacturing execution systems (MES), middleware, statistical process control (SPC), laboratory information management systems (LIMS), adaptive process control (APC), and fault detection and classification (FDC). EPCAM uses Six Sigma as the fundamental macro management process to supervise manufacturing-process control across the entire value chain for any given element, including raw materials, suppliers, and subcontractors.

EPCAM has largely been overlooked for three major reasons. First, process control is typically executed manually and is primarily focused on stand-alone unit operations. Second, data integration across operations is lacking. Third, the software to draw the relationships between process parameters and product attributes is not widely known or commercially available. Some examples of these issues include the following:

• Large automated equipment trains have their own control systems
• Manufacturing is typically split into functional areas such as raw-material preparation, mixing, compression, machining, coating, and sterilizing, with control autonomy in each area
• Control remains focused on an operation-by-operation basis versus an end-to-end, product end-state outcome view
• Manufacturing uses specialized control systems that addresses specific needs
• Process knowledge is kept by the worker and may not be documented or shared among peers
• Product test data (percent active, color, tablet hardness, and weight) reside in systems such as LIMS, while process data may be held in an MES or recorded manually.

The requirements of manufacturing in the regulated life-sciences and healthcare environments often necessitate that manufacturing-process improvement take second priority to validation and responding to nonconformances. As a result, process control in many manufacturing operations is not well understood.

Many companies turn to Six Sigma as the preferred method for process improvement, then apply it problem by problem. Six Sigma is well suited for improving business processes following the methodology of define, measure, analyze, improve, and control (DMAIC). The problem with most DMAIC efforts is the amount of time and resources needed to collect the relevant process data and analyze them with the corresponding product data.

For example, a plant site of about 350 people adopted the Six Sigma process hierarchy with the intention of continually improving the Sigma of the critical processes and the overall process. The idea was sound, but significant practical problems derailed the implementation. The first problem was that the effort required about 30 full-time employees to track and maintain the data. The second problem was uncertainty about what to do with 10,000 SPC charts after they had been gathered.

EPCAM solves this process-execution and data-management problem by:

• Establishing DMAIC as the macro process-control management method across the enterprise
• Integrating process and product data through DMAIC. The data are collected to enable the correlation of process parameters to product attributes. This technique eliminates the considerable manual effort required by a typical DMAIC project of data collection and analysis.
• Providing the adaptive process-control applications that analyze product and process data, correlate the data, and optimize performance. This step provides a mechanistic process understanding.
• Providing the automation to run operations based upon the above process understanding in real time.
• Identifying process variations early and determining the optimum parameters throughout an entire process chain, as opposed to focusing on a single operation.