Mergers and acquisitions have long been a staple of the life sciences industry — a reality that has resulted in disparate information technology (IT) environments within and across often far-flung enterprises. The situation is particularly prevalent in manufacturing operations because a pharmaceutical or biotechnology organization may run different management systems in nearly every separate facility (especially when contract manufacturers are involved). These “siloed” information environments are hindering efforts to evaluate and improve quality and operational efficiency at a time when quality and efficiency are most important to life-science executives.

One way to solve the problem is to implement a single IT application across all manufacturing plants. But that is risky, expensive, and time consuming — if at all feasible given good manufacturing practice (GMP) and validation requirements as well as business pressures. Biomanufacturers, therefore, seek strategies and tools to help them make the most of their existing environments while enabling quality and efficiency globally. The answer could be enterprise manufacturing intelligence.

The Integration Factor

Life-science organizations facing rising costs, declining pipelines, and margin erosion are increasingly focusing on improving manufacturing efficiency and reducing both cost and waste without compromising quality. To achieve those goals, companies must aggregate and rapidly analyze manufacturing data across devices, equipment, production lines, and plants — sometimes across borders.

Improved manufacturing intelligence offers significant benefits. AMR Research (www.amrresearch.com) estimates that such initiatives can yield production efficiency improvements of ≤25% and improve cycle times ≤20% by providing manufacturers with the information they need to make informed decisions about scheduling, production, and scrap (1). For example, pharmaceutical companies implementing enterprise manufacturing intelligence initiatives can determine the best way to efficiently produce high-quality drugs in the shortest possible time with existing and potential resources. They gain the ability to accurately assess options such as running double shifts, increasing or decreasing outsourcing, or expanding manufacturing capabilities.

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Enterprise manufacturing intelligence can also help organizations increase their asset use by ≤10% annually because they can tell when a machine may be slowing down and thus schedule preventive maintenance accordingly to prevent outages. Similarly, expanding intelligence into equipment availability enables companies to optimize the use of their production machinery through improved agility in scheduling and rerouting. These capabilities are critical when manufacturers want to optimize their investment in production facilities and tight working capital.

By integrating manufacturing data, life-science organizations can also eliminate discrepancies between manual and machine data. Thus, preventive maintenance can be performed on the basis of machine “actuals” versus “best guesses,” averting unplanned downtimes and ensuring optimal schedules for equipment maintenance. That in turn reduces on-hold and other production-delay issues.

Quality has always been under the microscope in bioindustry. Integrated manufacturing data provide critical support for quality initiatives such as lean manufacturing, six-sigma, and quality by design (QbD) that require real-time and enterprise-wide information. Business intelligence based on quality data will successfully identify waste and outliers as well as variability and inconsistencies.

Finally, biopharmaceutical companies that launch enterprise manufacturing intelligence initiatives stand to gain a competitive edge through their ability to share quality information with trading partners. Integrated manufacturing data enable companies to connect and collaborate more efficiently with a growing list of manufacturing partners. Integration helps them capture and analyze information more effectively to improve quality as well as comply with regulatory requirements.

Solving the Puzzle

Most organizations are clear about their enterprise manufacturing intelligence objectives. The challenge lies in achieving expanded visibility in a disparate and resource-constrained environment. Today, many life-science organizations still rely on spreadsheet-based attempts to gain greater manufacturing intelligence. But the process has several drawbacks: It is resource intensive (and error prone), and the end results do not yield real-time information needed to drive the level of agility required in today's market.

Manufacturers have several options for automating visibility into their manufacturing operations at an enterprise level. Implementing a unified manufacturing execution system (MES) across an entire enterprise could provide end-to-end visibility and real-time information required to improve manufacturing efficiency and assist with quality initiatives. But cost, time, and validation complexities all come into play with this approach — making it out of reach for most companies in today's economy.

Life-science organizations might also consider building a network of point-to-point integrations into a centralized analytics environment. But this approach is resource intensive to build and maintain. It also adds complexity to a company's IT environment and cannot be deployed quickly, ultimately increasing time to value and total cost of IT ownership.

A third approach involves creating a manufacturing data hub and common extraction layer that helps a company collect information from various plant systems. A reporting tool sits on top of the infrastructure. This approach provides the visibility required for precise management of a manufacturing environment, can be deployed relatively rapidly, and offers the flexibility to quickly incorporate data from new systems.