Life sciences organizations, squeezed by dwindling pipelines and reimbursements, are focused like never before on improving manufacturing efficiency and reducing costs and waste – without compromising quality. Disparate IT environments, often the result of mergers and acquisitions, however, hinder these efforts at a time when they are most important to life sciences executives.
To effectively improve manufacturing efficiency and quality, pharmaceutical manufacturers must be able to aggregate and rapidly analyze manufacturing data across devices, equipment, production lines and plants worldwide. Most do not have the option of installing a single manufacturing application across all plants ― a risky, expensive and time consuming proposition – to gain this level of visibility. Manufacturers, therefore, seek strategies and tools to help them make the most of their existing environments while enabling quality and efficiency globally. Enterprise manufacturing intelligence could be the answer.
The benefits of improved manufacturing intelligence are significant. AMR Research estimates that such initiatives can yield production efficiency improvements of up to 25% and improve cycle time by up to 20% because manufacturers have the information they require to make informed decisions about scheduling, production and scrap.
How Do We Get There?
Most organizations are clear about their enterprise manufacturing intelligence objectives. The bigger challenge lies in achieving expanded visibility in a disparate and resource-constrained environment. Today, many life sciences organizations still rely on spreadsheet-based processes in their attempts to gain greater manufacturing intelligence. This process is resource intensive and the end results do not yield the real-time information needed to drive the agility required in today’s market.
Some life sciences organizations seek to turn a network of point-to-point integrations into a centralized analytics environment to gain greater visibility. This approach is resource intensive to build and maintain. It also adds complexity to the organization’s IT environment and cannot be deployed quickly, increasing time to value and total cost of IT ownership.
Another approach is to create a manufacturing data hub and common extraction layer that enables manufacturers to collect information from various plant systems, along with a reporting tool that sits on top of the infrastructure. This approach affords the visibility required for more precise management of an organization’s manufacturing environment, can be deployed relatively rapidly and offers the flexibility to quickly incorporate data from new systems.
Best Practices to Consider
As life sciences organizations consider their approach to enterprise manufacturing intelligence, several best practices can guide them to a successful initiative.
Accommodate Diverse Data
An enterprise manufacturing intelligence environment must capture and analyze real-time, granular information from all types of systems, including those on the shop floor. Access to shop-floor information enables a company to build quality into its processes, reacting to exceptions and quality deviations as they are occurring, as opposed to after quality control testing is complete. In addition, pharmaceutical manufacturers should ensure that their manufacturing intelligence environments have the ability to integrate data from third-party sources, such as the contract manufacturers on which they increasingly rely.
Build on Standards
The most flexible enterprise manufacturing intelligence environments are built on standards that an organization can use to leverage pre-built data structures, which enable systems providers to the manufacturing industry to speak the same language – facilitating the integration process and providing faster time to value. For example, systems built on the ISA-95, the international standard for integrating enterprise and control systems, normalize data across the enterprise resource planning (ERP) and manufacturing execution systems (MES). Enterprise manufacturing intelligence environments require the integration of both types of data. In addition, it is important to consider whether the environment has incorporated a generic data model that supports a hierarchical structure for reporting key performance indicators (KPIs) and metrics. Establishing KPIs varies by industry and should be set to help companies focus on what is urgent and what can be postponed by comparing the current state to a pre-determined target.
Manufacturing facilities are dynamic environments with new systems and equipment being added, modified and removed at any time. The systems in use also vary from facility to facility, particularly when managing a contract manufacturing environment. To readily accommodate such disparate and dynamic environments, manufacturing intelligence systems should be open and extensible.
Enable Rapid Payback
In today’s environment, life sciences organizations cannot afford a slow payback on their IT investments. When considering an enterprise manufacturing intelligence solution, organizations should seek features that enable rapid roll out and use. For example, pre-built graphical dashboards and pre-built adapters for ERP and MES systems enable lower total cost of ownership and faster time to value.
Deliver Value from the Shop Floor to Executive Suite
The system should also provide benefits to multiple audiences – from the shop floor to the executive suite – with flexibility and configurability that enables various groups to get the information they need. A line operator, for example, should have access to raw information to detect problems in real time, such as a packaging line that is starting to overfill. Conversely, the executive suite may be looking for higher level trends, such as comparison of the time, cost and quality of manufacturing across lines, plants or contract manufacturers.
Provide Actionable Information
The bottom line for any business intelligence solution is, “Does it deliver actionable intelligence?” In the case of an enterprise manufacturing intelligence environment, the solution should enable users to clearly identify areas for improvement and yield insight into how to move forward.
While the need for expanded business intelligence has never been greater, today’s manufacturers must balance this requirement against the need to optimize their existing IT investments. With strategic planning and adherence to best practices, pharmaceutical manufacturers can achieve all of these goals, and, ultimately, drive new levels of quality, productivity and performance in their manufacturing operations.