Operational excellence is a major priority for the process manufacturing industry. While achieving this can be a challenge, many companies have turned to the use of data to make decisions that improve production operations. The value in visualization and analytics is in reducing the impact of negative events and creating a process for continuous improvement in operating efficiency.

With the correct visualization and data analysis software in place, companies can avoid unplanned outages, which typically result in losses upwards of seven figures. However, before implementing these types of solutions, organizations need to ensure they have the key analytics capabilities that are required to achieve operational excellence.

Selecting an Effective Solution

Collecting and preparing data can often be a very time-consuming activity. In many cases, users report that it takes hours, if not days to perform these tasks as they struggle to reach across multiple systems to amass all of the data needed for effective analysis.

The use of a data foundation -- that includes master data management -- is an important key to eliminating the drudgery and delays of preparing data. These tools can have strong visual and programmatic features for data transformation and manipulation as well as build diagnostic data hierarchies.

Advanced manufacturing execution system (MES) solutions are important tools that address the data management challenge. There is often a significant amount of time spent sifting through key performance indicators (KPIs) to report on performance. Important context is often lost during shift changes, when informal hand-off meetings take place and plant issues are described to the team coming on shift, but not recorded or saved for future reference.

By implementing a MES infrastructure, users have the ability to capture unstructured data. With this type of electronic system, data becomes searchable and enables users to investigate functionality that advises operators on the most effective actions to take in response to an issue.

MES tools can also enable organizations to identify and understand variability in batch processes to ensure product quality and profit. Genealogy and the ability to track the entire production chain for a product are crucial in some segments and a regulatory requirement in others. However, visualizing batch data has different requirements than those of continuous processes, and there is a need to visualize batch data in several ways.

Effective batch analysis depends on the ability to capture, align and analyze information with complete context. With the proper MES tools, companies can augment existing process data stores with new capabilities for managing master data.

Measurement and reporting are some other issue companies need to address. Measurement is not always easy, instruments drift, communication links fail and companies depend on KPIs that are mostly inferential measures. There are typically dozens, if not hundreds of KPIs within an organization. However, they are not always actionable and can be numerous and conflicting.

Benchmarking assets across the enterprise can allow plants to better track and improve plant performance. An effective asset management program uses benchmarks to identify poor performing assets early on, as well as identify star performers and demonstrate the highest achievable levels of performance for an asset class.

In terms of reporting, companies should invest in software that enables enterprise manufacturing intelligence (EMI) to create a bridge between production and business systems. EMI software provides a rich, intuitive graphic environment that can be accessed from various devices such as tablets and cell phones.

In addition, data from different sources and systems can be combined, put into a new context, or aggregated to provide users with a different and more complete perspective of operations, regardless of where the data originated. Enterprise historians provide business users with access to information without compromising cyber security.

Finally, companies need to understand how to develop a smart IT/OT Strategy. There hasn’t been a time in history where operational excellence has been so dependent on the effectiveness of the IT/OT strategies. The industrial internet of things (IIoT), smart manufacturing and Industrie 4.0 are all focused on leveraging models and data to improve operations.

Solutions that provide the features for model development in an IT strategy and architecture should be implemented, as they will support the modeling needs covering fundamental, empirical and procedural approaches and identify common methods of deploying model-based applications supporting the key cross-functional workflows.

Continuing a Profitable Future

Fortunately, there is a technology convergence phenomenon afoot that will yield incredible capabilities to operate plants with deeper insights into asset conditions. With the right tools, organizations can predict a likely machine failure and take effective maintenance actions.

Operator effectiveness will increase with procedural automation systems that can guide them through difficult or infrequent abnormalities. Business and operational information will be integrated more seamlessly than ever and will enable new efficiencies in quickly bringing new products to market, and quickly drive out production inefficiencies.

All of these capabilities will converge to raise operational excellence to new levels in the process industries.

(About the author: Robert Golightly is a senior manager at AspenTech )

Register or login for access to this item and much more

All Information Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access