Addressing Information Management Challenges to Improve Manufacturing Performance

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1 Addressing Information Management Challenges to Improve Application Brief

2 About AspenTech AspenTech is a leading supplier of software that optimizes process manufacturing for energy, chemicals, pharmaceuticals, engineering and construction, and other industries that manufacture and produce products from a chemical process. With integrated aspenone solutions, process manufacturers can implement best practices for optimizing their engineering, manufacturing, and supply chain operations. As a result, AspenTech customers are better able to increase capacity, improve margins, reduce costs, and become more energy efficient. To see how the world s leading process manufacturers rely on AspenTech to achieve their operational excellence goals, visit

3 Background Most manufacturing companies list one of their goals as the pursuit of manufacturing excellence. Achieving that goal is complex and progress can be difficult to measure. While manufacturing excellence programs have many components, information management systems are at the foundation providing process, production, and product information essential to analysis efforts to identify improvement opportunities. The value of the data within an information management system is not inherent; it comes from a company s ability to visualize the information in context, analyze and extract intelligence, then distribute or report findings to a larger audience. Three common challenges to deriving value from manufacturing information include: 1. Information Overload 2. Time-Consuming Analysis 3. Accessibility of Data for Sharing and Collaboration All of these things contribute to decreased operational insight, delayed decision making, and inefficient troubleshooting, which impede manufacturing excellence initiatives. When these challenges are understood and addressed with capable tools, they can be overcome. Challenges Challenge 1: Information Overload As process industry facilities have become better instrumented, the sheer volume of data available in the plant information management systems, enterprise historians, or manufacturing execution systems has grown substantially. Plant-related data may reside in PLC/DCS systems, information management systems, ERP systems, lab systems, etc. In many cases the information is not centrally consolidated, which then requires individuals to have some idea of where the information is located that they seek. Now consider the many other systems that most plant workers interact with on a daily basis either supplying information to or getting information from such as expense report systems, time sheet systems, plant inventory systems, and basic plant IT systems. The many systems and large amounts of information that workers interact with on a daily basis can be overwhelming and often leads to minimal use as no one has the time to gain the necessary expertise to truly leverage the value of these various systems. For example, as a production engineer with responsibility for several units, it can be difficult to find exactly what you are looking for among the vast amount of data. Assume there are three flow indications on a catalyst line inlet to the reactor of Unit A and one of the meters was temporarily plugged for a few minutes over the course of a week. A production engineer may need to determine which flow was in error to manually correct a production report. If one-minute data is collected on all three meters over the course of the week, there will be a large amount of information to analyze to help identify the specific tag responsible for the issue as well as the time period when the correction is needed. Remembering tag names, time periods, and other production-related information that will help narrow the search is not always practical. Challenge 2: Time-Consuming Analysis The analysis tools integrated with information management systems have historically been powerful but complex. As a result, advanced users were the only ones who developed the expertise to take advantage of these tools. This decreased 1

4 the value of the information contained within the systems for the average user. They often resorted to transferring data segments into spreadsheets so that they could analyze data with tools that were more accessible to them and with which they were more familiar. This was not always easy though because the data extraction process could be cumbersome and unforgiving. As an example, if an engineer wanted to pull data for a batch production run for a certain time period, she might run a query for a specific time period with a group of tags and extract to Microsoft Excel. When she gets the data into Excel, she looks through it and realizes that she left out a tag and has to go back through every step again. This process can be inefficient and delays the engineer getting to the analysis step that can bring value to the plant. One of the other obstacles to taking advantage of existing analysis tools is training requirements. Companies often can t afford to have resources spend days at a training class to learn about analysis tools. They have resource limitations and the time investment required for off-site training can be a deterrent to developing more organizational expertise on the specific tools available. Challenge 3: Accessibility of Data for Sharing and Collaboration While there are many users of information management systems, there are also many others who often need information from the system but don t have the time for analysis, expertise in the toolsets, or, in some cases, access to the source data. For these users, they often have limited knowledge of the system but the system is still valuable to them. They use data from the system that comes to them in pre-populated spreadsheets, pre-defined trends, graphics, reports, etc. It may be delivered via , Microsoft Excel, Microsoft SharePoint, hardcopy, or any number of other ways. This information is supplied to them already in a relevant context for their specific role. However, often the resolution level of this data is much lower than what is actually being collected in the historian. For example, while data may be collected at one-minute intervals, it may be reported as an average over a week. One of the issues with this approach is that in most cases the data used in reports is static. As a result, decisions are not necessarily made in real-time with the most current information but instead are based on what has already occurred. While in some instances this is perfectly adequate, many times this can be an issue. The ability to have better operational insight by having more real-time data dissemination to these users can lead to faster and, in some cases, better informed decisions. As real-time data access is expanded to a broader audience, the ability to collaborate with others who might not have previously been a part of the process for decision making, troubleshooting, and other vital operational and business functions also becomes a reality. New Features and Benefits Understanding the information management challenges that many companies face, AspenTech has focused on providing new capabilities within the Aspen InfoPlus.21 product family. New features and enhancements to existing functionality were designed to optimize data analysis, streamline workflow, and expand process information accessibility while improving the overall user experience. 2

5 Search AspenTech s new enterprise search platform provides instantaneous web-based search access to all tags across the enterprise with search results organized by relevance and use, bringing greater speed and intelligence to data discovery. Now an engineer responsible for several units can quickly search for a tag in any of those units and subsequently call up a trend using that tag. This new capability makes it easier to find specific and relevant information quickly. Some key search features include: Full-text search Highlighting of hits within search results Faceted search so tag discovery is based on all facets as opposed to only quick changing attributes Custom field searches Figure 1. New High Performance Trends with Integrated Search Speeds Data Discovery High Performance Trends New high performance trends expand accessibility and provide large amounts of contextualized data very fast. This helps speed the analysis process for large volumes of information within Aspen IP.21 Process Browser, Aspen Role Based Visualization, and Aspen Process Explorer. The stored best fit methodology also simplifies finding and evaluating specific production events. Key features include: Optimized performance Broader access with multi-platform support Enhanced data discovery with expanded search functionality Enhanced accessibility by eliminating hidden context menus and providing better annotation of key user inputs Better contextualization with the use of spark lines and trend previews 3

6 Batch Excel Data Add-ins Figure 2. A New Workflow Simplifies Data Extraction The New Batch Data Excel Add-in optimizes batch analysis. The new add-in enhances data extraction and expands selfservice analytics with Excel and PowerPivot via simplified data exchange workflow between Aspen Production Record Manager and Excel. In addition to the streamlined workflow, it also provides 64-bit performance for support of SharePoint The new Batch Excel Data Add-in helps reduce analysis time and enables users to take advantage of commonly available analysis tools that they may have more experience with such as Microsoft Excel Services and PowerPivot. Excel Services and SharePoint 2010 Support Figure 3. Expand Real-Time Data Distribution and Encourage Collaboration 4

7 Support of Excel Services, SharePoint 2010, and PowerPivot not only optimizes analysis capabilities, but also improves operational insight and increases information value by expanding information availability. These capabilities broaden realtime data sharing by enabling report parameterization for automatic updates upon recall and distribution to SharePoint clients through Excel services. SharePoint 2010 support also includes additional functionality such as 64-bit performance, improved visualization, and enhanced navigation that empowers users to share data, collaborate, and maximize information value. Integrated Tools and Online Training Increasing user effectiveness is also an essential part of helping companies drive toward manufacturing excellence. With this in mind, AspenTech has integrated aggregation and compliance tools into Aspen InfoPlus.21. This integration is intended to simplify setup and utilization of common accounting and compliance functions for users. AspenTech has also added online training to many of our products. This online training speeds time to knowledge by providing on-demand, focused training materials accessible from within the product. Training materials include product whitepapers, animated video-based tutorials, quick start tutorials, and other pertinent product-related information, empowering users to manage their own learning and knowledge growth in specific areas of need. Conclusion While information management challenges can impede progress toward manufacturing excellence, AspenTech is confident that new tools and innovations will continue to help users address these challenges. Key capabilities delivered in our latest release include: Broadened accessibility, better speed, and more intelligence for data discovery Streamlined batch workflow and expanded self-service analytics Expanded real-time information access leveraging Microsoft capabilities Integrated tools and training for greater user effectiveness These tools provide important functionality to help address key information management challenges. Ultimately, it is up to each company to determine how to best apply these tools to optimize the value of information management within its organization s efforts to improve manufacturing performance. 5

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