Comment: Mastering data management
The high cost of capital projects means that getting more from existing equipment is now a strategic incentive for those looking to maximise profits and meet customer demands, says Dr Robert Golightly
The challenges facing the process industries today are becoming more intense. Manufacturers need to reduce production variabilities and eliminate them to achieve consistency either in continuous or batch processes. New regulatory requirements affecting the industry place further demands on existing resources. The cost of non-compliance is expensive, and so is the need to demonstrate high standards with regards to environmental protection.
Time to market is critical to gaining market share and recovering the costs of bringing new products to market. Therefore, fine tuning processes and achieving production goals needs collaboration across the enterprise in order to diagnose and resolve issues quickly.
Improving asset effectiveness is a priority for chemical companies seeking to gain a competitive edge. With the high cost of capital projects, getting more from existing equipment is a strategic incentive to maximise profits and meet customer demands. Creating enterprise-wide benchmarks of asset performance helps drive out downtime, increases throughput, and improves product quality. Manufacturing execution systems (MES) are designed to address these challenges and at the core of these systems is the data foundation for collecting time-series process data, along with product characteristics, alarms and other event data.
With investment in MES, refineries, chemical and petrochemical companies can easily collect, merge, store and retrieve data from multiple sources to create a complete picture of production operations.
Making data meaningful
Many industry leaders recognise that technology can give their businesses a commercial edge and help them bring products to market with greater efficiency and with consistent product quality. Smart factories are embracing technologies, such as the Industrial Internet of Things (IIoT) and the use of mobile devices, which enable staff to control operations, gain deeper insights with visibility into manufacturing behaviour, and help make timely decisions to correct issues quickly.
The flexibility and agility that these innovations offer today can mean the difference between success and failure, especially when needing to meet production targets and protect profit margins.
As manufacturing production becomes more sophisticated, the need for sophisticated MES is important to make sense of the huge volume of data. Staff can often succumb to exhaustive manual tasks, such as deciphering terabytes of information, so having powerful analytical tools can alleviate this burden.
Interestingly, a report entitled Market Guide for Manufacturing Execution System Software by Gartner highlighted: “Over 70% of data generated during production rarely gets used. Add to this the anticipated onslaught of intelligent sensors and devices expected to be added via IoT technologies, and it can clearly be seen that the MES challenge has moved from how to collect data to how to determine what data is meaningful and should be leveraged.”
‘Data munging’ is a term often used to reflect the time it takes to convert data in one form to another in order to complete an analysis workflow. Time is critical to reducing the financial impact of process issues. To improve asset effectiveness, it is important to quickly identify the root causes of variance in plant behaviour. Understanding the reasons for the deviations from nominal behaviour is essential and to know how they can manifest themselves into poor products that will cause additional production costs. With the right data historian and complementary tools, it is possible to complete analyses and spend less time manipulating data and more time solving problems.
Data management relies on having powerful tools to contextualise information in rich, easy-to-understand formats. AspenTech’s visualisation and analysis capabilities help users create context-rich analyses of their production operations. Context is created by overlaying process time series data with production characteristics, events, alarms, comments and annotations from operators and engineers. Amassing this contextual data provides a complete picture of production operations and drives faster and more effective analysis, resulting in improved asset utilisation and more efficient use of human resources.
MES systems have been successfully deployed by the large and small process and discrete manufacturers to improve quality, get better visibility to production data, streamline processes and remove the burden of manual tasks. Payback at the plant level can usually be seen within six months of implementing MES tools that deliver benefits both at plant floor level and to executive decision-makers.
Turn data into profit
Context helps to make data meaningful and provides a complete picture of the production situation. Organising and sharing content is essential within a Big Data environment and helps to create a collaborative workforce that can operate in harmony to meet production goals. Content can be aggregated into tabbed displays and organised in customised views, so users can easily understand plant behaviour.
Breakthrough MES technology for the process industries includes powerful features for continuous and batch processes delivering desktop performance in the form of a thin client. Speed, power and flexibility help users resolve production problems quickly and reduce losses.
When rich, easy to understand analysis is the source of good decision-making, cutting-edge MES solutions allow decision-makers to master information in a timely, relevant and shareable fashion and to drive visible value throughout the production lifecycle.