Providing access to plant data at the right level and time is critical to effective operations and key to achieving the organisation’s targets. Decision makers need a clear understanding of the current situation and overall performance. Understanding trends and relationships enables experts to analyse and optimise the process and forecast the future. Operators are required to spot issues in real time to take corrective actions. A lot of effort and investment goes into building the infrastructure for gathering, storing, and processing of plant data. Effective delivery of information to users often requires the use of specific data visualisation techniques.
Data visualisation can be defined as “a collection of methods that use visual representations to explore, make sense of, and communicate quantitative data”. The main goal of visualisation is to improve the user’s understanding, and therefore requires knowledge of how users perceive data and interact with it. Studies in human perception provide guidelines on what users can observe and remember in a short time span. Likewise, domain knowledge is necessary to identify the most important pieces of information to the users. Aesthetics and responsiveness of the design help increase user engagement, while taking care not to sacrifice the quality of data presented.
The recent advancements in technology have lowered the deployment cost of visualisation solutions. High resolution displays enable the rendering of sharper graphics with readable information on both large monitors and mobile devices. Standardisation of web graphics in HTML5 enables rapid deployment of dashboards and interactive graphics on both desktop and mobile phones. Faster processing speed allows for a more responsive user experience while interacting with visualisation. Additionally, the recent rise of cloud analytics solutions enables quick integration of business intelligence capabilities to user data. User expectations of visualisation systems have risen accordingly.
Plant information dashboards
Dashboards have become a popular way of visualising plant information, often represented as KPIs that are calculated from multiple sources. Dashboards may be deployed as part of collaboration portals that enable different departments and functions to exchange data and execute business workflows. Multiple dashboards may be deployed to display different levels of information according to user roles and areas of operation. While they are mostly used for real-time monitoring, detailed reports and drilldown views are often provided for further exploration and analysis.
Dashboards enable users to become aware of the current situation on the plant and are often displayed on dedicated monitors or large displays that show the progress as the day goes on. Visualisations are used to add context to the displayed data. For example, gauges highlight the desirable targets of the plant and how close they are to be achieved, comparison charts show the contribution of each unit to the overall outcomes, and trends enable an understanding of behaviour over a specified period of time.
Applying data visualisation techniques is essential for effective dashboard design. The main goal is to focus on the most critical information, while reducing the noise generated from displaying excessive data or non-data visuals. Efficient use of the limited available space is required, especially on smaller display mediums. Providing visual cues that help the user quickly identify patterns or interact with data is essential. Furthermore, flexibility and continuous improvement of design as the user requirements evolve ensures retention of user engagement.
In Yokogawa’s experience deploying dashboard solutions, the following guidelines are helpful in effective solution design that meets user expectations:
1. Engage end users early.
2. Follow proven design principles.
3. Focus on the critical data.
4. Choose the right visualisation.
Engage end users early
It might occur that end users start interacting with the dashboards at or after project delivery, which may lead to gaps between the designed solution and user expectations. Engaging users with questionnaires, samples and prototypes helps close these gaps and reduces the overall time and effort required for delivery. It is recommended to involve domain experts and consultants to help users identify their requirements and arrive at the most effective methods for achieving them.
Follow proven design principles
Time constraints and pressure to complete the desired dashboard functionality may cause fundamental design principles to be overlooked. On the other hand, a tendency to rely on personal taste leads to subjective design choices that might not be well perceived by other users. An experienced designer would incorporate the design principles in their workflow and take user feedback into the process. Techniques such as A/B Testing can be used to measure user response to different choices in design. In particular, we found the following principles to have great effect on improving the quality of dashboards:
• Use colours to encode meaning. Different hues can encode different categories of data while colour saturation may encode emphasis.
• Users are quick to detect patterns instead of remembering sparse data. Use space, orientation, shapes and sizes to form patterns. Use borders to group relevant data together. Avoid over-crowded plots that show very high frequency data or large number of trends, instead, filter or zoom in the data to the correct level.
• Maximise the data ratio in visualisations. Give priority to flat, solid colour visualisations over 3D or unnecessary gradients. Remove unnecessary lines and keep the background colours consistent.
• Aim for a simple and easy to understand design. Complex, multidimensional visualisations may be broken into simpler ones using techniques such as Small Multiples for better results.
• Provide clear context to visualisations in terms of time, area, scale and reference to avoid confusion.
Focus on the critical data
Dashboards are ideally designed to fit the screen. Scrolling the dashboard to view the rest of it is not desirable as it leads to loss of important information. The limited available space requires prioritising which information to be displayed. The following techniques help in focusing the content of the dashboard:
• Well defined KPIs provide a great source of brief and concise information.
• Summaries and roll-ups of aggregated data would be more suitable to show on a dashboard, while further details may be explored in drill-down analysis.
• Using rounded up values, percentages or multiples instead of high-precision data might save some space without general loss of quality.
• Short lists of Top/Bottom performers help focus the user’s attention.
Choose the right visualisation
Visualisations are created for different purposes and can convey different aspects of the provided information. The choice of visualisation should be driven by the objectives that must be achieved. Moreover, visualisations can be fine-tuned using several parameters such as scale, range, and number of categories, etc. Poor choice of parameters might lead to loss of important information that the user would like to observe.
Gauges: these are commonly used to visualise KPIs and are often modelled after physical gauges that are familiar to the plant operator. They use a combination of indicator and colour to mark the current status against a certain range. Target values are often marked in green on the scale, while alarm regions are marked using yellow and red.
Line plots: these are used to display trends over a period of time. When designing the trends, it is important to select proper scales and labels for the axes. Horizontal lines can be used to plot thresholds for data, while vertical lines can be used to plot times for important events or differentiate between actual data and future predictions.
Bar charts: these are used to compare quantities of categorised data. Similar to trends, labels and scales should be defined properly. Categories on a bar chart can be sorted according to their values or based on a user defined order. Quantities in a bar chart can be stacked to show further details in values. Bar charts are efficient in conveying differences in quantities and are generally preferred to pie charts.
Scatter plots: these are used to compare two or more variables in a data set in a two dimensional plot. Two variables can be plotted on the X-Y axes while additional variables can be encoded using colour, size and shape of dots.
The way forward
The KPI team at Yokogawa consists of close-knit performance analysts, system professionals and domain experts that are ready to assist in conceptualising and implementing the appropriate KPI based system. Yokogawa experts can bring a new era of prosperity to a client’s enterprise, ensuring maximum value addition to the KPI project by way of performance boost and effective decision making.
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