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INDUSTRY 4.0/IIOT MEASURE ENVIRONMENTAL PERFORMANCE


Figure 1: Automatically generated reports


provide personnel with the information they need to act quickly


Data generated by Industry 4.0 and IIoT initiatives can be used to increase efficiency and improve sustainability, says Mariana Sandin, Seeq


decarbonisation is tangible, leading process manufacturers to increase their urgency toward achieving sustainability goals and reducing their environmental impact. Despite recognising sustainability as an


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area of importance, many organisations are at a standstill in this evolving space. Due to growing regulatory, investor, and social pressures, process manufacturers know progress towards goals is urgent, but many don’t know where to begin, and how to demonstrate success. Without a clear strategy for producing measurable improvements, well-intentioned pledges are not allocated the required investment of resources and time. But with the right Industry 4.0 solutions in


their digitalisation arsenals, process manufacturers can measure their environmental performance, and then improve efficiency and thus sustainability, specifically by generating more value from time series data by leveraging advanced analytics applications. Sustainability in the process industries is


often linked to new, niche technologies, such as alternative energy and carbon capture,


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ollowing numerous net-zero pledges from companies and countries across the globe, acceleration towards


which are expensive to implement and maintain, and provide benefits that are not immediately realised. For example, the benefits of a wind energy farm cannot be realised until the operation balances out the carbon footprint associated with the production of each wind turbine. When process manufactures focus solely


on these technologies, they often overlook what they can do immediately to improve their environmental performance, with much less investment required. This is where operational time series data collected from IIoT projects can help. Digitalisation and IIoT projects have been


providing operational leaders with real-time access to time series data for years. Stored in process historians, this data can be analysed used to optimise processes. However, as both the accessibility to and


the volume of data grows, legacy software options present serious limitations when it comes to finding and operationalising these insights. For example, many process manufacturers lack an accurate and efficient method for proactively tracking environmental performance. Instead, subject matter experts (SMEs), including process engineers and operations personnel, are left sorting through spreadsheets to wrangle their


data, instead of analysing models and patterns that lead to useful insights. Recognising these limitations, process


manufacturers are providing SMEs with self- service advanced analytics applications, like Seeq, that provides a streamlined approach and interface for accelerating insight into process data. Advanced analytics applications enable organisations to fully leverage their workforce’s expertise to define sustainability key performance indicators (KPIs) and track performance. With these insights, SMEs can determine areas for improvement to optimise performance of existing assets. Process experts can leverage the flexibility


and agility provided by these applications to overcome previously unsolvable use cases, using a proactive approach. Driven by regulation, environmental performance has traditionally been tracked by SMEs collecting and analysing data after an event. But by using advanced analytics


applications, process manufacturers can shift from this reactive approach to a proactive, or even predictive, approach – for example by automating report generation. This near real- time visibility enables organisations to assess best practice and benchmarking more easily, while detecting deviations quickly to decrease response times and mitigate impacts. Additionally, SMEs can build models within


advanced analytics applications to better understand how process changes will improve sustainability KPIs. Using “what if” analyses, SMEs can apply these models to predict environmental performance and


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