Internet of Thing

Artificial Intelligence

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4 May 2022
6 Min read

How To Get Operational Excellence Using loT?

Chemical manufacturers embrace new operating techniques when volatile markets, profit margins are thin, and all eyes are on corporate sustainability. Many firms are now investing in data analytics and related skills, particularly advanced analytics and typical spending on technology or process modifications to improve outcomes.

Engineers and other subject matter experts (SMEs) can swiftly examine data from legacy process historians, relational databases, and new Industrial Internet of Things (loT) sensors thanks to these investments, which allow chemical producers to break down data silos. Cloud-based advanced analytics applications are an essential aspect of this change. They solve concerns like time to return on investment, multi-source data access, and disseminating insights throughout the company.

Instead of spending hours wrangling and aligning data, frontline SMEs closest to the assets and processes may utilize that time to identify process, quality, and environmental improvements thanks to self-service analytics. By facilitating communication between information technology (IT) and operational technology (OT) leadership, cloud-based software speeds the enterprise-wide rollout of these enhancements. As a result, early adopters of these advances gain a competitive advantage, supporting prosperity in a challenging market climate.

Establishing A Data Strategy

Chemical manufacturers primarily relied on capital improvement projects to boost production capacity when crude oil was selling at near $100/bbl. This investment to increase production has resulted in a saturated chemicals market in many areas, lowering selling prices and forcing companies to consider how they might squeeze out an extra $0.05/lb profit margin.

Chemical manufacturers are exploring innovative techniques to create operational excellence because standard value-creation tactics are no longer yielding the returns they once did. Process modeling, optimization, key performance indicator (KPI) calculation, and loss tracking are all being used by many firms to extract more value from data.

To get the most out of analytics, you need to start with a plan and corporate alignment. It necessitates responses to questions such as:

  • What information is presently being stored?
  • Who are the key users of those data, and where do they live?
  • What kind of calculations are made using the data?
  • What is the data frequency required for the analysis?
  • Is the data used in the study historical, near-real-time, or forecasted?
  • What types of data sources are being combined to generate insights?

The focus changes to which types of analysis to prioritize once you’ve figured out how and where to store data. This could entail establishing a set of KPIs to evaluate, report on, and compare across production sites. It could mean applying machine learning, artificial intelligence, or digital twins to tackle predictive maintenance difficulties. It could mean standardizing the analysis and monitoring of critical process equipment (pumps, compressors, valves, and so on) across the business.

Empowering existing SMEs with user-friendly self-service advanced analytics solutions is one of the most effective methods to crank out analytics at scale. Chemical companies embrace this strategy, believing that they can get maximum value by combining powerful analytics tools with empowered personnel. Because of their process knowledge and awareness, SMEs can properly contextualize data, avoiding the contextualization feedback loop standard in firms with segregated data science units.

Getting Past Common Roadblocks

Data access remains one of the most significant impediments to engineering productivity, notwithstanding technological breakthroughs prompted by cloud-based software delivery. Near-real-time analytics and actionable insights require live links to process and contextual data sources, yet these connections are frequently unavailable. These and other difficulties are addressed by cloud-based advanced analytics, increasing productivity and reducing time to insight. Traditional, generally spreadsheet-based techniques of aggregating data from various sources or over long periods, as shown in Figure 1, can offer a variety of issues. The SME indexes the combined data on demand and displays it in an interactive point-and-click environment, making calculations simple and providing rapid visual feedback.

A user-friendly environment is required to make advanced analytics available to SMEs, many of whom lack programming experience. Another critical factor is using a complete dashboard and reporting tool to display the visualizations and results of various analyses. In browser-based advanced analytics solutions, reports provide fast access to insights and click-through functionality for further inquiry.

A sophisticated network of information flow among systems is required to convert an analytical insight into a physical action executed by frontline process employees. Chemical manufacturers can do this in near-real-time and at scale, thanks to the cloud and related services.

Conclusion

A company may take a single engineer’s study for one asset or batch at one facility and instantly share it with the peer group to scale it out to thousands of investments and collections. The cloud speeds up these enterprise rollouts by allowing vast amounts of data to be analyzed at increasingly quicker speeds, with nearly limitless computational power available.

The cloud, in particular, is ideally suited for using machine learning to develop fresh insights and predictions that are not possible when data is categorized and users are restricted to the limited computing capabilities available in their company’s data centers. The choke point changes downstream to how a company operationalizes the insights gained through analytics once the data access and computation issues are resolved.

So, if you wish to know more about operational excellence, contact the ONPASSIVE team.

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