#process analytics

Process Analytics Market : Global Opportunity Analysis and Industry Forecast, 2021-2031

The global process analytics market size was valued at $469.86 million in 2021, and is projected to reach $18.6 billion by 2031, growing at a CAGR of 44.6% from 2022 to 2031.

Read More: https://www.alliedmarketresearch.com/process-analytics-market

According to a new report published by Allied Market Research, titled, “Process Analytics Market, by Process Mining Type (Process discovery, Process conformance, Process enhancement), by Deployment Mode (On Premise, Cloud), by Organization Size (Large Enterprises, Small and Medium Enterprises), by Application (Business Process, Information Technology Process, Customer Interaction): Global Opportunity Analysis and Industry Forecast, 2021–2031”.

The process analytics market was valued at $469.86 million in 2021, and is estimated to reach $18.6 billion by 2031, growing at a CAGR of 44.6% from 2022 to 2031.

Process analytics is a type of system that helps in analyzing and controlling the manufacturing processes. It also measures critical quality parameters and performance attributes of raw and in-process materials. In addition, it enables in-process data to be used for assessing the quality of a batch during manufacture, significantly reduces the need for finished product testing, and as a result, improves lead times. Furthermore, digital transformation is empowering users’ awareness for analyzing and understanding business processes and increase in adoption of algorithmic business are the significant growth opportunities for the future prospects of process analytics market forecast. In addition, rise in implementation of task-level automation is positively impacting the growth of the process analytics market. However, competition from open-source alternatives is hampering the demand for commercial solutions and lack of skills and expertise limit the market growth. On the contrary, the need to focus on enhancing the customer experience is expected to offer remunerative opportunities for expansion of the process analytics industry during the forecast period.

On the basis of organization size, the large enterprises segment dominated the overall process analytics market in 2021, and is expected to continue this trend during the forecast period. This is attributed to process analytics solutions enabling enterprises to monitor and regulate complex business and Information Technology (IT) processes. However, small and medium size enterprise segment is expected to witness highest growth as various SMEs industries from different domains have been trying to build process system infrastructures that are more dynamic, agile, and cost-efficient, which is expected to further fuel the growth of the global process analytics industry.

Region wise, the process analytics market was dominated by North America in 2021, and is expected to retain its position during the forecast period, owing to rapidly growing digital landscape of the region and rise in initial adoption of advanced process analytics technology by the manufacturing enterprises. However, owing to its growing adoption of process analytics in manufacturing and IT & telecom sector across the developing countries in this region, is anticipated to drive the industry growth for the processing analytics market in Asia-Pacific region.

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The spread of COVID-19 has been disrupting the world, businesses, and economies and has impacted the way of living of the masses and approaches adopted by enterprises for business management. The ability of enterprises to sustain this pandemic has become challenging for them as they are shifting focus from growth opportunities and concentrate on implementing measures to mitigate the impact of COVID-19. The upcoming analytics projects are kept on hold owing to the pandemic. Several companies are competing with each other to gain a process analytical solution to adopt the process analytics. Businesses have already started efforts to return to their fully operating conditions and are facing multiple challenges in terms of enhancing the process, due to impact of the pandemic on customer base and operations. Therefore, companies are looking forward to update their manufacturing process by implementing process analytics.

KEY FINDINGS OF THE STUDY

By process mining type, the process discovery segment accounted for the largest process analytics market share in 2021.

By region, North America generated highest revenue in 2021.

By organization size, the large enterprise segment generated the highest revenue in 2021.

The key players profiled in the process analytics market analysis are Celonis, Fluxicon BV, Icaro Tech, Kofax Inc., Logpickr, Scheer GmbH, Worksoft, Inc., Minit, Monkey Mining BV, and ABBYY. These players have adopted various strategies to increase their market penetration and strengthen their position in the industry.

About Us:

Tridiagonal Solutions Process Analytics Group supports Data Science and Manufacturing Data Analytics initiatives of the organizations through its Guided Analytics Services & Solutions Framework. This framework enables organizations to devise KPI-based analytics strategy and selection of the right approach/methods/solutions for successful implementation at an enterprise level.  

Read more here: https://dataanalytics.tridiagonal.com/consulting/  

We fill the gap in terms of skillsets, know how and best practices in the data analytics space and help companies to get started in their data analytics journey. Our multi-skill teams consist of Domain experts in Chemical / Mechanical engineering with strong industry exposure in Oil & Energy, Chemicals, Pharmaceuticals, Metals & Mining, Cement sectors. The essential combination of data engineers, data scientists, and application engineers is instrumental in implementing data analytics solutions at our customer's end. We have a suite of solutions (Data analytics platform), which we implement as a part of our services.

Today most of the manufacturing data analytics is in the form of monitoring (what is happening) the current state of the system/ process or historical data investigation (What has happened); trying to understand the events. The basic application of statistics (such as covariance/ correlation) helps in doing deterministic analysis such as root cause analysis of the past events and identifying ‘Why’ such events happened.  

There are different applications of data science and analytics, which can help in performing various levels of analytics from diagnostics to probabilistic to augmented data modeling. The Machine learning techniques and application of the right methods can unlock the data and give meaningful insights. However, to realize the benefits of this technology, the organizations need to have the right ‘Analytics Strategy’, skills, methods, and right solutions.

Following are the service offerings

Core Data Science Group - ML Modeling Center

Analytics implementation Strategy – Roll-out plan with Returns on Analytics (ROA)

Building Analytics & Data-driven models for various applications (processes) – Model construction, application or validation and implementation – Lab/ R&D, Process Dev., Manufacturing, Supply chain, etc.

Department-level Value creation (Lab, Process R&D/ Dev., Quality, Operations)

Augmented modeling for complex processes (Mechanistic + Data-driven), working with SMEs for advanced analytics (level 4, 5)

Custom Application for a specific purpose – Templatizing Analytics

Model Management & Tuning – Enhancing the value of models

Knowledge-creation and building Analytics Culture / Champions – Analytics Transformation

Center of Analytics (COA) – Management of Data Insights

Extended team for Data Mining and Manufacturing Data Analytics – Managing multiple plants/ assets

Dynamic reports and KPI/dashboards for global department heads (Asset-level/ Process level) – R&D, Quality, Operations, maintenance, CXOs

Working with Plant / Operations head-on Operational effectiveness and productivity analytics

Working with MSAT team on Process & Optimization Projects using Data Modeling & Analytics

Back-end ‘Analytics Production Team’ to the Data Science/ Digitalization leaders

Establishment of Best Practices for Data Analytics

Knowledge-building Workshops

Hands-on training on Data Analytics – Data Preparation & Pre-processing – Data Cleansing, Conditioning, descriptive statistics

Feature identification, reduction, Covariance methods

How to create Data Twin – System / Process representation for data modeling/ Analytics

Model construction, application / validation and implementation – Right Methods (Use case-specific)

Best Practices or Guide & Methods for Manufacturing Data Analytics

Advanced Data modeling techniques on your data and results

Data Analytics Solution (Software) – Implementation

Providing Industry leader – Advanced Analytics Solution

Proof-of Value development for Seeq evaluation

Configuration & Implementation Plan and Global roll-out

Establishing Logical Data Lake using Seeq Cortex Server

Training / Support post-implementation

Custom Application / Dashboards Development using Seeq API/SDK

Remote monitoring and KPI-driven dashboard creation for plants

RPA Process Mining