Manufacturing Data Analytics – Guided Approach-Methods-Solutions

Many companies in the process industry have started investing in Industry 4.0, digital transformation and data science initiatives. The industry has experienced a paradigm shift from implementing Industrial automation systems, Manufacturing execution system to centralized historians and data lakes to collect and centralize data. There is a big push now to monetize data and realize the returns on huge investments made in data collection / aggregation. The focus on DATA ANALYTICS gets diluted and is looked as another software implementation /digitalization project.

Pilot your Analytics: The implementation of a data analytics solution needs a focused vision, coordinated strategy and a guided approach to realize Return on Analytics (ROA)’ before investing big dollars. The stakeholders need the guided approach (evaluation criteria), methods and solution for successful implementation of ‘Data Science & Analytics’ initiative.

Introducing ‘Pilot-Guided Analytics’ a framework for initiating, scaling and implementing Data Science and Analytics Solutions across the organization. Pilot Services framework is a methodical way of implementing data transformation and KPI-based data analytics strategies.

It takes the following layered approach:

Layer 1: Understanding Time series data and preparing (Quality) Data for Analytics

It is important to have an ‘Analytics view’ of a data and shift from statistics & monitoring view to KPI-driven analytics. The right data preparation (quantity, time period, structure, characteristics), transformation and scaling methods needs to be used for the desired outcome. A guided framework for data preparation, pre-processing and an appropriate solution/software, which make the quality data available is a key for successful data analytics.

Layer 2: Selecting Right Analytics & Validation Methods

Once the quality / conditioned data is available, applying right (data-driven) model selection, training and performance evaluation methods are important. It is driven by end objective and level of analytics under study (for e.g. Data clustering, Dimensionality reduction, Data visualization, Trend analysis, Process monitoring and fault diagnosis, Fault classification, Online soft sensing and Quality prediction, etc.). A guided framework, which enables the right selection and validation of data modeling methods is useful for embracing the data-driven and analytics culture across the board.

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Layer 3: Deploying Analytics Solution / Strategy

The ROA can be realized only if the data analytics solution is implemented in a production environment. Apart from deploying right methods / processes or a solution for data analytics, it is imperative to ‘Upskill’ the teams in various groups for successful deployment of data analytics. It needs a guided framework of Knowledge capture, transfer and collaboration, otherwise data analytics gets trapped into few teams and organization don’t realize the ROI on data investments. The Deployment of Analytics is a function of building Analytics culture all across the organization more than implementing solution/ software.

Learn how we implement Pilot-Guided Analytics through a PROOF OF VALUE (POV) PROGRAMME