Electronics manufacturers sitting on untapped data goldmines can drive revenue growth by breaking down operational silos and applying strategic analytics frameworks.
While sensors generate terabytes of data from production lines and quality systems that store data of good and defective material, most organisations cannot transform this scattered data into usable information for strategic advantage, leaving profits on the table that competitors are beginning to claim.
Why invest in analytical data?
Large manufacturing enterprises run complex, end-to-end operations—from raw-material sourcing and processing plants to warehouse storage, distribution centres, and logistics partners. Each operation generates a large volume of data.
The solution is not a revolutionary technology; it is simply about connecting previously isolated data streams and analysing air temperature, water temperature, and pipe conditions alongside production outcomes. Over time, this analytical framework generates value, suggesting total returns exceeding half a billion dollars.
Companies sitting on years of unused historical data, quality metrics, and operational information can, when properly analysed, reveal optimisation opportunities worth millions.
The five-step ROI framework every decision maker needs
To implement data analysis successfully, investments must follow a predictable pattern across electronics manufacturing:
Scenario creation
Quantify specific problems by analysing costs, production inefficiencies, and quality failures, with precise financial impact measurements.
Data integration
Connect all isolated systems (SCADA, MES, PLC, historian databases) that currently operate as information silos.
SCADA provides a real-time view of pumps, valves, and temperature sensors; MES tracks every production order and quality check as it happens; PLCs drive machine logic, mixing, conveying, and heating; while historian databases archive all data points with timestamps. Pulling these together turns isolated signals into insights needed to boost yield, cut scrap, and schedule maintenance proactively.
Predictive modelling
Build models that can analyse data to forecast outcomes rather than just reporting historical performance.
Resource optimisation
Use forecasts to create precise schedules. Instead of firefighting breakdowns with whoever is free and available for parts, the right technician is scheduled, correct spare parts are staged at the work cell, and shift mixes are aligned with predicted demand—eliminating scramble and keeping costs under control.
Continuous refinement
Establish feedback loops to compare forecasted outcomes with actual results, tweak model parameters, and feed in the newest data. Continuous refinement improves accuracy over time.
Organisations that have implemented this framework for over a decade report average cost reductions of 15-25% in first-year operations, primarily through reduced warranty claims, optimised maintenance scheduling, and improved production yield.
How piling up data drains profits?
A massive 20-year dataset from a flight carrier, containing 2.5 million flight hours of aircraft performance data, shows electronics components face dramatically different operating conditions across markets.
Working with enterprises from Apple’s supply chain to aerospace systems reveals a consistent crisis: organisations possess vast analytical potential trapped in fragmented systems. This fragmentation costs the electronics industry billions annually due to component failures in electric vehicles, with warranty claims averaging $2000-$5000 per incident.
| It does not matter if an enterprise is small or has little to no historical data; it is time for SMEs and medium-scale enterprises also to adopt this technology, learning from the successes of large enterprises. Do not reinvent the wheel. Take baby steps: start with a pilot on one line, then scale systematically. Organisations can grow rapidly and exponentially while always anchoring back to a clear business objective, rather than chasing shortcuts. |
Immediate action for supply chain leaders
The first step with this piled-up data is to audit all datasets and make inventory of all systems generating operational data. Most organisations discover 40-60% more usable data than they initially estimated.
Calculate poor quality costs
Quantify current expenses from scrap, rework, and warranty claims. This typically represents 8–12% of revenue for electronics manufacturers.
Identify integration opportunities
Start with production and quality data integration, as it is usually the fastest path to measurable ROI.
Analytics should go beyond traditional metrics
Electronics manufacturing has long relied on statistical process control methods—X-bar and R-bar charts, control limits, and variance analysis. These approaches work for stable and controlled environments, but usually break down under modern complexities.
Consider automotive electronics suppliers serving diverse markets. Components performing flawlessly in German testing facilities may behave differently when deployed in extreme climates, from Kashmir’s cold to Chennai’s heat. Traditional quality control cannot account for such environmental variables, leading to field failures that integrated analytics could predict and prevent.
How should a company adapt to change?
The most significant change is organisational, not technological. Chief Data Officers did not exist a decade ago, but now occupy board-level positions because analytics directly impacts key business metrics, production yield, quality enhancements, and warranty cost reductions.
A myopic vision is insufficient. A limited data set or isolated efforts will fail. The IoT program must evolve over time, bringing in design engineering, supply chain, and product management teams. Integration and a long-term vision are critical for success.
This article is based on the session titled Advanced Analytical Innovation for Electronics IoT Program Success Using AI-ML and Event Stream Processing, delivered by Arvind Kumar Prasad, Founder & Director of Vortexa Technology Advisors, at IEW25 in Bengaluru. The article was transcribed and curated by Janarthana Krishna Venkatesan, journalist at EFY.
