Client & Context
Raymonds , A long-established Indian textile and lifestyle manufacturing group operates fully integrated textile businesses spanning spinning, weaving, processing, and finishing.
Within the group, textile manufacturing represents a critical economic driver, where energy cost per unit output directly influences margin performance across high-volume, continuous operations.
The organization runs large, continuous textile manufacturing facilities in Western India, producing significant volumes of fabric and finished goods for both domestic and export markets. Given the energy-intensive nature of textile processing, sustained energy performance is central to operational competitiveness.
Business Problem
Energy consumption was visible, but Specific Energy Consumption (SEC) behaviour was unpredictable and inconsistent across shifts and production schedules.
Monitoring systems captured total energy usage, but they did not explain why energy intensity varied even when production plans and volumes were comparable. SEC fluctuations were influenced not only by production throughput but also by fixed baseloads, idle consumption, machine start stop behaviour, and how utilities like compressed air and humidification operated in relation to the process.
The plants lacked a framework to decompose SEC into structural versus behavioural components, making it difficult to identify whether variances were controllable, such as idle behaviour, or inherent to production. Without this insight, improvement initiatives lacked focus and operational teams could not intervene effectively during runs.
Approach
Greenovative was engaged to introduce an SEC intelligence layer that reframed how energy performance was interpreted.
Rather than collecting data for dashboards, the platform aligned energy and production information at a granular level and applied behaviour based modelling to quantify the relationship between output and energy use. This decomposition separated consumption into:
• Fixed baseload energy that did not vary with output
• Production linked variable energy
• Idle and non productive energy that could be influenced through operational practices
Energy production alignment allowed plant teams to see how specific behaviours, such as idle machinery, warm up periods, or utilities running independently of process demand, contributed to SEC variability.
Utilities such as compressed air and humidification were analysed in context with production output rather than as separate systems, revealing their latent impact on SEC even when throughput remained constant.
Results / Impact
Within a few months of deployment, the SEC intelligence translated into measurable, quantifiable outcomes at both Amravati and Jalgaon.
• Specific Energy Consumption improved by 3 to 5 percent across comparable production runs and shifts
• 10 to 15 percent of total energy consumption was identified as non productive or idle driven, creating clear, actionable control levers for operations teams
• The plants achieved a low single digit percentage improvement in energy cost, driven entirely by operational discipline rather than capital investment
• SEC variability across shifts reduced materially, enabling a more stable and predictable energy cost per unit produced
Most importantly, energy interventions shifted from post period analysis to intra shift control, allowing teams to correct deviations while production was still underway.
Strategic Insight
In continuous textile manufacturing, energy performance cannot be governed by monitoring alone. The transition from visibility to understanding, especially of how energy intensity relates to production and utility interaction, is the real value driver.
Raymond’s experience demonstrated that decomposing energy behaviour makes SEC actionable, enabling teams to stabilise energy intensity through operational levers rather than relying solely on asset upgrades. By institutionalising SEC intelligence, Raymond built a foundation for consistent, predictable energy cost performance across plants, shifts, and product mixes, converting energy from a lagging indicator into an operational management metric.
