About Us
About Stiperstone Analytics
Industrial experience. Sustainable purpose. Responsible innovation.
Stiperstone Analytics Ltd is an engineering-led consultancy focused on improving the operational, financial and environmental performance of asset-intensive industries.
Our experience spans chemical engineering, industrial operations, operational information systems, analytics and decision support. We use that experience to help organisations reduce energy losses, emissions, waste, off-spec production and downtime while improving reliability, yield and profitability.
Our purpose is straightforward: to help industrial organisations make better use of their existing knowledge, information and operational experience to achieve measurable and sustainable improvement.
Why Stiperstone Analytics Ltd exists
Industrial organisations have accumulated vast amounts of operational information and engineering knowledge. Yet the evidence required to make a good decision is often distributed across control systems, operating procedures, reports, maintenance records, spreadsheets, engineering documents and the experience of individual employees.
This fragmentation makes it difficult to understand why performance is being lost, which opportunities should be addressed first and whether previous lessons are being consistently applied.
Stiperstone Analytics Ltd was established to help close this gap. We combine engineering understanding with structured analysis so that information can be converted into practical action and sustained operational improvement.
A passion for energy and emissions improvement
We believe that better-run industrial operations are usually more sustainable operations.
Inefficient equipment, unstable processes, unplanned outages, excessive product giveaway and avoidable operating events consume additional energy and materials. They also increase emissions, waste, operating cost and production risk.
Our particular areas of interest include:
- Reducing avoidable energy losses
- Improving the performance of energy-intensive equipment
- Reducing flaring and other emissions-related events
- Minimising waste, rework and off-spec production
- Improving yield and material recovery
- Reducing unplanned outages and downtime
- Extending the intervals between energy-intensive activities
- Establishing reliable baselines for measuring improvement
We focus on operational changes that create both commercial and environmental value. Sustainability is therefore treated as part of plant performance, not as a separate reporting exercise.
Mapping improvement to the UN Sustainable Development Goals
Where relevant, we maps measured operational improvements to applicable United Nations Sustainable Development Goals, particularly those relating to energy efficiency, sustainable industry, responsible production and climate action.
The SDGs provide a recognised framework for communicating contribution and impact. Our primary focus remains on verified project outcomes such as energy saved, emissions avoided, waste reduced, improved resource efficiency and downtime prevented.
This communicates the differentiator without explaining your full mapping methodology.
Controlled AI for engineering decision support
Industrial organisations hold valuable knowledge across procedures, standards, engineering reports, maintenance histories, investigations and the experience of their people. We help organisations explore how this information can be organised into a governed engineering and operational knowledge base with controlled AI access.
The objective is to make approved knowledge easier to find and apply while maintaining appropriate permissions, source traceability, human oversight and established engineering governance. AI supports informed decision-making; it does not replace professional judgement or operational approval processes.
Experience developed through industrial change
Our experience began when industrial information was largely confined to control rooms and individual systems. Engineers often spent considerable time manually gathering and preparing information before any analysis could begin.
We subsequently worked with plant historians, operational-intelligence environments, enterprise information solutions and increasingly accessible analytical tools. This has provided a practical understanding of both the possibilities and the limitations of industrial information.
The tools have changed considerably, but the underlying challenge has remained consistent:
How can people obtain trusted, contextual information when they need it and use it to make a better decision?
That question continues to shape Stiperstone Analytics approach to sustainable operations, knowledge management and responsible AI-enhanced decision support.
How we think
Engineering first
Recommendations must make sense within the physical, operational and commercial realities of the plant.
Measurable improvement
Claims should be supported by agreed baselines and credible operational, financial and environmental measures.
Technology with purpose
Systems and analytical tools are selected according to the problem being solved, rather than treated as objectives in themselves.
Responsible innovation
AI and advanced analytics should be introduced with appropriate validation, governance, traceability and human oversight.
Sustained value
An improvement is only valuable if it can be implemented, adopted and maintained through normal operating practice.
Our Shropshire roots
Stiperstone Analytics takes its name from the Stiperstones ridge in the Shropshire Hills near the company’s origins in Shrewsbury.
Its rugged quartzite outcrops create a distinctive pattern across the landscape, resembling the irregular profiles often found in industrial operating trends. The name reflects both our local roots and our interest in finding meaning within complex operational evidence.
Better knowledge. Better decisions. More sustainable operations.
Stiperstone Analytics helps industrial organisations connect engineering experience, operational evidence and responsible innovation to reduce losses and improve performance.
Copyright © 2026 Stiperstone Analytics Limited
Acknowledgements:
https://www.flaticon.com/authors/freepik
Photos by Chromatograph / Crystal Kwok / Tim J / John
Cameron / Martin Adams / Chris Liverani on Unsplash