knowledge graph

what are our knowledge graphs?

They are powerful visual and interactive representations of a network of data connections and relationship. Our software – uniquely – can drill down to the chemical/molecular level of any material used in a production process.

They provide an exceptional method to analyse and model complex real-world relationships in large data sets (unstructured and structured data sets) and enable options, permutations and similarities to be evaluated in a truly dynamic way, and at a level previously impossible.

They also allows us to very effectively communicate and measure these aspects, and to explore multiple alternatives for viability, cost and environmental savings.

challenges of conventional LCA software

benefits of Ardhi LCA software

Time consuming inventory data collection process

Inventory collection becomes automated with integration of existing enterprise data system

Time consuming modelling

Quick LCA modelling once data is plugged in for an organization

Need to connect with multiple teams to identify the materials used, energy usage, waste generation, etc.

The system will have the details and hence needed less connect with multiple people

Current LCA software act as an interface for database and has no information of upstream or related flows

In Ardhi LCA all the upstream activates can be visualized and its related downstream use cases are shown

No transparency for the inflows and outflows - only software user can see what is used for modelling

High level of transparency for the flows - any person seeing the visualization can understand all the minute processes

Material or process alternatives cannot be seen

Material or process alternatives can be visualized

No warnings or error message for old database usages. Validity year is mentioned in documentation, but no referencing while modelling.

Timeline feature limits the use of old databases and highlights the usage of expired data or EPDs

Limitation of visualizing the different scenarios in a single modelling or window

During design phase timeline feature helps the visualize different scenarios and understand its impacts

Environmental impact matrices are non-customizable

Customizable environmental impact matrices can be developed. e.g. ECI

Highest impacting routes of a product system cannot be identified

Shortest path feature - can highlight the path of the most environmental impacting items in an LCA product system

Current LCA software’s cannot involve different stakeholders in the product design phase

Ardhi LCA visualization and its shortest path feature can be well used in new production planning or new design phases

fully integrated software

With conventional LCA software, the use of advanced software techniques such as machine learning require the export of data to a separate machine learning software platform and for data scientists to create models and algorithms, for example to identify similarities across material types and recommend alternatives to reduce carbon.

Our LCA software utilises an integrated machine learning framework and pre-build models to reduce the time, cost and complexity. It provides configurable dashboards which show evidenced-based reporting in a clear and understandable way. Objectivity and evidence – No more greenwashing!

get in touch to see our software in action

request a demo

the Environmental Cost Indicator (ECI)

This is a single-score indicator expressed in cost. It unites all relevant environmental impacts into a single score of environmental costs, representing the environmental shadow price of the project.

monetisation of data and analytics is hard

To what extent is your organisation currently generating measurable economic benefit from your data?

Source: 2019 Gartner CDO Survey
Respondents: 293

we can help you connect carbon reporting to revenue.

Data monetisation type 1

Using data to improve internal processes and/or sharing data internally and/or eternally.

Data monetisation type 2

Using data to improve the value of existing offerings and/or exchanging data insights internally for rewards.

Data monetisation type 3

Selling or licensing data for cash directly and/or through brokers or marketplaces.