How Process Intelligence for Sustainability Makes Claims
Trace sustainability figures from reports to the process events behind them.
Why does operational data belong in a sustainability report?
Process intelligence for sustainability connects a reported environmental measure to the process activities and source events used to calculate it.
A report may state that a process produced a certain amount of CO₂ or waste. To explain what that figure means, you need its scope, calculation method, and supporting data.
An event log records which activities occurred, when they occurred, and which case they belonged to. When you include environmental values in the dataset, you can examine those values by case, process variant, or reporting period.
That gives you a traceable path from a report to its evidence: the measure, the activity-level values, and the source events that identify the cases and activities included. The claim remains limited to the quality and coverage of those inputs.
Sustainability is one of the few goals that outranks the rest. We want to hand our children a world that is still healthy to live in. If you are working on sustainability with ProcessMind, get in touch and tell us what your data showed.
Where can process data help with environmental measures?
Process data can help you find patterns to investigate, then track relevant outcomes when suitable environmental data is available.
It does not reduce environmental impact by itself. It shows how work happened and can help you see where resource use, rework, transport, or waiting may be associated with process activities.
For example, you can compare process variants and examine whether differences in activity patterns align with differences in recorded energy or material values. Any conclusion depends on the data and method behind those values.
Resource use and energy
If your dataset includes energy use by activity, you can compare cases and process variants to see where recorded consumption differs.
A process log may show that some cases spend longer in a machine-related activity. That pattern can help you investigate whether operating time, idle time, or a process variation is associated with higher energy use.
The event log records when an activity occurred. It does not measure electricity use unless that information is included in your data or derived through a documented calculation.
Rework and waste
Repeated activities can show where cases return to an earlier step, which may indicate rework.
You can count those repetitions and compare the process paths involved. If reliable material or waste values are linked to the activities, you can also compare the recorded amounts.
A repeated activity is evidence of repetition, not proof of a specific quantity of material waste.
Transport and waiting
Process data can reveal handoffs, delays, and long gaps between activities. Those patterns may help you identify where goods or people wait, or where additional movement may occur.
To quantify travel emissions, you need relevant distance, transport mode, and emissions data. A process log alone cannot establish those values.
How do you calculate CO₂ per case?
You can calculate CO₂ per case by adding the activity-level CO₂ values assigned to the events in each case, within a defined process scope and reporting period.
Start with a dataset that identifies the process events and includes the environmental values you want to analyze. A useful structure includes:
| Field | What it records |
|---|---|
| Case ID | The process instance the event belongs to |
| Activity | The process step recorded in the event |
| Timestamp | When the event occurred |
| CO₂ value | The emissions value assigned to the activity |
| CO₂ unit | The unit used, such as kg CO₂e |
| Source or method | Where the value came from or how it was calculated |
Keep the unit and calculation method consistent. If some activities have no value, document how you handle them. Do not treat missing values as zero without a reason.
Worked example: trace a case-level figure
Suppose case C-104 has three recorded activities, each with an activity-level CO₂ estimate in the dataset:
| Case ID | Activity | CO₂ value | Source |
|---|---|---|---|
| C-104 | Activity A | 0.4 kg CO₂e | Activity-level estimate |
| C-104 | Activity B | 0.7 kg CO₂e | Activity-level estimate |
| C-104 | Activity C | 0.3 kg CO₂e | Activity-level estimate |
The values add up to 1.4 kg CO₂e for this case. You can trace the total to the three source events for C-104, inspect their timestamps and activities, and review the source or method recorded for each value.
This is an illustrative calculation, not a measured ProcessMind customer result. In a report, state the process scope, reporting period, unit, data source, and whether the activity values are measured or estimated.
ProcessMind lets you configure dataset fields for CO₂ impact. Your fields and source data determine what you can calculate; the platform does not independently measure emissions. See the dataset configuration documentation to prepare the relevant fields.
How do you make an ESG claim auditable?
An auditable claim defines its measure, scope, reporting period, unit, included activities, and method for assigning environmental values.
Keep the reported result connected to the data behind it. Retain case identifiers and source events so you can review which activities contributed to the figure.
Record the source or calculation method for each environmental value, and assign an owner who can explain the data and its assumptions. Track changes to field definitions, mappings, and calculation rules.
A dashboard can make results easier to review, but it does not replace the supporting information. Distinguish measured values from estimates, and describe limitations that affect interpretation. You can put the result on a dashboard to review it alongside the process data.
What can process intelligence not tell you?
Process intelligence analyzes the data you provide; it is not a meter or a life cycle assessment (LCA).
Event data can show which activities were recorded and when. It cannot independently verify energy use, material quantities, travel distances, or emissions factors.
A CO₂ figure is only as reliable as its inputs and method. If activity-level values are estimates, the result is an estimate. If the dataset omits part of the process, the result covers only the included scope. Incomplete source events limit traceability.
Keep environmental statements factual and specific to the evidence. Process analysis can help you identify where to investigate and track changes over time when relevant data is available. It does not, by itself, establish that your organization has achieved a broader environmental target.
How do you get started with process intelligence for sustainability?
Start with one process and one sustainability measure you already need to report, then check whether your data can support the calculation.
Define the scope and reporting period. Confirm that your dataset includes the case, activity, timestamp, and environmental fields you need, and document the unit and calculation method.
Use the dataset configuration documentation to plan the fields. For a wider view of how process intelligence can support continuous improvement, see the process improvement techniques guide.
Where to Go From Here
You have one reported figure and the dataset behind it. The next step is to configure the environmental fields, trace a result back to its source events, and review it on a dashboard.
