Axpo:

Automating invoicing, reporting, and providers switching for a renewable energy company

Client:

Axpo

Location:

Poland & Switzerland

Industry:

Green energy (renewables and gas)

Team:

Tech Coordinator,
Senior RPA developer, Agile Coach,
Project Manager

Technology:

UiPath

Timeframe:

1 year (ongoing)

Axpo automation case study

6.18

FTEs saved

$186.000

of pure profit in Year One

147%

ROI generated

54.000

automated actions/month

+90%

average bot accuracy

Client

Axpo is Switzerland's largest producer of renewable energy and an international leader in energy trading and the marketing of solar, wind, and gas power.

The Challenge

We were recommended to Axpo as a process automation agency by our technology partner, UiPath - a leading RPA development platform. The UiPath team knew the projects we had been delivering before, and they knew that our team was a perfect fit for Axpo’s needs.

Axpo executives brought three well-defined business challenges to the table:

  1. They were growing fast, and their existing processes were not fit for the scale of the organization;
  2. Some of their workflows (in billing and switching departments) were executed manually, and their growing volumes made them impossible to process on time;
  3. There are various regulations in the energy markets they operate, so a need for constant compliance is crucial.

Initially, Axpo’s executives considered two RPA technologies - UiPath and Power Automate. Our strong recommendation was to stay with UiPath, as it performs better with complex processes and has a better orchestrator module.

Axpo also came with a backlog of process automation ideas. We validated them all during the workshop and proceeded with automation development.

Solution:

To solve Axpo’s challenges, we created an army of RPA bots for their billing and switching departments.

Here are two examples of the most critical processes that we have automated:

Sending the purchase summaries to partners

Axpo has around 1500 partners that buy energy from them. Due to legal reasons, all of them must be sent an annual purchase summary before the set deadline. Before our engagement, all summaries had been sent manually. 

We have built an RPA bot that would:

  1. automatically log into various distribution network operator (DNO) systems
  2. extract the energy usage data 
  3. Fill out the summary and calculate the usage costs
  4. Create a report in an Excel file form
  5. Send the file to the Partner for approval

Automation outcome: 27 days of manual work delivered in one day.

Business outcome: faster work, eliminating process bottlenecks, no more missed deadlines, and less employee stress.

Issuing invoices and invoice corrections

Invoicing is one of the most time-consuming processes in Axpo’s billing department, with over 20K transactions being invoiced monthly via their CRM system.

Before our engagement, billing specialists would manually look for clients in the systems and issue invoices.

We have built an RPA bot that:

  1. Access the Power BI database and send an inquiry about the clients who are ready to be invoiced. 
  2. Log into the Axpo CRM and find the right clients
  3. Generate the invoice

Invoice corrections work with the same logic as invoicing. A bot accesses the database, sends inquiries about invoices that need to be corrected, logs into the CRM, and generates correct invoices.

Automation outcomes: 600+ transactions automated daily (20K/month)

Business outcomes: elimination of manual work, faster and more accurate invoicing, faster payment cycle.

The Outcome

After automating the first batch of processes, we saved Axpo the work equivalent of 6.18 FTEs with projected savings of $186.000 over one year.

We continue working with Axpo’s back office teams, identifying new processes and adding new RPA bots to the network.

Axpo, thank you for your trust!

Looking to automate processes in your energy company? Discover what we can do for you!

6.18

FTEs saved

$186.000

of pure profit in Year One

147%

ROI generated

54.000

automated actions/month

+90%

average bot accuracy

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