Field service reporting using an offline voice app

Suez chose the VDK to create an on-device mobile voice application for its deskless workforce to produce field reports with improved speed and data quality.

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11/2021

Voice dictation for field reports

Embedded app in smartphone

Automatic Speech Recognition

In a nutshell…

Suez is a leading corporation in the field of water and waste management in France and worldwide. Suez France launched an innovation project to reinvent the tools provided to its employees in order to improve the collected data quality. In this context, Smile and Vivoka worked together to voice-enable the mobile application used to create work reports.

Challenge & Requirements

Improving the collected data quality from field services reporting

Different problems were identified regarding reporting creation. Gaps in writing skills, environment conditions and existing tool ergonomics were impacting the data quality when technicians were doing their reports.

Letter & Digits Recognition

A large majority of the content technicians need to report are references that contains letters and digits. They are used to identify specific devices and give metrics about their status.

Application & Service Reliability

Technicians from Suez are often working on particularly hard conditions, in remote places. With such a vast area of action, network quality and stability cannot be taken as granted.

Solution & Specifications

Embedded professional voice dictation engine running on the company’s already-existing mobile application

About the speech recognition system itself

The ASR engine is based on a dynamic grammar to understand industry-specific vocabulary as well as multiple word-alternatives per items. In addition to the focused speech recognition, we added FreeSpeech capabilities so users could naturally transcribe long messages.

Focus on end-users' experience

+1000 technicians from Suez are the expected end-users of this voice update. The voice AI is seamlessly integrated inside the current UX and UI of the mobile app.

Seamlessly embedded inside the company's existing app

Speech recognition technology involved in this project is embedded inside mobile devices that are carried by technicians. The technology runs on Android 6.0 (API 23) and above. It doesn’t require specific modification to the device, the native microphone is totally adapted.

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Use cases & Workflows

We gathered some of the app’s user scenarios

User Scenario: Meter Replacement

1) User – The index of the deposited meter: “The index number is “xxxx“.
2) System – “Indication by the machine of the current meter number”: “Please confirm the number xxxx“.
User – “Yes/No” confirmation by the employee
> If “Yes“, the procedure continues.
> If “No“, “System” asks to spell out – User: the collaborator spells out.
Resume step 3.

3) User – Announcement of the new index of the installed meter: “The index of the installed meter is 0″.
User – Announcement if a non-return valve is present: “I have installed a non-return valve“.

– Possible text said by the User :
“I changed a water meter, the index of the installed meter is 0“
“I have changed a water meter, the index of the removed meter is 34458 M3“
“I have just renewed the meter, the removal index is 7899 M3, I have installed a check valve“

User Scenario: Metering Station Leakage

1) User – Indication of the part concerned by the leak: “The part to be repaired is “xxxx”.
(warning, the parts database must be filled in beforehand, the listing must be provided).

2) User – Indication of the replacement part: “The part “xxxx” has replaced the part “xxxx“.
> The possibility to indicate if the employee to use a pump is possible.

3) User – The index of the deposited meter: “The number of the index is “xxxx”.

4) System – “Indication by the machine of the current meter number“: “Please confirm the number xxxx“.
User – “Yes/No” confirmation by the employee
> If “Yes“, the procedure continues.
> If “No“, “System” asks to spell out – User: the collaborator spells out.
Resume step 3.

– Possible text said by the User :
“I have changed a water meter, the removal index is 34458 M3.”
“I fixed the leak, it was just a problem of a seal to renew at the meter.”
“I have just renewed the meter, the removal index is 7899 M3, I have installed a non-return valve.”

User Scenario: BAC Re-connection

1) User – The meter index on day D: “The index number is “xxxx“.

2) System – “Indication by the machine of the number of the current personnel number”: “Please confirm the number xxxx“.
User – “Yes/No” confirmation by the employee
> If “Yes“, the procedure continues.
> If “No“, “System” asks to spell out – User: the collaborator spells out.
Resume step 3.

3) System – The machine asks if a counter was in place: “Was a counter in place?“
> If “Yes“, the procedure continues.
> If “No“, “System“.

4) User – The re-connection to the BAC or to the meter is given by the employee: “I have re-connected the BAC connection.“

5) User – Procedure of the new meter installed (seen in Use Case 1 and 2)
> Possibility to add if the employee has used a pump to evacuate the water or not.

– Possible text said by the User :
“I changed a water meter, the removal index is 34458 M3.”
“The customer has water, I put the connection back on the BAC.”
“I have put the service line back into service, the meter reading is 12998 M3.”

Benefits and Improvements

What value does voice AI bring to field service reporting?

Customer satisfaction through data quality

Voice commands make it possible to standardize the data quality of the created reports. No missing informations, exhaustive and accurate data are increasing the service quality that clients are experiencing, thus increasing satisfaction.

Enhanced technician workforce engagement

Before going to a digital application, technicians were using paper to create their report. The mobile app helped with employees comfort and ease of training. This is where voice is taking another step ahead with intuitive interactions and hands-free use.

Improved productivity and competitiveness

In this project that concerns short work order (<30min) on which report take a lot of time, the expected time savings are important, 10 to 45 minutes a day, per user, on the current forecast. Voice recognition is on average 5 times faster than manual typing.

Robust, resilient and independent voice service

Network coverage is an important issue when working in remote areas and technical sites. Most of the technicians from Suez are not sure of the service availability when they operate. This is why embedded technology was used to work anywhere, anytime.