Based on a report of new officers and staff who require access to the DMS application, a digital worker creates accounts with various privileges for the users, based on their position within the force. After creating the account profiles, including new passwords for the accounts, the digital worker will provide the users with their account details and information about how to log in with their username and password.

This automation has enhanced user experience and enabled faster response to a greater number of requests.

Upon joining the force, new officers fill out an online form created via Oleeo. The output of this form is generated into an Excel spreadsheet that’s shared with the digital worker via email. Provided all the required fields are completed within the form, the digital worker can key in all the information into the relevant SAP transactions to onboard the employees’ information into the application for payroll purposes.

This automation has improved the time it takes for new staff to be on-boarded onto the force systems. Implementing this automation has helped reduce the workload of this repetitive task from the HR team and has improved the quality of data being entered into the SAP tool.

Linking unlinked phone numbers/e-mail addresses within Niche to pre-existing records.

This automation will automatically link known information to pre-existing records taking the manual load away from officers.

Referring victims of a crime to relevant unit to receive victim support. This automation will save both officer time and improve victim support - currently not all victims are referred for support due to capacity issues.

Officers order uniform in assyst and these orders are then automatically raised as shopping carts in SAP. This automation will free up BOU team for VA activities as well as speed up request fulfilment time.

Uploading redacted and non redacted Storm log reports from 999/101 calls into the related occurrence in Niche. This automatio nwill free up Comms support time (small team) as well as improve timely upload of all reports.

Automatic linking of victims as witnesses within an occurrence to ensure MG11 statement is sent with the case file to CPS. If they are not linked as witnesses, the MG11 (key witness statement) is not sent with case file to CPS and case will be rejected. This automation will ensure all victims of a crime appear in the occurrence tab and will not be rejected.

Deletion of all blank linked and orphan entities within Niche. Blank linked entities will result in case file rejection when two way interface between Niche and CPS is implemented as they are invalid entities within case file. Profiles can appear within occurrences that have no information but are linked to something. By deleting orphan entities (have a name but no linked information) from Niche this automation improves data quality by removing duplicates.

Merging duplicate phone numbers within Niche. This automation helps by improving data quality and removing phone number duplicates within Niche to assist linking.

Occurrences must have an attached MO for it to be used successfully in court. The information required for the MO is often misheld in the Summary field and an MO is not created. This automation removes the manual task for the officer as it is automatically done.

Automatic reseting of SAP, PNC, and ANPR passwords when requested by officers/staff. Immediate password reseting to prevent staff/officers being out of systems and freeing up service desk to focus on VA activities. Frees up officers waiting on phone for password to be reset.

This automation removes the onerous manual task of raising a ticket and then waiting for the IT service desk to action.

Searching in QAS to determine whether applicant is registered at current address and then searching in Experian to run a financial check. Any traces are then captured and uploaded into Corevet for researcher review.

This automation frees up researcher time to solely conduct review and speeds up vetting process in general.

Searching Pentip to locate any minor/driving offences. Offence details are then captured and entered into comments field in Corevet for researcher review. This automation also frees up researcher time to solely conduct reviews.

Once provided with Niche ID number, all information relating to the individual will be downloaded from Niche and uploaded into Corevet for Vetting Review. This automation speeds up vetting process in general.

Automatic completion of officer requests to receive audio of 999/101 calls to help in their enquiries. This automation ensures more timely sending of recordings to officers and free up of lightly resourced Comms support team (currently one member is responsible for this completeing this job as main workoad all week). Overnight, only 1 supervisor completes this, resulting in officers waiting with custody suspects for calls to help with case.

 

Creating Storm information markers on properties/phone numbers when requested by officers. This automation frees up Comms Support team time - currently this is bulk of their workload and is very manual.

Business Objects produces a report daily of all cases that hit VCOP referrals criteria but haven't been referred. This automation is able to reduce this list by about ~40% by detecting duplicates or cases without victims, as well as detecting any duplicates existant in the LSU task tray. However, manual judgement is still required to make the referral for the remaining 60%.

All crimes must be classified within 24 hours. This automation creates a Business Objects report which will provide a list of theft related crimes, their occurrence and the classification code.

A scheduled process running daily to detect if any duplicate tasks exist in the LSU task tray. If found, one task with the highest priority is retained, while the remaining duplicates are closed. Priority is measured by task type first, then the following methods if tasks have matching types: Priority level listed on the task, the amount of comments in the task and when the task was submitted. This automation speeds up tracking of tasks.

RPA controller processes carry out daily checks in order to ensure all RPAs are in good health. This automation ensures a good state across the board.

SAP tasks, substitutions & notifications remain on a users SAP account after they have left the force. If these are not cleared the related personal data will be accessible by the next employee who acquires the same collar number (minimum 3 months before a collar can be re used). This automation ensures a daily leavers report is created which will serve as a trigger to clear a users SAP account details one week (or older) after their leave date.

Niche occurrences with a specific crime classification and a victim age 17 or under (at date of offense) also need to have CSA/E qualifiers applied to the occurrence. Doing so ensures key information for child sexual abuse/exploitation victims is correctly recorded. This automation ensures a BO report of occurrences needing these qualifiers. All occurrences on the BO report will need CSA qualifier, with a sheet that also identifies Cyber Crime.

Between the dates of Nov 1st and Jan 31st, officers (constables and sergeants) can submit a request that specifies what days they would like to be their bank holidays for the coming year (it does not have to be statutory). Up to 900 people submit changes every year. They can amend these change as many times as they would like within the date range previously specified, and we previously experience roughly ~10% resubmissions. This data will be displayed in SAP and DMS. This automation ensures faster back office speeds, no need for manual forms or double checking by support staff.

When new officers join the force they will fill out an online form created via Oleeo. The output of this will be generated into an Excel spreadsheet which will be shared with the digital worker via email. Provided all the required fields are completed within the form, PO13 and PA30 SAP transactions to onboard the new employees information into SAP for Payroll purposes. This automation removes onerous tasks from support staff, freeing up officers for more worthy tasks.

A daily report is created for all off-boarding force members and emailed to the digital worker. Force members’ personal data is then cleared from their SAP tasks, substitutions and notifications, to ensure that the next officer who acquires the same collar number does not have access to the previous employee’s personal information.

This automation helps the force ensure adherence to their service-level agreements (SLAs) and eliminates the risk of breaching any General Data Protection Regulation (GDPR) rules due to residual data being left after a force member leaves.

A Qlik Sense report is run to identify duplicate tasks in the load store unit (LSU)task tray in Niche. The digital worker exports this report and scores each item based on a ruleset laid out by the LSU. The highest scoring task will be left open for action by LSU, while the digital worker closes down the remaining duplicates.

This automation has reduced the workload and time per case by removing unwanted duplicated tasks.

Shopping carts are created by the end user. Any shopping carts for an amount of £250 or lower will send an approval request to the digital worker’s task list in the SAP portal. The digital worker will search for any pending items in the SAP portal task list and approve the items.

This automation can help enhance user experience and reduce the time between users raising a shopping cart and receiving their goods. The shopping cart approval can help reduce the workload of different users across the police force.

A business object report is run to identify any address entities in Niche marked as single use. The digital worker will check the address entry for any existing links to an occurrence or individuals. If no links are found, the digital worker will delete the address record from the application.

This automation will reduce the number of unwanted records held in the Niche database and will assist with data cleansing and accuracy.

Telephone entities entered into Niche currently go through no quality assurance process and are often created as an unverified single use entity. This process would review all single-use (SU) telephone entities, review their format (such as digit count or area code) to ensure they are an appropriate number and then remove their SU status.

This automation has improved the telephone data accuracy within Niche and prevented sending incorrect data to the Home Office.

The digital worker searches the PNC ID in the application and automatically adds all offence summaries as a comment to CoreVet. Additionally, intelligence data will be printed and uploaded as a PDF to CoreVet.

This automation is part of a strategic force solution to help provide additional support to the vetting team, streamline the data-harvesting aspect of cases and seek to improve the operational efficiency of the overall vetting process.

The digital worker searches the PND ID provided in the application and exports any attachments (such as crimes, custody, intelligence and so on) to a PDF, ready to be uploaded to CoreVet.

This automation is part of a strategic force solution to help provide additional support to the Vetting team, streamline the data harvesting aspect of cases and seek to improve the operational efficiency of the overall Vetting process.