5 Ways AI can Advance your Social Housing's Repairs & Maintenance
askporter’s automated support and communication platform is an important step towards adopting intelligent AI into your social housing organisation. askporter's AI streamlines communication with residents by using intelligently friendly chatbots to gather diagnostics, triage, and allocate tasks - reducing misunderstandings and delays, and freeing help desk staff to focus on critical issues.
This is no more evident than in repairs and maintenance operations. Well-developed and flexible AI creates value for social housing landlords and their suppliers by removing barriers; ultimately speeding up the flow of information between residents and housing managers for an improved service delivery.
To fully realise the value of AI, organisations must take the next step: To understand where a self-learning algorithm can design better outcomes for the planners and field operatives tasked with servicing your residents.
In this post, we introduce five ways to extend AI into your field service delivery, and invite you to meet askporter’s ecosystem partner FLS – FAST LEAN SMART. FLS supports housing customers across the UK and Europe, ranging from small teams of 50 to large organisations with thousands of field operatives - between them maintaining over 500,000 units.
FLS is the technology leader for real-time field service scheduling and dynamic route planning. The award-winning FLS VISITOUR field service management platform is built on the unique PowerOpt algorithm - driving digital transformation, boosting productivity, and resident engagement to new heights.
1. Self-Service AI to generate an Accurate Diagnosis
When something goes wrong, such as the unwelcome experience of ‘no hot water’, residents cannot be expected to act as subject experts. At the same time, the average cost of six call centre workers talking through a fault for 10 minutes across 50,000 annual appointments can add around £145,500 to an organisation’s bottom line.
askporter’s multichannel outreach options (including chatbots, calls, WhatsApp, and SMS channels) can support your residents to diagnose, self resolve issues themselves, or triage issues with the right information to the right engineer, 24/7. By asking the right question at the right time, validation processes can identify and escalate to an appropriate level of assistance. As well as compliance and resident satisfaction KPIs, a standardised job order removes the risk of inconsistencies, or missing information.
This data is key to AI-powered field service scheduling. With increasing demand and technical skills shortages across all specialisms, it’s critical that your field technicians and engineers’ orders are dynamically planned to maximise a first-time-fix. Operatives need sufficient time, skills and materials, and knowledge and reliable data supports transparent decision-making throughout scheduling. Housing providers that combine clean data with dynamic field scheduling improve their capacity management and efficiency; reducing the use of sub-contractors and repeat visits.
2. AI-optimised Field Appointment Suggestions
What is an optimised field appointment, and how does scheduling with AI benefit residents and housing providers?
Scheduling involves balancing costs, SLAs, and KPIs with data such as a realistic fix window and skills and parts allocation. Integrating askporter with FLS VISITOUR scheduling optimisation greatly reduces the risk of 'no-access' and other common appointment failure issues. FLS VISITOUR removes batch and overnight processing and instead considers the entire mobile workforces’ activities for any given day. The algorithm, coupled with self-learning, designs schedules at a speed and accuracy greater than humanly possible. It will only propose optimised appointment options, and on average leads to 30% more jobs completed with the existing mobile workforce.
residents provided accurate appointment choices are more likely to make allowances to fit around their lifestyle, such as work schedules. With embedded travel routes, residents must no longer suffer with AM/PM proposals, and are sent reminders, enhancing satisfaction and reducing cost risks for planners by minimising unnecessary field service downtime and driving. residents can reschedule at their convenience to another cost-optimised appointment proposal.
3. AI to recognise Time-of-Day Travel Time
Inspection, installation, repairs and maintenance: How do you calculate your travel time? Do you plan site visits with ‘white space’ left in the schedules for driving between orders?
AI can remove planning pressures and design your field schedules with embedded routes that group orders into entire tours. This means there’s no longer a reliance on guesswork for the order in which they are to be completed, and how to react if a ticket is over-running. FLS VISITOUR calculates appointment proposals with realistic and precise time-of-day travel speed profiles, leading to an average 20% less travel time, fuel use, and CO2 production.
Separated route planning (simply from A to B to C) neglects the domino effect that a service request can create throughout the entire schedule. For example, FLS’ self-learning algorithm can eliminate vehicle idling in rush-hour traffic by discounting road size or static legal speed limits and instead using actual driving speeds. This creates a significant opportunity to enhance productivity and efficiency. Optimisation lowers fuel spend and promotes job completion outcomes.
4. AI to support an Emergency Response
askporter’s countless intelligent interactions produce high-value data for housing managers to make decisions with transparency. The necessity for same-day/emergency response repairs and maintenance risks the benefits of this transparency – through unassigned costs and a lack of up-front approvals. Housing providers are not only legally required to rectify in-day emergencies. When dealing with homes, they undertake a human-level responsibility to the communities they manage. This makes it tempting to immediately send the ‘closest’ field engineer or surveyor and this creates waste.
Extending the use of AI into responsive field operations means attending emergency callouts sooner, whilst controlling costs and providing a remedy more quickly: authentic optimisation. With the continuous optimisation power of FLS VISITOUR, dispatchers can clearly understand why the scheduling and dispatch algorithm has produced its suggestion.
In-day management handles events outside of a provider’s control, through balancing the countless parameters that designed the schedule in the first place. Examples include in-day restrictions such as available skills, geocoded locations, and parts available. It then adds traffic and driving time data and favours removing hard borders (service patches) for an overlapping radius. This clever use of intelligent AI supports all parties at all times; the resident in knowing the right skills are en-route, and the head office in complete visibility both during and after the event.
5. AI to keep Planned Maintenance compliant
In an inflationary environment, housing providers may feel compelled to cut resources and delay planned repairs and maintenance, prioritising reactive or emergency appointments instead. New rules, such as damp and mould inspections and carbon monoxide testing come at a time when DLOs and contractors find difficulties in retaining skilled workers (and heightened pressures to turn around Voids). Delaying planned work can damage resident satisfaction by pushing aside ‘lower priority’ events.
Overall, assets can depreciate, and complaints about housing disrepairs climb. This leads to an escalation where planned and preventative maintenance grows into reactive appointments.
Scheduling with AI integrates data in real time from askporter, Housing Management Systems (HMS) and CRM, to optimise for the best use of resources. Assisted scheduling puts the power into planners hands to offer a balance between the increased efficiency of fully automated dynamic scheduling and the intuition of an experienced dispatch agent.
Another way to use AI scheduling to meet your obligations is an integration to your Enterprise Resource Planning (ERP). ERP might know the cost of field employees, but cannot account for the true cost of operating a field service, and what can be learned from the data this generates. This includes management insights, where data can be extracted to identify holistic trends.
Replacing intuition and guesswork with comprehensive data analysis increases your organisational advantages: Enhanced strategic planning and cost savings for efficiency gains. Flagging failing assets, better customer insights, self-learning job durations and impacts – all examples that grow into a holistic evaluation of your field service with data sourced directly from FLS VISITOUR – and the opportunity to maintain productivity.
Want to discuss how askporter's AI and FLS scheduling can improve your repairs and maintenance processes - get in touch.
