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projectOne of the innovative streams that MoMRAH has followed was the "Risk Based Inspection" using AI and ML to direct this massive operation, that is afterwards changed into a whole Analytics centre to carry on the culture of analysis and raising the leadership vision to deliver data-driven decisions.
This information was sourced from "Analytics and AI Solutions for Risk Based Inspections" by Observatory of Public Sector Innovation (OECD), https://oecd-opsi.org/innovations/analytics-and-ai-solutions-for-risk-based-inspections/. Licensed under Creative Commons Attribution-ShareAlike 3.0 IGO (CC BY-SA 3.0 IGO).
If we take the sheer size of the above-mentioned mandate and challenges, we realize the reflection on the massive operation in order to inspect, and if excellence is aimed, structural innovation is needed to change the rules of the game and to inspect based on risk and incentive the compliant. One of the innovative streams that MoMRAH has followed was the "Risk Based Inspection" using AI and ML to direct this massive operation, that is afterwards changed into a whole Analytics centre to carry on the culture of analysis and raising the leadership vision to deliver data-driven decisions.
In a closer prospective, MoMRAH introduced a mobile platform called Baladi, that opened the e-participation to report municipal requests and incidents, which through it has been receiving growing number of reports that follows the growth of the platform adoption reaching to +3mn annual cases in 2021. Half of that, +1.5 counts for a major national campaign to fight visual pollution, which puts enormous pressure on the operation arm of the ministry, municipalities. Hence, MoMRAH, represented by the Digital Transformation and Smart Cities Deputyship, formed a small team, later called "ClearVision", of internal and external resources, to target Analysis and Solutioning using Advance Analytics and AI to tackle this challenge.
Fast forwarding, the team focused on solving pains of Inspection, specifically, on Visual Pollution. Following a root cause analysis and a prioritization exercise of business pains, the team followed an agile 5-step methodology of: Ideate, Source, Develop and Test, Create Output, Deploy and Scale. For the below-detailed solutions, there were three main problems in inspection in order to optimize dispatching: no visibility of location-near and similar cases, prioritization is fixed and static, with almost absence of proactive inspection. Those problems were addressed with the following AI Engines, amongst other various CX and procedural solutions; Case Clustering, Case Prioritization, and Risk Based Dispatching.
Each one of them targets to tackle a problem;
These solutions, were novel to the sector, and different from any usual "software" development witnessed before. Moreover, in order to scale and implement throughout the value chain in order to integrate into the daily inspection activities, it took altogether around 9 months with a steep learning curve, and many changes to data quality, procedures, and even organization structure; adding new departments to own the ideation and another to host and operate the models, a third to handle the smart operations.
With each engine tackled a problem, all were weaved together, as the Clustering providing up to 30% savings in inspectors visits, Case Clustering showing the 20% of the cases that requires imminent response, and Risk Based Dispatching helps identifying 1200+ focused areas.
That said, that was the seed for embodying the Risk Based Inspection concept, followed by undergoing expanding the same tools into Retail, Health, Excavation, and other 4 types of inspection. Moreover, introducing new tools such as inspector demand forecasting, and more.
Municipal operation in the kingdom always has been on static basis, or first in first out, never dynamic, or learning through historical cases or feedback loops coming from actual visits. Therefore, there was no much use of data, no data-based root cause analysis or solutioning.
Here comes the innovation we made around utilizing the plethora of data repositories MoMRAH sits on, where ClearVision provided;
The innovation has fully gone through the stages of development, from discovery and prioritizations, ideation and design, developing and testing, buy ins and business acceptance, implementation and full scale and process adaption. With a few months of reaping initial results benefits. Currently, the project is scaled up, and the innovation is in transition between next steps of full rollout and diffusing lessons and replicating the learning and expanding the concepts.
Collaborators to this innovation were private sector, government officials, inspectors, and citizens. Each of them played a role, where private sector provided technical capabilities, government officials share problem and procedures and championing, inspectors shared the on-ground expertise, and the citizens were the main source of data and the main final beneficiaries.
Government officials (business owners) have a way better visibility on the incident's hot spots, priorities, and most important areas. Inspectors, have better optimization of their time and output. Citizens, receive a better response to their most important problems, and minimizing their need to reporting though pre-emptive capturing.
Results of the innovation at hand is on the overall aspects of the operations of inspection, clustering is providing an average of 25% clustered cases that in great sense is saved trips. Also, the RBD was the only and main tool to help verify the results on an independent priority areas definition. On the other hand, Case Prioritization shows initial results where to identify the top quartile that the business wants to responds faster to, and the brackets are fully flexible.
The learning curve coming with this innovation is steep. Out of which, it worth highlighting two main challenges; Roles and Responsibilities in the scale up, and structural challenges.
For roles and responsibilities, since this is solutioning from scratch utilizing new concepts, therefore, by nature is different from the current software development value chain, it was hard to have internal teams to own their usual part of this new entrant. Resistance was present, until we identified team members that have higher inclination to deal with such solution, and developed a clear RACI matrix.
For structural challenges, handling models, in terms of quality checking, maintenance, and daily operations was challenging since it requires different set of capabilities that are not present. Therefore, we used external parties to QC and verify the models with another third party, and now, we are creating an internal team for technical matters, and smart operations to handle the pipeline of models.
For such novel ideas we believe it requires; Pressing topics, championing, agility mindset.
The concept has started with the visual pollution related activities. Since this is now maturing, we are expanding the same concept to all other CRM and Inspection activities. In more details, we are:
Which leaves us with more than 14 engines and we are introducing two master engines to work cross classification as well.
Three main lessons; Construct agile teams and assure openness, engage with clarity, and pave the role for industrialization. Creation of new concept can be frustrating for both teams and management; therefore, it is crucial to construct our teams well, and assure openness for discussing hurdles and support needed to make things happen.
Engage with clarity about responsibilities, once you have an MVP or so to have better planning and prioritization, as much as possible to minimize gaps of communication in such a cross-function innovation that is easily missed engaging critical teams, and by the time you reach to technical development or so, it would be quite late to engage.
Create with expansion in mind, lay procedure and transfer knowledge, to establish sustainability and growth, in this example, we laid the grounds for industrialization, now the new models, while more than double the number of engines, altogether, is taking almost half the time.
Budget: Not disclosed
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