Clients always want a quality job and to select a consultant, designer, contractor or tradesperson who will provide the level of quality specified. Unfortunately, using current tools, quality is often subjective and because poor quality can take time to reveal itself, often poor quality workers are as likely to succeed as good.
In the absence of objective data, quality firms struggle to differentiate themselves to clients. That leaves clients faced with subjective and often ambiguous information when they're trying to discriminate based on quality and engage those most suitable for their projects. This situation has led to a bad image of the industry from the outside and a combative attitude inside.
Government mandates, professional associations and institutes, industry schemes, and frameworks claim to help carry out robust due diligence to ensure stable businesses, a thriving industry, and assurances for construction clients. However, the recent case of Carillion shows, in the extreme, that the current approach simply isn’t working.
It’s simple — in an industry where information on past performance is unavailable or to complicated to be easily understood, great people and firms are forced to compete on even terms with even the worst performers. Current approaches don’t work because they are unable to utilise objective performance data to inform decision making.
Using blockchain and smart contracts within a project platform can change that.
A smart contract simply means that the terms of the contract, when met, create a record on a blockchain (shared ledger). By using a networked project platform requirements of the smart contracts can be codified and represented in the project platform as tasks, milestones and budget information. When these conditions are met the work is approved and so is payment and an immutable record is created.
How does that create a way for construction professionals to prove value for money and differentiate themselves in a commoditised market?
The answer is because the data collected is based on actual contractual performance, not word of mouth, not on if they seem like good guys, not because they were recommended. On the actual track record of work provided over time. This data can be used to produce a rating system based on Key Performance Indicators (KPI’s).
How might this might look in practice?
Painter A has won a smart contract which is reflected within the project platform as a set of tasks. Each task has the details of specification, programme, and price. These tasks are allocated to Painter A and can be tracked by the project manager who acts as the approver.
Painter A completes activities associated with a task to a level they believe meets the conditions in the contract. A request for approval is made on a mobile app and gets logged. The approver is notified and comes to inspect the work. If the work meets the contractual conditions then the work is approved and Painter A gets paid and a record of the successful delivery is created.
Any contractual workflow could be accommodated here. For example, the main contractor approves first then the client’s QS recommends to the contract administrator to approve — that all gets taken care of within the platform.
Painter A is a good painter and has a success rate for first-time approvals of 96%.
Painter B, on the other hand, isn’t so good and often has snags picked up as part of the approval process so his rating is 74%. Painter B’s score for requesting approval is also poor, often not requesting an inspection until after the programme date, so their score for that is 83%.
This creates a clear metric to be able to say that Painter A is better than Painter B.
Let’s put this into context and imagine a project to build a high-end hotel. The handover date must be hit for a big New Year’s Eve opening ceremony. If it’s missed the reputation damage and financial losses will far outweigh paying a premium for accelerated construction work.
In this case the client’s main driver is time. In order to de-risk the project only trades with a rating for hitting programme and quality of work of 95% and above will be eligible to tender for the work.
These trades charge more but that’s ok because they can prove, with objective data, that they provide value for money. The client is happy to pay a premium for a premium service because they are able to clearly see they are paying to de-risk the project.
Taking this data-driven approach will allow for construction business to develop a reputation, backed up by data, to differentiate themselves from their competition.
It could also provide the opportunity for clients to audit payment terms of the main contractor. That could go a long way to creating more transparent work practices, improving the reputation of the industry, and providing the industry with much needed financial stability.
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