A big part of an engineer task is to define meaningful measurable goals that are possible to work towards (i.e. S.M.A.R.T goals). From reliability tasks, performance improvements, as well as Data Science processes.
How to hold accountability inside an organisation is a different problem that has yet to be solved. For that, maybe blockchain tech. can be a tool to align everyone from day one.
After the initial hype, reality sinks in an tries to shift focus of any process change. Holding people and organisations accountable it is as important as any other technical process. Specially when dealing with several suppliers or partner organisations.
What if we could link the reliability performance of a physical asset with an incentive system based on blockchain tech.?
Or even better, what about if we could link a measurement agreed upon (i.e. key performance indicators, reliability plan, preventive maintenance plan, efficiency improvements, etc.) with governance systems like DAO (Decentralized Autonomous Organization).
Achieving a link between performance of a physical asset with an incentive system (game theory) for all parties.
Reliability professionals like Fred Schenkelberg advocate to sit down management and specialists and define what reliability means for them. Have a common understanding of what a failure means, so we can take action when it occurs, rather than to ignore it or dismiss it.
For example, one action that can be agreed upon might be the classification of a failure. Another the trigger of an specific analysis. Other actions might be review of the maintenance strategy, trigger corrective or preventive maintenance, etc.
Being able to follow the process leaving very small room to interpretations, gives transparency to what all parties are agreeing upon. This will help to lower the risk when audit by any financial institution that is lending money to the business. Reducing risk on a credit worthiness, rating process is another use case or WACC.
It is literally, like programming in a “computer” a legal contract.
In academia there are proponents of reproducible research. This is to avoid wrong scientific studies and conclusions. Ensuring that the right decisions are taken due to the right data is equally important. Being able to backtrace past decisions is even more important.
2. Data Quality & Penalties
Need to have some penalties in case you stop feeding the input data to the “contract” or feed “bad data”. Need to define measures for all participants in terms of quality, as well as in case they decide to step out of the agreements. For example, pull the plug on the input data streaming necessary to execute the computer programs or falsify them.
This can also be regarded as a way out of this type of incentive system for another one, like a legacy agreement. In this case, whom and when trigger the exit of the agreement is recorded and clear.
3. Eliminate Inefficiencies
Eliminate all intermediation processes and contractual legal battles with regards the interpretation or consequences of the results. Things are agreed upon, and in case the conditions need to be modified afterwards the DAO structure will need to agree upon.
The technology that is necessary to implement this kind of ideas is being developed right now. From smart contracts to the programming language necessary to implement them.
Cryptocurrency and blockchain technology in general is evolving very fast, therefore we should expect more plug & play solutions in the near future. For example is already possible to create smart contracts for creating and managing a decentralised organisation, contractor payment or manage the cap table in the Ethereum blockchain, thanks to initiatives like ARAGON.
This is true in business models not design for marginal revenue (brownfield). It will be smart for big organisations in this area to start to adapt to this new reality that is coming before disruption occur.