Prioritization & Triage
Prioritization is the centerpiece of the Auguri engine – the engine operates by identifying a hypothetical “ideal” solution, and prioritizing alternatives based on distance from the ideal. This can be effectively carried out regardless of the number of relevant criteria involved.
The traditional answers to today’s optimization challenges are found through an organization’s Operational Research division. Typically, these solutions are effective in optimizing around a single objective. For example, an airport may attempt to minimize waiting times at security checkpoints; a car manufacturer may want to minimize production costs while maintaining a 4-star safety rating; a computer manufacturer may want to maximize CPU speed while keeping production costs under a certain limit.
Reality and experience demonstrate that for many problems, optimizing around a single objective is simply inadequate. In real-world scenarios, there is rarely only one factor, or criteria, that we care about. Some factors may be more important than others, but generally we would prefer to optimize with regard to a number of priorities.
For example, modern GPS devices should ideally take a number of considerations into account when determining an optimized route from A to B. Route length, traffic conditions, estimated duration, weather conditions, and neighborhood safety may all be relevant factors in the optimization process.
Procurement is another example where multiple priorities must coexist within an optimization scheme – buyers generally are not concerned exclusively with price or quality, but rather a combination of the two. This is a reality which current RFX and Reverse Auction strategies struggle to address effectively.
Auguri provides a framework which can handle multi-objective optimization effectively.
It allows users to (i) identify and define the set of alternatives, (ii) define the various factors that impact the decision, (iii) determine the way these factors behave and impact the decision, (iv) set the weights of the various factors to reflect their relative importance.
By doing so the decision maker defines an ideal (optimal) solution in a multivariate system. Ultimately, each alternative is compared to the ideal solution, leading Auguri to recommend the alternatives that are closest to the ideal even if none of them are an exact match with the ideal.
There are many examples of prioritization and optimization scenarios where the Auguri engine is applicable. A few prominent examples are listed below:
Emergency Response Prioritization
When responding to a distress calls triggered by a weather change, the Coast guard needs to optimize their response. If for example they receive a call from the Queen Elizabeth with 2000 passengers on board and a low distress level (leaking skull) and a distress call from a fishing boat with 20 person on board with a high distress level (sinking). Auguri is the only solution that would allow them to trade-off the level of distress with the number of lives at risk. To compound the problem if you know that the fishing boat is 8 miles away from shore while the Queen Elizabeth is 30 miles away and that the chances of success of the operation is 95% for the dingy and 56% for the Queen Elizabeth, the necessity of a sophisticated real time decision support and prioritization solution that supports tradeoffs between several criteria emerges as the preferred solution.
CDC Outbreak Response
When health risks and outbreaks emerge, a large organization like the CDC must prioritize such outbreaks to determine which locations require the most urgent attention, or who to treat first, or which strategy is most effective. This prioritization must take a number of factors into account, including the number of people affected, whether or not an outbreak is contagious, treatability, public opinion, financial risk, etc. These prioritizations must be made quickly, as days and even hours of delay may jeopardize lives. Auguri can negotiate tradeoffs between each of these factors in real-time while leveraging expert know how collected in the system..
Few devices handle the safety of as many individuals, or demand more precision, coordination, and collaboration than an airplane. Airplanes are built from millions of parts which must work together seamlessly, and many of these parts demand unique maintenance schedules. Add to this that maintenance prioritization is also influenced by other factors, like worker schedules, new part prices and availability, and flight schedules, and things suddenly become quite complex. How should airplane maintenance be prioritized? The necessity of a tool which can handle multiple tradeoffs and knowledge collaboration efficiently is very clear.
In the financial world, investment decisions must be made in time-sensitive environments, and take a number of factors into account. Some factors are financial benchmarks which indicate the speculative value of a stock or option – values like price per share, PE ratio, dividend growth rate, net value, etc. Other factors may be more personal, like risk tolerance, desired liquidity, and long-term financial goals for a particular account. To base an investment decision on any one of these factors in isolation would be ridiculous; ultimately, all of these factors must be considered and analyzed simultaneously. Auguri provides a platform and engine which can perform sophisticated tradeoff analysis in real-time.