An Insight or a forecast isn’t actionableA data driven performance overview, insight or forecast can be useful information but has little value. That’s because the outcomes of descriptive or predictive analytics are not actionable. Real value is only created when insights and forecast are used to make better decisions. This is exactly what prescriptive analytics, a.k.a. optimisation, offers. Given your objective(s), conditions and decision variables it will provide explicit recommendations to achieve the best possible outcome. The recommendation results from considering all possible solutions to your decision problem in a smart way, not just considering a few, and choosing the one that results in the best objective value while satisfying all conditions.
Many analytics overview charts put optimisation at the top or as final step in a process of growing in analytical maturity, suggesting that predictive and descriptive analytics are prerequisites to start with optimisation. This is not the case. There are many organisations successful in optimisation without the ability or the need to forecast. For example, hospitals optimise the utilisation of human capital by constructing optimal shift rosters for nurses and maximise the utilisation of their operating theatres without the ability to forecast. Similarly, delivery firms construct routes for their delivery vans to maximise vehicle utilisation and customer service while minimising cost per km.
Prescriptive analytics translates a business decision into a mathematical model and uses optimisation algorithms or simulation to find the best answer. With a mathematical model the analysis becomes repeatable and can be re-done quickly, for example re-optimising the production schedule when certain demand conditions change. This brings agility to an organisation and allows it to quickly adapt to new conditions. Also, a mathematical model solidifies knowledge of specialists which enables decision makers to use that knowledge and take action without having to be a specialist themselves.
The whole is better than the sum of parts
Some mathematical optimisation problems are easy, but many of them become unsolvable as they increase in size. This is called the combinatorial explosion, expressing that time to solve the problem grows exponentially in problem size. The size of the mathematical problem that can be solved is therefore depended on the computing power available. Luckily the speedup of computing power is tremendous, for example my 4 year old iPad 2 has the same computing power as a CRAY2 supercomputer from 1985. This speedup, together with the progress in algorithmic optimisation, gives us the ability to solve larger and more complex mathematical problems than 30 years ago. To illustrate, decision problems that would have taken 85 years or ~45 million minutes to solve on the hardware and algorithmic capabilities of 1988 can now be solved within 1 (!) minute. In the 80’s and 90’s large decision problem had to be broken up into smaller parts and solved separately. With the progress in technology we don’t need to break up models into smaller arts but can solve decision problems covering multiple departments in an organisation or across organisations in supply chains in one go. This holistic way of optimisation most certainly will lead to better decisions as more relevant conditions are taken into account and it will consider more possible solutions.
Optimisation delivers real valueAttention for optimisation is rising, Gartner and other analyst firms signal that attention for this technology is growing. Optimisation has been around for over 70 years and has proven its value many times. To illustrate, each year INFORMS organises a competition in which the best business applications of analytics compete for the Franz Edelman Award. As a former participant and winner, together with TNT Express, I can tell it’s a tough competition where only the best analytics practitioners have a chance to win. Illustrative for the value optimisation can bring is a graph that contains the benefits of the selected finalists of the Edelman competition. Measured since 1972 total benefits exceed $223 Billion! What is interesting is that the graph seems to level up a bit, indicating that the reported benefits are rising. The benefits from optimisation are not only monetary, it increases the agility of an organisation, allows for continuous improvement, stimulates knowledge sharing, and leads to changes that improve health, safety, cooperation, decision making, and job satisfaction.
Although prescriptive analytics is very powerful, it is no substitute for human brainpower, experience and or judgment. In the end a mathematical model is a simplified version of reality and can’t possible cover all aspects of a decision. In my experience the best results are achieved when decision makers are supported by prescriptive analytic models in their decision making. The results from Edelman finalist proof that. As complexity and speed of decision making is growing you need a better tool than just descriptive or predictive analytics. You need actionable insights, access to prescriptive modelling therefore is a must. So, what is keeping you?