“Powerful prescriptive analytics are not possible without artificial intelligence (AI) and machine learning (ML).”
With the orange juice example, not only does this discipline analyze what is likely to happen, but the technology also provides concrete instructions or suggested solutions.
Schwenk’s colleague Axel Oppermann, head of the analyst firm Avispador, considers the technology to be “decisive for the war.”. “All companies, all those in charge who ask themselves, ‘What should I do,’ that is, who actively pursue future and business planning, benefit from the technologies and thinking patterns of prescriptive analytics,” Oppermann emphasizes.
A second, major reason for the strong demand for prescriptive analytics is that these technologies are mutually beneficial for other trendy digital developments. “Just as analytics evolves along these lines, they need support like machine learning, which can expose patterns in the data and continually build knowledge over time to predict problems and take the necessary action,” explains Schwenk. In other words, powerful prescriptive analytics are not possible without artificial intelligence (AI) and machine learning (ML). And, conversely, the two technologies only gain momentum and their special value through analysis.
In the future, the software will say: “Have 30 heads of lettuce on hand. This way you won’t run out and won’t have a lot left over.”
An investigation from last year shows just how realistic Oppermann’s assessment is. The costs of managing bottlenecks in the power grid today can already be reduced by more than 200 million euros per year. This was the result of an interdisciplinary working group led by the German Energy Agency and the Office for Energy Management and Technical Planning – and initiated by the Federal Ministry for Economic Affairs and Energy. Recently, it has cost almost one billion euros every year to avoid bottlenecks in the German power grid and to ensure system stability. Given the world’s ambitious targets for renewable energies, it is therefore more than understandable that prescriptive analytics will also play a major role here. Climate data and consumption parameters are included in the calculations, on the basis of which sound recommendations for energy management can then be made.
Another example comes from the procurement of raw materials. The experts at the Technical University of Munich found that data-driven optimization approaches with prescriptive analytics produce valid procurement strategies. For example, these industries can significantly reduce both the operating risk due to price fluctuations and their procurement costs. In this way, companies could achieve natural gas purchase prices up to 11 percent cheaper on average instead of relying on traditional cash transactions or futures.
Ultimately, companies benefit from prescriptive analytics across the board. And, according to industry experts, fast food chains are currently working on having the analysis tools independently suggest new recipes and burger ideas. So maybe big data on the menu will become reality within a few years – who knows.