Developing a robust strategy has never been easy. High business complexity, market dynamics and shorter planning horizons create uncertainty and make it harder to make the right decisions at the right time. The half-life of a strategy becomes shorter, the importance of a strategy increases. The call for more agility in strategic planning is understandable. A lever lies in a new handling of the already existing strategy data estimates. Machine learning methods are developing into useful service providers of the "strategic command center".
Many companies are already successfully using intelligent data analysis and evaluation for decision support in purchasing, production or warehousing. Patterns are discovered, outliers identified and possible developments predicted as a basis for future scenarios via the frequent and structured availability of large amounts of data from the past. Extended statistics and machine learning make this possible and the practical successes speak for this "digital consultant".
The strategy process and strategic planning are among the essential drivers of a business. Despite their central importance, the use of these "opaque" methods to support decision-making is still in its infancy.
The basic prerequisites for this are good:
In practice, it is not uncommon for the strategy process to pass through a more complex business for a total of 30 to 50 planning units annually in varying granularity. Let us assume that a business unit consists of several submarkets (e. g. market segments, application fields). Within each planning unit, data is often collected for further segmentation dimensions (e. g. region, products, customers). This collection has a comparable data structure and common denominators. This produces a wealth of new quantitative and qualitative information every year, which can be actively used to gain knowledge.
The hypothesis is that there are common qualitative and quantitative denominators across planning units with rolling, recurring updates:
Compared to other machine-learning applications, our strategy work involves relatively small amounts of data and a manageable number of time series points. It is manageable for the machine, but already difficult for the human being to grasp its entirety and to process it further for knowledge gains. The right methods applied in the right form open new doors of "strategic knowledge" for us.
Machine Learning and Artificial Intelligence address one of the core questions of corporate management: How do we obtain information from data and action-guiding knowledge from it? Today we do not suffer from a lack of data or strategy information. On the contrary. Due to the wealth of data available and the complexity of our business, we reach practical and mental limits in order to generate relevant and action-guiding knowledge.
Machine-learning approaches will help us to overcome some of the limitations and to use existing empirical data even smarter for better decisions:
Intelligent algorithms provide us with a change of perspective and a new approach to answering relevant questions of strategic control and planning.
A few examples:
Good answers to these questions consider the individual planning unit as well as the cross-sections across several units through different "cuts" in the planning dimensions (e. g. through regions, product groups, application fields) and planning variables (e. g. turnover, profit, volume). The dimension time and the rolling data update are taken into account in the form of plan values and the actual data. Qualitative and quantitative findings are "partners at eye level" and confirm each other or question each other for reasons.
Every year, many of the actors involved provide our organisations with quantitative and high-quality strategy information in considerable quantities and in various formats.
Conserving this for the organisation is right and good. To actively use this hidden treasure of information seems even smarter and is practically possible. The company will wonder what it knows!
This article is the first part of the publication series Strategic Insights. Ronald Herse will give you more exciting insights into the world of strategy and data in the future.