We are living in exciting and fast changing times. Technology has changed the way we live, and we view ourselves and manage our businesses. This changing environment also impacts the decision-making process inside our companies. Delay of the adaption of what we call "analytical-management approach" might have a high hidden cost, like loss of market share and profits. Only those companies that can find value in their data will prosper in the future. Still using only spreadsheets to run your business?
It's not only the data availability to make the difference but how processes are re-designed or re-invented, how your data leads to the best actions and decisions, to a higher risk tolerance.
This concept is valid for a wide range of contexts and industries, from the optimization of prices to the resource planning to the optimal design of products, to the early detection of diseases and the optimal-personalized supply of drugs to patients.
The benefit of a structural adaption of an analytical-management approach concerns profitability and effectiveness. To be considered that another hidden but precious benefit, comes from the gradual evolution of the culture inside the organizations. People learn how to use data as fuel to feed a human-machine collaborative decision-making process and, at the same time, learn from the numerical outcome if this process.
At ACT Operations Research (ACT OR) we have more than 20 years of experience in delivery of decision models and algorithm, based solutions, helping our customers to enhance their performances significantly.
Based on this experience I can say that, it is not always complex but often neither that easy, to apply the analytical approach to the management decision making. As a manager, you don't need to be an operations research specialist or a statistic professional.
What we recommend for modern, analytical based decision making is:
- a good trust and management leadership, that such approaches, when correctly applied, lead to a good ROI and an improving your cultural processes for your staff to make better decisions;
- the awareness that experienced modeling and data science specialists are a fundamental pillar to help to define the "what" and manage "the how";
- an open-mind to change the way your organization manages the processes. In some cases, you also need to reshape the responsibility dimensions of the different teams cooperating on a certain process;
- understand that even though, any analytical model is a "piece of software" running somewhere, the drivers to define the requirements and to manage the project is not classical method used to introduce others type of software;
- start with a simple problem but work on the long-term vision. Do not excessively stress the timeline of the first few projects because your organization will not be able to follow and, probably, the data you have will not be as good as supposed to be, to feed analytical engines.
Author: Raffaele Maccioni - CEO at ACT OR