Is not always so easy to make the best decisions, both strategic, like to redesign the supply chain, or operative like to deliver goods at min costs, or to define prices and promotions. Even more, sometimes it is not so clear what "best" really means. Often the outcomes depend on the sum of multiple-decisions and "multiple-bests" from different people within the organization.
As attested by ever-increasing evidence, an appropriate, data-driven and analytical decision-making approach enables better performances, a more robust vision of expected outcomes.
On the other hand, for organizations, it is not so easy to deeply introduce such techniques in their processes, but, it is an enthusiastic journey.
The competences of the team make the difference and details matter.
The following points give you a conceptual sequence of actions:
1) focus on the problem: are not the techniques the starting point but the issues, the goal the organization want to reach (for example cut logistics costs or reduce stock-outs, improve web-sales performances);
2) identify the potential levers you can use: which are the variables and the elements the organization can put in place (for example review supply chain, improve picking productivity, dynamic pricing to better shape the demand curve).
3) evaluate if the "to-be" solution, will require a change in the processes and change management;
4) depending on the level of confidence you have that the levers you identified will be corrected, implement the solution or pass through a validation phase. What to do it depends on the specific case.
5) remember: it is not only a question of software and features. Models behind such software have a dramatic importance as well as the quality of the available data, and how the decision-making process is re-engineered to lever the power of #Algorithms, #AI, #Simulation, and #PredictiveAnalytics.
6) Analytical experts should support the above steps, preferably since the early stage, that is people having a good experience on the application of models (math-optimization, AI, etc.) to real, often complex, cases. The view these people add has a high value reducing the risk of failure.
Author: Raffaele Maccioni - CEO at ACT Operations Research (ACT OR)