Update scenario.md

docs/manuals/core/concepts/scenario.md

@@ -1,28 +1,36 @@
-We introduce here the important concept of a `Scenario^`. A Taipy Scenario represents an instance of a
-business problem to solve on consistent data and parameter sets.
+Introducing the `Scenario`, a fundamental key concept in Taipy.
 
-As its name implies, with Taipy scenarios, the users can instantiate different versions of a business
-problem with different assumptions. This is extremely useful in a business context where impact analysis
-and what-if analysis are essential in the decision process.
+A Taipy *Scenario* represents a business problem with consistent data and parameters.
 
-After analyzing its first scenario, an end-user may be interested in modifying input
-data nodes (not the intermediate nor the output data nodes), re-running the same pipelines, and
-comparing the results with the previous run.
+Scenarios are a powerful tool to create different versions of a business problem under different
+assumptions. This is especially valuable for what-if analysis in decision-making processes,
+enabling users to create, store, edit, and execute multiple scenarios with various
+parameters within the same application.
 
-For this purpose, he needs to instantiate a second scenario, execute it and compare it with the
-first scenario. This process can be repeated across multiple scenarios.
+Once an initial scenario has been analyzed, users can modify the input data nodes (excluding
+the intermediates and output data nodes), rerun part of its tasks, and compare results.
 
+This involves instantiating a second scenario, changing the input data, executing it,
+and comparing the outcomes with the first scenario.
+
+This iterative process can be repeated across multiple scenarios, allowing for comprehensive
+exploration and analysis of different problem variations.
 
 !!! example "In the example"
 
-    Here, our scenario consists of the two pipelines described earlier. The external light blue box in the flowchart
-    below represents our scenario that contains both pipelines.
+    Here, our scenario consists of the two pipelines described earlier. The external light
+    blue box in the flowchart below represents our scenario that contains both pipelines.
 
     ![scenarios](../pic/scenarios.svg){ align=left }
 
-A scenario represents one instance of a business problem to solve. Each new business problem instance
-is represented by a new scenario. With Taipy, end-users can create, store, edit, and
-execute various scenarios within the same application.
+
+Two use-cases arise from the utilization of Taipy scenarios:
+
+### - Use case 1 :
+Each *scenario* represents a distinct instance of a business problem.
+
+With Taipy, end-users can create, store, edit, and execute various scenarios within the
+same application.
 
 !!! example
 
@@ -32,27 +40,30 @@ execute various scenarios within the same application.
 
     - then, based on that forecast, generate the planning for the production orders.
 
-    The end-user creates the first scenario for January. It must contain everything the end-user needs
-    to understand the January case, access input data, compute predictions, visualize our
-    forecast algorithm results, make production decisions, and publish the January production orders.
+    The end-user creates the first scenario for January. It must contain everything the
+    end-user needs to understand the January case, access input data, compute predictions,
+    visualize our forecast algorithm results, make production decisions, and publish the
+    January production orders.
 
-    Then the end-user creates another scenario for February using the new information provided for the
-    February period. And so on.
+    Then the end-user creates another scenario for February using the new information provided
+    for the February period. And so on.
 
-Two _scenarios_ can also represent the same instance of a business problem but with two different sets of
+### - Use case 2:
+Two *scenarios* represent the same business problem instance, but with different sets of
 assumptions.
 
 !!! example
 
-    The end-user would like to simulate the impact of our capacity data on production planning for the
-    February use case.
+    The end-user wants to simulate how the capacity data affects production planning for
+    the February situation.
 
-    The first scenario can forecast demand and compute production orders assuming a low capacity,
-    whereas the second assumes a higher capacity value.
+    In the first scenario, we forecast demand and calculate production orders based on a low
+    capacity assumption. In the second scenario, we assume a higher capacity value.
 
-    One can note that data scientists can also use scenarios. This is often referred to as ‘experiments’.
-    Scenarios are in fact, a generalization of experiments in such a way that both data sceintists
-    and end-users can finally use the same concept: the Scenario.
+    It's important to note that scenarios are not only useful for end-users, but also for
+    data scientists. They can use scenarios as experiments to test different hypotheses.
+    Essentially, scenarios provide a common concept that both data scientists and end-users
+    can utilize.
 
 
 [:material-arrow-right: The next section introduces the Cycle concept.](cycle.md)