Merge branch 'Pamela_Branch_4' into 'master'

Pamela's Merge Request: 10-26-2021

See merge request !45

models/Application_examples/Agile_Production_Platform_Customer_Interactions.mp

@@ -21,74 +21,8 @@ agile production / building of alternative supply chains.
 
 Specifically in this model, the focus is on the delivered 
 quality of 5,000 medical face shields (a Customer placed 
-the order using the APP). The narrative for this baseline 
-use case is as follows:
-
-Precondition: A Customer has an urgent need for 5,000 
-medical face shields.
-
-1.	Customer logs into the APP linkage platform.
- 
-2.	APP authenticates valid customer credentials and prompts the Customer for the need.
- 
-3.  Customer inputs a need for 5,000 units of a medical face shield with desired
-    attributes.
- 
-4.  APP identifies the individual components of the needed item.
- 
-5.  APP determines which Sector Resources to search for equivalent components (prepares
-    component search parameters).
- 
-6.  APP searches Sector Resources for available component matches and their attributes
-    (e.g., availability, timeliness, cost, reliability, quality, quantity).
- 
-7.  APP assembles combinations of available components having intersecting
-    attributes (find component matches).
- 
-8.  The APP constructs a list of valid supply chain elements.
- 
-8a. If the supply chain for the order is complete, the APP displays the order options to the
-    Customer along with soonest delivery time, total cost, and component attributes for
-    each participant in the chain (e.g., participant reputation, material specifications,
-    product dimensions, suitability for certification).
-    
-8b. Otherwise if one or more components is unavailable, the APP reprocesses the order with
-    a new search for equivalent alternatives to the missing component(s) (back to step 5).
- 
-9a. If the supply chain is complete, the Customer receives the order options from the APP
-    and filters, sorts, and inspects the presented solutions, selects a solution, and places
-    the order. 
-    
-    9a-1.  The order is delivered to the Customer and meets their expectations.(Favorable
-    Outcome)
-    9a-2.  The order is delivered to the Customer and does not meet their expectations.
-    (Unfavorable Outcome)
-    9a-3.  The order is not delivered to the Customer (Unfavorable Outcome).
-   
-9b. If no alternative supply chain can be found, the Customer is notified by the APP that the
-    order cannot be completed at this time. (Unfavorable Outcome)
- 
-Assumptions/Abstractions:
-The "Data for supply chain" event that is included in the APP and the Data Services/
-Resources root represents asynchronous and continuous data for support to the 
-alternative supply chain construction.  The individual interactions involved here
-are another level of refinement reserved for a separate MP model that can be elaborated
-separately from this model which focuses on the Customer-APP interactions.
-
-Known ways to get to an unfavorable outcome:
-- Customer asks for a disproportionately large amount of product and the system is strained
-for other users
-- Allow for aggregation
-
-Questions:
-- Does the APP prioritize orders?
-- Can a customer get orders with "some assembly required" or does it come assembled from
-some service provider? 
-
-Postcondition:  The Customer receives 5,000 face shields (not shown in the model yet).
-
-Running this model at Scope 1 generates 4 event traces, 
-described as follows:
+the order using the APP). Running this model at Scope 1 
+generates four event traces, described as follows:
 	Trace 1: The order is delivered to the Customer and 
 		meets their expectations (Favorable Outcome).
 	Trace 2: The order is delivered to the Customer and 
@@ -115,15 +49,15 @@ SAY statement; trace annotation; event sharing; table;
 behavior, unexpected; behavior, emergent; 
 behavior, agile production platform
 
-Instructions: Run for Scope 1.  Reviewers of this model are 
+Instructions: Run for Scope 1. Reviewers of this model are 
 invited to provide feedback for better-named events, other 
 events that may occur normally or as a disruption to the 
 sequence, missing or incorrect interactions, and the 
-partitioning of the high level APP functions for architectural 
-cohesion. Lower-level (implementation) activities have been 
-deliberately omitted for now in order to reach consensus on 
-intended use at the highest level of abstraction.  
-
+partitioning of the high level APP functions for 
+architectural cohesion. Lower-level (implementation) 
+activities have been deliberately omitted for now in order 
+to reach consensus on intended use at the highest level 
+of abstraction.
 	Scope 1: 4 traces in less than 1 sec.
 
 ==========================================================*/

models/Application_examples/Agile_Production_Platform_Supply_Chain_Interaction.mp

@@ -24,7 +24,7 @@ capacity even though the APP pooled more suppliers at all
 levels of production, so the order was not fulfilled (an 
 Unfavorable Outcome). (This involved imagining an event 
 change under the Customer from receive to did not receive
-product).  An example of Unexpected Emergent Behavior 
+product). An example of Unexpected Emergent Behavior 
 coming out of this model is shown in trace 36: a 
 nonconforming material still making its way to the customer, 
 either through a miscommunication or lack of inspection 

models/Application_examples/Correlation_and_Fusion_Process_with_Data_Governance.mp

@@ -9,18 +9,18 @@ governance framework.
 
 Description: Models the data flow from sensors all the way 
 to the human analyst at a tactical location incorporating 
-a data governance framework.  The AIS_Sensor represents an
+a data governance framework. The AIS_Sensor represents an
 example AIS GPS SENSOR; the Aggregation_Service represents 
 a local AIS Aggregator like a Radar system or local AIS 
 Broadcast; the Global_Distribution_Mechanism receives data 
 from multiple aggregation services, coalates the data, and 
-rebroadcasts the data globally.  Within the 
+rebroadcasts the data globally. Within the 
 Global_Distribution_Mechanism, Source A represents raw AIS 
 tracks that have not been modified in any way prior to 
 dissemination; Source B represents raw AIS tracks grouped 
 into geographic areas for limited broadcast; Source C 
 represents custom filtering and combining of AIS tracks 
-based on unique fields.  The Data Governance Framework 
+based on unique fields. The Data Governance Framework 
 applies an evaluation rubric to data sources and only allows 
 sources that are uncorrupted to be transmitted to the 
 correlator. The Data_Governance_Framework receives sources, 
@@ -39,7 +39,7 @@ Watson, Frank. "Design Methodologies for 21st Century
 Watson, Frank. "Model of Correlation and Fusion Process 
     Ungoverned." 20 August 2021. In "Design Methodologies 
     for 21st Century Battlefield Object Correlation and 
-    Fusion."  NPS Master's Thesis, Monterey, CA: September 
+    Fusion." NPS Master's Thesis, Monterey, CA: September 
     2021.
 
 Example 49. Histogram showing number of traces with 

models/Application_examples/Supply_Chain_with_Two_Cyber_Threats.mp

@@ -30,8 +30,8 @@ demonstrate calculating individual and total risk scores.
 Description: This model demonstrates performing detailed 
 risk analysis on a supply chain potentially affected by 
 two cyber threats. This is made possible by combining two 
-MP models that each contained one threat: a cyber attack on 
-the barge, and a cyber attack on the Colonial Pipeline. 
+MP models that each contained one threat: a cyber attack 
+on the barge, and a cyber attack on the Colonial Pipeline. 
 All three of these models can be found in (Palmieri 2021).
 
 This model first takes advantage of separate COORDINATE