code references added

README.md

@@ -12,7 +12,7 @@ On the other hand scalability or non functional requirements are different for d
 
 To accommodate to those differences, separate architectural patterns are applied:
 
-![Command Query CRUD Responsibility Segregation](command-query-crud.png)
+![Command Query CRUD Responsibility Segregation](doc/command-query-crud.png)
 
 **Simple Create Read Update Delete functionality** are exposed with leverage of CRUD framework.
 
@@ -76,7 +76,7 @@ Examples in code:
 Only the most valuable part of that enterprise software is embedded in hexagonal architecture -
 complex business processing modeled in form of the Domain Model.
 
-![Domain Model embedded in hexagonal architecture](hexagon.png)
+![Domain Model embedded in hexagonal architecture](doc/hexagon.png)
 
 **Application Services** - providing entry point to Domain Model functionality,
 Application Services are ports for Primary / Driving Adapters like RESTfull endpoints.
@@ -103,13 +103,13 @@ with **Model Exploration Whirlpool** and build **Ubiquitous Language** with your
 Adding infrastructure and technology later is easy thanks to Hexagonal Architecture.
 
 Simply starting from ZERO business knowledge through initial domain and opportunity exploration with **Big Picture Event Storming**:
-![Big Picture Event Storming](es-big-picture-original.jpg)
+![Big Picture Event Storming](doc/es-big-picture-original.jpg)
 
 after cleaning and trimming initial model to most valuable and needed areas: 
-![Big Picture Event Storming](es-big-picture-cleaned.jpg)
+![Big Picture Event Storming](doc/es-big-picture-cleaned.jpg)
 
 Deep dive in **Demand Forecasting** sub-domain with **Design Level Event Storming**:
-![Design Level Event Storming - Demand Forecasting](es-design-demand-forecasting.jpg)
+![Design Level Event Storming - Demand Forecasting](doc/es-design-demand-forecasting.jpg)
 
 is excellent canvas to cooperative exploration of:
 - impacted and required actors,

doc/demand-forecasting/README.md

@@ -0,0 +1,104 @@
+# Demand forecasting
+Demand forecasting refers to predicting future the sales of the product. 
+
+Demand forecasting, enables organization to take various business decision on operational level, such as:
+- production planning,
+- purchasing of materials,
+- shortage prediction,
+- product pricing,
+- inventory optimization,
+- business risk management.
+
+But to benefit from being demand-driven, demanded values need to be predicted, collected and kept accurate according to dynamic conditions.
+
+There is plenty of demand predicting approaches:
+- Qualitative approaches:
+  - Judgemental (surveys, specialist opinion, consensus methods)
+  - Experimental (test marketing, customer buying database, customer panels)
+- Quantitative:
+  - Relationship (econometric models, life cycle models, input-output models)
+  - Time Series (moving averages, exponential smoothing, threshold methods, GARCH, TBATS, adaptive models)
+- Scenario Modeling: refining quantitative forecast with specific adjustments reflecting upcoming situation.
+
+## Continuous demand forecasting
+Continuous demand forecasting refers to automated,
+continuous prediction no high granularity like demand for product-day-location.
+Continuous calculation involve predictive modeling,
+algorithmic modeling, pattern identification, scenario modeling and simulations
+by combining selected predicting approaches depending on demands mathematical properties.
+![Continuous demand forecasting](continuous-forecasting.gif)
+
+Review example [**demand forecasting
 expertise**](demand-forecasting-expertise.R) leveraging FactoryAnalytics and R-language.
+Expertise is generating pdf document while fetching and posting data to REST endpoints of **demand-forecasting** bounded context.
+
+Interactions with **demand-forecasting** bounded context can be found in source code:
+- External event **demand forecasting
 expertise** is handled by port
+  [DemandService.process(Document)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+- External event **forecast recalculated** is handled by port
+  [DemandService.process(Document)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+- Command **adjust demand** is handled by port
+  [DemandService.adjust(AdjustDemand)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+- Read model **stock forecast** is calculated on query time by
+  [StockForecastQuery.get(RefNoId)](../../app-monolith/src/main/java/io/dddbyexamples/factory/stock/forecast/StockForecastQuery.java)
+
+## Framework agreement and call‐off orders
+Demands information can be collected based on framework agreement and call‐off orders.
+In case of high volume production of low value consumables or materials,
+an framework agreement with customer specifies contracted demand (on annual, quarterly or monthly granularity).
+In addition call‐off order’s contains commitment values in short period of time
+with higher granularity like product-day-location or product-week-location.
+Call-off orders and be actualized continuous even on daily basis
+replacing need for in house continuous demand forecasting.
+![Framework agreement and call‐off orders](call-of-orders.gif)
+
+Review example **call-of order** document:
+
+![Call‐off order](call-of-order.png)
+
+Example document contains demand forecast for two products
+- 5 weeks ahead daily demand for first
+- 2 weeks ahead daily demand for second
+and some weekly demands aggregates.
+
+Interactions with **demand-forecasting** bounded context can be found in source code:
+- External event **framework agreement signed** is handled by port
+  [DemandService.process(Document)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+- External event **call-of order** is handled by port
+  [DemandService.process(Document)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+- Command **adjust demand** is handled by port
+  [DemandService.adjust(AdjustDemand)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+- Read model **stock forecast** is calculated on query time by
+  [StockForecastQuery.get(RefNoId)](../../app-monolith/src/main/java/io/dddbyexamples/factory/stock/forecast/StockForecastQuery.java)
+- Domain event **review required**
+  [ReviewRequired](../../shared-kernel-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/ReviewRequired.java)
+  can be published via push notification to Logistician, and is stored as read model and exposed by REST endpoint
+  [RequiredReviewDao.findByRefNoAndDecisionIsNull(String)](../../demand-forecasting-adapters/src/main/java/io/dddbyexamples/factory/demand/forecasting/command/RequiredReviewDao.java)
+- Command **apply review decision** is handled by port
+  [DemandService.review(ApplyReviewDecision)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+
+## Custom orders
+For custom goods ordered in advance, order based demand can replace demand forecasting.
+In case of lack of neither continuous demand forecasting nor call‐off orders,
+customer orders can be treated as product-day-location demand
+so any further processing requiring demand information may work normally.
+![Custom orders](custom-orders.gif)
+
+Interactions with **demand-forecasting** bounded context can be found in source code:
+- External event **demand forecasting
 expertise** is handled by port
+  [DemandService.process(Document)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+- External event **order** is handled by port
+  [DemandService.process(Document)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+
+## Manual management
+Ad-hoc adjustments of demands on top of any existing forecasts.
+
+Note that **adjust demand** can change demand for multiple days atomically.
+
+Interactions with **demand-forecasting** bounded context can be found in source code:
+- Command **adjust demand** is handled by port
+  [DemandService.adjust(AdjustDemand)](../../demand-forecasting-model/src/main/java/io/dddbyexamples/factory/demand/forecasting/DemandService.java)
+- Read model **stock forecast** is calculated on query time by
+  [StockForecastQuery.get(RefNoId)](../../app-monolith/src/main/java/io/dddbyexamples/factory/stock/forecast/StockForecastQuery.java)
+
+## Propagation of demand
+![Propagation of demand](demand-propagation.gif)

doc/demand-forecasting/demand-forecasting-expertise.R

@@ -0,0 +1,69 @@
+#' /* setup
+library(FactoryAnalytics)
+productRefNo <- "3009013"
+#' */
+
+#' ---
+#' title: "Initial demand forecasting expertise
+#' author: "Michał Michaluk"
+#' output: pdf_document
+#' ---
+
+#' # Initial demand forecasting expertise of new product release
+{{productRefNo}}
+
+#' ## Method exmplanation
+#' Demand forecasting for new product will be calculated based on relationship to existing products of the same family namly *3009002* and *3009004*.
+#' *3009002*, *3009004* are the most popular colors of our product _black_ and _blue_ respectivly.
+#' Retailers feedback sugests that new _dark gray_ color is havly demand, but unavaliable from our manufacture.
+
+#' Experts consensus suggests that introduction of new *3009013* _dark gray_ product will
+#' increase market share of related colors (_black_, _blue_ and new _dark gray_) in that product family about 15%.
+#' In addition it will take over about
+#' - 25% of *3009002* _black_ demands,
+#' - 40% of *3009004* _blue_ demands
+#' of existing products, becoming the most popular color in family.
+
+#' ## Calculation
+#' New color will be shipped at 2018/09/01
+#' forecast 60 days from release will be more than suficient.
+
+#' fetch current demand forecast for 3009002 (black)
+blackDemand <- demand.forecast.current(
+    refNo = "3009002",
+    from = as.Date("2018/09/01"),
+    to = as.Date("2018/10/30")
+)
+#' fetch current demand forecast for 3009004 (blue)
+blueDemand <- demand.forecast.current(
+    refNo = "3009004",
+    from = as.Date("2018/09/01"),
+    to = as.Date("2018/10/30")
+)
+
+#' Experts provided calculation
+expectedDemand <- (blackDemand$level + blueDemand$level) * 0.15 + blackDemand$level * 0.25 + blueDemand$level * 0.40
+
+darkgrayDemand <- data.frame(
+    refNo = rep(productRefNo, 60),
+    date = blackDemand$date,
+    level = expectedDemand
+)
+
+#' Adaptation of existing products demand only for presentation purpose
+blackDemand$level <- blackDemand$level * 0.25
+blueDemand$level <- blueDemand$level * 0.40
+
+#' ## Results
+plot(darkgrayDemand[, c("date", "level")], type = "l", col = "darkgray", lty = 1)
+lines(blueDemand[, c("date", "level")], type = "l", col = "blue", lty = 2)
+lines(blackDemand[, c("date", "level")], type = "l", col = "black", lty = 2)
+legend(0, 0,
+    legend = c(productRefNo, "3009002 (black)", "3009004 (blue)"),
+    col = c("darkgray", "black", "blue"),
+    lty = c(1,2,2), ncol=1
+)
+
+#' Demand forecast saved alongside report generation:
+darkgrayDemand
+demand.forecast.new(darkgrayDemand)