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HC = Holding Cost - - -Purchase Order Quantity ------------------------ - -For the monte carlo siimulation, the - -In release 0.0.3 the purchase order quantity will be compared against a forecast to assess whether it requires adjustment. - - -Backlog and Shortage units --------------------------- - -To aggregate the backlog and shortage units for the `summarise_frame` - - -Normal Sum Deviation --------------------- - -The standard deviation is calculated by first summing the average variance for backlog and quantity sold. - - -.. math:: - - \sigma_{xy}^2 = \sigma_{x}^2 + \sigma_{y}^2 - - + HC = Holding Cost \ No newline at end of file diff --git a/docs/_build/html/_sources/quickstart.txt b/docs/_build/html/_sources/quickstart.txt index 3350a9a..a70b285 100644 --- a/docs/_build/html/_sources/quickstart.txt +++ b/docs/_build/html/_sources/quickstart.txt @@ -10,13 +10,13 @@ Quick Guide All profiling has been carried out on the following systems: - +------------+------------------------------------+------------------+ - | system | CPU | Ram | - +============+====================================+==================+ - | PC | Xeon 1650v3 (15M Cache, 3.50 GHz) | 32gb ddr4 2133 | - +------------+------------------------------------+------------------+ - | Macbook Air| i7-4650U | 8gb ddr3 1600 | - +------------+------------------------------------+------------------+ + +------------+------------------------------------+------------------+----------------+ + | system | CPU | Ram | OS | + +============+====================================+==================+================+ + | PC | Xeon 1650v3 (15M Cache, 3.50 GHz) | 32gb ddr4 2133 | linux (xubuntu)| + +------------+------------------------------------+------------------+----------------+ + | Macbook Air| i7-4650U | 8gb ddr3 1600 | OS X (yosemite)| + +------------+------------------------------------+------------------+----------------+ Timings are provided as simple benchmarks and will vary based on system and current load. The computation here are mainly cpu bound, io may be a factor when reading from file (csv txt). @@ -324,7 +324,7 @@ when summarised later. The simulation is limited by the assumptions inherent in :ref:`calculations`). To start we need to analyse the orders again like we did in the inventory analysis above: - +388.324289560318 .. code:: python >>> from supplychainpy.model_inventory import analyse_orders_abcxyz_from_file @@ -508,7 +508,8 @@ monte carlo simulation and the original orders analysis. The service level achie reviewed and compared with the desired service level. If below a threshold, then the saftey stock is increased and the whole monte carlo simulation is run again. The new safety stock values are returned when finished. This optimisation step will take as long, if not longer, than the initial monte carlo simulation because the optimisation -step run the simulation again to simulate transactions based on the new safety stock values. Please take this into consideration and adjust your expectation for this optimisation step. +step run the simulation again to simulate transactions based on the new safety stock values. Please take this into +consideration and adjust your expectation for this optimisation step. This feature is in development as is the whole library but this feature will change in the next release. diff --git a/docs/_build/html/calculations.html b/docs/_build/html/calculations.html index c99f89d..2cf38e7 100644 --- a/docs/_build/html/calculations.html +++ b/docs/_build/html/calculations.html @@ -90,9 +90,6 @@
  • Reorder Level
  • Safety Stock
  • Economic Order Quantity (eoq)
    • -
  • Purchase Order Quantity
    • -
  • Backlog and Shortage units
    • -
  • Normal Sum Deviation
    • @@ -205,21 +202,6 @@

    Economic Order Quantity (eoq) -

    Purchase Order Quantity

    -

    For the monte carlo siimulation, the

    -

    In release 0.0.3 the purchase order quantity will be compared against a forecast to assess whether it requires adjustment.

    - -
    -

    Backlog and Shortage units

    -

    To aggregate the backlog and shortage units for the summarise_frame

    -
    -
    -

    Normal Sum Deviation

    -

    The standard deviation is calculated by first summing the average variance for backlog and quantity sold.

    -
    -\[\sigma_{xy}^2 = \sigma_{x}^2 + \sigma_{y}^2\]
    -
    diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html index 1f454d4..6f8e7ed 100644 --- a/docs/_build/html/genindex.html +++ b/docs/_build/html/genindex.html @@ -692,20 +692,12 @@

    R

    - -
    run() (in module supplychainpy.simulate) -
    -
    run_monte_carlo() (in module supplychainpy.simulate)
    - -
    run_monte_carlo_mt() (in module supplychainpy.simulate) -
    -
    diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html index eb8c36d..0a4c11b 100644 --- a/docs/_build/html/index.html +++ b/docs/_build/html/index.html @@ -157,9 +157,6 @@

    Welcome to supplychainpy’s documentation!Reorder Level
  • Safety Stock
  • Economic Order Quantity (eoq)
    • -
  • Purchase Order Quantity
    • -
  • Backlog and Shortage units
    • -
  • Normal Sum Deviation
    • diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv index fcb27b2..103b977 100644 Binary files a/docs/_build/html/objects.inv and b/docs/_build/html/objects.inv differ diff --git a/docs/_build/html/quickstart.html b/docs/_build/html/quickstart.html index fe636ed..d56dd22 100644 --- a/docs/_build/html/quickstart.html +++ b/docs/_build/html/quickstart.html @@ -147,24 +147,28 @@

    Quick Guide - - - + + + + system CPU Ram +OS PC Xeon 1650v3 (15M Cache, 3.50 GHz) 32gb ddr4 2133 +linux (xubuntu) Macbook Air i7-4650U 8gb ddr3 1600 +OS X (yosemite) @@ -481,8 +485,10 @@

    Monte Carlo simulationcalculations).

    -

    To start we need to analyse the orders again like we did in the inventory analysis above:

    -
    >>> from supplychainpy.model_inventory import analyse_orders_abcxyz_from_file
+
    To start we need to analyse the orders again like we did in the inventory analysis above:
+388.324289560318
+.. code:: python
    
+
    >>> from supplychainpy.model_inventory import analyse_orders_abcxyz_from_file
 >>> orders_analysis = analyse_orders_abcxyz_from_file(file_path="data.csv", z_value=Decimal(1.28),
 >>>                                        reorder_cost=Decimal(5000), file_type="csv")
 
    
@@ -681,7 +687,8 @@ 
    Monte Carlo simulation
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\ No newline at end of file diff --git a/docs/_build/html/supplychainpy.html b/docs/_build/html/supplychainpy.html index b4e8963..bec59af 100644 --- a/docs/_build/html/supplychainpy.html +++ b/docs/_build/html/supplychainpy.html @@ -499,8 +499,8 @@

    Submodules

    supplychainpy.simulate module

    -supplychainpy.simulate.optimise_service_level(frame_summary: list, orders_analysis: list, service_level: float, runs: int) → list
    -

    Optimises the safety stock based on the results of the monte carlo analysis.

    +supplychainpy.simulate.optimise_service_level(frame_summary: list, orders_analysis: list, service_level: float, runs: int, percentage_increase: float) → list +

    Optimises the safety stock for declared service level.

    Identifies which skus under performed (experiencing a service level lower than expected) after simulating transactions over a specific period. The safety stock for these items are increased and the analysis is monte carlo is run again.

    @@ -512,6 +512,7 @@

    Submodules -
    -supplychainpy.simulate.run(simulation_frame: list, period_length: int=12)
    -

    -
    supplychainpy.simulate.run_monte_carlo(orders_analysis, file_path: str, z_value: decimal.Decimal, runs: int, reorder_cost: decimal.Decimal, file_type: str, period_length: int=12) → list
    @@ -621,11 +617,6 @@

    Submodules -
    -supplychainpy.simulate.run_monte_carlo_mt(file_path: str, z_value: decimal.Decimal, runs: int, reorder_cost: decimal.Decimal, file_type: str, period_length: int=12) → list
    -

    -
    supplychainpy.simulate.summarise_frame(window_summary)
    diff --git a/docs/calculations.rst b/docs/calculations.rst index 2d181a1..e93585f 100644 --- a/docs/calculations.rst +++ b/docs/calculations.rst @@ -79,31 +79,4 @@ where: D = Demand - HC = Holding Cost - - -Purchase Order Quantity ------------------------ - -For the monte carlo siimulation, the - -In release 0.0.3 the purchase order quantity will be compared against a forecast to assess whether it requires adjustment. - - -Backlog and Shortage units --------------------------- - -To aggregate the backlog and shortage units for the `summarise_frame` - - -Normal Sum Deviation --------------------- - -The standard deviation is calculated by first summing the average variance for backlog and quantity sold. - - -.. math:: - - \sigma_{xy}^2 = \sigma_{x}^2 + \sigma_{y}^2 - - + HC = Holding Cost \ No newline at end of file