release 0.0.2 rc updated docs

docs/_build/html/_sources/calculations.txt

@@ -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

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.
 
 

docs/_build/html/calculations.html

@@ -90,9 +90,6 @@
 <li class="toctree-l2"><a class="reference internal" href="#reorder-level">Reorder Level</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#safety-stock">Safety Stock</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#economic-order-quantity-eoq">Economic Order Quantity (eoq)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#purchase-order-quantity">Purchase Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#backlog-and-shortage-units">Backlog and Shortage units</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#normal-sum-deviation">Normal Sum Deviation</a></li>
 </ul>
 </li>
 </ul>
@@ -205,21 +202,6 @@ <h2>Economic Order Quantity (eoq)<a class="headerlink" href="#economic-order-qua
 </dd>
 </dl>
 </div>
-<div class="section" id="purchase-order-quantity">
-<h2>Purchase Order Quantity<a class="headerlink" href="#purchase-order-quantity" title="Permalink to this headline">¶</a></h2>
-<p>For the monte carlo siimulation, the</p>
-<p>In release 0.0.3 the purchase order quantity will be compared against a forecast to assess whether it requires adjustment.</p>
-</div>
-<div class="section" id="backlog-and-shortage-units">
-<h2>Backlog and Shortage units<a class="headerlink" href="#backlog-and-shortage-units" title="Permalink to this headline">¶</a></h2>
-<p>To aggregate the backlog and shortage units for the <cite>summarise_frame</cite></p>
-</div>
-<div class="section" id="normal-sum-deviation">
-<h2>Normal Sum Deviation<a class="headerlink" href="#normal-sum-deviation" title="Permalink to this headline">¶</a></h2>
-<p>The standard deviation is calculated by first summing the average variance for backlog and quantity sold.</p>
-<div class="math">
-\[\sigma_{xy}^2 = \sigma_{x}^2 + \sigma_{y}^2\]</div>
-</div>
 </div>
 
 

docs/_build/html/genindex.html

@@ -692,20 +692,12 @@ <h2 id="R">R</h2>
   </dt>
 
       </dl></dd>
-
-  <dt><a href="supplychainpy.html#supplychainpy.simulate.run">run() (in module supplychainpy.simulate)</a>
-  </dt>
-
   </dl></td>
   <td style="width: 33%" valign="top"><dl>
 
   <dt><a href="supplychainpy.html#supplychainpy.simulate.run_monte_carlo">run_monte_carlo() (in module supplychainpy.simulate)</a>
   </dt>
 
-
-  <dt><a href="supplychainpy.html#supplychainpy.simulate.run_monte_carlo_mt">run_monte_carlo_mt() (in module supplychainpy.simulate)</a>
-  </dt>
-
   </dl></td>
 </tr></table>
 

docs/_build/html/index.html

@@ -157,9 +157,6 @@ <h1>Welcome to supplychainpy&#8217;s documentation!<a class="headerlink" href="#
 <li class="toctree-l2"><a class="reference internal" href="calculations.html#reorder-level">Reorder Level</a></li>
 <li class="toctree-l2"><a class="reference internal" href="calculations.html#safety-stock">Safety Stock</a></li>
 <li class="toctree-l2"><a class="reference internal" href="calculations.html#economic-order-quantity-eoq">Economic Order Quantity (eoq)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="calculations.html#purchase-order-quantity">Purchase Order Quantity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="calculations.html#backlog-and-shortage-units">Backlog and Shortage units</a></li>
-<li class="toctree-l2"><a class="reference internal" href="calculations.html#normal-sum-deviation">Normal Sum Deviation</a></li>
 </ul>
 </li>
 </ul>

docs/_build/html/quickstart.html

@@ -147,24 +147,28 @@ <h1>Quick Guide<a class="headerlink" href="#quick-guide" title="Permalink to thi
 <p>All profiling has been carried out on the following systems:</p>
 <table border="1" class="docutils">
 <colgroup>
-<col width="18%" />
-<col width="55%" />
-<col width="27%" />
+<col width="15%" />
+<col width="44%" />
+<col width="22%" />
+<col width="20%" />
 </colgroup>
 <thead valign="bottom">
 <tr class="row-odd"><th class="head">system</th>
 <th class="head">CPU</th>
 <th class="head">Ram</th>
+<th class="head">OS</th>
 </tr>
 </thead>
 <tbody valign="top">
 <tr class="row-even"><td>PC</td>
 <td>Xeon 1650v3 (15M Cache, 3.50 GHz)</td>
 <td>32gb ddr4 2133</td>
+<td>linux (xubuntu)</td>
 </tr>
 <tr class="row-odd"><td>Macbook Air</td>
 <td>i7-4650U</td>
 <td>8gb ddr3 1600</td>
+<td>OS X (yosemite)</td>
 </tr>
 </tbody>
 </table>
@@ -481,8 +485,10 @@ <h2>Monte Carlo simulation<a class="headerlink" href="#monte-carlo-simulation" t
 requested. The higher the number of runs the more accurately the simulation captures the dynamics of the system,
 when summarised later. The simulation is limited by the assumptions inherent in the simulations design (detailed in the
 <span class="xref std std-ref">calculations</span>).</p>
-<p>To start we need to analyse the orders again like we did in the inventory analysis above:</p>
-<div class="code python highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">supplychainpy.model_inventory</span> <span class="kn">import</span> <span class="n">analyse_orders_abcxyz_from_file</span>
+<p>To start we need to analyse the orders again like we did in the inventory analysis above:
+388.324289560318
+.. code:: python</p>
+<div class="highlight-python"><div class="highlight"><pre><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">supplychainpy.model_inventory</span> <span class="kn">import</span> <span class="n">analyse_orders_abcxyz_from_file</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">orders_analysis</span> <span class="o">=</span> <span class="n">analyse_orders_abcxyz_from_file</span><span class="p">(</span><span class="n">file_path</span><span class="o">=</span><span class="s2">&quot;data.csv&quot;</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="n">Decimal</span><span class="p">(</span><span class="mf">1.28</span><span class="p">),</span>
 <span class="gp">&gt;&gt;&gt; </span>                                       <span class="n">reorder_cost</span><span class="o">=</span><span class="n">Decimal</span><span class="p">(</span><span class="mi">5000</span><span class="p">),</span> <span class="n">file_type</span><span class="o">=</span><span class="s2">&quot;csv&quot;</span><span class="p">)</span>
 </pre></div>
@@ -681,7 +687,8 @@ <h2>Monte Carlo simulation<a class="headerlink" href="#monte-carlo-simulation" t
 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.</p>
 </div>
 </div>

docs/_build/html/supplychainpy.html

@@ -499,8 +499,8 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <span id="supplychainpy-simulate-module"></span><h2>supplychainpy.simulate module<a class="headerlink" href="#module-supplychainpy.simulate" title="Permalink to this headline">¶</a></h2>
 <dl class="function">
 <dt id="supplychainpy.simulate.optimise_service_level">
-<code class="descclassname">supplychainpy.simulate.</code><code class="descname">optimise_service_level</code><span class="sig-paren">(</span><em>frame_summary: list</em>, <em>orders_analysis: list</em>, <em>service_level: float</em>, <em>runs: int</em><span class="sig-paren">)</span> &rarr; list<a class="headerlink" href="#supplychainpy.simulate.optimise_service_level" title="Permalink to this definition">¶</a></dt>
-<dd><p>Optimises the safety stock based on the results of the monte carlo analysis.</p>
+<code class="descclassname">supplychainpy.simulate.</code><code class="descname">optimise_service_level</code><span class="sig-paren">(</span><em>frame_summary: list</em>, <em>orders_analysis: list</em>, <em>service_level: float</em>, <em>runs: int</em>, <em>percentage_increase: float</em><span class="sig-paren">)</span> &rarr; list<a class="headerlink" href="#supplychainpy.simulate.optimise_service_level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Optimises the safety stock for declared service level.</p>
 <p>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.</p>
@@ -512,6 +512,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <li><strong>frame_summary</strong> (<em>list</em>) &#8211; window summary for each period multiplied by the number of runs.</li>
 <li><strong>orders_analysis</strong> (<em>list</em>) &#8211; prior analysis of orders data.</li>
 <li><strong>service_level</strong> (<em>list</em>) &#8211; required service level as a percentage.</li>
+<li><strong>runs</strong> (<em>int</em>) &#8211; number of runs from previous</li>
 </ul>
 </td>
 </tr>
@@ -530,11 +531,6 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 </table>
 </dd></dl>
 
-<dl class="function">
-<dt id="supplychainpy.simulate.run">
-<code class="descclassname">supplychainpy.simulate.</code><code class="descname">run</code><span class="sig-paren">(</span><em>simulation_frame: list</em>, <em>period_length: int=12</em><span class="sig-paren">)</span><a class="headerlink" href="#supplychainpy.simulate.run" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
 <dl class="function">
 <dt id="supplychainpy.simulate.run_monte_carlo">
 <code class="descclassname">supplychainpy.simulate.</code><code class="descname">run_monte_carlo</code><span class="sig-paren">(</span><em>orders_analysis</em>, <em>file_path: str</em>, <em>z_value: decimal.Decimal</em>, <em>runs: int</em>, <em>reorder_cost: decimal.Decimal</em>, <em>file_type: str</em>, <em>period_length: int=12</em><span class="sig-paren">)</span> &rarr; list<a class="headerlink" href="#supplychainpy.simulate.run_monte_carlo" title="Permalink to this definition">¶</a></dt>
@@ -621,11 +617,6 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 </table>
 </dd></dl>
 
-<dl class="function">
-<dt id="supplychainpy.simulate.run_monte_carlo_mt">
-<code class="descclassname">supplychainpy.simulate.</code><code class="descname">run_monte_carlo_mt</code><span class="sig-paren">(</span><em>file_path: str</em>, <em>z_value: decimal.Decimal</em>, <em>runs: int</em>, <em>reorder_cost: decimal.Decimal</em>, <em>file_type: str</em>, <em>period_length: int=12</em><span class="sig-paren">)</span> &rarr; list<a class="headerlink" href="#supplychainpy.simulate.run_monte_carlo_mt" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
 <dl class="function">
 <dt id="supplychainpy.simulate.summarise_frame">
 <code class="descclassname">supplychainpy.simulate.</code><code class="descname">summarise_frame</code><span class="sig-paren">(</span><em>window_summary</em><span class="sig-paren">)</span><a class="headerlink" href="#supplychainpy.simulate.summarise_frame" title="Permalink to this definition">¶</a></dt>

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