diff --git a/inventory_simulation.py b/inventory_simulation.py
new file mode 100644
index 0000000..02890ff
--- /dev/null
+++ b/inventory_simulation.py
@@ -0,0 +1,288 @@
+import pandas as pd
+import csv
+import numpy as np
+import ipdb
+
+orders = pd.read_csv("2018_Transport Calc Slim copy.csv",
+                     usecols=[2, 3, 4, 7, 10, 14, 15, 16, 17, 21, 24, 28],
+                     dtype={"sh_ItemId": "object"})
+
+wh = pd.read_csv("wh_locations.csv")
+
+location_dic = orders.groupby("lo_LocationId")[
+    "sh_OriginLocationMasterLocation"].first()
+# get mapping of io_LocationId to general location
+location_dic = location_dic.to_dict()
+wh_address = {}
+for index, row in wh.iterrows():
+    # create mapping between address and ShipToID
+    wh_address[row["NavID"]] = row["Location"]
+
+
+# Data cleaning
+# only keep orders >0 pallets
+orders = orders[orders["sh_18_Ln_PalletsEquivalent"] > 0]
+
+orders = orders[orders["DirectShipmentInOriginal"] == "standard"]  # only keep standard shipment
+# remove orders with invalid locations
+orders = orders[orders["sh_OriginLocationMasterLocation"].notna()]
+
+
+# if shipment was internal point to where it was sent (e.g. LASEK if ShipToID = CZ__370 00__Ceské Budejovice)
+orders["internal_ship"] = orders["ShipToID"].map(wh_address)
+
+# if shipment was to the client writes "Client"
+orders["internal_ship"].fillna("Client", inplace=True)
+orders["internal_ship"][orders["internal_ship"] == orders["sh_OriginLocationMasterLocation"]
+                        ] = "Client"  # if orgin=destination treat it as a normal order
+fact = ["BRN", "VES", "BAB", "DOM", "DOM buffer", "ITT", "ITT buffer", "DOE"]
+orders["internal_ship"][orders["internal_ship"] == "IIT buffer"] = "ITT"
+
+
+lead_times = pd.read_csv("Lead Time per GIFC.csv",
+                         usecols=[0, 1, 3, 4, 6])  # load lead times spreadsheet
+# remove sh prefix from GIFC
+lead_times["sh_GIFC"] = lead_times["sh_GIFC"].str[4:]
+
+lead_times = lead_times.drop_duplicates()
+# lead times based on sh_GIFC and production location are assigned to the orders
+orders = orders.merge(
+    lead_times, on=["sh_GIFC", "ProducedBy"], how="left", sort=False)
+
+
+operation = {"sh_18_Ln_PalletsEquivalent": "sum",
+             "Effective Lead Time [weeks]": "first",
+             "Avg delay Lead time [weeks]": "first",
+             }  # assign operations to be performed on each column of interest
+
+orders["sh_ShipmentDate"] = pd.to_datetime(
+    orders["sh_ShipmentDate"],
+    format="%d/%m/%Y")  # converting dates to datetime objects
+
+
+
+test = orders.assign(period=pd.PeriodIndex(orders["sh_ShipmentDate"], freq="W-Sun")).groupby(["period",
+                       "sh_ItemId",
+                       "sh_OriginLocationMasterLocation",
+                       "sh_GIFC",
+                       "ProducedBy",
+                       "internal_ship"]).agg(operation)  # aggregate based on sku,location, week,... and perform operations defined above
+
+test = test.reset_index()
+
+aggs = orders.assign(period=pd.PeriodIndex(orders["sh_ShipmentDate"], freq="W-Sun")).groupby(["period",
+                       "sh_OriginLocationMasterLocation"]).agg({"sh_18_Ln_PalletsEquivalent": "sum"})
+aggs = aggs.reset_index()
+aggs.to_csv("demand_period_location.csv")
+print(test[test.duplicated(subset=["sh_ItemId", "sh_OriginLocationMasterLocation", "period"], keep=False)])
+
+prop = test[test["internal_ship"] != "Client"].groupby(
+    ["sh_ItemId", "internal_ship"])["sh_18_Ln_PalletsEquivalent"].sum()  # sums quantity sent internally for each lcoations and SKU
+
+total = test.groupby(["sh_ItemId", "sh_OriginLocationMasterLocation"])[
+    "sh_18_Ln_PalletsEquivalent"].sum()  # get total demand for each location/sku
+prop = prop.reset_index()
+total = total.reset_index()
+
+prop = prop.merge(total, how="left", left_on=["internal_ship", "sh_ItemId"], right_on=[
+                  "sh_OriginLocationMasterLocation", "sh_ItemId"])  # match quantity sent internally and total demand
+
+
+prop["perc_supplied_indirect"] = prop["sh_18_Ln_PalletsEquivalent_x"] / \
+    prop["sh_18_Ln_PalletsEquivalent_y"]  # determine proportion of demand that cames from indirect flow (qty that came from other warehouse / total demand)
+prop.dropna(inplace=True, subset=["perc_supplied_indirect"])
+prop.to_csv("prop.csv")
+
+
+prop.drop(["sh_18_Ln_PalletsEquivalent_x",
+           "sh_18_Ln_PalletsEquivalent_y"], inplace=True, axis=1)
+
+op = {"sh_18_Ln_PalletsEquivalent": ["size", "mean", "std", "sum"],
+      "Effective Lead Time [weeks]": "first",
+      "Avg delay Lead time [weeks]": "first"}
+
+ss_computation = test.groupby(
+    ["sh_OriginLocationMasterLocation", "sh_ItemId", "sh_GIFC"]).agg(op)  # mean demand and std (per week) is computed
+
+
+ss_computation = ss_computation.reset_index()
+ss_computation = ss_computation.merge(prop, how="left", right_on=["sh_ItemId", "internal_ship"],
+                                      left_on=["sh_ItemId", "sh_OriginLocationMasterLocation"])  # assign prop for each order with mathcing sku and location
+
+ss_computation.drop(
+    ["internal_ship", "sh_OriginLocationMasterLocation"], axis=1, inplace=True)
+ss_computation.columns = ["sh_ItemId", "sh_OriginLocationMasterLocation", "copy", "sh_GIFC",
+                          "size", "mean", "std", "sum", "lt_mean", "lt_std", "perc_supplied_indirect"]  # renaming columns
+ss_computation["perc_supplied_indirect"].fillna(
+    0, inplace=True)  # if not internal shipment -> 0%
+# fill missing lead times with median values of present values
+ss_computation["lt_mean"].fillna(
+    ss_computation["lt_mean"].median(), inplace=True)
+ss_computation["lt_std"].fillna(
+    ss_computation["lt_std"].median(), inplace=True)
+
+
+''' 
+compute SS by using the demand for each sku and location
+weighted average is used to assign portion of safety stock that covers for demand variability (using the entire formula for SS) and SS that cover for lead time variability
++1 is added to the factory lead time to account for internal transport lead time
+'''
+ss_computation["Safety Stock_0.98"] = (1 - ss_computation["perc_supplied_indirect"]) * (
+    2.05 * ((ss_computation["lt_mean"] + 1) * ss_computation["std"]**2 + (ss_computation["mean"]**2) * ss_computation["lt_std"]**2)**0.5) + ss_computation["perc_supplied_indirect"] * (2.05 * (1 * ss_computation["std"]**2)**0.5)
+
+ss_computation["Safety Stock_0.98"][ss_computation["sh_OriginLocationMasterLocation"].isin(
+    fact)] = 0
+ss_computation["Safety Stock_0.98"][ss_computation["size"] < 2] = 0
+ss_computation.to_csv("ss.csv")
+
+test = test.merge(ss_computation, how="left", left_on=["sh_OriginLocationMasterLocation", "sh_ItemId", "sh_GIFC"], right_on=[
+                  "sh_OriginLocationMasterLocation", "sh_ItemId", "sh_GIFC"])  # Assign SS levels to matching SKU and lcoation
+
+test.drop(["copy", "mean", "std", "sum",
+           "lt_mean", "lt_std"], inplace=True, axis=1)
+
+
+test.rename(
+    columns={"sh_18_Ln_PalletsEquivalent": "sh_18_Ln_PalletsEquivalent"}, inplace=True)
+test.drop(["Effective Lead Time [weeks]",
+           "Avg delay Lead time [weeks]"], axis=1, inplace=True)
+
+holding_costs = pd.read_csv("inventory_data.csv",
+                            usecols=["Item No_",
+                                     "Cost per UOM",
+                                     "KW 39 Quantity",
+                                     "Qty per Container",
+                                     "EUR pallet indicator",
+                                     "Location Code",
+                                     "Pallets"])
+
+holding_costs["Qty per Container"] = holding_costs["Qty per Container"].str.strip()
+holding_costs["Qty per Container"] = holding_costs["Qty per Container"].str.replace(" ", "")
+holding_costs["Qty per Container"] = holding_costs["Qty per Container"].astype(float)
+
+
+
+holding_costs["Location"] = holding_costs["Location Code"].map(location_dic)
+holding_costs.dropna(inplace=True, subset=["Location Code"])
+
+holding_costs["Location"][holding_costs["Location Code"].str.contains("TABOR")] = "DC Tabor"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("TÁBOR")] = "DC Tabor"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("SKLAD")] = "DC SK"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("312")] = "CoP NL"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("WABERE")] = "DC HU DRY"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("LASEK")] = "LASEK"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("LGI")] = "LGI"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("TRANSFER")] = "DC Tabor"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("BAL_FG_VYR")] = "DC Tabor"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("900")] = "DC NL"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("203")] = "DC NL"
+holding_costs["Location"][holding_costs["Location Code"].str.contains("405")] = "ITT"
+
+
+holding_costs["inv_costs_20"] = holding_costs["Cost per UOM"] * 0.2 
+
+holding_costs = holding_costs[["Location", "Item No_", "inv_costs_20", "Location Code"]]
+holding_costs = holding_costs.drop_duplicates(subset=["Item No_", "Location", "inv_costs_20"])
+# holding_costs.to_csv("hol.csv")
+test = test.merge(holding_costs, how="left", left_on=["sh_ItemId", "sh_OriginLocationMasterLocation"], right_on=["Item No_", "Location"])
+# test = test.drop_duplicates()
+
+test["inv_costs_20"] = test["inv_costs_20"].fillna(test.groupby("sh_ItemId")["inv_costs_20"].transform("mean"))
+
+
+test = test.dropna(subset=["size"])
+
+test.to_csv('zdzad.csv')
+
+# test = pd.read_csv("inv.csv",
+#                    index_col=[0])
+
+
+def ABC_segmentation(perc):
+    '''
+    Creates the 3 classes A, B, and C based
+    on quantity percentages (A-60%, B-25%, C-15%)
+    '''
+    if perc > 0 and perc < 0.6:
+        return 'A'
+    elif perc >= 0.6 and perc < 0.85:
+        return 'B'
+    elif perc >= 0.85:
+        return 'C'
+
+test_agg = test.groupby(["sh_ItemId"], as_index=False).agg(
+    {"sh_18_Ln_PalletsEquivalent": "sum", "inv_costs_20": "mean"})
+
+
+def ABC_segmentation(perc):
+    '''
+    Creates the 3 classes A, B, and C based 
+    on quantity percentages (A-60%, B-25%, C-15%)
+    '''
+    if perc > 0 and perc < 0.6:
+        return 'A'
+    elif perc >= 0.6 and perc < 0.85:
+        return 'B'
+    elif perc >= 0.85:
+        return 'C'
+
+
+test_agg["AddCost"] = test_agg["sh_18_Ln_PalletsEquivalent"] * \
+    test_agg["inv_costs_20"]
+
+test_agg = test_agg.sort_values(by=["AddCost"], ascending=False)
+test_agg["CumCost"] = test_agg["AddCost"].cumsum()
+test_agg["TotSum"] = test_agg["AddCost"].sum()
+test_agg["RunPerc"] = test_agg["CumCost"] / test_agg["TotSum"]
+test_agg["ABC_cluster"] = test_agg["RunPerc"].apply(ABC_segmentation)
+
+abc_map = dict(zip(test_agg["sh_ItemId"], test_agg["ABC_cluster"]))
+
+test["ABC_cluster"] = test["sh_ItemId"].map(abc_map)
+
+test_agg = test.groupby(["sh_ItemId"], as_index=False)[
+    "sh_18_Ln_PalletsEquivalent"].agg(["mean", "std", "count"]).reset_index()
+
+test_agg["CV"] = test_agg["std"] / test_agg["mean"]
+
+
+def XYZ_segmentation(perc):
+    '''
+    Creates the 3 classes A, B, and C based
+    on quantity percentages (A-60%, B-25%, C-15%)
+    '''
+    if perc > 0 and perc <= 0.5:
+        return "X"
+    elif perc > 0.5 and perc < 1:
+        return 'Y'
+    elif perc >= 1:
+        return 'Z'
+
+
+test_agg["XYZ_cluster"] = test_agg["CV"].apply(XYZ_segmentation)
+print(test_agg)
+
+xyz_map = dict(zip(test_agg["sh_ItemId"], test_agg["XYZ_cluster"]))
+
+
+test["XYZ_cluster"] = test["sh_ItemId"].map(xyz_map)
+
+
+test["Safety Stock_0.98"][(test["ABC_cluster"] == "C") & (test["XYZ_cluster"]=="Z")] = 0
+
+
+
+test["Total Inventory"] = test["sh_18_Ln_PalletsEquivalent"] + \
+    test["Safety Stock_0.98"].fillna(
+    0)  # add demand from orders with SS for weekly inventory
+ 
+uniq_skuloc = test.groupby("sh_ItemId")["sh_OriginLocationMasterLocation"].apply(set).to_dict()
+
+print(list(uniq_skuloc.keys())[0])
+
+ipdb.set_trace()
+test.set_index(["period", "sh_ItemId", "sh_GIFC", ""])
+test.to_csv("inventory final.csv")
+
+
+