diff --git a/src/wattanalyse/pipelines.py b/src/wattanalyse/pipelines.py
index e4bcf0b..8b6c5d3 100644
--- a/src/wattanalyse/pipelines.py
+++ b/src/wattanalyse/pipelines.py
@@ -58,6 +58,18 @@ RENAMING_SCHEME_PSM: dict[str, str] = {
 
 PRIM_KEYS: Final[list[str]] = ["PA", "PA_Pos"]
 
+NOT_NULL_COLS: Final[tuple[str, ...]] = ("PA", "PA_Pos", "Meldezeitpunkt_Historie")
+NOT_IN_FUTURE_COLS_DATETIME: Final[tuple[str, ...]] = ("Meldezeitpunkt_Historie",)
+NOT_IN_FUTURE_COLS_DATE: Final[tuple[str, ...]] = ("Wareneingang am", "Prod-Start_Historie")
+
+PLAUSI_FEATURES: Final[list[str]] = [
+    "Prod-EP10_Historie",
+    "Prod-EP20_Historie",
+    "Prod-EP30_Historie",
+    "Prod-EP40_Historie",
+    "Prod-EP50_Historie",
+]
+
 LOWER_BOUND_DATE_DEVIATION: Final[int] = (
     USER_CFG.Datenpipelines_PSM.Terminabweichung_untere_Schranke
 )
@@ -133,7 +145,6 @@ def preprocess_psm(
     data = data.drop("null_count")
 
     # any NULL values in critical columns
-    NOT_NULL_COLS = ("PA", "PA_Pos", "Meldezeitpunkt_Historie")
     conds = [pl.col(col).is_null() for col in NOT_NULL_COLS]
     filtered_data = pl.concat([filtered_data, data.filter(pl.any_horizontal(*conds))])
     data = data.filter(~pl.any_horizontal(*conds))
@@ -142,8 +153,6 @@ def preprocess_psm(
     # dates not allowed to be in the future
     current_datetime = datetime.datetime.now()
     current_date = current_datetime.date()
-    NOT_IN_FUTURE_COLS_DATETIME = ("Meldezeitpunkt_Historie",)
-    NOT_IN_FUTURE_COLS_DATE = ("Wareneingang am", "Prod-Start_Historie")
     conds = [
         (pl.col(col) > current_datetime).fill_null(False)
         for col in NOT_IN_FUTURE_COLS_DATETIME
@@ -188,13 +197,6 @@ def process_order_level(
     data = data.sort(PRIM_KEYS + ["Meldezeitpunkt_Historie"], descending=False)
 
     # ** plausibility check of order quantities
-    PLAUSI_FEATURES: list[str] = [
-        "Prod-EP10_Historie",
-        "Prod-EP20_Historie",
-        "Prod-EP30_Historie",
-        "Prod-EP40_Historie",
-        "Prod-EP50_Historie",
-    ]
     data = data.with_columns(
         pl.all_horizontal(
             pl.col(PLAUSI_FEATURES).is_null() | (pl.col(PLAUSI_FEATURES) == 0)