diff --git a/data_analysis/02-3_oracle_workflow_test.py b/data_analysis/02-3_oracle_workflow_test.py
index c66b41b..3287106 100644
--- a/data_analysis/02-3_oracle_workflow_test.py
+++ b/data_analysis/02-3_oracle_workflow_test.py
@@ -22,6 +22,9 @@ from sqlalchemy import event
 
 from umbreit import db, types
 
+oracledb.defaults.arraysize = 1000
+oracledb.defaults.prefetchrows = 1000
+
 
 # %%
 # import importlib
@@ -72,13 +75,22 @@ conn_string = (
     f"oracle+oracledb://{USER_NAME}:{USER_PASS}@{HOST}:{PORT}?service_name={SERVICE}"
 )
 # engine = sql.create_engine(conn_string)
-engine = sql.create_engine(conn_string, execution_options={"stream_results": True})
+engine = sql.create_engine(
+    conn_string,
+    execution_options={"stream_results": True},
+)
 
 
-@event.listens_for(engine, "after_cursor_execute")
-def set_fetch_sizes(conn, cursor, statement, parameters, context, executemany):
-    cursor.arraysize = 1000
-    cursor.prefetchrows = 1000
+# @event.listens_for(engine, "after_cursor_execute")
+# def set_fetch_sizes(conn, cursor, statement, parameters, context, executemany):
+#     cursor.arraysize = 1000
+#     cursor.prefetchrows = 1000
+
+
+# @event.listens_for(engine, "before_cursor_execute")
+# def set_fetch_sizes(conn, cursor, statement, parameters, context, executemany):
+#     cursor.arraysize = 1000
+#     cursor.prefetchrows = 1000
 
 
 # %%
@@ -397,7 +409,7 @@ df.filter(pl.col("MELDENUMMER") == 18).select(pl.col("MENGE_VORMERKER").is_null(
 # BEDP_MENGE_BEDARF_VM as reference or ground truth not suitable
 df_diff_VM_bedp_tinfo = df.filter(pl.col("BEDP_MENGE_BEDARF_VM") > pl.col("MENGE_VORMERKER"))
 p_save_diff_VM_bedp_tinfo = (
-    Path.cwd() / "diff_BEDP-MENGE-BEDARF-VM_TINF-MENGE-VORMERKER_20251211-1.xlsx"
+    Path.cwd() / "diff_BEDP-MENGE-BEDARF-VM_TINF-MENGE-VORMERKER_20260130-1.xlsx"
 )
 
 df_diff_VM_bedp_tinfo.to_pandas().to_excel(p_save_diff_VM_bedp_tinfo, index=False)
@@ -440,6 +452,13 @@ def save_tmp_file(
     if filename is None:
         filename = str(uuid.uuid4())
     pth = (TMP_DIR / filename).with_suffix(".arrow")
+
+    n: int = 1
+    while pth.exists():
+        filename_new = pth.stem + f"_{n}"
+        pth = (TMP_DIR / filename_new).with_suffix(".arrow")
+        n += 1
+
     data.write_ipc(pth)
 
 
@@ -453,13 +472,13 @@ def load_tmp_file(
     return pl.read_ipc(pth)
 
 
-def load_all_tmp_files() -> tuple[pl.DataFrame, ...]:
-    all_dfs: list[pl.DataFrame] = []
+def load_all_tmp_files() -> dict[str, pl.DataFrame]:
+    all_dfs: dict[str, pl.DataFrame] = {}
     for file in TMP_DIR.glob("*.arrow"):
         df = pl.read_ipc(file)
-        all_dfs.append(df)
+        all_dfs[file.stem] = df
 
-    return tuple(all_dfs)
+    return all_dfs
 
 
 def get_starting_date(
@@ -486,6 +505,7 @@ def get_raw_data() -> pl.DataFrame:
         db.ext_titel_info.c.VERLAGSNR,
         db.ext_titel_info.c.MENGE_VORMERKER,
         db.ext_titel_info.c.MANDFUEHR,
+        db.ext_titel_info.c.EINKAEUFER,
     ).select_from(db.ext_bedpbed.join(db.ext_titel_info, join_condition, isouter=True))
 
     return pl.read_database(
@@ -938,7 +958,16 @@ def _wf100_petersen_sub1_wdb(
         .group_by(sub1.c.BEDP_TITELNR)
         .having(count_col > 1)
     )
-    # !! this is a sub result which muste be used in the result set
+    if SAVE_TMP_FILES:
+        stmt = (
+            sql.select(
+                sub1.c.BEDP_TITELNR,
+                count_col.label("count"),
+            )
+            .select_from(sub1)
+            .group_by(sub1.c.BEDP_TITELNR)
+        )
+    # !! this is a sub result which must be used in the result set
     # !! for testing and feedback by the customer
     relevant_titles = pl.read_database(
         stmt,
@@ -947,6 +976,7 @@ def _wf100_petersen_sub1_wdb(
 
     if SAVE_TMP_FILES:
         save_tmp_file(relevant_titles, TMPFILE_WF100_SUB1_WDB)
+        relevant_titles = relevant_titles.filter(pl.col.COUNT > 1)
 
     entries_to_show = pipe_result.open.filter(
         pl.col.BEDP_TITELNR.is_in(relevant_titles["BEDP_TITELNR"].unique().implode())
@@ -1063,7 +1093,17 @@ def _wf200_sub1(
         .group_by(sub1.c.BEDP_TITELNR)
         .having(unique_count_col >= 3)
     )
-    # !! this is a sub result which muste be used in the result set
+    if SAVE_TMP_FILES:
+        stmt = (
+            sql.select(
+                sub1.c.BEDP_TITELNR,
+                sql.func.count().label("count"),
+                unique_count_col.label("customer_count"),
+            )
+            .select_from(sub1)
+            .group_by(sub1.c.BEDP_TITELNR)
+        )
+    # !! this is a sub result which must be used in the result set
     # !! for testing and feedback by the customer
     relevant_titles = pl.read_database(
         stmt,
@@ -1071,7 +1111,8 @@ def _wf200_sub1(
     )
 
     if SAVE_TMP_FILES:
-        save_tmp_file(relevant_titles, None)
+        save_tmp_file(relevant_titles, TMPFILE_WF200_SUB1)
+        relevant_titles = relevant_titles.filter(pl.col.CUSTOMER_COUNT >= 3)
 
     entries_to_show = pipe_result.open.filter(
         pl.col.BEDP_TITELNR.is_in(relevant_titles["BEDP_TITELNR"].unique().implode())
@@ -1099,27 +1140,13 @@ def _wf200_sub1(
 # SAVING/LOADING
 p_save = Path.cwd() / "raw_data_from_sql_query_20260116-1.arrow"
 df = pl.read_ipc(p_save)
+raw_data = df.clone()
 print(f"Number of entries: {len(df)}")
 clear_tmp_dir()
 clear_result_data()
 # %%
 df.head()
 # %%
-# df.filter(pl.col.BEDP_TITELNR == 4314750)
-# # %%
-# RELEVANT_DATE = get_starting_date(180)
-# join_condition = db.tmp_data.c.BEDP_TITELNR == db.EXT_BESPBES_INFO.c.BESP_TITELNR
-# filter_ = sql.and_(
-#     db.EXT_BESPBES_INFO.c.BES_DATUM >= RELEVANT_DATE,
-#     db.EXT_BESPBES_INFO.c.BESP_TITELNR == 4314750,
-# )
-# stmt = sql.select(db.EXT_BESPBES_INFO).where(filter_)
-# relevant_titles = pl.read_database(
-#     stmt,
-#     engine,
-# )
-# print(relevant_titles)
-# %%
 raw_data = df.clone()
 # pipe_res = get_empty_pipeline_result(raw_data)
 pipe_res = PipelineResult(raw_data)
@@ -1167,11 +1194,99 @@ pipe_res.open.filter(pl.col.MELDENUMMER.is_in((17, 18)))
 # %%
 pipe_res.results.select(pl.col("VORLAGE").value_counts())
 # %%
+pipe_res.results.height
+# %%
 # ** aggregate test results
 all_tmps = load_all_tmp_files()
 print(len(all_tmps))
 # %%
-all_tmps[2]
+WF_100_TMP_RENAME = {"COUNT": "WF-100_WDB_Anz-Best-Petersen_verg_6_Monate"}
+WF_200_TMP_RENAME = {
+    "COUNT": "WF-200_Anz-Best-Kunde_verg_3_Monate",
+    "CUSTOMER_COUNT": "WF-200_Anz-Kunden_verg_3_Monate",
+}
+
+WF_100: list[pl.DataFrame] = []
+WF_200: list[pl.DataFrame] = []
+
+for name, df in all_tmps.items():
+    if TMPFILE_WF100_SUB1_WDB in name:
+        rename_schema = WF_100_TMP_RENAME
+        df = df.rename(rename_schema)
+        WF_100.append(df)
+    if TMPFILE_WF200_SUB1 in name:
+        rename_schema = WF_200_TMP_RENAME
+        df = df.rename(rename_schema)
+        WF_200.append(df)
+
+tmp_WF_collects = (WF_100, WF_200)
+all_tmps_preproc = []
+
+for collect in tmp_WF_collects:
+    if len(collect) > 1:
+        df = pl.concat(collect)
+    elif len(collect) == 1:
+        df = collect[0].clone()
+    else:
+        raise RuntimeError()
+
+    all_tmps_preproc.append(df)
+
+all_tmps_preproc
+
+#############################
+# %%
+res_table = pipe_res.results.clone()
+# %%
+res_table.head()
+# %%
+raw_data.head()
+# raw_data = raw_data.rename({"BEDARFNR": "BEDARF_NR", "BEDP_SEQUENZ": "BEDARF_SEQUENZ"})
+# raw_data
+# %%
+res_title_info = res_table.join(
+    raw_data,
+    left_on=["BEDARF_NR", "BEDARF_SEQUENZ"],
+    right_on=["BEDARFNR", "BEDP_SEQUENZ"],
+    how="inner",
+)
+exclude_cols = ("BEDARF_NR", "BEDARF_SEQUENZ")
+res_title_info = res_title_info.select(pl.exclude(exclude_cols))
+res_title_info.head()
+# %%
+df1 = all_tmps["WF-100_Sub1-WDB"]
+df2 = all_tmps["WF-200_Sub1"]
+df3 = all_tmps["WF-200_Sub1_1"]
+df1.head()
+# %%
+test_results = res_title_info.clone()
+
+for df in all_tmps_preproc:
+    test_results = test_results.join(df, on="BEDP_TITELNR", how="left")
+
+test_results = test_results.sort(by=["WF_ID", "BEDP_MAN"], descending=False)
+test_results = test_results.select(pl.int_range(1, pl.len() + 1).alias("Index"), pl.all())
+# %%
+test_results
+
+# %%
+date_str = datetime.datetime.now().strftime("%Y-%m-%d")
+p_save = Path.cwd() / f"Testdatensatz_WF-100-200_{date_str}.xlsx"
+test_results.to_pandas().set_index("Index").to_excel(p_save, freeze_panes=(1, 1))
+# %%
+# ** deviating titles where BEDP_MENGE_BEDARF_VM > MENGE_VORMERKER
+deviation_vm = test_results.with_columns(pl.col.MENGE_VORMERKER.fill_null(0)).filter(
+    pl.col.BEDP_MENGE_BEDARF_VM > pl.col.MENGE_VORMERKER
+)
+deviation_vm = test_results.filter(pl.col.BEDP_TITELNR.is_in(dev["BEDP_TITELNR"].implode()))
+
+date_str = datetime.datetime.now().strftime("%Y-%m-%d")
+p_save = Path.cwd() / f"Abweichungen-VM_{date_str}.xlsx"
+deviation_vm.to_pandas().set_index("Index").to_excel(p_save, freeze_panes=(1, 1))
+# ** WF-200 potentially triggered
+raw_data.filter(pl.col.MELDENUMMER.is_in((17, 18))).filter(
+    pl.col.BEDP_TITELNR.is_duplicated()
+).sort("BEDP_TITELNR")
 
 # %%
 # ---------------------------------------------------------------------------- #
diff --git a/src/umbreit/db.py b/src/umbreit/db.py
index 05a4964..ddd3c56 100644
--- a/src/umbreit/db.py
+++ b/src/umbreit/db.py
@@ -188,6 +188,7 @@ raw_data_query_schema_map: PolarsSchema = {
     "VERLAGSNR": pl.UInt32,
     "MENGE_VORMERKER": pl.UInt32,
     "MANDFUEHR": pl.UInt8,
+    "EINKAEUFER": pl.String,
 }
 
 tmp_data = Table(