diff --git a/src/calculation_time_constrains/service.py b/src/calculation_time_constrains/service.py
index 4aa8f3b..f53c5c6 100644
--- a/src/calculation_time_constrains/service.py
+++ b/src/calculation_time_constrains/service.py
@@ -138,14 +138,14 @@ async def get_corrective_cost_time_chart(
         raise
 
 
-# async def get_corrective_cost_time_chart(
-#     material_cost: float,
-#     service_cost: float,
-#     location_tag: str,
-#     token,
-#     start_date: datetime.datetime,
-#     end_date: datetime.datetime
-# ) -> Tuple[np.ndarray, np.ndarray]:
+async def get_corrective_cost_time_chart(
+    material_cost: float,
+    service_cost: float,
+    location_tag: str,
+    token,
+    start_date: datetime.datetime,
+    end_date: datetime.datetime
+) -> Tuple[np.ndarray, np.ndarray]:
     days_difference = (end_date - start_date).days
 
     today = datetime.datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
@@ -186,17 +186,17 @@ async def get_corrective_cost_time_chart(
                     history_dict[month_key] += item["num_fail"]
 
             # Sort months chronologically
-            sorted_months = sorted(monthly_failures.keys())
+            sorted_months = sorted(history_dict.keys())
+
+            failures = np.array([history_dict[month] for month in sorted_months])
+            cum_failure = np.cumsum(failures)
 
-            # Calculate cumulative failures
-            running_total = 0
-            for month in sorted_months:
-                running_total += monthly_failures[month]
-                history_dict[month] = running_total
+            for month_key in sorted_months:
+                monthly_failures[month_key] = int(cum_failure[sorted_months.index(month_key)])
 
 
             # Update monthly_data with cumulative historical data
-            monthly_data.update(history_dict)
+            monthly_data.update(monthly_failures)
         except Exception as e:
             # print(f"Error fetching historical data: {e}")
             raise Exception(e)
@@ -239,7 +239,29 @@ async def get_corrective_cost_time_chart(
     current_date = datetime.datetime(start_date.year, start_date.month, 1)
     while current_date <= end_date:
         if current_date not in monthly_data:
-            monthly_data[current_date] = 0
+             # Initialize to check previous months
+            previous_month = current_date.replace(day=1) - datetime.timedelta(days=1)
+            # Now previous_month is the last day of the previous month
+            # Convert back to first day of previous month for consistency
+            previous_month = previous_month.replace(day=1)
+
+            # Keep going back until we find data or run out of months to check
+            month_diff = (current_date.year - start_date.year) * 12 + (current_date.month - start_date.month)
+            max_attempts = max(1, month_diff)  # Ensure at least 1 attempt
+            attempts = 0
+
+            while previous_month not in monthly_data and attempts < max_attempts:
+                # Move to the previous month (last day of the month before)
+                previous_month = previous_month.replace(day=1) - datetime.timedelta(days=1)
+                # Convert to first day of month
+                previous_month = previous_month.replace(day=1)
+                attempts += 1
+
+            # Use the found value or default to 0 if no previous month with data exists
+            if previous_month in monthly_data:
+                monthly_data[current_date] = monthly_data[previous_month]
+            else:
+                monthly_data[current_date] = 0
 
         # Move to next month
         if current_date.month == 12:
@@ -247,11 +269,13 @@ async def get_corrective_cost_time_chart(
         else:
             current_date = datetime.datetime(current_date.year, current_date.month + 1, 1)
 
-    # Convert to list maintaining chronological order
+
+    # # Convert to list maintaining chronological order
     complete_data = []
     for month in sorted(monthly_data.keys()):
         complete_data.append(monthly_data[month])
 
+
     # Convert to numpy array
     monthly_failure = np.array(complete_data)