diff --git a/src/calculation_time_constrains/service.py b/src/calculation_time_constrains/service.py
index 315bd2f..e6a7778 100644
--- a/src/calculation_time_constrains/service.py
+++ b/src/calculation_time_constrains/service.py
@@ -877,7 +877,7 @@ class OptimumCostModel:
     retry_delay: float = 1.0
 ) -> Optional[float]:
         date_str = target_date.strftime('%Y-%m-%d %H:%M:%S.%f')
-        url = f"{self.api_base_url}/calculate/reliability/{location_tag}/{date_str}"
+        url = f"{self.api_base_url}/calculate/failure-rate/{location_tag}/{date_str}"
 
         for attempt in range(max_retries + 1):
             try:
@@ -1055,9 +1055,29 @@ class OptimumCostModel:
 
         return fr
 
-    def _calculate_costs_vectorized(self, reliabilities: Dict[datetime, float],
-                              preventive_cost: float, failure_replacement_cost: float, failure_rate) -> List[Dict]:
-        valid_data = [(date, rel) for date, rel in reliabilities.items() if rel is not None]
+    def _get_failure_rate(self, location_tag: str, token: str):
+        failure_rate_url = f"{self.api_base_url}/asset/failure-rate/{location_tag}"
+        try:
+            response = requests.get(
+                failure_rate_url,
+                headers={
+                    "Content-Type": "application/json",
+                    "Authorization": f"Bearer {token}",
+                },
+                timeout=10
+            )
+            response.raise_for_status()
+            result = response.json()
+        except (requests.RequestException, ValueError) as e:
+            raise Exception(f"Failed to fetch or parse mdt data: {e}")
+
+        fr = result["data"]["failure_rate"]
+
+        return fr
+
+    def _calculate_costs_vectorized(self, failure_rate: Dict[datetime, float],
+                              preventive_cost: float, failure_replacement_cost: float) -> List[Dict]:
+        valid_data = [(date, rel) for date, rel in failure_rate.items() if rel is not None]
         if not valid_data:
             return []
 
@@ -1069,28 +1089,33 @@ class OptimumCostModel:
         reliability_values = np.array(reliability_values)
 
         # Calculate days from last OH
-        days_from_last_oh = np.array([(date - self.last_oh_date).days for date in dates])
+        days_from_last_oh = np.array([(date - self.last_oh_date).days * 24 for date in dates])
+        date_point = np.array([i+1 for i in range(len(dates))])
 
         # Calculate failure probabilities
         failure_probs = 1 - reliability_values
 
         # Calculate expected operating times using trapezoidal integration
         # This is the denominator: ∫₀ᵀ R(t) dt for each T
-        expected_operating_times = np.zeros_like(days_from_last_oh, dtype=float)
-        for i in range(len(days_from_last_oh)):
-            # Time points from 0 to current point T
-            time_points = [(d - self.last_oh_date).days for d in dates[:i+1]]
-            # Reliability values (assuming reliability at time 0 is 1.0)
-            rel_values = reliability_values[:i+1]
-            # Calculate expected operating time up to this point
-            expected_operating_times[i] = np.trapz(rel_values, time_points)
+        # expected_operating_times = np.zeros_like(days_from_last_oh, dtype=float)
+        # for i in range(len(days_from_last_oh)):
+        #     # Time points from 0 to current point T
+        #     time_points = [(d - self.last_oh_date).days for d in dates[:i+1]]
+        #     # Reliability values (assuming reliability at time 0 is 1.0)
+        #     rel_values = reliability_values[:i+1]
+        #     # Calculate expected operating time up to this point
+        #     expected_operating_times[i] = np.trapz(rel_values, time_points)
 
 
         # Calculate costs according to the formula
         # Failure cost = (1-R(T)) × IDRu / ∫₀ᵀ R(t) dt
-        failure_costs = (failure_rate * failure_replacement_cost * expected_operating_times)
-        # Preventive cost = R(T) × IDRp / ∫₀ᵀ R(t) dt
-        preventive_costs = (reliability_values * preventive_cost) / expected_operating_times
+        # failure_costs = (failure_rate * failure_replacement_cost * expected_operating_times)
+        # # Preventive cost = R(T) × IDRp / ∫₀ᵀ R(t) dt
+        # preventive_costs = (reliability_values * preventive_cost) / expected_operating_times
+
+        failure_costs = reliability_values * failure_replacement_cost * days_from_last_oh
+        preventive_costs = preventive_cost / date_point
+
 
 
         # Total cost = Failure cost + Preventive cost
@@ -1099,14 +1124,13 @@ class OptimumCostModel:
         # Convert back to list of dictionaries
         results = []
         for i in range(len(dates)):
-            if i == 0:
-                continue
             results.append({
                 'date': dates[i],
-                'days_from_last_oh': days_from_last_oh[i],
-                'reliability': reliability_values[i],
+                'days_from_last_oh': days_from_last_oh[i] / 24,
+                'failure_rate': reliability_values[i],
                 'failure_probability': failure_probs[i],
-                'expected_operating_time': expected_operating_times[i],
+                'number_of_failure': round(reliability_values[i] * days_from_last_oh[i]),
+                'expected_operating_time': days_from_last_oh[i],
                 'failure_replacement_cost': failure_costs[i],
                 'preventive_replacement_cost': preventive_costs[i],
                 'total_cost': total_costs[i],
@@ -1124,6 +1148,7 @@ class OptimumCostModel:
             return None
 
         total_costs = np.array([r['total_cost'] for r in results])
+
         min_idx = np.argmin(total_costs)
         optimal_result = results[min_idx]
 
@@ -1131,13 +1156,31 @@ class OptimumCostModel:
             'optimal_index': min_idx,
             'optimal_date': optimal_result['date'],
             'days_from_last_oh': optimal_result['days_from_last_oh'],
-            'reliability': optimal_result['reliability'],
+            'failure_rate': optimal_result['failure_rate'],
+            'number_of_failure': optimal_result['number_of_failure'],
             'failure_cost': optimal_result['failure_replacement_cost'],
             'preventive_cost': optimal_result['preventive_replacement_cost'],
             'total_cost': optimal_result['total_cost'],
             'procurement_cost': 0
         }
 
+    def _get_number_of_failure_after_last_oh(self, target_date, token, location_tag):
+        date = target_date.strftime('%Y-%m-%d %H:%M:%S.%f')
+
+        nof = f"http://192.168.1.82:8000/reliability/calculate/failures/{location_tag}/{date}"
+
+        header = {
+            'Content-Type': 'application/json',
+            'Authorization': 'Bearer ' + token
+        }
+
+        response = requests.get(nof, headers=header)
+
+        data = response.json()
+
+        return data['data']['value']
+
+
     async def calculate_cost_all_equipment(
         self,
         db_session,  # DbSession type
@@ -1177,17 +1220,11 @@ class OptimumCostModel:
                 cost_per_failure = equipment.material_cost
 
                 # Get pre-fetched reliability data
-                reliabilities = all_reliabilities.get(location_tag, {})
-                failure_rate = self._get_equipment_fr(location_tag, self.token)
-
-
-                if not reliabilities:
-                    self.logger.warning(f"No reliability data for equipment {location_tag}")
-                    continue
+                failure_rate = all_reliabilities.get(location_tag, {})
+                # failure_rate = self._get_equipment_fr(location_tag, self.token)
 
                 # Calculate costs using vectorized operations
                 predicted_costs = self._calculate_costs_vectorized(
-                    reliabilities=reliabilities,
                     preventive_cost=preventive_cost_per_equipment,
                     failure_replacement_cost=cost_per_failure,
                     failure_rate=failure_rate
@@ -1204,8 +1241,8 @@ class OptimumCostModel:
                 preventive_costs = [r["preventive_replacement_cost"] for r in predicted_costs]
                 procurement_costs = [r["procurement_cost"] for r in predicted_costs]
                 procurement_details = [r["procurement_details"] for r in predicted_costs]
-                failures = [(1-r["reliability"]) for r in predicted_costs]
-                total_costs = [r['total_cost'] for r in predicted_costs]
+                failures = [r["number_of_failure"] for r in predicted_costs]
+                total_costs_per_equipment = [r['total_cost'] for r in predicted_costs]
 
                 # Pad arrays to max_interval length
                 def pad_array(arr, target_length):
@@ -1217,7 +1254,7 @@ class OptimumCostModel:
                 preventive_costs = pad_array(preventive_costs, max_interval)
                 procurement_costs = pad_array(procurement_costs, max_interval)
                 failures = pad_array(failures, max_interval)
-                total_costs = pad_array(total_costs, max_interval)
+                total_costs_per_equipment = pad_array(total_costs_per_equipment, max_interval)
 
                 fleet_results.append(
                     CalculationEquipmentResult(  # Assuming this class exists
@@ -1238,7 +1275,7 @@ class OptimumCostModel:
                 total_corrective_costs += np.array(corrective_costs)
                 total_preventive_costs += np.array(preventive_costs)
                 total_procurement_costs += np.array(procurement_costs)
-                total_costs += np.array(total_costs)
+                total_costs += np.array(total_costs_per_equipment)
 
             # Calculate fleet optimal interval
             # total_costs = total_corrective_costs + total_preventive_costs + total_procurement_costs
@@ -1285,8 +1322,6 @@ async def run_simulation(*, db_session: DbSession, calculation: CalculationData,
         db_session=db_session, overhaul_session_id=calculation.overhaul_session_id
     )
 
-    equipments = equipments[:100]
-
 
     scope = await get_scope(db_session=db_session, overhaul_session_id=calculation.overhaul_session_id)
 
diff --git a/src/overhaul_activity/cost_failure.json b/src/overhaul_activity/cost_failure.json
new file mode 100644
index 0000000..c38a531
--- /dev/null
+++ b/src/overhaul_activity/cost_failure.json
@@ -0,0 +1,9561 @@
+{
+"data": [
+	{
+		"location" : "00CHB-SKR805A",
+		"total_work_orders" : 416,
+		"total_cost" : 5.342133299E9,
+		"avg_cost" : 12841666.584134615385
+	},
+	{
+		"location" : "00CHA-SU801B",
+		"total_work_orders" : 388,
+		"total_cost" : 1.2727581122E10,
+		"avg_cost" : 32803044.128865979381
+	},
+	{
+		"location" : "00CHB-SKR805B",
+		"total_work_orders" : 358,
+		"total_cost" : 4.359637724E9,
+		"avg_cost" : 12177759.005586592179
+	},
+	{
+		"location" : "00CHA-SU801A",
+		"total_work_orders" : 354,
+		"total_cost" : 1.2511368626E10,
+		"avg_cost" : 35342849.225988700565
+	},
+	{
+		"location" : "00CHB-CV805A",
+		"total_work_orders" : 160,
+		"total_cost" : 4.5531383E8,
+		"avg_cost" : 2845711.437500000000
+	},
+	{
+		"location" : "00CHB-CV805B",
+		"total_work_orders" : 152,
+		"total_cost" : 1.995510155E9,
+		"avg_cost" : 13128356.282894736842
+	},
+	{
+		"location" : "00CHA-CV803A",
+		"total_work_orders" : 102,
+		"total_cost" : 1.085878938E9,
+		"avg_cost" : 10645871.941176470588
+	},
+	{
+		"location" : "00RO-S170A",
+		"total_work_orders" : 93,
+		"total_cost" : 8.5102543E8,
+		"avg_cost" : 9150811.075268817204
+	},
+	{
+		"location" : "00RO-S170C",
+		"total_work_orders" : 79,
+		"total_cost" : 8.1201957E8,
+		"avg_cost" : 10278728.734177215190
+	},
+	{
+		"location" : "00CHA-CV801A",
+		"total_work_orders" : 77,
+		"total_cost" : 9.75924205E9,
+		"avg_cost" : 126743403.24675325
+	},
+	{
+		"location" : "00CHA-CV803B",
+		"total_work_orders" : 75,
+		"total_cost" : 4.34169656E8,
+		"avg_cost" : 5788928.746666666667
+	},
+	{
+		"location" : "00CHA-CV802A",
+		"total_work_orders" : 74,
+		"total_cost" : 1.2157889526E10,
+		"avg_cost" : 164295804.40540541
+	},
+	{
+		"location" : "00DS-VF900",
+		"total_work_orders" : 72,
+		"total_cost" : 1.312066733E9,
+		"avg_cost" : 18223149.069444444444
+	},
+	{
+		"location" : "00RO-S170B",
+		"total_work_orders" : 70,
+		"total_cost" : 5.8583154E8,
+		"avg_cost" : 8369022.000000000000
+	},
+	{
+		"location" : "00DS-VF935",
+		"total_work_orders" : 68,
+		"total_cost" : 3.43762508E8,
+		"avg_cost" : 5055331.000000000000
+	},
+	{
+		"location" : "00CHA-CV804",
+		"total_work_orders" : 60,
+		"total_cost" : 1.7704475E8,
+		"avg_cost" : 2950745.833333333333
+	},
+	{
+		"location" : "00CHA-CV802B",
+		"total_work_orders" : 56,
+		"total_cost" : 1.91170166E8,
+		"avg_cost" : 3413752.964285714286
+	},
+	{
+		"location" : "00RO-S170D",
+		"total_work_orders" : 55,
+		"total_cost" : 8.79536795E8,
+		"avg_cost" : 15991578.090909090909
+	},
+	{
+		"location" : "00CHA-CV801B",
+		"total_work_orders" : 54,
+		"total_cost" : 5.5111054E8,
+		"avg_cost" : 10205750.740740740741
+	},
+	{
+		"location" : "00CHA-LBX801A",
+		"total_work_orders" : 43,
+		"total_cost" : 1.9083332E7,
+		"avg_cost" : 443798.418604651163
+	},
+	{
+		"location" : null,
+		"total_work_orders" : 38,
+		"total_cost" : 3.3254667E8,
+		"avg_cost" : 8751228.157894736842
+	},
+	{
+		"location" : "00MOB-WL009",
+		"total_work_orders" : 37,
+		"total_cost" : 8.1E7,
+		"avg_cost" : 2189189.189189189189
+	},
+	{
+		"location" : "00RO-T160C",
+		"total_work_orders" : 34,
+		"total_cost" : 1.97062538E8,
+		"avg_cost" : 5795957.000000000000
+	},
+	{
+		"location" : "00MOB-WL011",
+		"total_work_orders" : 33,
+		"total_cost" : 9.2156193E7,
+		"avg_cost" : 2792611.909090909091
+	},
+	{
+		"location" : "3SCR-S002A",
+		"total_work_orders" : 33,
+		"total_cost" : 3.05091179E9,
+		"avg_cost" : 92451872.424242424242
+	},
+	{
+		"location" : "00LSH-VI851",
+		"total_work_orders" : 33,
+		"total_cost" : 7180000.0,
+		"avg_cost" : 217575.757575757576
+	},
+	{
+		"location" : "00CHA-CS803",
+		"total_work_orders" : 32,
+		"total_cost" : 6.1908053E7,
+		"avg_cost" : 1934626.656250000000
+	},
+	{
+		"location" : "00RO-T160D",
+		"total_work_orders" : 30,
+		"total_cost" : 7.87525413E8,
+		"avg_cost" : 26250847.100000000000
+	},
+	{
+		"location" : "00LSH-CV852",
+		"total_work_orders" : 29,
+		"total_cost" : 1.6894841E7,
+		"avg_cost" : 582580.724137931034
+	},
+	{
+		"location" : "00MOB-BD006",
+		"total_work_orders" : 28,
+		"total_cost" : 1619543.0,
+		"avg_cost" : 57840.821428571429
+	},
+	{
+		"location" : "00RO-T160B",
+		"total_work_orders" : 28,
+		"total_cost" : 1.094721756E9,
+		"avg_cost" : 39097205.571428571429
+	},
+	{
+		"location" : "00LSH-CV801",
+		"total_work_orders" : 28,
+		"total_cost" : 1253500.0,
+		"avg_cost" : 44767.857142857143
+	},
+	{
+		"location" : "00RP-Z856B",
+		"total_work_orders" : 27,
+		"total_cost" : 3.69232E8,
+		"avg_cost" : 13675259.259259259259
+	},
+	{
+		"location" : "00RO-T160A",
+		"total_work_orders" : 27,
+		"total_cost" : 7.8789444E7,
+		"avg_cost" : 2918127.555555555556
+	},
+	{
+		"location" : "3SCR-W004A",
+		"total_work_orders" : 26,
+		"total_cost" : 5.917886E7,
+		"avg_cost" : 2276110.000000000000
+	},
+	{
+		"location" : "00RP-Z856A",
+		"total_work_orders" : 26,
+		"total_cost" : 9.1500205E7,
+		"avg_cost" : 3519238.653846153846
+	},
+	{
+		"location" : "00MOB-WL008",
+		"total_work_orders" : 24,
+		"total_cost" : 4.16495451E8,
+		"avg_cost" : 17353977.125000000000
+	},
+	{
+		"location" : "00RP-P972B",
+		"total_work_orders" : 21,
+		"total_cost" : 4.41908807E8,
+		"avg_cost" : 21043276.523809523810
+	},
+	{
+		"location" : "00CL-S001A",
+		"total_work_orders" : 21,
+		"total_cost" : 1.8145E7,
+		"avg_cost" : 864047.619047619048
+	},
+	{
+		"location" : "00DS-CY851A",
+		"total_work_orders" : 21,
+		"total_cost" : 5.6862177E8,
+		"avg_cost" : 27077227.142857142857
+	},
+	{
+		"location" : "3ABS-P910A",
+		"total_work_orders" : 20,
+		"total_cost" : 9.261E7,
+		"avg_cost" : 4630500.000000000000
+	},
+	{
+		"location" : "4LOM-I",
+		"total_work_orders" : 20,
+		"total_cost" : 5.79693318E8,
+		"avg_cost" : 28984665.900000000000
+	},
+	{
+		"location" : "00RO-S180",
+		"total_work_orders" : 20,
+		"total_cost" : 1.2219178E8,
+		"avg_cost" : 6109589.000000000000
+	},
+	{
+		"location" : "4TSI-I",
+		"total_work_orders" : 19,
+		"total_cost" : 4.98118132E8,
+		"avg_cost" : 26216743.789473684211
+	},
+	{
+		"location" : "3BAD-AX501",
+		"total_work_orders" : 19,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00LSH-CR853",
+		"total_work_orders" : 19,
+		"total_cost" : 6.9657265E7,
+		"avg_cost" : 3666171.842105263158
+	},
+	{
+		"location" : "00CL-P002A",
+		"total_work_orders" : 19,
+		"total_cost" : 2.55407127E8,
+		"avg_cost" : 13442480.368421052632
+	},
+	{
+		"location" : "3BAD-CV501",
+		"total_work_orders" : 19,
+		"total_cost" : 1.5930233E7,
+		"avg_cost" : 838433.315789473684
+	},
+	{
+		"location" : "00RO-P250A",
+		"total_work_orders" : 18,
+		"total_cost" : 2.0319E7,
+		"avg_cost" : 1128833.333333333333
+	},
+	{
+		"location" : "3ABS-P910B",
+		"total_work_orders" : 18,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "4OL-E",
+		"total_work_orders" : 18,
+		"total_cost" : 5.7196E7,
+		"avg_cost" : 3177555.555555555556
+	},
+	{
+		"location" : "00RO-P170C",
+		"total_work_orders" : 18,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RP-BM871",
+		"total_work_orders" : 17,
+		"total_cost" : 7405182.0,
+		"avg_cost" : 435598.941176470588
+	},
+	{
+		"location" : "3AI-Y511L",
+		"total_work_orders" : 17,
+		"total_cost" : 1232108.0,
+		"avg_cost" : 72476.941176470588
+	},
+	{
+		"location" : "00APC-PD807",
+		"total_work_orders" : 16,
+		"total_cost" : 3791500.0,
+		"avg_cost" : 236968.750000000000
+	},
+	{
+		"location" : "3DP-BM731E",
+		"total_work_orders" : 16,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AI-Y504R",
+		"total_work_orders" : 16,
+		"total_cost" : 4.616929E7,
+		"avg_cost" : 2885580.625000000000
+	},
+	{
+		"location" : "3DP-BM731D",
+		"total_work_orders" : 16,
+		"total_cost" : 9.0,
+		"avg_cost" : 0.56250000000000000000
+	},
+	{
+		"location" : "3ABS-P888A",
+		"total_work_orders" : 16,
+		"total_cost" : 4.4929222E7,
+		"avg_cost" : 2808076.375000000000
+	},
+	{
+		"location" : "00CHA-MB806A",
+		"total_work_orders" : 16,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RP-CY911",
+		"total_work_orders" : 16,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
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+	{
+		"location" : "00RO-PG105A-RV",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00FO-P803A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AI-Y508R",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AI-PN502",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AI-Y513B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3SCW-M001A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3DP-PG702C",
+		"total_work_orders" : 1,
+		"total_cost" : 1650000.0,
+		"avg_cost" : 1650000.000000000000
+	},
+	{
+		"location" : "3FO-FCV501",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3FW-PG001A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3GG-M851B",
+		"total_work_orders" : 1,
+		"total_cost" : 1.83E8,
+		"avg_cost" : 183000000.000000000000
+	},
+	{
+		"location" : "00RO-AIX107A-RV",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AF-TE531A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-V086B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00DMW-LG330",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3GG-TE986",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3GMC-Z002",
+		"total_work_orders" : 1,
+		"total_cost" : 1653300.0,
+		"avg_cost" : 1653300.000000000000
+	},
+	{
+		"location" : "3ABS-V913",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AH-TE751A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AL-PN503A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AL-TE511A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-M195A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3ESP-RD805",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00CHA-PCS881B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AL-DPIX703E",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AI-Y515C",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3ESP-RD823",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00FF-EHD845",
+		"total_work_orders" : 1,
+		"total_cost" : 2.345E7,
+		"avg_cost" : 23450000.000000000000
+	},
+	{
+		"location" : "3FW-P010A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3GG-PX511A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00DS-W980B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00FF-SN810",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3FO-PN502",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3FO-PN711A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-V051F",
+		"total_work_orders" : 1,
+		"total_cost" : 1.4469E7,
+		"avg_cost" : 14469000.000000000000
+	},
+	{
+		"location" : "3AI-Y514A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-ARV013D",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00FF-V849",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-PN114B",
+		"total_work_orders" : 1,
+		"total_cost" : 2.397E7,
+		"avg_cost" : 23970000.000000000000
+	},
+	{
+		"location" : "00FF-SRN895",
+		"total_work_orders" : 1,
+		"total_cost" : 3979875.0,
+		"avg_cost" : 3979875.000000000000
+	},
+	{
+		"location" : "3AL-VE502B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "4GG-F853A",
+		"total_work_orders" : 1,
+		"total_cost" : 1.31947E7,
+		"avg_cost" : 13194700.000000000000
+	},
+	{
+		"location" : "00RP-W978B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3CL",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-FIX103C",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AL-TE522B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3CW-LBX005B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00DMW-V282A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00APC-LV004A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3ATT-V006A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3GG-F803A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00FF-V806",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00FF-FS808",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-V010C",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AL-PN512C",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3SCR-Z007A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00DMW-FIX031B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3ESP-RD827",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-V068B",
+		"total_work_orders" : 1,
+		"total_cost" : 1.96248E7,
+		"avg_cost" : 19624800.000000000000
+	},
+	{
+		"location" : "3ABS-PIX910",
+		"total_work_orders" : 1,
+		"total_cost" : 8.52369E7,
+		"avg_cost" : 85236900.000000000000
+	},
+	{
+		"location" : "00FF-SRN852",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3GG-PN853",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3SO-P001",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3FW-V219B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3DP-PX701B",
+		"total_work_orders" : 1,
+		"total_cost" : 2442000.0,
+		"avg_cost" : 2442000.000000000000
+	},
+	{
+		"location" : "3CAE-PIX001B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RP-W957B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3CRH-PIX001",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00CL-V012B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-V072A",
+		"total_work_orders" : 1,
+		"total_cost" : 9.1464E7,
+		"avg_cost" : 91464000.000000000000
+	},
+	{
+		"location" : "00RO-V045A",
+		"total_work_orders" : 1,
+		"total_cost" : 5.16E7,
+		"avg_cost" : 51600000.000000000000
+	},
+	{
+		"location" : "00RO-P300B",
+		"total_work_orders" : 1,
+		"total_cost" : 3.4633333E7,
+		"avg_cost" : 34633333.000000000000
+	},
+	{
+		"location" : "3ABS-M932B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3FW-V227",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3SCR-V012A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AS-V506",
+		"total_work_orders" : 1,
+		"total_cost" : 1.0,
+		"avg_cost" : 1.00000000000000000000
+	},
+	{
+		"location" : "00FF-SN809",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RP-FS995",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3DP-PN721E",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3CCCW-PG002B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3EHB-P010B",
+		"total_work_orders" : 1,
+		"total_cost" : 1.71717E7,
+		"avg_cost" : 17171700.000000000000
+	},
+	{
+		"location" : "3GG-TE817A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3DCS-CAB014A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3ABS-W933B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00CL-V151D",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00APC-PD891",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00RO-M170C",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3FF-PG501A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3AH-H501A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3CO-LIX001A",
+		"total_work_orders" : 1,
+		"total_cost" : 1.468325E7,
+		"avg_cost" : 14683250.000000000000
+	},
+	{
+		"location" : "00RP-P991B",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00FF-V960",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "3CW-LBX001A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00FF-V001",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	},
+	{
+		"location" : "00DMW-PN132A",
+		"total_work_orders" : 1,
+		"total_cost" : 0.0,
+		"avg_cost" : 0E-20
+	}
+]}
diff --git a/src/overhaul_activity/service.py b/src/overhaul_activity/service.py
index 83c1f96..72c509a 100644
--- a/src/overhaul_activity/service.py
+++ b/src/overhaul_activity/service.py
@@ -25,6 +25,7 @@ from .model import OverhaulActivity
 from .schema import (OverhaulActivityCreate, OverhaulActivityRead,
                      OverhaulActivityUpdate)
 
+import json
 
 async def get(
     *, db_session: DbSession, assetnum: str, overhaul_session_id: Optional[UUID] = None
@@ -42,7 +43,11 @@ async def get(
     result = await db_session.execute(query)
     return result.scalar()
 
+def get_cost_per_failute():
+        with open('src/overhaul_activity/cost_failure.json', 'r') as f:
+            data = json.load(f)
 
+        return data['data']
 
 async def get_all(
     *,
@@ -97,20 +102,24 @@ async def get_all(
 
     num_equipments = len((await common['db_session'].execute(query)).scalars().all())
 
-    material_cost = get_material_cost("B", num_equipments)
+    data_cost = get_cost_per_failute()
 
 
     equipments = await search_filter_sort_paginate(model=query, **common)
     data = equipments['items']
 
+
+
     results = []
 
     for equipment in data:
         if not equipment.master_equipment:
             continue
+
+        cost = next((item for item in data_cost if item['location'] == equipment.location_tag), None)
         res = OverhaulActivityRead(
             id=equipment.id,
-            material_cost=material_cost,
+            material_cost=cost.get('avg_cost', 0) if cost else 0,
             service_cost=200000000,
             location_tag=equipment.location_tag,
             equipment_name=equipment.master_equipment.name if equipment.master_equipment else None,
@@ -146,11 +155,13 @@ async def get_standard_scope_by_session_id(*, db_session: DbSession, overhaul_se
 
     material_cost = get_material_cost("B", len(eqs))
     results = []
+    data_cost = get_cost_per_failute()
 
     for equipment in eqs:
+        cost = next((item for item in data_cost if item['location'] == equipment.location_tag), None)
         res = OverhaulActivityRead(
             id=equipment.id,
-            material_cost=material_cost,
+            material_cost=cost.get('avg_cost', 0) if cost else 0,
             service_cost=200000000,
             location_tag=equipment.location_tag,
             equipment_name=equipment.master_equipment.name if equipment.master_equipment else None,