ENGINATICS SYSTEM_ADMINISTRATOR SYSADMIN 3A365C0F53C0AEFEE06362FB09051731Y with opm_batch_lot_costs as ( select x.top_batch_id, x.top_item_id, x.batch_id, x.material_detail_id, x.organization_id, x.product_item_id, x.inventory_item_id, x.primary_uom_code, x.dtl_um, x.dtl_prim_uom_conv_rate, x.plan_qty, x.actual_qty, x.scaled_actual_qty, x.ingredient_usage_factor, x.lot_number, x.lot_cost * x.dtl_prim_uom_conv_rate lot_cost, x.lot_qty, x.scaled_lot_qty, x.intermediate_ingred, x.uom_conv_error, x.line_type, x.lot_source, x.step_level, x.sort_order, x.group_id from xmltable ( '/BATCHLOTCOST/ROW' passing xxen_opm.get_report_xml columns top_batch_id number path 'TOP_BATCH_ID' , top_item_id number path 'TOP_ITEM_ID' , batch_id number path 'BATCH_ID' , material_detail_id number path 'MATERIAL_DETAIL_ID' , organization_id number path 'ORGANIZATION_ID' , product_item_id number path 'PRODUCT_ITEM_ID' , inventory_item_id number path 'INVENTORY_ITEM_ID' , primary_uom_code varchar2(3) path 'PRIMARY_UOM_CODE' , dtl_um varchar2(3) path 'DTL_UM' , dtl_prim_uom_conv_rate number path 'DTL_PRIM_UOM_CONV_RATE' , plan_qty number path 'PLAN_QTY' , actual_qty number path 'ACTUAL_QTY' , scaled_actual_qty number path 'SCALED_ACTUAL_QTY' , ingredient_usage_factor number path 'SCALING_FACTOR' , lot_number varchar2(150) path 'LOT_NUMBER' , lot_cost number path 'LOT_COST' , lot_qty number path 'LOT_QTY' , scaled_lot_qty number path 'SCALED_LOT_QTY' , intermediate_ingred varchar2(1) path 'INTERMEDIATE_INGRED' , uom_conv_error varchar2(1) path 'UOM_CONV_ERROR' , line_type number path 'LINE_TYPE' , lot_source varchar2(150) path 'LOT_SOURCE' , step_level number path 'STEP_LEVEL' , sort_order varchar2(2000) path 'SORT_ORDER' , group_id number path 'GROUP_ID' ) x ) -- -- select ood.organization_code organization, ood.organization_code mfg_unit, ood.organization_name warehouse, gbh1.batch_no, (select gfc.formula_class_desc from fm_form_mst ffm,gmd_formula_class gfc where ffm.formula_class = gfc.formula_class and ffm.formula_id = gbh1.formula_id) batch_type, gbh1.actual_cmplt_date batch_completion_date, (select gllv.currency_code from gl_ledger_le_v gllv where gllv.ledger_id = ood.set_of_books_id) currency_code, -- product msiv1.concatenated_segments "FP Code", msiv1.concatenated_segments product_code, msiv1.description product, case when oblc.step_level = 0 then gmd1.plan_qty else null end batch_size, case when oblc.step_level = 0 then gmd1.actual_qty else null end batch_actual_output, case when oblc.step_level = 0 then gmd1.plan_qty else null end "Batch Size Qty in Nos", case when oblc.step_level = 0 then gmd1.actual_qty else null end "Actuals Output Qty in Nos", case when oblc.step_level = 0 and nvl((select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'PACK SIZE'),0) > 0 then trunc(gmd1.actual_qty/(select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'PACK SIZE'),:p_precision) else null end batch_pack_count, case when oblc.step_level = 0 then round(gmd1.actual_qty/gmd1.plan_qty*100,2) else null end batch_yield, case when oblc.step_level = 0 then trunc(trunc(oblc.lot_cost,:p_precision) * oblc.actual_qty,:p_precision) else null end batch_cost, case when oblc.step_level = 0 then trunc(oblc.lot_cost,:p_precision) else null end batch_unit_cost, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'SIZE') product_size, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'REGION') country, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'PACK STYLE') pack_style, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'PACK TYPE') pack_type, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'PACK SIZE') pack_size, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'PACK SIZE') pack_count, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'GENERIC NAME') generic_name, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'BRAND NAME') brand_name, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'STRENGTH') strength, (select mdev.element_value from mtl_descr_element_values_v mdev where mdev.inventory_item_id = msiv1.inventory_item_id and mdev.item_catalog_group_id = msiv1.item_catalog_group_id and mdev.element_name = 'FORM') dosage_form, -- ingredients xxen_util.meaning(oblc.line_type,'GMD_FORMULA_ITEM_TYPE',552) type, msiv2.concatenated_segments item, msiv2.description item_desc, -- lot oblc.lot_number, gbh2.batch_no lot_batch_no, xxapl_batch_lot_costing_pkg.get_lot_source_name(gmd2.organization_id,gmd2.inventory_item_id,msiv2.concatenated_segments,oblc.lot_number) lot_source, trunc(oblc.lot_cost,:p_precision) lot_cost, trunc(oblc.scaled_lot_qty,:p_precision) lot_quantity, trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) lot_extended_cost, -- case when oblc.line_type = 1 then trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) else null end product_cost, case when oblc.intermediate_ingred = 'N' and oblc.step_level > 0 then trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) else null end ingredient_cost, case when oblc.intermediate_ingred = 'Y' and oblc.step_level > 0 then trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) else null end intermediate_ingredient_cost, -- case when substr(msiv2.concatenated_segments,1,1) = 'C' and substr(msiv2.concatenated_segments,2,1) != '5' then trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) else null end "API Cost", case when substr(msiv2.concatenated_segments,1,2) = 'C5' then trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) else null end "Excipient Cost", case when substr(msiv2.concatenated_segments,1,1) = 'P' then trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) else null end "PM Cost", case when oblc.step_level > 0 and substr(msiv2.concatenated_segments,1,1) in ('C','P') then trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) else null end total_material_cost, case when substr(msiv2.concatenated_segments,1,1) in ('C','P','F') then to_number(null) else trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) end other_item_cost, case when substr(msiv2.concatenated_segments,1,1) = 'F' then trunc(trunc(oblc.lot_cost,:p_precision) * oblc.scaled_lot_qty,:p_precision) else null end finished_product_cost, -- oblc.ingredient_usage_factor, oblc.primary_uom_code, oblc.dtl_um detail_uom_code, oblc.dtl_prim_uom_conv_rate, xxen_util.meaning(oblc.uom_conv_error,'YES_NO',0) uom_conv_error, xxen_util.meaning(decode(oblc.intermediate_ingred,'Y','Y',null),'YES_NO',0) intermediate_ingredient, oblc.step_level "Level", oblc.sort_order, oblc.group_id, oblc.top_batch_id, oblc.batch_id, oblc.material_detail_id from opm_batch_lot_costs oblc, gme_batch_header gbh1, gme_batch_header gbh2, gme_material_details gmd1, gme_material_details gmd2, org_organization_definitions ood, mtl_system_items_vl msiv1, mtl_system_items_vl msiv2 where oblc.top_batch_id = gbh1.batch_id and oblc.top_batch_id = gmd1.batch_id and oblc.top_item_id = gmd1.inventory_item_id and gmd1.line_type = 1 and oblc.batch_id = gbh2.batch_id and oblc.material_detail_id = gmd2.material_detail_id and gbh1.organization_id = ood.organization_id and oblc.organization_id = msiv1.organization_id and oblc.top_item_id = msiv1.inventory_item_id and oblc.organization_id = msiv2.organization_id and oblc.inventory_item_id = msiv2.inventory_item_id and -- -- only the top batch product line to be selected (oblc.step_level = 0 or oblc.line_type = -1) and -- oblc.intermediate_ingred = 'N' and 1=1 order by gbh1.batch_no, oblc.sort_order XXEN_OPM US APL Lot Cost Trend APL OPM Lot Costing Trend Report 1=1 :p_batch_no :p_conc_request_id :p_cost_type :p_cost_type_id :p_date_from :p_date_to :p_formula_class :p_item :p_master_org_id :p_org_code :p_org_id :p_precision :p_report_code 1 -50 :p_report_code Char OPM_BATCH_LOT_COST US Report Code 2 -45 :p_precision Number 3 US Precision 3 -40 :p_batch_no LOV custom Y select gbh.batch_no value, to_char(gbh.actual_cmplt_date,'DD-MM-YYYY HH24:MI:SS') description from mtl_parameters mp, gme_batch_header gbh where gbh.organization_id = mp.organization_id and xxen_util.contains(:$flex$.organization_code,mp.organization_code) = 'Y' and gbh.actual_cmplt_date is not null and trunc(gbh.actual_cmplt_date) between :$flex$.from_date and :$flex$.to_date and (:$flex$.formula_class is null or gbh.formula_id in (select ffm.formula_id from fm_form_mst ffm where ffm.formula_class = :$flex$.formula_class and ffm.owner_organization_id = mp.organization_id) ) and exists (select null from gme_material_details gmd, mtl_system_items_vl msiv, mtl_material_transactions mmt, mtl_transaction_lot_numbers mln where gmd.batch_id = gbh.batch_id and gmd.line_type = 1 and gmd.organization_id = msiv.organization_id and gmd.inventory_item_id = msiv.inventory_item_id and gmd.batch_id = mmt.transaction_source_id and gmd.material_detail_id = mmt.trx_source_line_id and gmd.inventory_item_id = mmt.inventory_item_id and mmt.transaction_source_type_id = 5 and mmt.transaction_id = mln.transaction_id and msiv.concatenated_segments = :$flex$.product ) order by value US Batch 4 -30 :p_formula_class LOV custom Y select gfc.formula_class id, gfc.formula_class_desc value, null description from gmd_formula_class gfc order by value 53 US Formula Class 5 10 :p_org_id LOV custom Y select goav.organization_id id, goav.organization_code value, goav.organization_name description from gmd_org_access_vw goav order by value Y US Organization 6 20 :p_date_from Date Y US From Date 7 30 :p_date_to Date Y US To Date 8 40 :p_item LOV custom Y Y select msiv.concatenated_segments value, msiv.description from mtl_system_items_vl msiv where msiv.organization_id=:$flex$.organization and msiv.inventory_item_id in (select gmd.inventory_item_id from gme_batch_header gbh, gme_material_details gmd where gbh.batch_id = gmd.batch_id and gbh.organization_id = :$flex$.organization and gmd.line_type = 1 and gbh.actual_cmplt_date is not null and trunc(gbh.actual_cmplt_date) between :$flex$.from_date and :$flex$.to_date and (:$flex$.formula_class is null or gbh.formula_id in (select ffm.formula_id from fm_form_mst ffm where ffm.formula_class = :$flex$.formula_class and ffm.owner_organization_id = :$flex$.organization) ) ) order by value Y US Product 9 60 :p_cost_type_id LOV custom Y select cmm.cost_type_id id, cmm.cost_mthd_code value, cmm.cost_mthd_desc description from cm_mthd_mst cmm where cmm.cost_type = 6 order by value select cmm.cost_mthd_code from cm_mthd_mst cmm, gmf_fiscal_policies gfp, org_organization_definitions ood where cmm.default_lot_cost_type_id = gfp.cost_type_id and gfp.legal_entity_id = ood.legal_entity and ood.organization_id = :$flex$.organization Y US Cost Type E1301C11D9BBF6C86EF0551045126579 Detail ENGINATICS -8.0009 ACTUAL_QUANTITY SUM 2 BATCH_NO 1 12 BATCH_SIZE 13 BATCH_YIELD -8.0009 EXTENDED_COST 11 INGREDIENT_COST SUM 4 ITEM 3 7 LOT_COST 9 LOT_EXTENDED_COST SUM 5 LOT_NUMBER 4 8 LOT_QUANTITY SUM 6 LOT_SOURCE -8.0009 LOT_VALUE 1 ORGANIZATION 10 PRODUCT_COST SUM -6.0007 SOURCE -8.0009 TOTAL_COST 3 TYPE -2 -8.0009 VALUE S Site 7124527E28D36F0944D841991F9FA4BF Detail Pivot ENGINATICS 9 BATCH_ACTUAL_OUTPUT 4 BATCH_COMPLETION_DATE 11 BATCH_COST 2 BATCH_NO 8 BATCH_SIZE 3 BATCH_TYPE 12 BATCH_UNIT_COST 10 BATCH_YIELD 5 CURRENCY_CODE 25 DETAIL_UOM_CODE 26 DTL_PRIM_UOM_CONV_RATE 23 INGREDIENT_COST 27 INGREDIENT_USAGE_FACTOR 14 ITEM 15 ITEM_DESC -16.0017 ITEM_TYPE 20 LOT_BATCH_NO 17 LOT_COST 19 LOT_EXTENDED_COST 16 LOT_NUMBER 18 LOT_QUANTITY 21 LOT_SOURCE 29 Level 31 MATERIAL_DETAIL_ID 1 ORGANIZATION 24 PRIMARY_UOM_CODE 7 PRODUCT 6 PRODUCT_CODE 22 PRODUCT_COST -7.0008 PRODUCT_ITEM_TYPE 30 SORT_ORDER 13 TYPE 28 UOM_CONV_ERROR APL Lot Cost Trend - Detail Pivot Template 20250821.xlsx US 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xl/worksheets/sheet1.xml xl/worksheets/sheet3.xml 1 0 Batch Completion Date 1 Batch No 2 Type 3 Item 4 Item Description 5 Lot Number 6 Lot Source 7 Lot Cost 8 Lot Quantity 9 Lot Extended Cost 10 Product Cost 11 Ingredient Cost 12 Batch Size 13 Batch Yield % 14 BATCH1 15 Product 16 NA 17 LOT1 18 (blank) 19 Ingredient 20 LOT2 21 BATCH1 Total data A1 1 data AA1 1 data AB1 1 data AC1 1 data AD1 1 data AE1 1 data AF1 1 data AG1 1 data B1 1 data C1 1 data D 13 data D1 14 data E1 1 data F1 1 data G1 1 data H1 1 data I 3 data I1 1 data J 3 data J1 1 data K 4 data K1 1 data L 3 data L1 1 data M 3 data M1 1 data N1 1 data O1 1 data P1 1 data Q1 1 data R1 1 data S 3 data S1 1 data T 3 data T1 1 data U 3 data U1 1 data V1 1 data W1 1 data X 3 data X1 1 data Y 3 data Y1 1 data Z1 1 parameter A1 1 parameter A11 1 parameter A12 1 parameter A13 1 parameter A14 1 parameter A15 1 parameter A2 1 parameter A3 1 parameter A4 1 parameter A5 1 parameter A6 1 parameter A7 1 parameter A8 1 parameter A9 1 parameter B3 2 S Site FDEBD9136906360C2B7CC3E2FB49E804 Summary SYSADMIN 12 API Cost SUM 11 Actuals Output Qty in Nos SUM 4 BATCH_NO 3 16 BATCH_YIELD SUM 10 Batch Size Qty in Nos SUM 6 DOSAGE_FORM -11.0012 EXCIPIENT_COST SUM 13 Excipient Cost SUM -9.001 FINAL_NUMBER_OF_UNITS -7.0008 FINAL_PACK_COUNT 1 FP Code 1 2 MFG_UNIT 2 9 PACK_COUNT 8 PACK_STYLE 14 PM Cost SUM 5 PRODUCT -4.0005 PRODUCT_DESC 7 STRENGTH 15 TOTAL_MATERIAL_COST SUM 3 WAREHOUSE APL Lot Cost Trend - Summary 13-Aug-2025 - AHCL- FGDLA1TC.xlsx US 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xl/worksheets/sheet1.xml xl/worksheets/sheet3.xml 1 0 Row Labels 1 Sum of API Cost (Sum) 2 Sum of Excipient Cost (Sum) 3 Sum of PM Cost (Sum) 4 H3D401A 5 H3D402A 6 H3D403A 7 H3D404A 8 H3D405A 9 H3D406A 10 H3D407A 11 H3D408A 12 H3D409A 13 H3D410A 14 (blank) 15 Grand Total 16 Sum of Batch Yield (Sum) 17 Column Labels 18 Diclofenac Sodium Topical Gel ,1% 19 Sum of Total Material Cost (Sum) data A1 7 data B1 7 data C1 7 data D1 7 data E1 7 data F1 7 data G1 7 data H1 7 data I1 7 data J 3 data J1 7 data K 3 data K1 7 data L 3 data L1 7 data M 3 data M1 7 data N 3 data N1 7 data O 3 data O1 7 data P 4 data P1 7 parameter A1 1 parameter A11 1 parameter A12 1 parameter A13 1 parameter A14 1 parameter A15 1 parameter A2 1 parameter A3 1 parameter A4 1 parameter A5 1 parameter A6 1 parameter A7 1 parameter A8 1 parameter A9 1 parameter B3 2 S Site ENGINATICS E1301C11D9BBF6C86EF0551045126579 7124527E28D36F0944D841991F9FA4BF