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Full S&OP Data Element Inventory

Version: 1.0 Date: September 9, 2025

Introduction​

Purpose: To provide a comprehensive inventory of S&OP data elements. This document breaks down each element into its potential constituent data fields, with mappings to common ERP systems like SAP and Odoo where applicable. It serves as a master guide for the ChainAlign data model and AI training.

1. Demand Data & Forecasting​

This category covers all aspects of understanding and predicting customer demand.

1.1. Bookings (Order Inflow)​

The total volume of new customer orders received.

  • Data Owner: Sales Operations
  • Source System Example: SAP S/4HANA, Odoo Sales Module
  • Update Frequency: Real-time or Daily
  • Constituent Data:
    • order_id: Unique identifier for the order. (SAP: VBAK-VBELN, Odoo: sale.order-id)
    • customer_id: Identifier for the customer. (SAP: KNA1-KUNNR, Odoo: res.partner-id)
    • product_sku: The specific product being ordered. (SAP: VBAP-MATNR, Odoo: product.product-id)
    • order_date: Date the order was placed. (SAP: VBAK-AUDAT, Odoo: sale.order-date_order)
    • requested_delivery_date: The date the customer wants the product. (SAP: VBAP-EDATU, Odoo: sale.order-commitment_date)
    • order_quantity: Number of units ordered. (SAP: VBAP-KWMENG, Odoo: sale.order.line-product_uom_qty)
    • unit_price: Price per unit at the time of order. (SAP: VBAP-NETPR, Odoo: sale.order.line-price_unit)
    • order_status: e.g., ‘Confirmed’, ‘Pending’, ‘Cancelled’. (SAP: VBAK-STATU, Odoo: sale.order-state)
  • Metadata:
    • source_table: SAP: VBAK, Odoo: sale_order
    • last_updated: SAP: VBAK-AEDAT, Odoo: sale_order.write_date
    • notes: Order status reflects the latest state of the sales order in the system.

1.2. Shipments (Order Outflow)​

The total volume of orders fulfilled and sent to customers.

  • Data Owner: Logistics/Distribution Team
  • Source System Example: SAP EWM, Odoo Inventory/Delivery
  • Update Frequency: Real-time or Daily
  • Constituent Data:
    • shipment_id: Unique identifier for the shipment. (SAP: LIKP-VBELN, Odoo: stock.picking-id)
    • order_id: The corresponding sales order. (SAP: LIKP-VBELN → VBAK-VBELN, Odoo: stock.picking-sale_id)
    • actual_ship_date: The date the product was actually shipped. (SAP: LIPS-LFDAT, Odoo: stock_picking.scheduled_date)
    • shipped_quantity: Number of units shipped. (SAP: LIPS-LFIMG, Odoo: stock_move.product_uom_qty)
    • carrier_id: The logistics provider used. (SAP: LIKP-VSBED / VTTK-VSART, Odoo: delivery_carrier.id)
    • tracking_number: For tracing the shipment. (SAP: LIKP-TKNUM, Odoo: delivery_carrier.tracking_ref)
  • Metadata:
    • source_table: SAP: LIKP, Odoo: delivery_carrier
    • last_updated: SAP: LIKP-AEDAT, Odoo: delivery_carrier.write_date
    • notes: Tracking number may be updated post-shipment.

1.3. Backlog​

The buffer of open, unfulfilled customer orders.

  • Data Owner: Customer Service / Order Management
  • Source System Example: SAP SD, Odoo Sales
  • Update Frequency: Daily
  • Constituent Data:
    • backlog_id: Unique identifier. (SAP: Custom or generated key, Odoo: Custom ID)
    • order_id: The open order. (SAP: VBAK-VBELN, Odoo: sale.order-id)
    • product_sku: The product on backlog. (SAP: VBAP-MATNR, Odoo: sale.order.line-product_id)
    • backlog_quantity: The quantity of units pending fulfillment. (SAP: Calculated from VBAK + VBAP + delivery schedules, Odoo: sale_order_line.product_uom_qty - stock_moves done)
    • age_of_backlog_days: How long the order has been open. (SAP: Calculated from VBAK-AUDAT to current date, Odoo: sale_order.date_order to current date)
    • reason_code: e.g., ‘Awaiting Production’, ‘Awaiting Components’. (SAP: Custom field or status codes, Odoo: Custom field on sale_order or stock_move)
  • Metadata:
    • source_table: SAP: VBAK or custom, Odoo: sale_order or stock_move
    • last_updated: SAP: VBAK-AEDAT or custom, Odoo: sale_order.write_date or stock_move.write_date
    • notes: Used for backlog analysis; may require business-specific mapping.

1.4. Sales Forecasts​

The core forward-looking view of expected demand.

  • Data Owner: Demand Planning / FP&A
  • Source System Example: SAP IBP, Odoo custom forecast
  • Update Frequency: Weekly or Monthly
  • Constituent Data:
    • forecast_id: Unique identifier for the forecast version. (SAP: SOPC-FORECAST_ID or similar, Odoo: product.forecast_id or custom)
    • product_sku: The product being forecasted. (SAP: SOPC-MATNR, Odoo: product_product.id)
    • region_id: The geographic area. (SAP: SOPC-REGION or similar, Odoo: Custom field or analytic account)
    • time_period: e.g., ‘2025-10’ (monthly bucket). (SAP: SOPC-PLAN_DATE, Odoo: Custom date field)
    • forecasted_units: The number of units projected to be sold. (SAP: SOPC-FORECAST_QTY, Odoo: product.forecast_units)
    • forecasted_revenue: The projected revenue (forecasted_units * projected_price). (SAP: Calculated from SOPC forecast and MVKE-MATPR, Odoo: Calculated from product.forecast_units × product.list_price)
    • currency_code: e.g., ‘USD’, ‘EUR’. (SAP: T001-WAERS or SOPC currency field, Odoo: res_currency.name)
    • forecast_type: e.g., ‘Statistical’, ‘Sales Input’, ‘Consensus’. (SAP: SOPC-FORECAST_TYPE, Odoo: Custom field)
  • Metadata:
    • source_table: SAP: SOPC, Odoo: custom forecast table
    • last_updated: SAP: SOPC-AEDAT or custom, Odoo: custom field
    • notes: Indicates method or source of forecast.

1.5. Historical & Current Sales Data​

Past and real-time sales figures used for analysis and forecasting.

  • Data Owner: Sales Operations / Finance
  • Source System Example: SAP SD Billing, Odoo Invoicing
  • Update Frequency: Daily / Real-time
  • Constituent Data:
    • transaction_id: Unique identifier for a past sale. (SAP: VBRK-VBELN or VBELN, Odoo: account_move.id or sale_order.id)
    • product_sku: The product sold. (SAP: VBRP-MATNR, Odoo: sale_order_line.product_id)
    • transaction_date: The date of the sale. (SAP: VBRK-FKDAT, Odoo: account_move.date or sale_order.date_order)
    • units_sold: The quantity sold. (SAP: VBRP-FKIMG, Odoo: sale_order_line.product_uom_qty)
    • net_revenue: The revenue from the sale. (SAP: VBRP-NETWR, Odoo: account_move_line.price_subtotal)
  • Metadata:
    • source_table: SAP: VBRP, Odoo: account_move_line
    • last_updated: SAP: VBRP-AEDAT, Odoo: account_move_line.write_date
    • notes: Revenue may be subject to adjustments or credits.

1.6. Unconstrained Demand Plan​

The consensus view of what customers would buy if no supply limitations existed.

  • Data Owner: Demand Planning / S&OP Facilitator
  • Source System Example: SAP IBP, Odoo custom plan
  • Update Frequency: Monthly
  • Constituent Data:
    • plan_version_id: Identifier for the locked plan. (SAP: SOPC-PLAN_VERSION, Odoo: Custom plan version ID)
    • product_sku: The product. (SAP: SOPC-MATNR, Odoo: product_product.id)
    • time_period: The planning period. (SAP: SOPC-PLAN_DATE, Odoo: Custom date field)
    • unconstrained_units: The final agreed-upon demand number. (SAP: SOPC-UNCONSTRAINED_QTY, Odoo: Custom field)
    • rationale_text: The documented assumptions behind the number. (SAP: Custom notes field, Odoo: Custom notes field)
  • Metadata:
    • source_table: SAP: custom notes, Odoo: custom notes field
    • last_updated: SAP: custom timestamp, Odoo: custom field
    • notes: Free-text; may require standardization for reporting.

1.7. Market & Customer Insights​

Data on market dynamics and customer preferences.

  • Data Owner: Strategy / Market Insights
  • Source System Example: Market Research Tools, SAP BW, Odoo custom, CRM
  • Update Frequency: Quarterly or Monthly
  • Constituent Data:
    • market_segment_id: The specific market (e.g., "High-End Watches, Europe"). (SAP: Custom segmentation field, Odoo: Custom analytic tag)
    • time_period: The period of measurement. (SAP: Custom date field, Odoo: Custom date field)
    • total_market_volume: The estimated total sales in the market. (SAP: External data or custom, Odoo: External data or custom)
    • company_sales_volume: The company's sales in that market. (SAP: Sales data filtered by segment, Odoo: Sales filtered by analytic tag)
    • market_share_percentage: The calculated share. (SAP: Calculated field, Odoo: Calculated field)
    • customer_satisfaction_score: e.g., CSAT, NPS.
    • social_sentiment_score: Positive/negative brand perception.
  • Metadata:
    • source_table: SAP: calculation from external + internal data, Odoo: calculated field
    • last_updated: SAP/Odoo: calculation timestamp
    • notes: Requires integration of external market data.

1.8. Demand Shaping Inputs​

Data related to initiatives that actively influence demand.

  • Data Owner: Marketing
  • Source System Example: SAP CRM, Odoo Marketing
  • Update Frequency: As needed / Quarterly
  • Constituent Data:
    • promotion_id: Unique identifier. (SAP: ZPROMO-PROMO_ID or custom, Odoo: marketing_promotion.id)
    • promotion_name: e.g., "Q4 Holiday Sale". (SAP: ZPROMO-PROMO_NAME, Odoo: marketing_promotion.name)
    • start_date / end_date: Duration of the promotion. (SAP: ZPROMO-START_DATE / END_DATE, Odoo: marketing_promotion.start_date / end_date)
    • product_skus_affected: Which products are included. (SAP: ZPROMO-MATNR list, Odoo: marketing_promotion.product_ids)
    • expected_lift_percentage: The projected impact on sales. (SAP: ZPROMO-LIFT_PCT, Odoo: Custom field)
    • pricing_data: Current and planned pricing for products.
  • Metadata:
    • source_table: SAP: ZPROMO, Odoo: marketing_promotion or custom
    • last_updated: SAP: ZPROMO-AEDAT, Odoo: marketing_promotion.write_date
    • notes: May be estimated; validate with marketing team.

1.9. Product Portfolio Data​

Data related to the product lifecycle.

  • Data Owner: Product Management / NPI Project Lead
  • Source System Example: SAP PLM, Odoo Projects
  • Update Frequency: As needed / Project-based
  • Constituent Data:
    • npi_project_id: Identifier for the launch project. (SAP: ZNPI-PROJECT_ID, Odoo: project.project.id)
    • new_product_sku: The new product. (SAP: ZNPI-MATNR, Odoo: product_product.id)
    • launch_date: The target market release date. (SAP: ZNPI-LAUNCH_DATE, Odoo: project.project.start_date or custom)
    • initial_ramp_up_volume: The planned initial production/sales volume. (SAP: ZNPI-RAMP_UP_VOL, Odoo: Custom field)
    • eol_plans: Phase-out plans for discontinued products.
    • product_mix: The range and hierarchy of products offered.
  • Metadata:
    • source_table: SAP: ZNPI, Odoo: custom NPI plan table
    • last_updated: SAP: ZNPI-AEDAT, Odoo: custom field
    • notes: Critical for NPI readiness tracking.

1.10. Segmentation & Context​

Data used to segment and understand demand patterns.

  • Data Owner: Commercial / IT
  • Source System Example: ERP, CRM, BI Tool
  • Update Frequency: Varies
  • Constituent Data:
    • demand_streams: Segmentation by channel, region, or go-to-market strategy.
    • demand_variability: Coefficient of variation for demand.
    • context_aware_entities: Products, regions, dates identified from conversations.

2. Supply, Capacity & Resources​

This category includes all data related to the capability to produce and deliver goods.

2.1. Production & Manufacturing​

Data related to the output of goods.

  • Data Owner: Production Planning / Manufacturing Operations
  • Source System Example: SAP PPDS, Odoo Manufacturing, MES
  • Update Frequency: Daily / Weekly
  • Constituent Data:
    • plan_id: (SAP: PPDS-PLAN_ID, Odoo: mrp.production.id)
    • product_sku: (SAP: PPDS-MATNR, Odoo: product_product.id)
    • plant_id: (SAP: PPDS-WERKS, Odoo: stock_location.warehouse_id)
    • time_period: (SAP: PPDS-PLAN_DATE, Odoo: mrp.production.date_planned_start)
    • planned_production_units: (SAP: PPDS-PLAN_QTY, Odoo: mrp.production.product_qty)
    • actual_units_produced: (SAP: AFRU-GMNG, Odoo: mrp.production.qty_produced)
    • yield_rate: Percentage of good units produced. (SAP: Calculated or custom field, Odoo: Custom field)
    • production_capacity: Maximum achievable output. (SAP: CRHD-KAP, Odoo: mrp.workcenter-capacity)
    • upside_flex: Capacity to increase production beyond the plan. (SAP: Custom, Odoo: Custom)
    • bottlenecks: Identified constraints in the production process. (SAP: Custom, Odoo: Custom)
  • Metadata:
    • source_table: SAP: PPDS, AFKO, AFRU, CRHD; Odoo: mrp.production, mrp.workcenter
    • last_updated: Varies by source table.
    • notes: Combines planning, execution, and capacity data.

2.2. Sourcing & Procurement​

Data related to acquiring materials and components.

  • Data Owner: Procurement / Supplier Quality
  • Source System Example: SAP MM, Odoo Purchase
  • Update Frequency: Varies
  • Constituent Data:
    • supplier_id: (SAP: LFA1-LIFNR, Odoo: res_partner.id)
    • component_sku: (SAP: MARA-MATNR, Odoo: product_product.id)
    • planned_lead_time_days: (SAP: MRP2-BDTER or custom, Odoo: product_supplierinfo.delay)
    • actual_lead_time_days: (SAP: Calculated from EKKO-BEDAT to MSEG-BUDAT, Odoo: Calculated field)
    • otif_percentage: (SAP: Calculated field, Odoo: Calculated field)
    • raw_material_availability: Stock levels of key inputs. (SAP: MARD-LABST, Odoo: stock.quant-quantity)
    • unreliable_suppliers: List of suppliers with high variability. (SAP: Custom, Odoo: Custom)
  • Metadata:
    • source_table: SAP: LFA1, EINE, EKKO, MSEG; Odoo: res.partner, product_supplierinfo, purchase_order, stock_picking
    • last_updated: Varies by source table.
    • notes: Combines planned lead times, actuals, and performance metrics.

2.3. Logistics & Distribution​

Data related to storing and moving goods.

  • Data Owner: Supply Chain (Logistics/Warehouse) / Maintenance
  • Source System Example: SAP WM/EWM, Odoo Inventory, WMS, SAP PM, Odoo Maintenance
  • Update Frequency: Daily / Weekly
  • Constituent Data:
    • distribution_capacity: Warehouse throughput capacity.
    • transportation_routes: Data on logistics optimization.
    • maintenance_id: (SAP: PM-ORDERID, Odoo: maintenance.request.id)
    • plant_id: (SAP: T001-WERKS, Odoo: stock_location.warehouse_id)
    • asset_id: (SAP: EQUN-EQUNR, Odoo: maintenance.equipment.id)
    • start_datetime: (SAP: PM-START_DATE, Odoo: maintenance.request.start_datetime)
    • end_datetime: (SAP: PM-END_DATE, Odoo: maintenance.request.end_datetime)
    • impact_on_capacity_percent: (SAP: Custom field, Odoo: Custom field)
  • Metadata:
    • source_table: SAP: LIKP, VTTK, PM tables; Odoo: stock.picking, delivery.carrier, maintenance.request
    • last_updated: Varies by source table.
    • notes: Combines logistics and maintenance data.

2.4. Human Resources​

Data related to the workforce.

  • Data Owner: HR / Finance
  • Source System Example: SAP HR/CO, Odoo Payroll, Workday
  • Update Frequency: Monthly
  • Constituent Data:
    • staffing_levels: Current headcount vs. plan.
    • labor_availability: Availability of key skills.
    • time_period: (SAP: HR module or CO data, Odoo: Custom date field)
    • plant_id: (SAP: HR or CO data, Odoo: stock_location.warehouse_id)
    • department_id: (SAP: HR module, Odoo: hr.department.id)
    • standard_labor_cost: (SAP: HR or CO data, Odoo: Payroll or analytic account)
    • overtime_labor_cost: (SAP: HR or CO data, Odoo: Payroll or analytic account)
  • Metadata:
    • source_table: SAP: HR/CO tables; Odoo: hr.employee, payroll tables
    • last_updated: Varies by source table.
    • notes: Combines headcount, availability, and cost data.

3. Inventory Data​

This category includes all data points related to on-hand and in-transit stock.

3.1. Inventory Levels & Composition​

The amount and type of stock on hand.

  • Data Owner: Inventory Control / Warehouse Management
  • Source System Example: SAP IM, Odoo Inventory
  • Update Frequency: Real-time or Daily
  • Constituent Data:
    • snapshot_date: (SAP: MARD-ERDAT or custom, Odoo: stock_quant.inventory_date)
    • location_id (warehouse): (SAP: MARD-WERKS, Odoo: stock_location.id)
    • product_sku: (SAP: MARD-MATNR, Odoo: product_product.id)
    • quantity_on_hand: (SAP: MARD-LABST, Odoo: stock_quant.quantity)
    • in_transit_units: Quantity shipped but not yet received.
    • available_units: Total stock available to promise.
    • work_in_progress_units: Value/quantity of partially finished goods.
  • Metadata:
    • source_table: SAP: MARD; Odoo: stock.quant
    • last_updated: Varies by source table.
    • notes: Represents a snapshot of physical inventory.

3.2. Inventory Policy & Strategy​

The rules and targets governing inventory.

  • Data Owner: Supply Planning / Inventory Management
  • Source System Example: ERP, Advanced Planning System
  • Update Frequency: Varies
  • Constituent Data:
    • product_sku: (SAP: MARA-MATNR, Odoo: product_product.id)
    • location_id: (SAP: MARD-WERKS, Odoo: stock_location.id)
    • safety_stock_units: (SAP: MRP2-SOBSL, Odoo: product.safety_stock)
    • safety_stock_days: (SAP: Custom or calculated, Odoo: Custom or calculated)
    • days_of_supply: (SAP: Calculated field, Odoo: Calculated field)
    • stockout_risk_alerts: Proactive warnings of potential shortages. (Calculated)
    • obsolete_quantity: (SAP: Custom field or flag, Odoo: Custom field)
    • obsolete_value: (SAP: Calculated, Odoo: Calculated)
    • decoupling_point_positioning: Strategic locations where inventory is held.
    • multi_echelon_inventory_data: Data for optimizing inventory across the network.
  • Metadata:
    • source_table: Varies (MARC, MARD, custom tables)
    • last_updated: Varies by source.
    • notes: Defines inventory targets and risk levels.

4. Financial Data​

This category translates operational data into financial terms.

4.1. Profitability Metrics​

Data related to the profitability of the plan.

  • Data Owner: Finance / FP&A
  • Source System Example: SAP FI/CO, Odoo Accounting
  • Update Frequency: Monthly
  • Constituent Data:
    • revenue: Top-line sales revenue. (SAP: VBRP-NETWR, Odoo: account.move.line-price_subtotal)
    • cost_of_goods_sold: Direct costs of production. (SAP: CO-PA or FI data, Odoo: Calculated)
    • gross_margin: Revenue - COGS. (SAP: Calculated, Odoo: Calculated)
    • gross_margin_percentage: Gross Margin / Revenue. (SAP: Calculated, Odoo: Calculated)
  • Metadata:
    • source_table: SAP: VBRP, CO-PA; Odoo: account.move.line
    • last_updated: Varies by source table.
    • notes: Core metrics for financial performance.

4.2. Balance Sheet & Cash Flow Metrics​

Data related to the financial health of the company.

  • Data Owner: Finance / Treasury
  • Source System Example: SAP FI, Odoo Accounting
  • Update Frequency: Monthly / Quarterly
  • Constituent Data:
    • snapshot_date: (SAP: Custom date, Odoo: Custom date)
    • inventory_value: The financial value tied up in inventory. (SAP: MBEW-SALK3, Odoo: stock.valuation.layer-value)
    • inventory_carrying_costs: The cost to hold inventory.
    • working_capital: Current Assets - Current Liabilities. (SAP: FI-GL accounts, Odoo: account.account with tags)
    • cash_flow_impact: The effect of the plan on cash.
    • return_on_net_assets: RONA, a key profitability ratio.
  • Metadata:
    • source_table: SAP: MBEW, FI-GL; Odoo: stock.valuation_layer, account.account
    • last_updated: Varies by source table.
    • notes: Key indicators of financial health and liquidity.

4.3. Planning & Decision Metrics​

Financial data used to guide decisions.

  • Data Owner: Finance / Logistics
  • Source System Example: FP&A Tool, ERP, SAP SD, Odoo Delivery/Accounting
  • Update Frequency: Monthly / As needed
  • Constituent Data:
    • financial_impact_of_scenarios: Real-time calculation of scenario costs/benefits.
    • budgetary_constraints: Financial limits the plan must adhere to.
    • shipment_id: (SAP: LIKP-VBELN, Odoo: stock_picking.id)
    • standard_freight_cost: (SAP: LIKP-FRKST, Odoo: delivery.carrier.price)
    • expedite_premium_cost: (SAP: Custom field, Odoo: Custom field)
  • Metadata:
    • source_table: Varies (FP&A system, LIKP, etc.)
    • last_updated: Varies by source.
    • notes: Key inputs for financial modeling and decision-making.

5. Performance Metrics (KPIs)​

This category includes key indicators that measure the health and effectiveness of the S&OP process and execution.

5.1. Forecast Performance​

Metrics measuring the quality of the demand forecast.

  • Data Owner: Demand Planning / Analytics
  • Source System Example: SAP IBP, Odoo custom
  • Update Frequency: Monthly
  • Constituent Data:
    • time_period: (SAP: Custom date, Odoo: Custom date)
    • product_sku: (SAP: MARA-MATNR, Odoo: product_product.id)
    • forecast_value: (SAP: SOPC-FORECAST_QTY, Odoo: product.forecast_units)
    • actual_value: (SAP: Sales actuals, Odoo: Sales actuals)
    • absolute_percentage_error: (SAP: Calculated, Odoo: Calculated)
    • forecast_error_sum: (SAP: Calculated, Odoo: Calculated)
    • bias_metric: (SAP: Calculated, Odoo: Calculated)
  • Metadata:
    • source_table: Calculated from forecast and actuals data.
    • last_updated: Monthly, after cycle closes.
    • notes: Key indicators of forecast quality.

5.2. Customer Service Performance​

Metrics measuring the ability to meet customer promises.

  • Data Owner: Customer Service / Logistics
  • Source System Example: SAP SD, Odoo Delivery
  • Update Frequency: Monthly
  • Constituent Data:
    • time_period: (SAP: Custom date, Odoo: Custom date)
    • customer_segment: (SAP: KNA1-KDGRP or custom, Odoo: res_partner.category_id)
    • total_orders: (SAP: Custom count, Odoo: Custom count)
    • otif_orders: (SAP: Custom count, Odoo: Custom count)
    • otif_percentage: (SAP: Calculated, Odoo: Calculated)
    • service_level: e.g., Fill Rate.
    • perfect_order_fulfillment: Percentage of error-free orders.
  • Metadata:
    • source_table: Calculated from sales and delivery data.
    • last_updated: Monthly.
    • notes: Key indicator of customer satisfaction.

5.3. Supply Chain Efficiency​

Metrics measuring the efficiency of the supply chain.

  • Data Owner: Supply Chain / Finance / Inventory Management
  • Source System Example: SAP Analytics, Odoo Reporting
  • Update Frequency: Monthly / Quarterly
  • Constituent Data:
    • time_period: (SAP: Custom date field, Odoo: Custom date field)
    • cost_of_goods_sold: (SAP: CO-PA or FI-CO data, Odoo: account_move_line filtered)
    • average_inventory_value: (SAP: Calculated from inventory balances, Odoo: Calculated)
    • inventory_turns_ratio: (SAP: Calculated, Odoo: Calculated)
    • lead_time: Actual vs. planned lead time for fulfillment.
    • asset_management_efficiency: How effectively assets are used.
  • Metadata:
    • source_table: Calculated from financial and inventory data.
    • last_updated: Monthly / Quarterly.
    • notes: Key indicators of operational efficiency.

5.4. Process Performance​

Metrics measuring the effectiveness of the S&OP process itself.

  • Data Owner: S&OP Facilitator / Analytics
  • Source System Example: ChainAlign (Generated), SAP IBP/Analytics, Odoo custom
  • Update Frequency: Per cycle / Monthly
  • Constituent Data:
    • plan_adherence_score: A weighted score combining Forecast Accuracy, Service Level, and Production Attainment. (SAP: Custom calculation, Odoo: Custom calculation)
    • decision_velocity: Time from issue identification to decision.
    • decision_id: (SAP: Custom ID, Odoo: Custom record ID)
    • meeting_date: (SAP: Custom date, Odoo: Custom date)
    • decision_text: (SAP: Custom text field, Odoo: Custom text field)
    • rolling_12_month_view: A forward-looking indicator of business direction.
  • Metadata:
    • source_table: ChainAlign internal tables, custom SAP/Odoo tables.
    • last_updated: Per cycle.
    • notes: Metrics to measure the health of the S&OP process itself.

6. Strategic & Contextual Data​

This category provides the high-level business context that guides S&OP decisions.

6.1. Corporate Strategy & Objectives​

The "why" behind the S&OP process.

  • Data Owner: Strategy / Executive Team
  • Source System Example: SAP Strategy Mgmt, Odoo custom
  • Update Frequency: Annually / As needed
  • Constituent Data:
    • objective_id: (SAP: Custom ID, Odoo: Custom ID)
    • objective_name: (SAP: Custom text, Odoo: Custom text)
    • target_value: (SAP: Custom numeric, Odoo: Custom numeric)
    • current_value: (SAP: Custom numeric, Odoo: Custom numeric)
    • weighting: (SAP: Custom numeric, Odoo: Custom numeric)
    • corporate_strategy: e.g., Operational Excellence, Product Leadership.
    • service_level_agreements: Documented service targets.
  • Metadata:
    • source_table: SAP: custom table, Odoo: custom field
    • last_updated: SAP: custom timestamp, Odoo: custom field
    • notes: Used for prioritization or scoring.

6.2. Planning & Decision Context​

Data that frames the planning process.

  • Data Owner: S&OP Facilitator / Planners
  • Source System Example: ChainAlign (Generated), SAP IBP, Odoo custom
  • Update Frequency: Per cycle / As needed
  • Constituent Data:
    • assumption_id: (SAP: Custom ID, Odoo: Custom ID)
    • plan_version_id: (SAP: SOPC-PLAN_VERSION, Odoo: Custom plan version ID)
    • assumption_text: (SAP: Custom text field, Odoo: Custom text field)
    • author: (SAP: User ID, Odoo: res.users.id)
    • date_created: (SAP: Timestamp, Odoo: Timestamp)
    • scenario_plans: The details of what-if analyses.
    • decision_log: A record of all key decisions made.
    • risk_assessment_matrices: Tools for evaluating potential risks.
  • Metadata:
    • source_table: SAP: any relevant table, Odoo: any relevant table
    • last_updated: SAP: date_created field, Odoo: create_date
    • notes: Indicates when the record was first created.

7. Technology & Data Management​

Data related to the ChainAlign system itself.

7.1. System & AI Data​

Data generated by or about the ChainAlign platform.

  • Data Owner: ChainAlign Admin
  • Source System Example: ChainAlign (Internal)
  • Update Frequency: Real-time
  • Constituent Data:
    • conversational_intelligence_data: Transcripts, intents, entities.
    • data_health_score: The overall quality score for ingested data.
    • data_freshness_indicators: Timestamps for source and sync.
    • system_performance_data: API latency, uptime, etc.
    • security_data: Audit logs, access records.

This comprehensive inventory will guide the development of ChainAlign's data model, ingestion engine, and AI features, ensuring we can support the full breadth of a mature S&OP process.