Metrics (measures) are performance measurements that allow you to analyze business performance. They are usually numeric values. A metric can be as simple as the sum of the values in a fact column, or a highly complex calculation that contains mathematical operators.
A metric can be viewed as a statement that specifies how a performance measure is calculated. The basic component of a metric is a formula that specifies the calculation to be made. A metric can contain other components that specify additional criteria for calculating the metric.
Oracle Retail Analytics provides an extensive set of predefined business measures and key performance indicators for business intelligence in a retail environment. You can create your own metrics with the tools available in Oracle BI.
Retail Analytics metrics are stored in presentation tables. These tables contain table descriptions that include the level and nature of information provided and the functional area in which the metrics are used. For each metric, the presentation tables contain a description that includes the following:
Metric type, such as count or system metric
Functional area, such as net cost
Definition (for example, base cost is defined as the initial cost before any discounts are applied)
Constraints (for example, net profit data is only available by primary supplier)
In the Oracle BI interface, you can access a summary description of a metric as follows:
Right-click on the metric name.
Select Properties.
Note: See Appendix B, "Reporting on Oracle BI Repository Objects" for information about producing comprehensive listings of Oracle BI repository objects. |
Note: See Appendix C, "Retail Analytics Metric Definitions" for a complete list of Retail Analytics metrics. |
Comparable (comp) stores are stores that are open for business for a set period of time and were in operation within the time period of analysis. In other words, comp stores are really established stores as opposed to new or closed stores. Comp store measurements are important to an analyst because profits and sales from the more established stores provide stable indicators of business performance. New or closed stores tend to be more volatile and can have a skewing effect on business performance indicators. Sales and profits from new or closed stores are not really comparable in business analysis, and as a result, they are not included in the comp store measurements.
Retail Analytics utilizes a mathematical formula based on the store open date to determine if a store is comp or not. When a store location is open for 53 weeks, and is still in operation, the store becomes comp.
The Comp Store Measurements measure the growth in sales and profit, excluding the impact of newly opened stores. Sales and profits from new stores are not reflected in same-store comparisons until those stores are open for 53 weeks before the beginning of the current year's comp period. These stores must also be still open at the end of the current year's comp period. A comp period can be a month, a quarter, or a year.
With this approach, stores whose open dates are not captured in the source system are not included in these comparisons. Each store needs to have a store open date as well as a store close date, if a store is closed. If there is no close date, the store is assumed to be still in operation.
Cost and profit analysis helps a retailer to understand the financial impact of various business decisions such as:
Stock levels for high-profitability items
Deals negotiation for low-profitability items
Promotions worthiness
Profit is calculated as the difference between sales amount and cost of the item in the transaction. The cost of the item in the sales transaction is based on the weighted average cost of that item in the merchandising system at the end of the day.
Net cost (sometimes called deal cost) measures are held at the supplier level.
Net cost is populated with data from Oracle Retail Merchandising System (RMS) or another source system. The data from RMS consists of cost values that represent different discounts on base cost that the supplier provides. These discounts can include the following:
Deals with deal partners for items, or items at specific locations
Deal partners can be suppliers, wholesalers, distributors, and manufacturers. Within a deal, you create deal components, specify the items for the deal component, and define thresholds.
Fixed deals with suppliers
Your organization receives payments from suppliers in return for mentioning their products in promotions, or for displaying their products on prime shelf space.
Bracket costing deals with suppliers
Your organization receives a certain deal price on an order, depending on the size of the order. Different types of brackets can be established based on mass, volume, pallet, case, each, or stat case.
Retailers plan markdown strategies carefully, as they make pricing decisions for their products with an eye toward keeping inventories at optimal levels, while driving gross margin revenue across key areas of the merchandise hierarchy.
Retail Analytics markdown analysis allows reporting on a range of data related to markdowns and markups. These include permanent and point-of-sale markdowns and markups, as well as markup and markdown cancellations, at granularities of item, location, day, and retail type (regular, promotion, and clearance).
A buyer planning a promotion strategy for a category of goods might be interested in promotional markdown totals in a certain department, year to date. On the other hand, a finance executive might want to analyze clearance markdown amounts compared to promotional markdown amounts at the corporate level, on the same report with profit comparisons from clearance versus promotional sales.
A sales forecast is a calculation of the potential sales of an item for a future period, based on past performance of the product. Sales forecast analysis helps a retailer to develop a marketing budget, allocate resources, and get a early sense of deviations from financial goals. Sales forecast analysis also helps the retailer to determine the effectiveness of forecasting techniques.
Retail Analytics stores sales forecast data at the item-location-week level. The sales forecast quantities exclude value-added tax (VAT).
Inventory Adjustments are changes to inventory level in units, retail and cost value. Inventory Adjustment analysis provides visibility to Inventory analysts, Inventory controllers, Inventory managers, Category managers and store managers for analyzing the reasons and plan accordingly to overcome the potential problems that are causing the stock adjustments. Inventory Adjustments impact the stock ledger as the inventory value for a location is impacted. Ending stock value will be increase/decreased making the stock as over-valued or de-valued.
Retail Analytics holds the inventory adjustment units and value by reason code at item, location and day level.
Inventory transfer is the movement of stock between the retailer's locations. Inventory transfers analysis will help the retailer in taking appropriate and profitable decisions to improve the sales by initiating stock transfer from nearby locations to avoid lost sales.
Retail Analytics supports three types of transfers, normal, book and intercompany, with an attribute called transfer type.
Book transfer items are inventory units moved from one part of the retailer's location to the virtual location.
Normal transfers are the inventory moved between the retailer's physical locations. (Store or warehouse)
Intercompany transfer items are inventory units moved from one legal entity into another legal entity. RDW holds transfer units and cost and retail values of transferred units.
Inventory transfers are held at the item or subclass, destination (to) location, shipping (from) location, and day or week levels.
Inventory receipts are units purchased and placed in inventory. Inventory receipts analysis provides visibility to and control of your accrued liabilities for inventory items. Inventory receipts transactions are recorded in the general ledger at the time of receipts.
Retail Analytics holds the number of units purchased at the day and week level, at both retail and cost value. Inventory receipts are held at item level for day and week and at subclass (segment) level for day and week.
RTV units are units returned to the vendor for any reason (overstock, poor quality, etc.). Return to vendor analysis gives retailer valuable insights for evaluating vendor performance.
Retail Analytics maintains record of RTV units and the value of RTV units in cost and retail amount. RTV facts are held at the item/supplier/location/day/return reason level.
Sales reporting helps the merchandising executive to identify sales key performance indicators and determine the operational effectiveness of sales, to evaluate whether sales achieve the results set during sales planning. This can help sales managers to take timely corrective actions when they see deviations from projected values.
Gross sales value is the total amount the retailer sells to consumers. Gross sales value is calculated by multiplying the unit price of an item by the number sold to consumers. Returns are the portion of sales that are returned to the store for a refund. Sales value is the net value after customer returns are subtracted from gross sales value.
Retail Analytics maintains gross sales and returns for amounts and numbers of units in separate fact columns. Separation of these values allows analysis of returns and the use of gross sales in calculations where this is desirable. Net sales value is required for most calculations.
In addition, the retailer may need to track sales according to price type to allow analysis of sales for promotional and clearance items. Retail Analytics holds sales amount and units by retail price type to allow analysis at this level.
A sales pack is a group of individual items grouped together by the retailer to be sold as one item. An example is a bottle of shampoo and a bottle of conditioner, both individual items on their own, but packaged together to be sold as a unique pack item.
Retailers require visibility to pack sales contribution information by regular, clearance, and promotion retail types. This analysis provides the ability to compare and contrast location performance of pack sales using retail type measures.
These metrics can help to determine:
How a SKU sold as a single item
How the pack itself has sold historically
How a SKU sold when it was included in a specific pack
Retail Analytics extraction, transformation, and loading processing prorates the value of a pack into its component items (see "Prorating of Packs" later in this section). This helps in analysis of component pack item contribution to pack sales.
Prorating of Packs
The prorating of a pack's value into its component items requires calculation. The following formulas are used for prorating packs:
Item Prorated Sales Value = Pack Sales Value * Item Prorate % Item Prorate % = (Item Price * Pack Item Qty) / Pack Component Sales Value Pack Component Sales Value = (Item A Price * Item A Qty) + (Item B Price * Item B Qty) + (Item C Price * Item C Qty) + …+ (Item n Price * Item n Qty)
Example
Pack A has a pack sales value of $90,000. Each pack is priced at $9 and contains the following:
Calculation Steps
Calculate pack component sales value:
Item A Price * Quantity of Item A in Pack A
4 * 2 = 8
Item B Price * Quantity of Item B in Pack A
2 * 1 = 2
Item C Price * Quantity of Item C in Pack A
1 * 1 = 1
8 + 2 + 1 = 11
Calculate item prorate percent:
8/11 = .7273 (Item A)
2/11 = .1818 (Item B)
1/11 = .0909 (Item C)
Calculate item prorated sales value:
$90,000 * .7273 = $65,457.00 = Item A Prorated Sales Value
$90,000 * .1818 = $16,362.00 = Item B Prorated Sales Value
$90,000 * .0909 = $8,181.00 = Item C Prorated Sales Value
Supplier invoice reporting can help retailers achieve control of a supplier's payment process and assess the discrepancies for a supplier.
Supplier invoice cost is the actual cost as shown on the supplier invoice (from Oracle Retail Invoice Matching or other source system). Supplier invoice purchase order cost is the expected cost previously agreed upon in the purchase order, before any deals or discounts. A difference between the two can reflect deals, discounts, clerical errors, or dishonesty.
Supplier invoice cost and supplier invoice purchase order cost are held at the supplier-item-location-day level.
The merchandising organization must carefully select, monitor, and adjust relationships with suppliers. Before negotiations with suppliers, the retailer can prepare by running supplier performance and compliance reports.
Supplier performance considers typical merchandising measures such as net sales, profit/margin, markups, and return rates, to compare the profitability and inventory costs of goods provided by different primary suppliers.
Supplier compliance measures allow buyers to assess supplier delivery timeliness and purchase order fill rates. For example, how many advance shipping notices came in early, on time, and late? Were overall purchase order counts at expected levels, under, or over?
This analysis can help the retailer to negotiate supplier-funded promotion negotiations and supplier bill-backs, and reward responsive and flexible suppliers. This in turn can reduce inventory costs, prevent out-of-stock conditions, and increase profitability.
This functional area focuses on reporting that provides supplier performance information based on key performance indicators. Collection of this data makes the following types of analyses possible:
Compare and contrast supplier performance over time
Compare and contrast department performance by primary supplier
Monitor department performance in terms of sales volume and value
Compare and contrast market supplier with supplier performance
Department managers in particular need to understand sales and profit contribution information about their suppliers. Retailers can monitor supplier performance better by identifying suppliers of profitable items, measuring contributions to total department performance, and identifying how categories are performing relative to other categories, and relative to last year.
Unless facts (such as net cost) are stored by supplier, all facts in that data can only be attributed to the primary supplier.
The following types of measures are a part of supplier performance:
Sales and profit
Sales value and variance in sales value from last year
Sales units and variance in sales units from last year
Profit amount and variance in percent profit from last year
Percent contribution to total sales value for the department
Inventory position and movement
Sell-through
Stock turns
Beginning stock on hand (BOH) and ending stock on hand (EOH) retail value
Receipts
Gross margin return per dollar of inventory (GMROI)
Net (deal) cost
Net cost (sometimes referred to as deal cost) measures are held at the supplier level. Net cost is populated with data from Oracle Retail Merchandising System (RMS) or another source system. The data consists of cost values that represent different discounts on base cost that the supplier provides. These discounts may be:
Deals with deal partners for items, or items at specific locations
Deal partners can be suppliers, wholesalers, distributors, and manufacturers. Within a deal, you create deal components, specify the items for the deal component, and define thresholds.
Fixed deals with suppliers
Your organization receives payments from suppliers in return for mentioning their products in promotions or for displaying their products on prime shelf space.
Bracket costing deals with suppliers
Your organization receives a certain deal price on an order, depending on the size of the order. Different types of brackets can be established based on mass, volume, pallet, case, each, or stat case.
Supplier compliance measures supplier performance based on key performance indicators such as timeliness and accuracy of deliveries. The supplier compliance functionality supports supplier evaluation based on the following parameters:
Timeliness
Delivery accuracy
Order fulfillment
Supplier invoice cost is the actual cost as shown on the supplier invoice (from Oracle Retail Invoice Matching or other application). Supplier invoice purchase order cost is the expected cost previously agreed upon in the purchase order, before any deals or discounts. A difference between the two can be reflective of deals, discounts, clerical errors, or dishonesty.
Supplier invoice cost and supplier invoice purchase order cost are held at the supplier-item-location-day level.
Retail Analytics supplier compliance data provides the ability to report receipt units grouped by supplier, item, location, and day. For example, the fact column RECEIVED_QTY contains the quantity from the qty_received column in the RMS SHIPSKU table.
The supplier compliance data does not contain cost or sales data, so it cannot be used to report sales or cost by supplier. The quantity in the supplier compliance data should not be confused with receipt units in the inventory movement data.
Timeliness measures the supplier's ability to deliver according to schedule. Early, late, and on-time shipments are tracked in the supplier compliance area. You can measure supplier timeliness on a daily basis.
Timeliness = No of On Time Deliveries / ( No of On Time Deliveries + No of Early Deliveries + No of Late Deliveries )
For example, if the number of on-time deliveries is 75 and the total of all deliveries is 100, the timeliness rating is 75 percent.
Missed deliveries are deliveries that did not take place within the time frame specified. A late delivery is also a missed delivery. Because the timeliness measure would not be meaningful if two of its components were counted twice, missed deliveries are not included in the timeliness measure. Missed deliveries can be reported at the supplier-location-time level as a separate metric.
Delivery accuracy measures the supplier's ability to deliver the correct items and quantities on the order. The rating is determined by comparing the total number of deliveries for the supplier to the number of deliveries where the quantity or item was incorrect.
Delivery Accuracy = Number of ASN Expected Deliveries / Number of Deliveries
where:
Number of Deliveries = No of ASN Expected Deliveries + No of ASN Over Deliveries + No of ASN Under Deliveries + No of Mismatched Deliveries
A mismatched delivery is a delivery that contains at least one mismatched item.
For example, if the number of on-time deliveries is 75 and the total number of deliveries is 100, the delivery accuracy rating is 75 percent.
Order fulfillment measures the supplier's ability to deliver on order in full. The rating is determined by calculating the ratio of completely filled order to the total number of orders.
Order Fulfillment = No of Full Order Deliveries/ Total Orders
where:
Total Orders = Orders Received in Full + Orders Received in Part + Orders Received in Excess
For example, a supplier earns an order fulfillment rating of 75 percent if the total number of orders is 4 and the number of partial deliveries is 1.
Retail Analytics holds stock position at a very low level, which is the ending position for every day for every item at every stockholding location. The available stock position measures include quantity, retail value, and cost amount (usually interfaced from source systems based on weighted average cost calculation).
There are three distinct groupings of stock position in Retail Analytics:
On-hand stock (goods owned by the retailer and received in a location)
In-transit stock (goods owned by the retailer, received into one location such as a distribution center, but currently in transit to another store or warehouse)
On-order stock (goods on an approved Purchase Order which have not yet been received)
Two examples of on-hand measures are ending on-hand (EOH) for a time period, as well as beginning on-hand (BOH) for a time period. The EOH position for week 1 is the BOH position for week 2.
Stock position is a constant state in which a value or position shifts over time. Stock on hand is at a certain position at the beginning and end of a week and at any point between. Positional values cannot be added together to arrive at a meaningful number. For example, the ending stock-on-hand values for the days in a week do not add up to the ending value for a week. Rather, there is a position at the end of each day and, in this example, the ending position for the week is the same as the position for the last day of the week. For this reason, positional measurements are semi-additive. They are not additive in the time dimension. In other dimensions, they act much like transactions. For example, the ending on-hand value for a subclass can be determined by adding the ending on-hand values for all items in that subclass.
Comparing ending inventory value to the same period last year is a typical scorecard measure, but deeper analysis and more complex calculations are also required. Retail Analytics offers critical inventory calculations such as gross margin return on investment (GMROI), weeks of supply, stock turnover, sell-through, and the critical out of stock percentage measures.
A buyer might use one of these calculations to pair net sales and net profit measures on the same report with the out-of-stock percentage for the current month, to assess whether a certain department had low sales performance because of stock unavailability.
Wholesale metrics enable reporting on wholesale transactions as distinct from regular retail transactions, allowing retailers to understand how their wholesale business is working as a stand-alone operation. This will keep the wholesale business from being lost in the noise of their overall sales. The list below is unique wholesale metrics, but also all the regular sales metrics can be used to do wholesale analysis by filtering for transactions at wholesale locations.
Oracle Retail Analytics has three types of franchise metrics: Stockholding Franchise, Non-Stockholding Franchise, and Franchise. Which one a retailer uses will depend on their relationship with their franchise locations: if they manage inventory and replenishment for their franchisees, then Stockholding Franchise metrics are more useful, but if their franchisees operate relatively independently, Non-Stockholding Franchise metrics would be appropriate. Markdown and Markup metrics are simply known as Franchise metrics because there is no way to distinguish between stockholding and non-stockholding for this type of metric.
Consumer analysis is a method by which retailers will analyze their target consumers in order to determine the most effective strategies to improve both their sales and profitability. This analysis can be done by consumer, date, trade areas, consumer segments, historical performance, price strategies, promotions, clearances, demographics and loyalty programs.
Pricing analytics can help retailers determine the optimal pricing of products. It focuses on the proposed pricing of merchandise. Cost elements and profit components are not evaluated as part of pricing.
Retail Analytics holds price as a retail value for an item, day, and location. For the purpose of analysis, the average price is calculated over the time period selected for the report.
Retail Analytics holds facts for both preseason (original) and in-season (current) planning in several reporting areas, including sales, markdowns, receipts, inventory, gross margin, and open-to-buy, in both dollars and units.
The following abbreviations are used in the names of Planning metrics:
CPC: Current plan for cost-based planning
CPR: Current plan for retail-based planning
OPC: Original plan for cost-based planning
OPR: Original plan for retail-based planning
Retail Analytics information for stock ledger analysis comes from Oracle Retail Merchandising System (RMS).
The lowest-level stock ledger facts are kept at the subclass and week level. This gives Retail Analytics visibility to store/subclass/week level and subclass/month level. Stock ledger reporting is not available at the item and day levels. Reports and drills into data that are lower than the subclass/week level return null values for stock ledger facts.
If you receive stock ledger information from RMS, the RMS stock ledger feed to Retail Analytics supports either a 4-5-4 fiscal calendar or Gregorian calendar.
If you have a Gregorian stock ledger, reporting in Retail Analytics can be done at the subclass, location, and month levels. Reports and drills into data that are lower than the subclass/month level return null values for stock ledger facts.
If you have a 4-5-4 stock ledger, you can analyze the stock ledger at the subclass, location, week, and month levels. Reports and drills into data that are lower than the subclass/week level return null values for stock ledger facts.
Any other calendars, such as a 13-period time calendar, are not supported by the RMS interface to Retail Analytics for stock ledger facts. If an RMS user customizes the stock ledger to use a 13-period calendar, there are inconsistencies with the RMS stock ledger interface to Retail Analytics unless modifications are made.
Because the month-level stock ledger is directly related to the RMS MONTH_DATA table, data for a specific month is available in Retail Analytics after the close of that month.
Baseline metrics are derived from data mined during a period of time when an item is not on promotion.
The baseline process brings sales transaction data from Retail Analytics into a suitable structure for performing baseline calculations. The process first transfers sales data by week, identifying which weeks are suitable to be included for baseline calculation. A set of item/location weekly sales is suitable for baseline calculation only if it does not have promotion sales for the week. The number of weeks of sales data to use for baseline calculation is configurable, with a default suggested value of 16 weeks, eight weeks prior to the promotional week and eight weeks after. You can configure both the number of weeks included and whether they are pre-promotion or post-promotion weeks. For example, 14 weeks might be included in the calculation, with eight weeks pre-promotion and six weeks post-promotion. After processing, the calculated baseline metrics are returned to Retail Analytics.
These metrics are calculated at the promotion component/item/location/week level. They include baseline units, sales, profit, and transactions.
Baseline metrics can be used by a buyer during category planning, to establish expected sales for a category before promotions are added. This can help identify the level of promotion needed for the category to hit sales targets. A planner might decide that sales goals can be reached without promotions, or by promoting very little, thus saving money and adding to category margins.
Baseline metrics can also be used to calculate lift for promoted products; that is, how much over the baseline did sales increase when this category was promoted? If the difference between baseline and promoted weeks is large, and baseline sales are unacceptably low, it might be concluded that customers are shopping the category only for promoted items. Promotions might need to be cut back or changed, to avoid conditioning customers to buy items only when they are on promotion. If the difference is too small, the promotions might not be effective and not worth the cost to run them.
Retailers want to analyze the impact of new item introductions, and item promotions, to see whether customers come back a second and third time after trying something new. Something new may be a new item introduction, or the first time an item is put on promotion, perhaps as part of raising that item (or Brand's) profile, and so on. Trial and Repeat Metrics can help to analyze the repeat purchase behavior of customer household for the merchandise.
Market Basket Analysis reports can be used to understand what sells with what, including probability and profitability of market baskets. Such reports can be used to shape promotions, optimize product placement and support store planogram decisions. These metrics can help you understand the statistical relationship between sales of different merchandise.
The Customer Analytics module enables you to perform retail analysis around customers and customer segments. The following are some example business questions that Customer metrics can help to answer:
Who are my most profitable customers? Who are my most frequent shoppers?
Are my customers only buying items from me when they are on promotion?
What does a customer buy from me on a typical shopping trip? Does it vary by where they live or how much money they make?
Which of my departments appeal to which of my customers? That is, who is shopping in my stores and what are they shopping for?
Retail Analytics has a number of metrics against which to measure a promotional sales, cost and forecast as well as Promotion Campaign costs. These metric provide useful insight into the processes of managing actual marketing cost, evaluating financial performance of marketing tactics, and analyzing forecast and actual spending.
Note: Promotion Budget only supports as-is reporting. |
Note: Amount facts are in local and primary currency only. |
A cluster is a group of stores. Retailers make store clusters for various reasons, but the general idea is that stores in a cluster should have some key element or elements in common, which differentiates them from stores in other clusters. These elements could involve business objectives like store performance benchmarking, inventory management, and assortment/space planning. Then clusters can be used for analysis of sales, inventory, and promotions. Performance, inventory, ranging, trade area analysis, and union analysis are examples of elements around which clusters are built.
Oracle Retail Analytics' cluster metrics enable retailers to analyze their clusters' sales, inventory position, inventory receipts and promotions, so that any analysis that might normally be limited to some aspect of the organizational hierarchy can instead be performed on a retailers' customized store cluster, enabling precise, actionable analysis.
Customer orders lie at the heart of the modern retail experience. Virtually every customer transaction that takes place outside of a brick-and-mortar store is captured as part of a customer order, whether it is a normal sale, cancellation, return or exchange. A customer order consists of a customer order header that contains one or more customer order lines. Oracle Retail Analytics' customer order metrics allow retailers the flexibility to analyze the performance of their business across the various selling channels their customers use.
Retail Analytics supports a number of different metrics related to customer orders to allow performance analyses of omnichannel retailing. A list of the major metrics (minus the time transformations such as LY and LW) is below.
The following chart shows Retail Analytics metric metadata. Users should be aware that you cannot mix facts across as-is, as-was, and point-in-time subject areas.
Note: Performance of reports that contain YTD metrics may become less optimal as the end of the fiscal year approaches, due to the increasing amount of data that accumulates. Users should be aware of this and take steps to mitigate any performance effects, such as being specific with filters and prompts to get back the smallest amount of data necessary for analysis. |
Table 6-2 Metric Metadata
Merchandising Analytics | Customer Analytics | Metrics | As-Is | As-Was | Point in Time |
---|---|---|---|---|---|
X |
Cost and Profit |
X |
X |
X |
|
X |
Markdowns and Markups |
X |
X |
X |
|
X |
Sales Forecast |
X |
X |
X |
|
X |
Inventory Receipts |
X |
X |
X |
|
X |
X |
Sales |
X |
X |
X |
X |
Sales Pack |
X |
X |
X |
|
X |
Supplier Invoice |
X |
X |
X |
|
X |
Supplier Performance and Compliance |
X |
X |
X |
|
X |
Inventory Position |
X |
X |
X |
|
X |
Wholesale |
X |
X |
||
X |
Franchise |
X |
X |
X |
|
X |
Price |
X |
X |
X |
|
X |
Planning |
X |
|||
X |
Stock Ledger |
X |
|||
X |
Trial and Repeat |
X |
X |
||
X |
Sales Promotion |
X |
X |
X |
|
X |
Customer Order |
X |
X |
||
X |
Customer Order Promotion Transaction |
X |
X |
||
X |
Customer Order Status Fact |
X |
X |
||
X |
Customer Order Transaction |
X |
X |
||
X |
Touch Point |
X |
X |
||
X |
Retail Promotion Actuals |
X |
X |
X |
|
X |
Retail Promotion Forecast |
X |
X |
X |
|
X |
Promotion Baseline |
X |
X |
||
X |
Promotion Budget |
X |
X |
||
X |
Consumer Spend |
X |
X |
||
X |
Sales Promotion |
X |
X |
||
X |
Inventory Position |
X |
|||
X |
Return to Vendor |
X |
X |
||
X |
Inventory Adjustment |
X |
X |
||
X |
Inventory Transfers |
X |
X |