You can download demo data here.
In order to load data to Augment, you can either use one of our connectors if your data reside on the connected platforms or use this general import to include your data into the Augment platform. The data should contain your product and sales information for optimum at least last 2 years in order to perfectly compute seasonality or any other trends.
This article describes the data structure exactly how we need it in order for Augment to work correctly. Once you have the structure ready, you will be ready to upload the data and create a connection.
Data spaces
There are 2 data spaces for the Augment.
- Sales Data
- Promotion Data
Sales Data contains all your product, warehouse and transaction information. Based on past transactions we can forecast future sales and based on product-warehouse data (current state of amount on stock for example) we can tell you what to order and when.
In order for perfectly analyse sales and predict future we need information about your promotions. Past promotions serve as a benchmark to see if the high sales are due to higher demand or due to promotion, future promotion serves us to higher the expected forecast because of it. In the best scenario both past and planned promotions will help us compute better forecast and orders.
Entity types
There are several entity types we import in order to compute all necessary data. Each fits into its own .csv file.
- Warehouse - this entity holds information about warehouse or any other place where we store items (point of sales etc)
- Product - basic information about product - name, category
- Supplier - basic information about suppliers
- Category - categorisation of the products (like product lines, brands etc.) - Augment currently supports only one level of categories (so no nested or parent-child categories are taken into account)
- Warehouse-product - In warehouse product connecting data are specific data for each product on given warehouse (so for example product A on Warehouse 1 has different inventory on hand or price than on Warehouse 2)
- Transactions - Each transaction is one sales item, so if one order has 2 products sold, it is 2 transactions in Augment. Data can be grouped per item per day (so all sold Product A per day from Warehouse 1)
- Currency - Indicating primary and (if applicable) secondary currency of the system to be displayed.
Data
Currency (currency.csv)
| Field | Type | Comment |
| currency_id | ISO code of currency | for example USD |
| default | tinyint | 0/1 There should be only one default currency per file (the default one) |
Warehouse (warehouse.csv)
| Field | Type | Comment |
| warehouse_id | generated id [0-9a-zA-Z_-] | |
| name | Varchar 64 | |
| parent_warehouse_id | generated id [0-9a-zA-Z_-] | ID of parent warehouse, for use in case of supplying your warehouse/shop from central warehouse for example |
Product (product.csv)
| Field | Type | Comment |
| product_id | generated id [0-9a-zA-Z_-] | |
| name | Varchar 64 | |
| category_id | FK - category_id | FK = Foreign key - link to category based on the category_id (that category must exist) |
| shortcut | Varchar 255 | ID that is displayed to the user in the app as id of the product, if empty, it is filled with product_id |
| brand | Varchar 255 | tags which will help you to cluster your portfolio by brands (you can use it on the replenishment as a filter etc.) |
| collection | Varchar 255 | especially useful for fashion industry (limited collections etc) |
| product_id_by_supplier | Varchar 255 | Product ID which is helping your suppliers to identify items on the purchase orders |
| ean | Varchar 255 | EAN code (European Article Number (International Article Number)) |
Supplier (supplier.csv)
| Field | Type | Comment |
| supplier_id | generated id [0-9a-zA-Z_-] | |
| name | Varchar 64 | |
| currency_id | ISO code of currency |
Category (category.csv)
| Field | Type | Comment |
| category_id | generated id [0-9a-zA-Z_-] | |
| name | Varchar 64 |
Warehouse product (warehouse_product.csv)
| Field | Type | Comment |
| warehouse_id | FK - warehouse_id | link to warehouse via ID |
| product_id | FK - product_id | link to product via ID |
| available_supply | Decimal | available amount of this goods on stock |
| stock_price | Decimal | price of cost for this unit |
| sale_price | Decimal | usual selling price of this goods |
| supplier_id | FK - supplier_id | |
| minimal_order_quantity | Decimal | minimal order quantity for this product, if not available empty value (as NULL) |
| minimal_order_set | Decimal | minimal order set (for example packaging) for this product, if not available empty value (as NULL) If the minimal quantity is 100 and minimal set is 10 the orders will be 100,110,120 etc. if minimal quantity is 0, the orders are in multiply of minimal_order_set (10,20,30..) |
| visible | tinyint | if 1 then product is processed by Augment (is active), if 0 then product is skipped completely |
| order | tinyint | if 1 then product can be proposed in replenishment, if 0 product cannot be proposed |
| created_at | string | the exact date when the SKU/item was created, Format W3C Y-m-d\TH:i:sP example: 2005-08-15T15:52:01+00:00 |
| similar_product_id | FK - product_id | ID of the predecessor, Augment will automatically utilize the sales history from this predecessor |
| basestock_min | Decimal | minimum inventory you would like to keep |
| basestock_max | Decimal | maximum inventory you would like to keep |
| expiration_in_days | Decimal | real expiration in days after delivery to your warehouse |
| minimal_period | Decimal | purchase order period in days |
| leadtime | Decimal | lead time in days |
Transaction (transaction.csv)
| Field | Type | Comment |
| transaction_id | generated id [0-9a-zA-Z_-] | usually line number |
| transaction_type_id | see transaction types | |
| warehouse_id | FK - warehouse_id | warehouse from which this product was sold |
| product_id | FK - product_id | |
| date_of_transaction | YYYY-MM-DD | |
| amount | Decimal | amount sold in units |
| price | Decimal | unit price of transaction, in case of sales transaction it is unit sell price, in case of other transactions it is unit stock price |
| stock_price | Decimal | cost of this goods (per unit) |
| supplier_id | FK - supplier_id | if the transaction is of type Purchase |
| promo_sale | 0,1 | If this sale had some discount or other promo |
| extreme_sale | 0,1 | if the sale is extreme(1) it is not used for forecasting |
| date_of_order | YYYY-MM-DD | if the transaction os of type Purchase, empty otherwise |
Transaction types:
In Augment we can import multiple types of transaction (sales, purchase, stock-taking, transfer of goods etc.). The must for the integration are sales and purchase transaction. For explanation of the rest please contact us.
For the initial import, please consider using sales and purchase transactions - each has its own transaction_type_id.
1 = Sales Transaction (if the amount is <0 than it is Cancellation of the sales)
2 = Purchase Transaction (if the amount is <0 than it is Cancellation) - you are purchasing the product from your supplier
If you want to include cancelations (return of the goods) use the same transaction_type_id but use negative value in the amount field. The logic is always - positive amount = succesful transaction, negative amount = return or cancelation. Augment does not calculate the cancelation exactly in the month as it occurred but tries to find the transaction which is cancelled and negate that previous sales. So that if you on 2020-31-07 sell 10 items and 1.8 Augment see -10 items, you will see 0 sell in month 07 and 08 as well.
Replenishment
If you have multiple warehouses and want the orders to be generated only to the central warehouse please fill in the data as follows:
1) Central warehouse should be first in the warehouse file and its parent_warehouse_id should be empty
2) All stores which are replenished through central warehouse should :
- in warehouse.csv have filled in parent_warehouse_id as central warehouse
- for each item in warehouse_product which is replenished through central warehouse the "supplier_id" field should be empty
- for each item in warehouse_product which is supplied directly - supplier_id should be filled in
3) please make sure that each good in replenished warehouse there is the same product on central warehouse.
Optional Delivery Data
In order to tell Augment that you are already expecting some goods (goods in transit) you can fill data for future_delivery. Please put in future_delivery only the goods which might come in near future. If the order was partially fulfilled we expect that the fulfilled part is in transactions as transaction_type_id=2 and the unfulfilled amount (should it still arrive) should be inserted into future_delivery data. The second type is customer order you are expecting in the future to be delivered.
Future delivery or Inventory in transit or Customer Orders (future_delivery.csv)
| Field | Type | Comment |
| future_delivery_id | generated id [0-9a-zA-Z_-] | Unique identifier of Inventory in transit |
| future_delivery_type | use SUPPLIER_ORDER for Purchase Orders in Transit or CLIENT_ORDER for Customer Orders | Optional. If you don't use column future_delivery_type at all. All records are considered type=SUPPLIER_ORDER |
| product_id | FK - product_id | link to the product - must exist in products and warehouse_product |
| warehouse_id | FK - warehouse_id | link to warehouse where the goods will be delivered |
| supplier_id | FK - supplier_id | link to supplier (empty if the order is between two locations (warehouses) |
| parent_warehouse_id | FK - warehouse_id | link to warehouse(empty if the order is from the supplier) |
| date_of_order | YYYY-MM-DD | when the purchase order was created |
| estimated_date_of_delivery | YYYY-MM-DD | when the purchase order will be delivered |
| amount | Decimal | amount of goods in transit |
| purchase_price | Decimal | purchasing unit price |
If you wish to give us during the integration phase your minimal periods (how often you buy such goods) and leadtimes (how fast you can receive goods upon placing an order) you can give us data in this format
Supplier configuration (supplier_configuration.csv)
| Field | Type | Comment |
| supplier_id | FK - supplier_id | |
| minimal_period | int | minimal purchase period |
| leadtime | int | usual lead time in days for this provider/supplier |
| leadtime_std | int | days of standard deviation for lead time |
Later it is recommended to update the values in the Augment application.
Promotion Data (promotion.csv)
| Field | Type | Comment |
| promotion_type | coupon, gift, discount, other, listing, delisting | |
| promotion_id | generated id [0-9a-zA-Z_-] | identifier of promotion to separate multiple promotions |
| product_id | FK - product_id | |
| warehouse_id | FK - warehouse_id | if empty applies to products on all warehouses |
| promotion_date_from | YYYY-MM-DD | start of the promotion |
| promotion_date_to | YYYY-MM-DD | end of the promotion |
| power | Decimal | power of the promotion (for example 10 represents 10% discount) |
| estimated_amount | Decimal | if you know how many you expect to sell during that promotion |
Forecast only Sales data
If you need to compute only forecast and do not need to compute orders or help with stock, the minimal amount of information we need to get from you is this information.
The format of the files should be the same, the information not in the table below should be empty column or 0 for numbering fields.
Data Import CSV
Data are sent as CSV files. Entity type (entity type is for example Warehouse or Product) consists of all records of that entity type (so all warehouses are lines in one file warehouse.csv, we do not support multiple csv per type - for example it is not possible to send half of the products in one csv file and half in other csv file).
Foreign key - FK fields(connections between entities - like for example category to product link) must be filled and must match foreign entity, otherwise the row will not be inserted. For example if we find transaction which does not have product attached we ignore it for both forecast and orders suggestions.
Filename for each entity type is a possible to set up in initial setting, but should follow these naming conventions:
- warehouse.csv
- product.csv
- supplier.csv
- category.csv
- warehouse_product.csv
- transaction.csv
- supplier_configuration.csv
- currency.csv
You can download demo data here.