User Guide
1. Streamline Client
2.Streamline Server
3. Starting Up
4. Connecting data
5. Demand and Sales Forecasting
6. Inventory Planning
7. Reference
1. Streamline Client
2.Streamline Server
3. Starting Up
4. Connecting data
5. Demand and Sales Forecasting
6. Inventory Planning
7. Reference
An ERP system operates and stores a vast variety of transactions. Streamline is interested only in those that relate to changes in your inventory. In other words, Streamline needs transactions that represent a sale or, generally, an inventory movement. A transaction is usually described by its origin, and what, where, when, and how much was sold (or changed the inventory level).
On the other hand, all transactions in the ERP system can be divided into closed and open. In terms of Streamline:
The main aim of this data piece is to import sales history and on-hand history into Streamline. Typically, sales history is represented by sales (sales order or invoice) and return (credit memo or customer return document) transactions. To import on-hand history you should provide all the transactions that affected on-hand in the past. Those could include:
Despite it is not easy, and sometimes not possible, to collect and provide all the inventory transactions, this is not obligatory for Streamline to work. To create forecasts and calculate procurement plans, it needs only sales history to be imported.
However, on-hand history allows Streamline to:
Below, we describe the required and optional data types which you should provide for:
The required data types for this data piece and for demand planning in particular are shown in the table below.
Data name | Description | Datatype |
---|---|---|
Date | The transaction date. | Date or DateTime |
Quantity sold | The item amount that was sold in the transaction in the base units of measure. | Integer |
Item code | The item identifier. | String |
If you need to plan your demand by location (store, warehouse, or region) and by channel, additionally provide the data types represented in the table below.
Data name | Description | Datatype | Is not given | |
---|---|---|---|---|
Default | Provided | |||
Location | A code of the location where the Item code is sold. It is used to forecast the consumption of each Item code in each location. | String | Empty string | NULL or empty string |
Channel | It represents a channel by which an item is sold. For example, e-commerce, direct sales, distributors, or a single customer. | |||
Channel category, Channel sub-category, … | Used to plan your demand by channel categories. You can import as many categories as you need. |
An empty string for Location or Channel means that there is no location or channel set for this transaction.
Besides the automatic determination of outliers, there is an ability to mark a period as an outlier when providing transactional data. To do this, provide the data type shown in the table below.
Data name | Description | Given in | Datatype |
---|---|---|---|
Outlier | Used to mark the data aggregation period this transaction belongs to as an outlier. | Provide 1 or 'true', or 'yes' to put this setting in action, or 0, 'false', or 'no', to ignore it. | Integer or String |
After importing the data, the marked periods will have checked checkboxes in the Ignore actual sales row of the table on the Demand tab.
To forecast revenue, the required data types should be extended with one of the types shown in the table below.
Data name | Description | Datatype |
---|---|---|
Sales price/unit | The item price of one unit in the base UOM in the sale or customer return transaction. | Float |
Transaction revenue | The amount of the sale or customer return transaction. Typically it equals to Sales price/unit multiplied by Quantity sold. |
This information is also used to perform revenue-based ABC analysis and calculate selling price-dependent KPIs such as annual revenue, revenue next year, gross margin, and turn-earn index.
As we mentioned above, providing on-hand history to Streamline allows for calculating several KPIs, such as inventory turnover and stockout days. There are two data types that can be used to import on-hand history into Streamline. You can choose one of them. They are shown in the table below.
Data name | Description | Datatype |
---|---|---|
On hand | The on-hand remaining after the transaction. | Integer |
On hand change | How much on-hand quantity has changed due to the transaction. |
You can provide Streamline with the profit obtained from each transaction by importing the data type shown in the table below. This allows Streamline to calculate the total gross profit for each aggregation period and also over the last 12 months.
Data name | Description | Datatype |
---|---|---|
Transaction profit | The profit obtained from the transaction. | Float |