A location curve is used to estimate the productivity of specific choices (style/color) in each location. A location curve is used to spread an aggregate demand curve down to a store level. For instance, if a choice is expected to sell 10 units per week / per store on average, then web may sell 25 units per week and the lowest performing store may sell 2 units per week. The location curve allows us analyze similar products to make this estimate of productivity.
Location curve blending provides users with the capability to blend perspectives on how a new product may perform when allocating. The goal is to take more than one relevant data set into account when estimating the expected performance of a product.
Some examples of location curve blending are
- When launching a new season, evaluate similar products in the previous year, same season. But, blend that with the latest trends of those products over the preceding X periods of time.
- When evaluating a product that may not fit a single mold, for instance evaluate the footwear category performance but blend that with high priced footwear only to understand the unique characteristics of price point on demand.
- Use a unique product as the basis for evaluation but blend that with a similar category due to the potential lack of information on the unique product.
Allocation Demand Retrend
Retrend of the demand forecast in allocations is available, the system will measure the actual sales performance against the original plan and change the mix of store/size contribution in the case of Item Plan demand type, and increase/decrease store/size sales demand in the case of total sales or ROS demand type.
The goal of retrend is to evaluate the original assumption of how each SKU sells in each location and adjust that assumption based on actuals recorded over time. The retrend is expected to get more accurate as more actual data is recorded but it will begin retrending as soon as the first sale is recorded in each location. In addition, retrend has outlier detection which will eliminate the effect of exceptional sales that were not planned in a given period.
In the example presented, the trend of the actual sales against the original forecast has increased and as a result the retrend has pushed up the future forecast for this particular size in this store
Column Stats in Master Data
Have you ever wanted to see a quick summary of a column in Master Data? The new Column Stats feature enables you to view statistics at a glance for each column, displaying the number of total rows, unique values, empty cells, as well as the sum, max, and min for each column. In addition, you can also see which values appeared most and least frequently in the data set. These metrics are particularly useful for data validation and for comparing against import scorecard summaries.
To use this feature, simply click the context menu for the column you want to enable it on, and select ‘Column Stats’.
Current Inventory Units Metric
In most scenarios planning is based on BOP and EOP of each period that is planned, however there are cases where current inventory visibility is helpful and useful, especially when allocation recommendations are being calculated on a daily basis.
Toolio has added the Current Inventory units metric to all modules, it will represent the latest unit inventory provided to Toolio through integration. As a result, users will be able to see the latest current inventory vs BOP in the current week. In Merchandise Planning and Item Planning, the current inventory is populated historically based on the last update provided in each week, this will typically match the EOP of the week but not always.