Understanding the Excel report, classic and quantile forecasts
Understanding the Excel report, classic and quantile forecasts
- The Quantitative Supply Chain Manifesto
- The Lokad test of supply chain performance
- An overview of quantitative supply chain
- Generalized probabilistic forecasting
- Decision-driven optimization
- Economic drivers
- Data preparation
- The Supply Chain Scientist
- Timeline of a typical project
- Project deliverables
- Assessing success
- Antipatterns in supply chain
- Inventory forecasting
- Prioritized ordering report
- Old forecasting input file format
- Old forecasting output file format
- Choosing the service levels
- Managing your inventory settings
- The old Excel forecast report
- Using tags to improve accuracy
- Oddities in classic forecasts
- Oddities in quantile forecasts
- Stock-out's bias on quantile forecasts
- Daily, weekly and monthly aggregations
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This report is considered as deprecated by Lokad. Indeed, since 2014, Lokad has vastly improved its reporting capabilities, and the reports that can be produced by the forecasting engine itself are crude compared to the reports that can be generated through Envision, a distinct component within Lokad. Then, this report also does not benefit from our latest and most accurate forecasts expressed as prioritized orders.
This page documents the Excel inventory forecast report as produced by the forecasting engine of Lokad. This report contains classic forecasts or quantile forecasts, or both. Below, we details the meaning of each column found in the Excel report.
Download sample Excel report: salescast-northwind-sample.xlsx
Big Picture
Lokad starts a project run by retrieving the data from files stored in your account. Then, quantile and/or classic forecasts are produced. Finally Lokad creates a consolidated Excel forecasting report gathering key inventory optimization metrics.This page documents a typical inventory forecasting Excel report produced by Lokad. More details are provided below for each section. Depending on the information present in your IT systems, your mileage may vary. Lokad is flexible and can produce reports with extra columns and eventually less. We do not cover those aspects here.
This Excel report is a somewhat inflexible report produced by the forecasting engine of Lokad. It was introduced very early on in Lokad, long before we introduced more capable reporting features. While we have no plan to remove this feature from Lokad, we are gradually transitioning all reports toward our generic commerce analytics platform. This platform offers much more flexible web-based reports while preserving the possibility to export to Excel, also with much greater flexibility.
Context data
The data columns, in gray in the schema here above, represents the context, that is to say all the information directly imported from the original data without any statistical processing involved.a) Item ID: identifier for each product / SKU / barcode, depending on the applicable terminology.
b) Item Name: human-readable entry for the item. This field is not required, however it illustrates the capability of Lokad to insert extra fields in the Excel report for pure reporting purposes.
c) Service Level: desired probability of not hitting a shortage. This value represents the performance goal in term of stock availability. Read more on setting the right service level. This value is provided as input to Lokad.
d) Lead Time: delay expressed in calendar days that represent the sum of the reordering delay plus the supply delay. This value typically depends on your supplier. Read more on calculating lead times. This value is provided as input to Lokad.
e) Stock On Hand, Stock On Orders: number of units readily available, resp. number of units already reordered.
f) History: aggregated sales (per day, week or month) over the last 12 periods. Those values are provided to facilitate quick validation of the sales forecasts. History not displayed is still used while computing the forecasts (both classic and quantiles).
Forecasts and optimization metrics
The forecasts are produced by Lokad. Based on those forecasts, we also infers a few inventory optimization metrics. Those are represented over an orange background in the schema here above.1) Reorder Point: The number of units that should trigger a replenishment order when the stock on hand gets strictly lower than the reorder point value. This value is calculated by Lokad, if quantile forecasts are active.
2) Order Quantity: The number of units that should be reordered. If zero then Lokad suggests that the item should not be reordered at this point of time. This value is defined by the relationship
ReorderQuantity = ReorderPoint - StockOnHand - StockOnOrder (minimal value is zero). If LotMultiplier is also provided, Lokad will round up OrderQuantity to be a multiple of it.3) Lead Demand (short-hand for lead time demand): the number of units that will be sold during the lead time. This value is inferred from the classic forecasts combined with the lead time.
4) Stock Cover: the number of days left before stock-out if no reorder is made. This value is inferred from the classic forecasts and the stock on hand.
5) Accuracy (followed by Classic Forecasts): The expected accuracy of the forecasts delivered by Lokad, which can be enabled in the project settings. This value is a percentage, and included between zero and one. The higher the value the more accurate forecasts are. We have the following relationship Accuracy = 1 - MAPE, where MAPE is the Mean Absolute Percentage Error. The expected accuracy is computed by Lokad - like the forecasts themselves - through advanced statistical analysis. Check Measuring forecast accuracy for more details.
6) Classic Forecasts: The classic demand forecasts produced by Lokad at the daily, weekly or monthly level. These classic forecasts can be absent if the quantile-only option is selected in Lokad. Check our Forecasting Technology page for more details.