Managing inventory settings

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The reorder points and the reorder quantities are the two key inventory control values calculated using Lokad's forecasting technology. However, these values highly depend on a variety of inventory settings such as the service level and the lead time. Whenever possible, Lokad tries to import these values from a third-party app from where the data is loaded. However, sometimes data does not exist and/or cannot be imported into Lokad. As a result, Lokad offers a mechanism to locally manage these settings through an Excel sheet named Lokad_Settings.xlsx. This page aims to explain how to setup and maintain this Excel sheet. We assume here that you have already successfully connected Lokad to your business app.

Inventory reorder settings

The lead time, the service level and the lot multiplier are the three settings that are used to determine the final reorder quantities suggested by Lokad. Under the hood, both the lead time and the service level directly impact the underlying demand forecast, while the lot multiplier only impacts the quantity to be reordered - rounding it off to the next multiple.

Lokad's general philosophy is to rely, as much as possible, on data that already exists in your business management app - the app where your historical data is imported from. Some apps like Brightpearl or Linnworks support custom properties, whereby it is possible to enrich product information with arbitrary settings. When such a feature is present in the business app itself, it is better to leverage this built-in feature of the business app in order to keep all the business data centralized in one place. However, not all apps support the notion of custom properties. In this case, these settings need to be managed on Lokad's side.

Creating the file Lokad_Settings.xlsx


Inventory settings can be gathered into an Excel sheet to be named Lokad_Settings.xlsx. This sheet is typically expected to contain three columns: a first column named Id that contains the item identifier, a second column named LeadTime that contains the applicable lead time values in days, and a third column named ServiceLevel that contains the desired percentage of not getting a stock-out. This file should be completed with the relevant values, and uploaded to Lokad (we have a web upload interface). In practice however, you will not have to create this file from scratch.

Lokad is designed using a cloud-based file hosting service internally named BigFiles. In your Lokad account, it appears as the Files' tab. When you connect Lokad with your business app, the data imported into Lokad is consolidated into tabular text files such as Lokad_Items.tsv (list of products) or Lokad_Orders.tsv (list of orders). Thus, all you require to do is to run an inventory forecast through Lokad, and then click on the Input folder link. You will be redirected to the Files section, and will directly be able to see the folder where all your business data has been gathered.

Here, click the file Lokad_Items.tsv. BigFiles should offer you a preview of the file content. You will probably see quite a few columns. Click Download as Excel link. The tabular text file is converted into an Excel sheet, and the download starts immediately. Once you have downloaded the file, move the file to a folder that makes sense to you (for example My Documents\Lokad), as you will need to find it again afterwards. Rename the file as Lokad_Settings.xlsx, and then open the spreadsheet.

The spreadsheet contains all the applicable items (which can be products, barcodes, SKUs, depending on the business case). We suggest not to touch the Id column (the first column), as the content of this column will be used to carry out the matching between the values that you are about to enter and the entries found in the file Lokad_Items.tsv. The other columns can be preserved or removed as you see fit. If a column is not named Id, LeadTime, ServiceLevel or LotMultiplier, it will just be ignored. In particular, preserving certain columns like ProductName, Brand or Supplier can be useful to make sense of the content in the sheet.

Add two new columns named LeadTime and ServiceLevel in the sheet. You can then start adding the relevant values for every item. The column LeadTime specifically requires integral numbers, while the column ServiceLevel requires fractional numbers between zero and one (exclusive), typically formatted in percentage form for readability purposes. Any cell can be left blank, and in this case, Lokad will simply inject the default value as specified in the project configuration. Once you have finished your edits, just save the spreadsheet.

Refreshing your forecasting report with the new settings

At this point, you have an Excel sheet named Lokad_Settings.xlsx originally produced as a copy of the Lokad_Items.tsv file and containing all your newly entered settings. Go back to your Lokad account, click the Files tab, and navigate to the folder from which you originally downloaded the Lokad_Items.tsv file. Once you are in the folder, click the Upload files button, select your new Lokad_Settings.xlsx and upload the file. Depending on the size of the sheet and the quality of your network, this may take anywhere from a few seconds to a few minutes.


When the upload is complete, go back to the Projects tab in your Lokad account, select your inventory forecasting project, and click Start Run. A new report starts to be generated. If you click the link View run details, you should see that the file Lokad_Settings.xlsx appears in the list of files loaded by Lokad. Once the new report is available, download the new report. There should be two columns named LeadTime and ServiceLevel in this report, and the content of these columns should match the values that you had originally entered into the Lokad_Settings.xlsx sheet.

Tip: refreshing business data is typically a relatively slow operation. If you do not need to refresh your business data, but to merely update your inventory forecasting settings, then, click the small triangle to the right of Start Run, which will give you an extra option Start run without refreshing data from xyz which executes much faster.