Why you should not care about seasonality

Published on by Joannes Vermorel.

We are often asked is if Lokad supports seasonality in its forecasting technology. Indeed, neither our add-ons nor the web application provide settings for seasonality.

Our answer to this question is twofold: yes, Lokad handles seasonality but no, there is no settings to tweak seasonality.

Indeed, seasonality or rather seasonalities - because beside yearly patterns, weekly and daily patterns should be taken into account as well - are natively handled by Lokad.

Thus, there is no need to tell Lokad about it because such types of frequent patterns are always explored and evaluated, and eventually rejected if data do not exhibit the seasonal patterns.

Our goal is to provide best possible forecasts right out of the box, batteries included, and not asking users to partially address forecasting questions for us.

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Video tutorial for Safety Stock Calculator

Published on by Joannes Vermorel.

The video produced by Kirix about Lokad was a great source of inspiration for us. We realized that videos were a pretty efficient way to communicate on software. Thus, we decided to produce a couple of video tutorials for Lokad.

Our first tutorial is about Lokad Safety Stock Calculator. Check out how to start optimizing your inventory with Lokad within minutes.

Play Video
(English subtitles are available, just click the CC button)

In this video, you will learn how get started with Lokad Safety Stock Calculator, how to import your sales data and how to generate your first report. We will also explain how the report works and what are the key figures to look at.

You can also download the Windows Media Player file of the video. Special thanks to Rex Teodosio for helping us producing this video.

Categories: docs, safety stock, supply chain, video Tags: No Comments

LSSC v1.6- bugfix release

Published on by Joannes Vermorel.

Lokad Safety Stock Calculator v1.6 has been released. This version includes a couple of important bugfixes. In particular, this version should be more reliable on large datasets. We have improved the handling of network timeouts. An issue has also been fixed with reorder points that were incorrectly set to zero in specific situations.


Concerning the improvements, the version 1.6 automatically saves the report at the start and at the end of the full refresh process. Thus, if Windows gets restarted (say because of an automated security update), the newly refreshed report does not get lost in the process.

We have also fixed a bug in the Check for Updates operation. The auto-update operation was failing if the MSI file had been renamed before launching the installation. If the auto-update fails, then just uninstall and reinstall.

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Keeping track of errors to improve later on

Published on by Joannes Vermorel.

Lately a couple of customers have been asking whether Lokad was keeping track of its past forecast errors in order to improve its future forecasts.

The answer is simple: yes, we do, but there are more than that. In particular, we do not wait for

  • the forecasts to be requested,
  • the course of events to happen,
  • the historical data to be updated,

to finally compare our past forecasts with what really happened. Indeed, such an approach would be way too slow and inefficient.

Instead, we are using cross-validation methods adapted for the purpose of time-series forecasting.The process is more simple than it sounds, let's start with an example.

Let assume that we have a single time-series worth 1 year of weekly sales data (i.e 52 points). We want to produce 4-weeks sales forecasts - but we also want to estimate the forecasting error.

  • take the N first points (with N = 10 initially).
  • create a forecasting model based on those N points.
  • create a 4-weeks ahead forecast based on this model.
  • compare the forecast with the complete series.
  • increment N of 1 point (i.e. 1 week).
  • repeat.

With cross-validation, we can accurately estimate the expected forecast error of a forecasting model. In particular, if you have two different models, cross-validation can help you choosing the best one (*). Cross-validation can also be used to adjust model parameters - in order to find the parameters that best fit the data.

The Lokad team continuously monitors accuracy on delivered forecasts with such cross-validation methods and keeps working on more accurate forecasting models. Thus, we do keep track of our forecast errors, but without waiting for them to happen.

(*) If you try too many models, then you are likely to end-up with overfitting issues, but this problem is beyond the scope of this post.

Categories: accuracy, forecasting, insights Tags: No Comments