The illustrated stock reward function

The stock reward function is a key ingredient to make the most of probabilistic forecasts in order to boost your supply chain performance. The stock reward is used for computing the return on investment for every extra unit of stock to be purchased or manufactured.

The stock reward function is expressive and can be used like a mini-framework for addressing many different situations. However, as a minor downside, it’s not always easy to make sense of the calculations performed with the stock reward function. Below you’ll find a short list of graphs that represent the various transformations applied to the forecasts.

The Supply Chain Scientist

Artificial intelligence has been making steady progress over the last few decades. However, while self-driving cars might be just around the corner, we are still decades away from having software smart enough to devise a supply chain strategy. Yet, at the same time, it would be incorrect to conclude that supply chain as a whole is still decades away from being positively impacted by machine learning algorithms. Lokad’s supply chain science competency was born out of the observation that while algorithms alone were insufficient, they actually became formidable enablers in the hands of capable supply chain experts.

Fashion demand forecasting

Forecasting is hard. Forecasting the future of fashion is insanely hard. As a result, for most part, the fashion industry still relies on crude methods such as Open-To-Buy which are nothing but glorified top-down moving averages. Yet, most supply chain practitioners would argue that as long as there isn’t something that can actually beat Open-To-Buy in the real world, then Open-To-Buy isn’t outdated, no matter how crude the method might be.

Machine learning jobs at Lokad

Machine learning along with artificial intelligence have become buzzwords. Given that Lokad has become identified as one of the key European companies that generate real-world decisions driven by machine learning - supply chain decisions actually - we are getting a growing number of applicants. The good news: we are still hiring! In this post, we review the three realms of machine learning that exist at Lokad and what you need to do to maximize the odds of getting an interview with us, and ideally be hired afterwards.

The test of supply chain performance

Answering these 12 questions tell more about your supply chain performance than nearly all benchmarks and audits that the market has to offer. This test should take about 5 minutes of your time. Can supply chain operate without Excel? Is ABC analysis regarded as obsolete? Is all relevant data documented by the supply chain teams? Do you record historical stock levels? Do supply chain teams monitor the quality of their data?

2017, year of quantitative supply chain

Thanks to the probabilistic forecasting engine that we released last year, our capacity to optimize supply chains has dramatically improved over the last couple of months. Through our growing experience, we have come to realize that there are 5 principles that drive the success of the supply chain initiatives undertaken by Lokad: All possible futures must be considered; a probability for each possibility. All feasible decisions must considered; an economic score for each possibility.

Markdown tile and Summary tile

The dashboards produced by Lokad are composite: they are built of tiles that can be rearranged as you see fit. We have many different tiles available: linechart, barchart, piechart, table, histogram, etc. This tile approach offers great flexibility when it comes to crafting a dashboard that contains the exact figures your company needs. Recently, we have introduced two extra tiles in order to help fine-tune your dashboards even further. The Summary tile offers a compact approach for displaying KPIs (key performance indicators).

Preparing enterprise data takes 6 months

How long does it take to get started with Lokad? Answering this question is tough because often our answer is about 3 to 6 months. Hell, 6 months! How can your software be so clunky that it can take up to 6 months to get started? Well, our typical set-up phases can be broken down as follows: 90 to 180 days: preparing the data 3 to 30 days: configuring Lokad This shows that Lokad’s setup is actually lightweight.

Probabilistic promotions forecasting

Forecasting promotions is notoriously difficult. It involves data challenges, process challenges and optimization challenges. As promotions are present everywhere in the retail sector, they have been a long-term concern for Lokad. However, while nearly every single retailer has its share of promotions, and while nearly every forecasting vendor claims to provide full support for handling promotions, the reality is that nearly all forecasting solutions out there are far from being satisfying in this regard.

Ionic data storage for high scalability in supply chain

Supply chains moved quite early on towards computer-based management systems. Yet, as a result, many large companies have decade-old supply chain systems which tend to be sluggish when it comes to crunching a lot of data. Certainly, tons of Big Data technologies are available nowadays, but companies are treading carefully. Many, if not most, of those Big Data companies are critically dependent on top-notch engineering talent to get their technologies working smoothly; and not all companies succeed, unlike Facebook, in rewriting layers of Big Data technologies for making them work.

Will compilation save supply chains?

Yes. To a noticeable extent. And I would never have ventured to put forward this opinion when founding Lokad nearly a decade ago. By compilation I refer to the art of crafting compilers, that is, computer programs that translate source code into another language. Few people outside the ranks of programmers know what a compiler does, and few people within the ranks of programmers know how a compiler is designed. At first, compilation concerns appear distant (to say the least) to supply chain concerns.

Visualizing probabilities with histograms

The future is uncertain, and one of the best mathematical tools we have for coping with this fact is the distribution of probability. Lokad features both a probabilistic forecasting engine and an algebra of distributions. These two capabilities get along pretty well when it comes to dealing with complex, erratic and very uncertain supply chain situations. At their core, these capabilities rely enormously on processing distributions of probabilities. Yet, until recently, Lokad was lacking convenient ways to visualize these distributions.

Hiring Big Data Analyst and Software Engineer

Once again, we are hiring. We are looking for a Software Engineer and a Business Data Analyst. Software Engineer You will integrate a team of talented software engineers in order to further develop our cloud-based data crunching apps. We have infrastructure, data processing, scalability and reliability challenges, and need your help in addressing them. At Lokad, you will benefit from the coaching of an awesome dev team. You will gain skills in Big Data processing and cloud computing apps.

Multicolor line charts

The releases of Lokad are done on Tuesdays, and every Tuesday, we release a few more useful bits. Sometimes we release major components - like our latest probabilistic forecasting engine - but nearly every week comes with a few more features and enhancements. Software development at Lokad is very incremental. A few weeks ago, we improved our line chart. So far, it was only possible to specify one color - the primary color - for the line chart, and then, if multiple lines were to be present, Envision was auto-picking one color for each line.

Senior software engineer wanted!

We are hiring again! You will integrate a team of talented software engineers in order to further develop our cloud-based data crunching apps. We have infrastructure, data processing, scalability and reliability challenges. We need your help to get those challenges addressed. At Lokad, you will benefit from the coaching of an awesome dev team. You will gain skills in Big Data processing and cloud computing apps. Our codebase is clean, documented and heavily (unit) tested.

Working with uncertain futures

The future is uncertain. Yet, nearly all predictive supply chain solutions make the opposite assumption: they assume that their forecasts are correct, and hence roll out their simulations based on those forecasts. Implicitly, the future is assumed to be certain and complications ensue. From a historical perspective, software engineers were not making those assumptions without a reason: a deterministic future was the only option that the early - and not so early - computers could process at best.

WinZip and 7z file formats now supported

File formats are staggeringly diverse. At Lokad, our ambition is to support all the (reasonable) tabular file formats. We were already supporting CSV (comma-separated values) files with all their variants - which can involve varying separators or varying line returns. However, tabular files can become very large, and in order to make the file transfer to Lokad faster, these files can be compressed. Lossless compression of flat text files works very well, frequently yielding a compression ratio below 10%, i.

Forecasting 4.0 with Probabilistic Forecasts

A little over one year ago, we unveiled quantile grids as our 3.0 forecasting technology. More than ever, Lokad remains committed to delivering the best forecasts that technology can produce, and today, our 4th generation of forecasting technology, namely our probabilistic forecasting engine, is live and available in production for all clients. This new engine consists of a complete rewrite of our forecasting technology stack, and addresses many long-standing challenges that we were facing.

Autocomplete file paths with Envision

When data scientists work with Envision, our domain-specific language tailored for quantitative optimization for commerce, we want to ensure that they are as productive as possible. Indeed, data scientists don’t grow on trees, and when you happen to have one available, you want to make the most of his time. A data analysis begins by loading input data, which happens to be stored as flat files within Lokad. Therefore, an Envision script always starts with a few statements such as:

Proofs of concept don’t work in quantitative supply chain optimization

Proofs of concept are one of the most frequent requests we get from our prospect clients looking to try out our supply chain optimization service. Yet, we frequently decline such requests; first because they hurt client’s company itself, and second because they also hurt Lokad in the process. Since POCs – or proofs-of-concept – are so widespread in B2B software, it is usually hard to grasp why they can be downright harmful in the specific case of quantitative supply chain optimization (1).