Sep 3, 2017
Back in 2014, Spairliners became one of the first companies to deploy probabilistic forecasts at scale to improve their supply chain. It turned out that those forecasts are an excellent fit for aerospace challenges. Fast-forward present time, Spairliners has agreed to let us produce a video case study. Enjoy!
Aug 10, 2017
As a rule of thumb, whenever supply chain is involved, probabilistic forecasts yield superior results compared to traditional periodic forecasts; i.e., forecasts expressed per day, week or month. Yet, there are also a few situations where demand uncertainty is very low such as where demand is very steady and non-sparse. In those situations, it might still make sense to consider periodic demand forecasts.
Therefore, we have extended our latest forecasting engine to support periodic forecasts.
Jun 15, 2017
We are proud to announce that Lokad is now featuring text mining capabilities that assist its forecasting engine in delivering accurate demand forecasts, even when looking at products associated with sparse and intermittent demand that do not benefit from attributes such as categories and hierarchies. This feature is live, check out the label option of our forecasting engine.
The primary forecasting challenge faced by supply chains is the sparsity of the data: most products don’t have a decade’s worth of relevant historical data and aren’t served by thousands of units when considering the edges of the supply chain network.
Jun 7, 2017
The IT landscape of supply chains is nearly always complex. Indeed, by nature, supply chains involve multiple actors, multiple sites, multiple systems, etc. As a result, building data-driven insights in supply chains is a challenge due to the sheer heterogeneity of the IT landscape. Too frequently, supply chain analytics deliver nonsensical results precisely because of underlying garbage in, garbage out problems.
At Lokad, we have not only developed a practice that thoroughly surveys the IT landscape and the datasets inhabiting it, but we have also created some bits of technology to facilitate the surveying operations themselves.
May 8, 2017
A few weeks ago, in Berlin, Lokad was invited on stage at Vizions 2017 to give a talk about probabilistic forecasting and how this paradigm will change the future of supply chain for the fashion industry.
Apr 29, 2017
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.
Mar 28, 2017
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.
Feb 8, 2017
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.
Jan 24, 2017
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.
Jan 16, 2017
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?
Jan 3, 2017
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.
Dec 6, 2016
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).
Nov 21, 2016
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.
Oct 18, 2016
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.
Oct 3, 2016
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.
Sep 26, 2016
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.
Sep 2, 2016
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.
Aug 22, 2016
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.
Jun 30, 2016
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.
Jun 24, 2016
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.