Lokad Forecasting SDK

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 ... [More] 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. [Less]

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 ... [More] 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. [Less]

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 ... [More] 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. [Less]

Artificial intelligence has been making steady progress over the last few decades.

Artificial intelligence has been making steady progress over the last few decades.

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 ... [More] , 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. [Less]

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 ... [More] , 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. Machine learning offers the possibility to achieve unprecedented levels of supply chain performance by taking care of all the extensive but otherwise clerical micro-decisions that your supply chain requires: when to order a product, when to move a unit of stock, when to produce more items, etc. The Supply Chain Scientist is a mix between a data scientist and a supply chain expert. This person is responsible for the proper data preparation and the proper quantitative modelling of your supply chain. Indeed, it takes human supply chain insights to realize that some relevant data may be missing from a project and to align the optimization parameters with the supply chain strategy of the company. Too often, supply chain initiatives come with fragmented responsibilities: Data preparation is owned by the IT team Statistics and reporting is owned by the BI (business intelligence) team Supply chain execution is owned by the supply chain team The traditional S&OP answer to this issue is the creation of collective ownership through monthly meetings between many stakeholders, ideally having the whole thing owned by the CEO. However, while we are certainly not opposed to the principle of collective ownership, our experience indicates that things tend to move forward rather slowly when it comes to traditional S&OP. In contrast to the collective ownership established through scheduled meetings, the Supply Chain Scientist holds the vital role of taking on the end-to-end ownership of all the quantitative aspects of a supply chain initiative. This focused ownership is critical in order to avoid too common pitfalls associated with traditional supply chain organizations which are: Data is incorrectly extracted and prepared, primarily because the IT team has limited insights in relation to the use of the data. Statistics and reporting misrepresent the business; they provide less-than-useful insights and suffer from less-than-perfect data inputs. Execution rely heavily on ad-hoc Excel sheets in order to try to mitigate the two problems described above, while creating an entire category of new problems. When we begin a quantitative supply chain initiative with a client company, we start by making sure that a Supply Chain Scientist is available to execute the initiative. Learn more about supply chain scientists [Less]

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 ... [More] , 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. Machine learning offers the possibility to achieve unprecedented levels of supply chain performance by taking care of all the extensive but otherwise clerical micro-decisions that your supply chain requires: when to order a product, when to move a unit of stock, when to produce more items, etc. The Supply Chain Scientist is a mix between a data scientist and a supply chain expert. This person is responsible for the proper data preparation and the proper quantitative modelling of your supply chain. Indeed, it takes human supply chain insights to realize that some relevant data may be missing from a project and to align the optimization parameters with the supply chain strategy of the company. Too often, supply chain initiatives come with fragmented responsibilities: Data preparation is owned by the IT team Statistics and reporting is owned by the BI (business intelligence) team Supply chain execution is owned by the supply chain team The traditional S&OP answer to this issue is the creation of collective ownership through monthly meetings between many stakeholders, ideally having the whole thing owned by the CEO. However, while we are certainly not opposed to the principle of collective ownership, our experience indicates that things tend to move forward rather slowly when it comes to traditional S&OP. In contrast to the collective ownership established through scheduled meetings, the Supply Chain Scientist holds the vital role of taking on the end-to-end ownership of all the quantitative aspects of a supply chain initiative. This focused ownership is critical in order to avoid too common pitfalls associated with traditional supply chain organizations which are: Data is incorrectly extracted and prepared, primarily because the IT team has limited insights in relation to the use of the data. Statistics and reporting misrepresent the business; they provide less-than-useful insights and suffer from less-than-perfect data inputs. Execution rely heavily on ad-hoc Excel sheets in order to try to mitigate the two problems described above, while creating an entire category of new problems. When we begin a quantitative supply chain initiative with a client company, we start by making sure that a Supply Chain Scientist is available to execute the initiative. Learn more about supply chain scientists [Less]

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 ... [More] 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. In fact, until recently, our own observations were aligned with what fashion companies were telling us: nothing really works for fashion, and guesswork remains the best option among all the other, even less satisfactory, alternatives. Our probabilistic forecasting engine, released last year, became a game changer for fashion. After years of struggling with fashion demand patterns, we finally have a forecasting engine that is natively architectured towards the specific challenges of the fashion sector. Over the last couple of months, we have been driving the supply chains of multiple fashion companies, and well, it actually works! Considering the track record of forecasting vendors in the fashion industry, the odds weren’t really in our favor. Demand in fashion is typically driven by novelty, and new products come together through collections. Collections are essential from the fashion perspective; yet, at the same time, they represent a massive forecasting challenge. The demand needs to be forecast for products that haven’t been sold yet. Fashion isn’t about products that haven’t been sold for long, fashion is about products that haven’t been sold at all. This perspective is a fundamental mismatch with the time-series forecasting approach that represents the foundation of nearly all forecasting systems - not Lokad though. Indeed, from a time-series perspective, in the case of fashion, time-series have zero historical depth, hence there is nothing to rely on for the purpose of forecasting. Lokad’s probabilistic forecasting engine takes a completely different stance: it actively leverages the different product attributes: brand, style, color, fabric, size, price point, category, etc, in order to build a demand forecast based on the performance of similar products in the previous collections. One of the things that Lokad’s forecasting engine does not do is to require products to be manually paired between collections. First, establishing those pairs is very complicated and extremely time-consuming. Supply chain practitioners are not supposed to be the slaves of their own systems; if the systems require thousands of products to be manually paired, chances are that this time would be better invested in producing a manual forecast that directly benefits from human insights. Second, in fashion, 1-to-1 mapping between the old and the new collections does not actually make any sense most of the time. New collections are likely to redefine the codes in subtle yet important ways: one product may become many, and vice-versa. A methodology that exclusively relies on 1-to-1 pairings is guaranteed to deliver rather naive results about the future collections. Lokad’s forecasting engine is all about computing all those similarities in a fully automated manner through machine learning algorithms. Artificial Intelligence is now all the rage in the media, but under the surface it boils down to machine learning algorithms that have undergone steady and yet gradual progress over the last 3 decades. Lokad leverages several classes of machine learning algorithms, tailored for supply chain purposes. In addition, Lokad delivers probabilistic forecasts. Instead delivering one demand forecast - the median or the mean - that is (nearly) guaranteed to be incorrect, Lokad delivers the probabilities for (nearly) all the demand scenarios. This aspect is of critical importance for the fashion industry because uncertainty is irreducible; and good supply order frequently boils down to a risk analysis. In fashion, the two main risks are lost opportunities if there is not enough stock, and stock depreciations if the goods have to be sold with a very aggressive discount during the sales period - in order to liquidate the remaining stocks of a collection. Lokad has native capabilities to deal with this specific risk analysis that is so important in fashion. Intrigued by Lokad’s capabilities for fashion? Don’t hesitate to book a demo call with us. [Less]

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 ... [More] 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. In fact, until recently, our own observations were aligned with what fashion companies were telling us: nothing really works for fashion, and guesswork remains the best option among all the other, even less satisfactory, alternatives. Our probabilistic forecasting engine, released last year, became a game changer for fashion. After years of struggling with fashion demand patterns, we finally have a forecasting engine that is natively architectured towards the specific challenges of the fashion sector. Over the last couple of months, we have been driving the supply chains of multiple fashion companies, and well, it actually works! Considering the track record of forecasting vendors in the fashion industry, the odds weren’t really in our favor. Demand in fashion is typically driven by novelty, and new products come together through collections. Collections are essential from the fashion perspective; yet, at the same time, they represent a massive forecasting challenge. The demand needs to be forecast for products that haven’t been sold yet. Fashion isn’t about products that haven’t been sold for long, fashion is about products that haven’t been sold at all. This perspective is a fundamental mismatch with the time-series forecasting approach that represents the foundation of nearly all forecasting systems - not Lokad though. Indeed, from a time-series perspective, in the case of fashion, time-series have zero historical depth, hence there is nothing to rely on for the purpose of forecasting. Lokad’s probabilistic forecasting engine takes a completely different stance: it actively leverages the different product attributes: brand, style, color, fabric, size, price point, category, etc, in order to build a demand forecast based on the performance of similar products in the previous collections. One of the things that Lokad’s forecasting engine does not do is to require products to be manually paired between collections. First, establishing those pairs is very complicated and extremely time-consuming. Supply chain practitioners are not supposed to be the slaves of their own systems; if the systems require thousands of products to be manually paired, chances are that this time would be better invested in producing a manual forecast that directly benefits from human insights. Second, in fashion, 1-to-1 mapping between the old and the new collections does not actually make any sense most of the time. New collections are likely to redefine the codes in subtle yet important ways: one product may become many, and vice-versa. A methodology that exclusively relies on 1-to-1 pairings is guaranteed to deliver rather naive results about the future collections. Lokad’s forecasting engine is all about computing all those similarities in a fully automated manner through machine learning algorithms. Artificial Intelligence is now all the rage in the media, but under the surface it boils down to machine learning algorithms that have undergone steady and yet gradual progress over the last 3 decades. Lokad leverages several classes of machine learning algorithms, tailored for supply chain purposes. In addition, Lokad delivers probabilistic forecasts. Instead delivering one demand forecast - the median or the mean - that is (nearly) guaranteed to be incorrect, Lokad delivers the probabilities for (nearly) all the demand scenarios. This aspect is of critical importance for the fashion industry because uncertainty is irreducible; and good supply order frequently boils down to a risk analysis. In fashion, the two main risks are lost opportunities if there is not enough stock, and stock depreciations if the goods have to be sold with a very aggressive discount during the sales period - in order to liquidate the remaining stocks of a collection. Lokad has native capabilities to deal with this specific risk analysis that is so important in fashion. Intrigued by Lokad’s capabilities for fashion? Don’t hesitate to book a demo call with us. [Less]