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. Traditional forecasting methods, which rely on the assumption that the time series are both long and non-sparse, perform poorly for this very reason.
Lokad is looking at supply chain historical data from another angle: instead of looking at the depth of the data, which tends to be nonexistent, we are looking at the width of the data, that is, all the correlations that exist between the products. As there are frequently thousands of products, many correlations can be leveraged to improve the forecasting accuracy significantly. Yet, when establishing those correlations, we cannot count on relying on the demand history because many products, such as the products that are about to be launched, don’t even have historical data yet. Thus, the forecasting engine of Lokad has introduced a mechanism to leverage categories and hierarchies instead.
Leveraging categories and hierarchies for increased forecasting accuracy works great. However, this approach suffers from one specific limitation: it relies on the availability of categories and hierarchies. Indeed, many companies haven’t invested much in master data setups, and, as a result, cannot benefit from much fine-grained information about the products that flow through the supply chain. Previously, when no category and no hierarchy were available, our forecasting engine was essentially crippled in its capability to cope with sparse and intermittent demand.
The new text mining capabilities of the Lokad forecasting engine is a game changer: the engine is now capable of processing the plain-text description of products to establish the correlations between the products. In practice, we observe that while companies may be lacking proper categorizations for their products, a plain-text description of the products is nearly always available, dramatically improving the applicability of the width-first forecasting perspective of Lokad.
For example, if a diverse set of products happens to be named Something Christmas, and all those products exhibit a consistent seasonal spike before Christmas, then the forecasting engine can identify this pattern and automatically apply the inferred seasonality to a new product that has the keyword Christmas in its description. This is exactly what happens under the hood at Lokad when plain-text labels are fed to the forecasting engine.
Our example above is simplistic, but, in practice, text mining involves uncovering complex relationships between words and demand patterns that can be observed in the historical data. Products sharing similar descriptions may share similar trends, similar life-cycles, similar seasonalities. However, two products with similar descriptions may share the same trend but not the same seasonality, etc. The forecasting engine of Lokad is based on machine learning algorithms that automatically identify the relevant information from the plain-text descriptions of the products. The engine requires no preprocessing of the product descriptions.
Our motto is to make the most of the data you have. With text mining capabilities, we are once again lowering the requirements to bring your company to age of quantitative supply chains. Any question? Just drop us a line at email@example.com.