Occubee automatically turns vast amounts of transactional data, information on thousands of products and dozens of trade- and manufacturing-specific factors into valuable sales and demand forecasts. Holistic analysis of data from the entire supply chain and advanced forecasting methods is the ticket to building a sustainable competitive advantage and increasing your company’s profitability.
Holistic and detailed data analysis
We take into account data of a wide and diverse range, including sales and stock data, internal variables (e.g. price, marketing campaign) and external variables (e.g. holiday calendar, weather forecast). In doing so, we maintain a detailed level of data aggregation.
Our forecasts can cover the entire available assortment and can be created at the level of points of sale, warehouses or markets served.
Sophisticated forecasting methods
We select the attributes of the models to reflect the specific character of the industry and respond to the requirements of a particular business.
We use automated AI algorithms thanks to which the selection of information feeding the model is also automated. This ensures speed and accuracy.
Forecast parameters tailored to business needs
horizon – short-term forecasts (e.g. 7 days), medium-term forecasts (e.g. 7 weeks), or long-term forecasts (e.g. 7 months),
frequency – forecasts generated daily, weekly or monthly,
granularity – forecast with daily, weekly or monthly aggregation,
level of detail – e.g. sales forecast per SKU/PoS, demand forecast per product category/market, sales forecast per master category/sales channel.
Expert’s response in unusual situations
This process does not require you to have expertise in Data Science, nor to have your own data center.
As an expert, however, you have the ability to modify the results of the forecasts using adjustment factors. This allows you to react quickly to dynamic changes in your business and the market.
Download of receipt, store and warehouse data and information on past and future prices, planned promotions, etc..
Verification of data consistency and completeness. Aggregation of sales data.
Preparation of data for modeling
Preparation of data including historical sales, internal and external factors, and auxiliary variables.
Selection of data that improves the quality of test predictions from all available information.
Taking into account data used in the process of training the model, but including the periods covered by the forecast.
Verification of model quality
Determination of quality measures for the generated forecasts and their analysis.
Generation of forecasts
Forecasts are delivered to the client’s system.
How often do you answer the question of how a particular product sells in a particular store of your chain?
Forecasting based on intuition and time-consuming expert analyses.
Analysis of 20% of the assortment.
High-level data aggregation
Forecasting for a product category within a store category (division into low-, medium- and high-volume stores).
Forecasting using advanced machine learning and artificial intelligence algorithms.
Analysis of 100% of the assortment.
Low-level data aggregation
Forecasting for a specific product at a specific point of sale.