Demand, forecasts & promotions

The basis for more precise planning of order quantities, with more security and transparency

Periodically automated forecasting processes are monitored and optimized with the support of artificial intelligence (AI). Depending on the current sales pattern (item character) impacted by e.g. the weather, seasonality, trends, variance, accuracy or cannibalistic effects are examined by machine learning methods to assign individually best matchingforecast strategies.

The focus is on which forecasting technique achieves the highest accuracy at the respective point in time compared to the historical course. Results are visualized via evaluations and KPIs and prepared for strategic management decisions.

The investigation of fluctuations in demand (variances), trends and the quality of errors allows for more precise assessments of the expected development of demand and the resulting dynamic calculation of safety stocks!

Forecasts alone are not enough…

But forecasts are only as good as their database. It is the data that is often not yet prepared and stored with sufficient accuracy in many companies. Because data is always made up of information and noise. It is important to separate data from noise with the necessary experience, to isolate outliers, to split sales in regular, promotional, lost or exceptional demand and to filter out errors (incorrect bookings, returns, inventory errors…).

Additional accuracy will be achieved if sales history is examined for dependencies like weather (temperature impact), parallel promotional impact, seasonality, vacation periods, delivery constrains, etc.

Differentiate sales channels

Differentiate between sales channels (such as B2C, B2B, retail stores, online business, key customers) in order to examine forecasts and your planning even more precisely for peculiarities that arise there in order to react faster and plan more precisely.

Use of AI to define the right strategy

Rely on self-learning mechanisms (machine learning) to react quicker to changes in demand, to better plan new product launches (sell in) from the start through analytical comparisons and to sell out items over a planned period of time.

Use machine learning to plan based on experience, to measure KPIs more precisely and to be able to make decisions based on well-founded, autonomously cleaned data and information.

Projections, for a better planning

Demand projections are created based on cleansed data. Projections help to plan stock levels (shelve utilization), organize your receivings and shippments, plan for your investments. Often they are also exchanged with industry partners (suppliers) to assure required service levels.

Optimize special campaigns with promotional forecasting

Promotion need to be accumulated and iInformation about the duration, promotion range and scope, weather impact, terms and conditions, target groups and promotion type historically updated. Information obtained in this way will be used in the Promo Portal of Demandsoft, to forecast promotional sales more precisely. With the demand expectations determined in this way, orders can be placed at the right time and for a hight availability, while undesired residual quantities will be minimized. Order processes, order triggers and monitoring mechanisms are adjusted accordingly!

Outlier monitored & managed by exceptions

The world is never perfect! If – for unknown reasans – it happens that unexpected demand accours, buyers need to be informed quickly to examined the situation and to understand if there are actually unexpected developments in demand or whether there are errors in inventory management, in data provision, receivings or shippments.