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Introduction and challenges

Demand management is the first step in securing the performance of operations, through the structuring and reliability of the input signal used for upstream (development, purchasing, procurement, production) and downstream (inventory management, distribution, order management) operations.

In a random context (currency fluctuation, economic instability, uncertain geopolitical environment), a controlled demand management process must have the flexibility to address new challenges: rapid market evolution, development of customized products, accelerated launch rates, systematic use of promotions, and the growth of new sales channels.

Demand management is based on the implementation of forecast development and sharing processes with all key internal stakeholders (supply chain, marketing, finance, commerce, regions and subsidiaries, etc.) and, where applicable, external stakeholders (suppliers, distributors, customers), addressing all related departments and entities.

The contribution of forecasting tools is essential to develop a statistical forecast based on historical sales data (sell-in and sell-out), to identify the correlation between different factors (via advanced analysis algorithms), and to calculate demand according to different aspects (geographical, time and product) to facilitate decision-making.

To do this, it is mandatory to have the right level of skills, organization and processes (both at central level and in the regions/subsidiaries). 

How we can help 

Argon & Co supports its clients in the:

  • Assessment of the current performance of forecasting and demand management processes against industry best practice 
  • The implementation of alerts, performance indicators and demand management dashboards
  • Estimating the cost of changing processes, organizations and tools (“case for change”)
  • Redesign of existing processes and the development of collaborative models with internal and external participants
  • Clarification of the target organization in terms of skills, size, structure (central, regional and local; by product line or market)
  • The development of the most appropriate models (through the application of data science in the choice of the best mesh and parameters for statistical forecasting), to address demand according to product type (catalog, POS, one-shot) and life cycle stages (launch, mature, end of life)
  • The choice and implementation of forecasting tools (drafting specifications, managing RFPs, deployment strategy, implementation assistance, project management and risk management)
  • Implementation of the improvement plan (training, model configuration, etc.) and change management (evolution of culture, behaviors, skills, communication, etc.)