Demand Prediction and Efficient Allocation of Printed Press

The Client

The client is a nationwide distributor responsible for delivering newspapers and magazines across a large network of retail locations. Their operations depend on accurately matching supply with local demand. As distribution scale and variability increased, they needed a data-driven solution to forecast demand and optimize allocation decisions.

The Challenge

Printed press distribution requires precise forecasting to avoid both shortages and over-supply. Excess inventory leads to waste and increased costs, while under-supply results in lost sales and dissatisfied customers.

The client relied on historical demand signals, including advance orders and actual sales, but variability across locations and product types made accurate prediction difficult. Demand patterns differed significantly between newspapers, magazines, and regions, requiring granular modeling.

Additionally, operational constraints such as minimum delivery quantities limited flexibility in supply decisions. Even when demand was predicted accurately, the system needed to determine how to allocate surplus inventory in a way that maximized overall efficiency.

Without an integrated forecasting and optimization framework, the distribution process remained suboptimal, leading to inefficiencies across the supply chain.
 

What We Did

Approach

We developed a two-stage solution combining machine learning-based demand forecasting with an optimization layer for supply allocation. The first stage focused on predicting demand at the level of individual press items and locations. The second stage addressed the redistribution of surplus supply under operational constraints.

During the discovery phase, we identified that demand prediction alone would not solve the business problem — the key value lay in linking predictions directly to allocation decisions. This led to the integration of a portfolio allocation-inspired optimization model to balance supply across locations.

A key design decision was to incorporate real-world constraints, such as minimum delivery quantities, directly into the optimization process to ensure deployability.
 

Methodology

• Data assessment:
  Analyzed historical data on advance demand and actual sales across press types and distribution locations
• Approach selection:
  Combined supervised machine learning for demand prediction with a modified portfolio allocation model for supply redistribution
• Model development:
  Built predictive models to estimate demand per item and location, capturing temporal and regional patterns
• Validation framework:
  Evaluated forecasting accuracy and tested allocation strategies against historical scenarios
• Integration:
  Deployed an end-to-end system linking demand forecasts with allocation decisions under operational constraints
• Compliance:
  Ensured alignment with logistical requirements, including minimum delivery thresholds and distribution policies
   

The Outcome

Improved demand prediction and optimized distribution efficiency

The solution enabled accurate forecasting of press demand at a granular level, combined with optimized allocation of surplus supply across the distribution network.
 

Secondary outcomes:

• Reduced waste from unsold press inventory
• Improved availability of high-demand items at key locations
• Data-driven allocation decisions replacing manual planning
• Scalable framework adaptable to changing demand patterns

Facing a similar challenge?

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