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Stock management system

As explained in Machine Learning System Design

A system that manages inventory availability, replenishment, and related stock decisions.

A stock management system turns demand forecasts into inventory decisions, balancing service levels against the costs of overstock, expired goods, and missed sales. section 5.3.1 section 9.4.1

Practical design and evaluation choices include:

  • Use quantile forecasts when the target is a service level such as 95%, then assess forecast quality through stock position and out-of-stocks. section 5.3.1
  • Weight errors by SKU price or apply asymmetric penalties when underforecasting is more damaging than overforecasting. section 5.3.1 section 9.4.1
  • Choose data granularity deliberately: moving from day-store-item to week-store-item can reduce computation while preserving or improving performance. section 9.4.1
  • Review residual bias and spread; slight, controlled overprediction is preferable to underprediction, while excessive overprediction remains harmful. section 9.4.1

Choosing the forecasting grain

A retailer deciding between daily and weekly demand records should involve product managers, category managers, users, and stakeholders, because granularity changes service speed, performance, and the system’s possible use cases. section 9.4.1

How Machine Learning System Design covers it

How deep it goes
Explains forecast metrics, quantiles, residual preferences, and data-granularity tradeoffs, but stops short of specifying system architecture.
Examples
Uses SKU prices, service-level quantiles, residual distributions, and day-store-item versus week-store-item datasets.
What it assumes
Assumes familiarity with demand forecasting, validation metrics, residuals, and inventory costs.
What you can do after
Evaluate demand forecasts against inventory costs, diagnose residual bias, and choose an operationally appropriate data grain.
How it connects
Links stock control with demand forecasting, quantile forecasts, loss functions, residual analysis, overstock, and out-of-stocks.
Cover of Machine Learning System Design
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