Updated: May 11
Supply Chain and Risks management
Aggregate planning represents the acquisition and allocation of limited resources to production activities so as to satisfy customer demand over a specified time horizon.
In aggregate planning you calculate the operating profit associated with the aggregate plan and use optimization (via linear programming) to find the plan that maximizes profit while satisfying the constrains that the organization faces.
The weakness of aggregate planning is that it neglects uncertainty which can be problematic in fast-moving industries with high demand uncertainty.
To mitigate shortage risk one can always add safety buffers in the form of minimal safety stock requirements, but optimization does not tell you how to choose between safety levels; the classic newsvendor model gives an analytical prescription of the optimal safety stock for normally distributed demand.
The downside of this procedure is that it is overly conservative as it assumes that demand would take on its maximal levels in every period. In reality, demand is not perfectly correlated over time and peaks in one period need not be sustained in the subsequent period.
Furthermore, increasing inventory increases service level and reduces shortage risk, but increases profit variance risk: with an abundance of stock, shortages are eliminated, sales equal demand and the company is exposed to total demand risk, and profit standard deviation is maximized; by reducing the stocking level, sales are capped by inventory and profit risk decreases (to the standard deviation of the minimum of demand and stocking level). The optimal risk-return trade-off for a company can be determined using the Markowitz’s portfolio management approach, and it depends on the company’s sensitivity to risk expressed by its coefficient of absolute risk aversion.
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With our network of Technology providers we assist our customers from the initial assessment and design of Supply Chain assets to the implementation of the technology solutions to digitize Supply Chain processes and to provide machine learning and AI to improve forecasting productivity and accuracy.