Inventory Planning

Introduction

Inventory Planning is the process by which target stock levels at each stockholding location are set. It provides tools and methodologies to trade off the costs of finished goods versus production capacity and customer service levels. The principles can equally apply for materials.

Inventory Planning helps reduce stocks by:
- Increasing the focus on manufacturing conformance to plan
- Focusing on improving forecast accuracy
- Providing a case for increasing production frequency

Stock is held for a number of different reasons:

· Process stock
o Generated by process specifications that delay product release, including hardening, quality assurance etc.

· Pipeline Stock
o To cover administrative and physical lags between customer order and delivery or invoicing.

· Cycle Stock
o To cover anticipated demand over the cycle time. Consequence of manufacturing in batches.

· Safety Stock
o To cover variations in demand and supply over the cycle time.

· Smoothing Stock
o To cope with seasonal demand where product has to be pre-produced because of limited capacity.
o To cope with promotions which create high demand over short intervals.
o To cover manufacturing shutdowns.
o To gain a price advantage on raw or packaging materials.

Objectives

To determine what stock target levels are required to ensure customer service levels are met at acceptable cost.

Tools

Simple spreadsheets may be used with statistical functions for calculating safety stock and cycle stock applying square root formulae. These have the following characteristics:
· Customer service measure as risk of not satisfying from stock
· Assumption about demand or forecast error variability (Normal Distribution)
· Not necessarily appropriate if trend in demand or lumpy demand
· Not necessarily appropriate for seasonal products.
· Normally single stage
· Capacity unconstrained

Simulation may be a better solution as it allows "what if" analysis on safety stock, cycle stock and smoothing stock whilst taking account of any capacity constraints. Other performance measures may also be evaluated. Any assumptions are not hidden. Simulation can be used with trends and lumpy demand and can handle multi-stage. The disadvantages are the number of 'what if' runs that may be necessary and simulation can be data intensive.