Problem Statement: A
consumer electronics company with operations around the world
is facing strong, growing demand for its products in the key South
American markets of Argentina, Brazil and Chile.
Currently, the company has a single Central DC in Asuncion, Paraguay,
and has the option to lease or buy local warehouse space. Since
the possible feasible warehouse locations are already well known,
optimization won’t help. We need to simulate various inventory
deployment strategies, to determine the best trade-off between
cost and service [figure1].
1. Using Guru's Demand
Flow policies, establish a baseline of customer delivery times
in a pure “Make To Order” environment. Customer delivery
times are consistently between seven and eight days for the baseline
demand flow strategy [figure 2].
2. Using a baseline identical R-Q with a periodic
review appears to be acceptable for the Chilean DC, but for Brazil
and Argentina, this policy will cause large service problems [figure
3]. The network inventory investment holding cost = $7382.
3. Implementing an inventory policy using DOS - days
of supply, using a moving average forecasting method immediately
produces improvements:
The blue lines are the inventory levels, and the yellow lines
are the back orders. After an initial adjustment period, inventory
levels stabilize and service rates hover around 95% or so. However
the inventory carrying cost under this scenario is $43,311 to
achieve these service levels [figure 4].
Inventory in Supply Chain Guru is continuously tracked, and the
inventory investment calculated by multiplying the Inventory Value
x Inventory Level for each product, summed over all products.
In a supply chain with random variance in the demand and in segments
of the supply delivery system, simulation is the only way to predict
the actual service and inventory costs. This is a lot more intelligent
than guessing and simply "hoping for the best" when
you evaluate inventory strategies.
Guru Inventory Policy Table
Supply Chain Guru has numerous capabilities to model alternative inventory strategies and approaches, including:
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R,Q: fixed reorder point, fixed reorder quantity
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s,S: reorder threshold, variable reorder quantity
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Demand Flow: one for one unit replacement (with or without base inventory level)
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Days of Supply Demand: dynamic inventory targets based on moving average of demand
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Days of Supply Forecast: dynamic inventory targets based on user specified forecasts and targets
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Custom Inventory Policy: use Guru's powerful scripting capabilities to emulate your own planning algorithms |
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