Customer Information Driven After Sales Service ... - RePub

3. Inventory Planning & Information Enrichment
We should also note that the above model does not consider uncertainty of demand,
which is the source of non-linearity in Kranenburg and Van Houtum (2009) and IBM’s
patented model by Erke et al. (2003). We noted earlier, that Kranenburg and Van Houtum
(2009) and Erke et al. (2003) handle demand uncertainty by developing approximations.
As a result, in comparison to deterministic planning model, base stock levels
estimated by the approximated model are inflated proportionally to the approximation
parameters. In the presented simplified model, we assume deterministic setting as it reduces
the computational complexity while retaining validity of presented results for the
approximated version. We argue this due to following two reasons. First, we performed
comparative experiments between the presented model and IBM’s patented tool and results
were found to be similar. This is due to the approximation coefficients chosen by
IBM for application of planning tool, that essentially reduce the approximated model to
deterministic setting.
Secondly, our objective in this chapter is not to present an inventory planning tool.
Instead, our focus here is to analyze the benefits of using installed base information to
support inventory planning versus no installed base information usage by observing reduced
inventory planning costs due to installed base usage. As we shall notice in Section
3.2.1, that by using installed base data, we do not change the overall network demand
forecast. We should note the amount of stocks that are needed to be placed in the stock
network is bounded from below by network demand forecast and depends on equilibrium
between holding and transportation costs, and time based service levels. If transportation
costs are of lower magnitude in comparison to holding cost then optimization attempts
to push base stock levels (integer values) towards network demand forecasts while accounting
for time based service level. Therefore, any inflation in base stock level, due to
approximation parameter, impacts installed base usage scenarios and no installed usage
scenarios in a consistent manner. The consistent impact of approximation parameter
also holds if holding costs rates are lower then transportation costs rates. Therefore, the
results presented in this chapter (see Section 3.7) are also valid for stochastic settings.
IBM developed this planning tool to replace the existing hierarchal planning system
(i.e. Cohen et al. (1990)). The existing system did not consider lateral transshipment and
time based service levels, which are specifically considered in this approach. Currently,
IBM is in the deployment phase of the developed tool. For the regions, where it has
been already deployed, the deployment of this tool has resulted in considerable inventory
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