An optimization approach between service level andinventory via simulation : an example from the semiconductor industry (2017)

Company & Motivation
This graduation project is conducted as part of the Industrial Engineering and Management
master program in cooperation with Infineon Technologies AG. Infineon is a German semiconductor
manufacturer producing chips, sensors, and microcontrollers. To stay competitive
and satisfy customer demand quickly Infineon places inventory at various stock points within
their supply chain. However, the more products are stored, the higher the costs due to the
binding capital effect of stock. Thus, Infineon has to balance the trade off between high stocks
(characterized by a high α-service level) and high costs when examining its supply chain planning
processes. In this thesis, we concentrate on the planning process of two products: chips
for contactbased and contactless payment of the Chip Card & Security (CCS) department.
The relevance lies in their high production volume and revenue share of more than 25% of
CCS‘s total revenues.
Research objective
The graduation project aims to solve the below stated research objective:
Improve the supply chain planning process according to the service level and
respective costs at CCS for two particular products considering the stocking
strategies as well as the approach of quantifying the amount of wafers to be
released to production.
The stocking strategy concerns the decision where to place inventory and which amount
to be stored. The production release approach examines the question how to quantify the
amount of wafers (release quantity) to be started in production in advance. Usually, the production
of wafers, which are thin slices of semiconductor material and serve as basis for many
products, is started on forecast due to long processing times. This enables faster response to
customer demand. A clever chosen approach of estimating the needed quantity helps cutting
costs as stock levels can be reduced at the same α-service level.
Methodology
The existing simulation model (discrete event simulation) of Infineon‘s flexibility & econometrics
team is used to study various scenarios. These combine different stock strategies
and production release approaches among others the current practice. Before conducting the
simulation study we require to parametrize the simulation model to the needs of the two
exemplary products. This involves to ensure that the generated demand by the simulation
model is similar to the observed demand of the products such that results are valid. An
iterative approach is performed consisting of setting the parameters in the demand generation
method and subsequently assessing the fit between the generated demand and observed
demand. The fit is assessed by a modified Kolmogorov-Smirnov approach where we compare the total area between the cumulative distributions of the two demand series. The iterations
are stopped when the total area is < 10% of the area below the cumulative distribution of
the observed demand. To check the consistency of the chosen demand series we further apply
the Chi-Square test.
Analysis of current situation
Infineons supply chain has three stock points (up- to downstream): the master storage, die
bank, and distribution centre. The amount of products stored at these stock points is determined
by the target reach. The target reach is defined as the safety stock in number of weeks.
Currently, CCS has a target reach of 13 weeks at the master storage, and no stocks at the
die bank nor the distribution centre since the customer order decoupling point (CODP) lies
at the master storage and thus products become customer specific in the downstream manufacturing
steps. Storing at the master storage employs the risk pooling effect. The stocks
are managed periodically (per week). The production up to the master storage is done on
forecast by using a four month moving average (MA) over the historical data. The remaining
manufacturing steps are continued when a customer order arrives. The overall performance
can be given by the α-service level. The α-service level becomes either 100% when all orders
are satisfied by the on-hand inventory during period t, or it becomes 0% when demand is not
satisfied completely from stock. Currently, the α-service level is 98%.
Conclusion
• Both new production release approaches: a simple MA over five weeks as well as single
exponential smoothing (SES) outperform the current approach that uses a simple four
months MA since they allow faster reaction in production as fluctuations are not as
smoothed out as with a large time horizon of four months.
• Applying either of these new approaches costs can be cut by 40% since the target reach
can be reduced from 13 to five weeks while keeping an α-service level of 98%.
• Comparing the simpleMA over five weeks and SES, the moving average performs slightly
better. In addition, as it is easy to understand and to apply, we recommend to use the
simple MA with a five week time window. That is, reducing the current time window
from 16 to five weeks.
• When keeping the current production release approach, the target reach at the master
storage can be reduced from 13 to about eight weeks while having only a marginal drop
by around 0.5% in the α-service level of currently 98%.
Recommendations
• Enhance the demand generation method of the simulation model such that it is able to
create intermittent and autocorrelated demand which is currently not supported by the
simulation model. Note, the products we are considering do not show autocorrelation
nor are classified as intermittent, however there are autocorrelated and intermittent
products at Infineon.
• In addition, implement machine capacity and idle costs as currently capacity is unlimited
and costs are solely evaluate according to the WIP and stock levels. However, in
reality capacity is restricted and idle costs play an important role as machines are very
expensive. 
• Last, more elaborated approaches of quantifying the amount of wafers to be started
in production in advance such as advanced exponential smoothing techniques, Holt
Winter procedure, or ARIMA models may be examined when the demand shows a
trend, seasonality, or autocorrelation.