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The approach first segments customers with respect to their importance and profitability into different priority classes. ATP quantities are allocated to these classes based on short-term demand information. Seitz et al. (2020) extend the allocation planning approach of Meyr (2009) by exploiting the known demand forecast bias of customers. Using data from a large semiconductor manufacturer, it is shown by designed experiments that average stock levels are reduced and the overall service level is increased.

1 Simulation infrastructure and supply chain simulation model

• We further compared it with the single-period and forward-looking approaches, indicating that the optimal result of LCOS varied from $406 to $520 for the different approaches.
• It is not obvious which one of the two decomposition approaches, i.e. the STDSM or the RBR procedure, is better.
• The upper value is obtained by the RBR whereas the lower value is computed by the STDSM.
• Caps on the capacity to be allocated can be seen as a strategy to deal with forecast inaccuracy.
• We discuss the overall planning approach and the underlying assumptions in Sect.
• This reentrant behavior results in complex competition for scarce capacity.

In the present paper, we are interested in proposing a STDSM approach for semiconductor supply chains. Since it is not reasonable to computationally assess the performance of the STDSM approach in isolation, we embed it into a hierarchical approach that contains master planning, allocation planning, release planning, and scheduling. The STDSM approach is based on decomposition that exploits the structure of the semiconductor supply chain. An iterative method is proposed to improve previously made matching decisions.

2.2 Allocation planning

• The approach first segments customers with respect to their importance and profitability into different priority classes.
• Finally, the fabrication position (FPOS) aggregate is used to represent the FE level in the supply picture offered by master planning.
• A capable-to-match (CTM) algorithm for the APS system SAP APO is discussed by Kallrath and Maindl (2006).
• Demand is generated based on the additive martingale model of forecast evolution (MMFE) by Heath and Jackson (1994).
• The semiconductor industry which manufactures integrated circuits (ICs) is one of the most complex industries in today’s world (Mönch et al. 2013).

Semiconductor supply chains are challenging for existing planning and control approaches and the related information systems (Chien et al. 2011). Mathematical optimization problems including a time dimension abound. For example, logistics, process optimization and production planning tasks must often be optimized for a range of time periods.

Implementing a Model with a Rolling Horizon

However, orders are not considered in IMPReSS in contrast to the present paper. Next, we will describe the different ingredients of the proposed planning approach. The hydrogen-based steelmaking system (HBSS) presents a multi-energy interaction, encompassing processes from hydrogen production and ironmaking to the final steelmaking processes.

3.5 Computing times for the optimization- and rule-based approaches

The literature for demand fulfillment in semiconductor supply chains is limited (see Sect. 2.2). To the best of our knowledge STDSM approaches in semiconductor supply chains are rule-based taking into account ATP quantities (Herding et al. 2017). Because of the large size of semiconductor supply chains, the proposed STDSM approach is based on decomposition. The cost settings for the STDSM approach are summarized in Table 4. Inventory holding costs are rather small relative to backlog costs since they only represent delayed revenue. FE WIP costs are chosen higher than inventory holding cost due to the limited available clean room space within wafer fabs.

The same is also shown for allocation planning, the STDSM/RBR procedure, and lot-to-order matching (green colored). We observe that the frequency of applying the different planning functions is different. For instance, allocation planning is more frequently used than master planning, whereas the OOP procedure is applied whenever an order is placed by a customer. We discuss the overall planning approach and the underlying assumptions in Sect. Master planning and allocation planning as prerequisite for the STDSM are briefly sketched in Sect. The reference approach and the remaining planning and control functions are discussed in Sect.

This approach repromises rolling horizon approach orders taking into account the finite capacity of the shop floor. Decomposition is used to obtain computationally tractable subproblems. The STDSM approach is applied together with master planning and allocation planning in a rolling horizon setting. A simulation model of a simplified semiconductor supply chain is used for the rolling horizon experiments. The experiments demonstrate that the proposed STDSM scheme outperforms conventional business rule-based heuristics with respect to several delivery performance-related measures and with respect to stability.