Model description

We use a simulation model to investigate the consequences of implementing the postponement of replenishments. Several aspects of the solution alternatives are important to evaluate the proposed solutions. These aspects are described below and translated into KPIs in Section 5.3.

• Levelled workload: the main objective is a levelled workload at the part feeding process. To be more precise: the supply of pallets should be balanced over the different supply cycles. As the workload at part feeding is directly linked to the supply of pallets, the levelling of workload can be achieved by levelling the supply of pallets. That is also the reason why we model the supply of pallets as one process step, i.e. we aggregate the different sub-process steps of the supply of pallets (such as transport by trains and changing pallets at the assembly line by forklifts).

• Capacity utilisation: the reason behind levelling workload is that less capacity is needed for a more balanced workload. The simulation model incorporates both the utilisation of the regular capacity as well as the use of extra capacity.

• Stock outs: a stock out at the assembly line should be prevented, as it could result in a costly line stoppage. The proposed alternatives of Chapter 4 are designed such that stock outs are prevented. We do not explicitly model these stock outs in the simulation model, as they are already incorporated in postponement decision, i.e. we only postpone pallets that cannot result into costly line stoppage.

Modelling replenishment requests

The core of the simulation model is the decision of which replenishments are supplied immediately and which are postponed. Hence, these replenishment requests need to be generated by the model. We state several alternatives for modelling these requests:

• The simplest solution is to directly use historical data of the requests as input of the model. The main advantage of historical data is that interdependencies between requests are incorporated in the historical data. Disadvantages are a lack of data and that only the historical situation can be evaluated. This last disadvantage is the main reason why we do not directly use historical data in our model.

• Requests can be generated by modelling the time between two consecutive requests of one part. However, interdependencies between parts cannot be modelled via this alternative, e.g. between the part demand of a left tail light and a right tail light. It does not incorporate the difference in part demand between certain truck models. Assuming independence between parts would underestimate peaks in requests. Therefore, we do not use this modelling method.

• A third way of modelling is deriving the part demand from the production sequence and bill of materials. An advantage of this method is that the interdependencies between the part demand for one truck are incorporated in the model. More processes such as the production

required for modelling the postponement decision based on real time information (see Section 4.3). Section 5.2 describes in more detail how we model the input of our model.

Overview of the model

This subsection describes the model briefly, before explaining the model more technically. The simulation time series is divided in supply cycles. In each supply cycle the production progress is updated and requests are created. At the initialisation of the model the production sequence is generated by drawing trucks randomly from a historical set of trucks. Each supply cycle, the production of trucks is simulated. For the trucks that are produced, the part demand is obtained via the bill of materials. This part demand is deducted from the line inventory and requests are created for empty pallets. The created requests are supplied or postponed according to the methods that Chapter 4 proposes.

Scania produces trucks on multiple assembly lines. We define the production flow 𝑎 as all the trucks (or components of trucks) that are produced on the same assembly line 𝑎. Workstations at the assembly line produce for only one production flow 𝑎, whereas pre-assembly workstations can produce components for multiple production flows.

Section 5.2 explains the generation of the production sequence at the initialisation of the model. At the start of each supply cycle, the model generates requests via the following modelling steps:

1. The demand during the lead time depends on the trucks that are produced in the previous supply cycle (Figure 25). We define 𝑇𝑤,𝑎as the number of trucks that have been produced in the previous supply cycle for flow 𝑎 on (pre-assembly) workstation 𝑤. 𝑇𝑤,𝑎 is drawn from an empirical distribution that Section 5.2 explains in more detail. In other words, 𝑇𝑤,𝑎 trucks have been produced since the start of the previous supply cycle.

Figure 25: 𝑇𝑤,𝑎 is the number of produced trucks between the start of the previous supply cycle and the current supply cycle.

2. We define 𝑉𝑝 as the demand for part 𝑝 in the previous supply cycle. Retrieve this part demand for the 𝑇𝑤,𝑎produced trucks from the production sequence and the bill of materials.

3. Update 𝐼𝑝, the line inventory of part 𝑝, by subtracting the part demand from the inventory level at the start of the supply cycle.

4. Create replenishment requests for empty pallets, just as in the current situation.

5. For all replenishment requests, determine whether the replenishment is supplied immediately or is postponed. This postponement decision is taken according to Sections 4.2, 4.3 and 4.4. Appendix B contains a flowchart of the implementation of the postponement decision in the simulation model. In the model, information about the production progress or line inventory is known that is not available as defined in each alternative. The model only uses the information as described in Chapter 4; additional information is not used at the postponement

Model assumptions

A simulation model is a simplification of reality; hence we list our important choices and assumptions:

• The actual supply of pallets is modelled as one process step with a deterministic process time. This process time is equal to the lead time starting from the postponement decision moment (see Figure 20 in Section 4.2).

• Only the supply of pallets is incorporated in the model; the return flow is left out of the simulation model.

• The capacity of supplying pallets is restricted by the physical capacity of the tugger trains/ pallet trailers. Other restrictions, e.g. driving times, are not incorporated in the model.

• The part demand is fully defined by the need according to the bill of materials, e.g. the rejection of parts is not taken into account.

• Rescheduling trucks is not incorporated in the simulation model.

• We do not change the current division of parts over the supply zones.

• The production process is modelled by means of generating 𝑇𝑤,𝑎 (see Section 5.2). Line stoppages and other disruptions of the production process are implicitly incorporated in this number of produced trucks. Moreover, stochastic cycle times are indirectly modelled via 𝑇𝑤,𝑎.