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Effect of quality on supply chain performance. Quality problems/variability have a big effect on supply chain performance, especially on customer service level. In SCMB, quality defects are simply modelled as the percentage of products taken out of a supply chain after production. Although more complicated and realistic modelling can be carried out, this simple modelling of quality problem does however illustrates the effect of quality on customer service level. In a pure pull supply chain, accumulated quality problems will make customer order fill rates lower and lower.

Supply chain risk measurement. The ability to measure supply chain risks and its resilience to these risks is important to the survival and profitability of a company. In SCMB, a risk is quantified by two parameters: the probability and the magnitude of a disruption. Resilience is defined as the time a supply chain returns to a normal state after a big disruption. The SCMB can clearly demonstrate different levels of supply chain resilience given different supply chain risk magnitude and probability combinations. The following diagram shows the periods after each disruption (with the probability and the magnitude of a disruption set to 3% and 3 times respectively) when the system returns to normal (signified by the 100% order fill rate) in the PW2000 TEC push supply chain. The average resilience time can be calculated as about four weeks.

Comparison of tied-up capital between ordering sequentially and through Collaboration Hub. The reduction of lead times at different stages of a supply chain will naturally translates into a reduction of inventory levels and accordingly tied-up capital. This results from two aspects: the longer lead time will require more cycle stock and more safety stock in case of volatile market demands. Since in the PW2000 TEC use case, demand volatility is not modelled, tied-up capital change will mainly be reflected on the higher cycle stock requirements. In this case, tied-up capital is reduced by about 3% while keeping the other performance measures to similar values.

Demonstration of the bullwhip effect. The bullwhip effect (Lee et al, 1997) is the amplification of fluctuations in processed quantities and inventory levels upstream along a supply chain process. There are many different individual reasons for the bullwhip effect. A SCMB simulation model can demonstrate the bullwhip effects caused by demand signal processing and order batching. Demand signal processing refers to the situation where demand is non-stationary and one uses past demand information to update forecasts. If a participant, e.g. a first tier supplier in an aerospace supply chain, experiences a surge in demand, this will be interpreted as an increase in future demand. Besides, long lead times will exaggerate the problem even further as each supplier will order a little more to cover demand volatility during the additional lead time period. Order batching refers to the situation where orders are batched until there are enough amounts to justify total minimum cost of ordering and holding costs. It also results from the operational practice where inventory is checked periodically. The SCMB shows that tied-up capital changes much more dramatically than changes in demand in a push supply chain with both material and finished goods inventory.

Analysing Supply Chain Performance with SCMB

Model prediction is one of the main purposes of a simulation model. SCMB can be used to analyse supply chain performance under various scenarios. The emphasis is on comparing some specific outputs from two scenarios to illustrate the concepts.

Comparison of lead times between ordering sequentially and through Collaboration Hub. One of the Collaboration Hub’s main effects is the reduction of order lead-time, especially in a pure pull supply chain. The diagram below shows the mean lead-time for the Volvo Aero PW2000 TEC in a pure pull supply chain from five replications for both sequential order passing and for order passing through the Collaboration Hub. It can be seen that the average difference between the two lead times is about five days, which conforms to our expectation since the average order processing time is five days and ordering through the hub saves exactly one order processing time.