Estructura y diferentes generaciones de CAR-T

The ordering pattern has been defined in this study as a combination of the ordering rate (OR) and effective average order quantity (EAOQ) of each ordering policy. The ordering rate is expressed as a percentage and represents the portion of the number of days an order is placed against the total number of simulation days. For instance, a value of 100 shows that an order is placed 100% of the time, which means that an order is placed to the upstream member every day. A value of 50 means that an order is placed on the number of days that is equivalent to half the total simulation time of 701 days. The EAOQ on the other hand is the total order quantity divided by the actual ordering days. As a simple illustration, say 1000 units were made in total and the number of ordering days were 500 out of 701 total simulation days, then the average order quantity would be 1000/701. However the EAOQ would be 1000/500. Appendix 4.2 presents the percentage of time an order is placed (ordering rate) and the effective average order quantity for each supply chain agent under all examined supply chain scenarios.

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In the methodology chapter it has been established that option I places an order whenever the inventory position falls below the re-order point, the quantity of which is equivalent to the difference between the order-up-to level (also re-order level) and the inventory position. From the ordering pattern result in Appendix 4.2 under the non-information sharing serial supply chain scenario (i.e. the base model) the order quantity for the retailer averages 9.98 which is less than the average demand quantity of 10. This means that the inventory position more often than not would be less than the re-order point prompting a daily order placement. The results suggest a 100% ordering rate which confirms that the retailer places an order every day. This of course would mean that the fill rate of the retailer would be low and consequently the backlog cost would be high. Since order quantity is not able to sufficiently satisfy demand, the expectation is that the holding inventory will be very low or near zero as there is constantly a demand that needs to be fulfilled. The result in Appendix 4.1 confirms this with the retailer fill rate very low (at 45%) and the backlog cost high (at £140). The holding cost for the retailer is near zero at £0.02. A similar observation is seen at the wholesaler and the manufacturer where the respective effective average order quantity is lower than the order from the downstream agent. Therefore the ordering rate is the same for all supply chain agents at 100% but the average effective order quantity decreases slightly as one goes upstream the supply chain.

For option II however, the order quantity is equivalent to the amount determined by a dynamic optimal EOQ model which is larger than that determined in the option I scenario. This quantity is large enough to better satisfy initial demand and raise the inventory position. Raising the inventory position implies that there would be less number of times the inventory position is lower than the re-order point. Therefore, orders would be placed less frequently and the inventory holding cost would be higher than in the option I scenario but the cumulative fixed ordering cost would be much lower. In addition the fill rate performance is expected to be higher under option II than in option I and consequently the backlog cost should be lower. From the result in Appendix 4.1, this expectation is confirmed to be true. Interestingly, the ordering rate decreases as one goes up the chain and the average effective order quantity significantly increases. This scale of increase in effective order quantity as one goes up the chain is high enough to create external economies of scale which

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translates into an improvement in operational efficiency at the manufacturer. Therefore the manufacturer is able to enjoy a reduction in daily average inventory holding cost that would have otherwise been high considering the size of the wholesaler orders to it. The consequence of this again is that the backlog cost would then be higher than anticipated. Hence the fill rate performance of the manufacturer in option II (92%) is less than that in option I (95%) meaning the daily average backlog cost in Option II (£9) is higher than that in option I (£5). This would have been the other way round if not for the scale increase effect.

Option III is an extension of option I (the base stock policy) with the addition of the simple EOQ component to its order quantity determination. This quantity is larger than the quantity in option I but less than that in option II. Therefore, orders would be placed less frequently in option III than in option I but more frequently than in option II. Consequently inventory holding cost would be higher under option III than in the option I scenario, but less than the option II scenario and the cumulative fixed ordering cost would be lower in option III than in option I but higher than the option II scenario. However the holding inventory for the manufacturer in option III is higher than in option II. This has been explained in the previous paragraph. The reason is because the scale increase effect was observed at the manufacturer under option II but not under option III as the order size is not large enough to create this effect in option III. This effect in option II is however large enough to ensure better daily average inventory holding cost performance in option II than in option III. Option III appears to be in between option I and option II in terms of the ordering pattern performance and the fill rate performance under this option is better than the other two ordering options. This suggests that the balance between the ordering rate and the EAOQ in option III creates a better balance between how much is ordered against how much is being demanded making it the best cost performer of the three ordering options.

In summary, option I is such that the ordering rate is high and the EAOQ is low and this is classed as a Case-1 ordering state. Option II is such that the ordering rate is low and the EAOQ is high and this is classed as a Case-2 ordering state. Comparatively, option II (£310) performed better than option I (£397) which means Case-2 scenario is more desirable than Case-1 scenario under normal circumstances. Option III, however, has median ordering rate and median average effective order

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quantity existing between the Case-1 and Case-2 ordering states. This state appear to be the nearer-optimal state making it the best overall cost performer (£300) of the three ordering policy.

4.2.2 Anatomy of the Bullwhip Effect in a Non-Integrated Serial Chain