Recap on current situation

Throughout Chapter 2, we highlighted a number of remarkable things, indicating that there are some things that could be improved regarding the decision making. First of all in Section 2.2.2, we noticed that the supplies and requirements matched over an entire year. However, during the year, a lot of uctuation was found in the weekly supplies. Therefore it is important to determine proper thresholds in the buers to cope with the uctuation in the weekly supplies. Currently the thresholds are around 15% and 85% and based on the mass of the waste. The forecasts for the throughput of the dierent incineration lines were straight lines, whereas the real throughputs had more uctuation. The caloric value is something that could be taken into consideration in order to achieve a proper forecast for the throughput. In Section 2.2.3, we presented the inventory development of the bulk storages. Those were constant, indicating that during a year there is not much uctuation in the bulk storages. So in order to save costs, the maximum capacity of the bulk storages could be lowered.

In Section 2.2.4, the costs associated with having too much or too less supplies were presented. The costs for downtime, shown in Table 2.4, are so high, that the most important goal of Twence is to never have unscheduled downtime. The storage costs do not outweigh the cost of downtime, but are still a reasonable amount of the entire rate that Twence receives for combustible waste. Currently a lot more waste is diverted and retrieved than is required based on the expiration date. This leads

to a average costs per week for diverting and retrieving ofe10.414,81 for the last 4 years, and evene11.447,69 for the last year. We use the costs per week as our rst performance indicator.

We have shown in Section 2.2.5, that there is a correlation between the caloric value of waste and the throughput. Caloric value eects the throughput, so Twence could also consider to determine the requirements based on the caloric value. With that Twence can make better forecasts about the throughput, because when only look- ing at the mass of the waste, equal portions of waste with dierent caloric values will give dierent throughputs. We stated that having a constant caloric value as input increases the process eciency. Currently, the standard deviation of the caloric value is 0,491 and 0,810 for resp. incinerator lines 1+2 and 3. Ecient processing is something that Twence desires and although we do not have insight in the costs of variations in caloric value, we still use the standard deviation of the caloric value in the buer as our second (but less important) performance indicator. To get a clear idea about the next steps in this research and nd out how decisions on these subject are commonly approached, we perform a literature study in Chapter 3.

In this research it is important to know what the conditions are under which Twence should decide to divert to or retrieve from bulk-storage, which can be seen as a kind of safety storage. We therefore, look at dierent inventory models to see how this is handled in general (Section 3.1). Another issue we addressed, was the quality of the waste. We want to know if the eect of the quality is really that important or that Twence does not need to take the quality into account while making decisions (Section 3.2). The last part of our literature study is about ways in which we can analyze and model all these dierent variables in the most ecient way (Section 3.3).

3.1 Inventory policies safety stocks

There exists lots of academic literature about inventory policies working with safety stocks. Natarajan and Goyal (1994) point out that there are two kinds of uncer- tainty to deal with, namely, demand-side and supply-side uncertainty. Inventory models deal with these uncertainties by introducing a distribution for lead time and for the demand as is done in Van der Heijden and De Kok (1992). In their research a (R, S) inventory system is used where the lead time of an order has a probability distribution function as well as the demand per customer. (R, S) inventory systems are widely used and discussed in for example Hadley and Whitin (1963) and Peter- son and Silver (1979).

Rawata and Altiokb (2008) introduce three policies for inventory control. All work with the same review period, but dier on other parameters. One has a safety stock level equal to the order up to level. So whenever the inventory level drops blow the safety stock, it is replenished back to the safety stock level. The second policy works with a dynamic safety stock based on the intensity of demand. The last policy has an order up to level higher than safety stock, which is calculated every S periods. When working with dierent product types or varying product qualities the heuris- tics of Zhou et al. (2011) can be studied. They introduce a pull policy with and without sorting of the dierent types of products that are used for remanufacturing. In the policy without sorting, if the inventory level is below the safety stock, the inventory is supplemented with returned products, ignoring their quality. In the policy with sorting, rst the returned products with the lowest remanufacturing costs are used. This means when looking at the way in which the safety stock is maintained, it does not matter how many products of each quality are available. In the case described in Zhou et al. (2011) it would be best to have as much returned products with a quality that results in low remanufacturing costs.

In our research, we are dealing with a client that needs to be served all the time (i.e., Service level 100%). Also the demand is known, when the caloric value is known. The most important thing is to prevent stockout. In all previously mentioned literature, and especially in Graves (1996) and Moinzadeh and Aggarwal (1997), it is stated that the amount of safety stock should be enough to reduce the probability of stockout to the desired level. We can not use policies directly found from these articles. However, the general idea we found about that more safety stock is needed for less probability of stockout, is useful for our research and we determine the situations in which stockout could occur in order to determine the safety stock.