### Table 1: Replenishment Policies to be Evaluated

"... In PAGE 3: ... For the specific inventory problem in this paper, the model parameters were established as R=1 week, s was given by each corresponding demand distribution with a 95% service level. Q was calculated using expression (3) for each item, and c taking four different values as shown in Table1 , to represent different replenishment coordination levels. Table 1: Replenishment Policies to be Evaluated... ..."

### Table 5 Enforcing tighter upper bounds for optimal replenishment cycle lengths - Partial solution in Table 4, underlined figures are closing inventory levels of the optimal policy

"... In PAGE 15: ... We now consider the partial solution shown in Table 4. Table5 shows the reduced domains obtained when we enforce tighter upper bounds for optimal replenishment cycle lengths con- sidering the partial solution in Table 4. From Theorem 1 it directly follows that the filtering is performed by removing from decision variables domains (Table 3) values that do not appear in Table 5, which contains the computed reduced domains with respect to the partial solution given.... In PAGE 15: ... Table 5 shows the reduced domains obtained when we enforce tighter upper bounds for optimal replenishment cycle lengths con- sidering the partial solution in Table 4. From Theorem 1 it directly follows that the filtering is performed by removing from decision variables domains (Table 3) values that do not appear in Table5 , which contains the computed reduced domains with respect to the partial solution given. We shall now see in details how feasible expected closing-inventory-levels in the reduced domains (Table 5) are computed for the first 5 periods.... In PAGE 15: ... From Theorem 1 it directly follows that the filtering is performed by removing from decision variables domains (Table 3) values that do not appear in Table 5, which contains the computed reduced domains with respect to the partial solution given. We shall now see in details how feasible expected closing-inventory-levels in the reduced domains ( Table5 ) are computed for the first 5 periods. In the given partial solution we place an order in period 1 but not in period 2.... ..."

### Table 4 Replenishment policy of TK

2003

"... In PAGE 10: ...results are presented in Table4 and Fig. 2.... ..."

### Table 3. Indifference level, profit and return on investment for the case study components when the first w components and replenished JIT and the m-w-1 components remain batch replenished

"... In PAGE 10: ... These may be wiser as the results of the analysis depend on the assumed product demands i A . Table3 shows the results of this analysis using both the exact method of (Betts and Johnston 2004) and the approximate method described above for the stochastic case using the case study data. (The deterministic case is not shown here as the above method reproduces the exact analysis as no approximations are involved).... In PAGE 10: ... Indifference level, profit and return on investment for the case study components when the first w components and replenished JIT and the m-w-1 components remain batch replenished Again we find reasonably good agreement between the values calculated for w K~ , w K P~ and w K ROI ~ by the two methods. Table3 shows that as JIT replenishment policies are implemented cumulatively for components with the highest ranks determined in the previous section, initially return on investment in inventory for the business increases considerably despite a slight reduction in profit. The greatest effect occurs when the first three components (Screw 1, Screw 2 and Piping 2) are simultaneously replenished JIT.... ..."

### Table 3: Performance of truncated linear replenishment policy

2007

### Table 2: Performance of truncated linear replenishment policy T = 10

2007

"... In PAGE 26: ...the sample means over all the runs. The results for the T = 5; 10; 20 and 30 problems solved are given in Table 1, Table2 , Table 3 and Table 4 respectively. The robust policies were obtained using the bounds of Theorem 1 and Theorem 4 where the support, covariance, directional deviations associated with random factors are specifled.... ..."

### Table 3: Performance of truncated linear replenishment policy T = 20

2007

"... In PAGE 26: ...the sample means over all the runs. The results for the T = 5; 10; 20 and 30 problems solved are given in Table 1, Table 2, Table3 and Table 4 respectively. The robust policies were obtained using the bounds of Theorem 1 and Theorem 4 where the support, covariance, directional deviations associated with random factors are specifled.... ..."

### Table 4: Performance of truncated linear replenishment policy T = 30

2007

"... In PAGE 26: ...the sample means over all the runs. The results for the T = 5; 10; 20 and 30 problems solved are given in Table 1, Table 2, Table 3 and Table4 respectively. The robust policies were obtained using the bounds of Theorem 1 and Theorem 4 where the support, covariance, directional deviations associated with random factors are specifled.... ..."

### Table 2: A sample path of the truncated linear replenishment policy.

2007

### Table 2: Parameters for Material Replenishment Planned

"... In PAGE 6: ... In the example being illustrated the lot sizes are assumed fixed, as indicated in Table 1. The other parameters as- sumed for each of the planning and control system alterna- tives are shown in Table2 . These parameters were set so that the average expected demand during the MRP/DRP planned lead times are equal to the reorder point and Kan- ban inventory positions at the time an order is triggered.... ..."