## 4 Evaluating Improvement Effects  
After suggesting the improvement plan, Table 3 shows the material utilization result from the 2 DCSP-TS algorithm that was found better than the manual method to an average of 7.7% and was used to calculate by the MFCA technique again, such as material balance and MFCA cost allocation, in order to convert to financial data of the after improvement. Then, cost of prior lingerie production shown in Figure 4 and Table 1 has been improved. More details in the comparison of cost in production before and after improvement are shown in Table 4.  
According to Table 4, the total cost of lingerie after improvement remains the same at 7217.00 USD because the improvement did not focus on the input factor but rather,  
Figure 10: The arrangement using heuristic Two-Dimensional Cutting Stock Problem—Tabu Search algorithm (2 DCSP-TS) algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{2}{c}{**Average Material Utilization (\%)**} & \multirow{2}{*}{\% Improvement} & \multicolumn{2}{c}{**Best Utilization Found**} & \multicolumn{2}{c}{**Average Calculation Time (sec.)**} & \multirow{2}{*}{
\begin{tabular}{c} **Speedup** \\ **(Times)** \\ \end{tabular} } \\ \cline{2-2} \cline{5-8}  & **Manual** & **2 DCSP-TS** & & & & **Manual** & **2 DCSP-TS** & \\ \hline
1 & 54.64 & 59.154 & 8.26\% & 60.05 & 960.00 & 44.507 & 20.57 \\
2 & 53.68 & 57.291 & 6.73\% & 58.812 & 900.00 & 49.191 & 17.30 \\
3 & 44.44 & 47.000 & 5.76\% & 48.393 & 1020.00 & 55.071 & 17.52 \\
4 & 69.82 & 74.977 & 7.39\% & 78.383 & 1020.00 & 9.623 & 105.00 \\
5 & 68.55 & 73.862 & 7.75\% & 75.701 & 840.00 & 8.023 & 103.70 \\
6 & 61.63 & 67.330 & 9.25\% & 70.618 & 1080.00 & 11.396 & 93.77 \\
7 & 58.20 & 62.511 & 7.41\% & 63.19 & 3789.30 & 251.141 & 14.09 \\
8 & 55.58 & 61.089 & 9.91\% & 62.047 & 3362.42 & 206.450 & 15.29 \\
9 & 53.57 & 57.106 & 6.60\% & 58.314 & 3640.27 & 235.224 & 14.48 \\
10 & 69.84 & 75.255 & 7.75\% & 76.031 & 1860.00 & 46.771 & 38.77 \\
11 & 69.35 & 73.434 & 5.89\% & 75.248 & 1680.00 & 32.656 & 50.45 \\
12 & 67.87 & 74.426 & 9.66\% & 76.930 & 1920.00 & 26.543 & 71.34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of the results from the 2 DCSP-TS algorithm and present case.  
the cutting process in which the highest negative product cost was generated. The result after improvement increased 7.62% in positive product cost from 5207.10 USD (72.15%) to 5756.74 USD (79.77%). The positive product costs are allocated to material cost 3806.02 USD, system cost 1784.76 USD, and energy cost 165.96 USD. Meanwhile, the negative product costs decreased from 2009.90 USD (27.85%) to 1460.26 USD (20.23%) which was allocated to material cost, 1361.49 USD, system cost, 63.24 USD, energy cost, 22.20 USD, and waste management cost, 13.33 USD. The results clearly show that positive product cost ratio has increased whilst the negative product cost has declined according to the improvement in the cutting process where the highest cost occurred. The negative product cost is mainly from material cost rather than other allocations. It means that, in future planning, we can reduce the input (raw material) from the initial planning stage, while still getting the same finished goods, and the cause helps entreprene more by saving cost.