In such a special period and environment condition, how to maintain the quality of the exposition will be a big challenge. The authority has to replace withered flowers by fresh ones. Two major challenges are faced by the authority while counting flora products. The first consideration is how to use less labors to get all flowers data within the shortest path, and the other one is how to reduce counting time. Labors, for serving in Taipei Flora Exposition, need to count of every flower manually. It means that we have to walk every route for every flower and need a lot of papers works to check the flowers life cycle, replacement date, planted date, and etc. These paper works not only need a lot of labors to deal with but also take a lot of time to process these data. What`s worse, we will waste a lot of time to walk every route for counting.
These problems will increase our labors cost and time cost greatly in this case.
Therefore, we will implement CIM system into this case.
At the beginning of applying this technology into this case, we need to divide all flowers into different areas, different blocks, and different batches in order. After clustering flowers, we will embed RFID tag into every batch of flowers with name data, life cycle data, region data and etc.
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For the CIM system used in Taipei Flora Exposition, we use cloud database to integrate current data. When inventory personnel count, they use handheld reader to check, revise, and confirm the amount of flowers after connecting with the end users in the cloud. With the integration of cloud computing database, rear end users can easily get the amount of flowers and RFID tags. Preparation for the system is to collect data for different flowers into RFID tags, classify flowers, and input in electronic database for integration when flower and RFID tags suppliers transport products to warehouse.
Before we apply cloud inventory management system into this case, labors, for serving in Taipei Flora Exposition, need to count of every flower manually. It means that we need a lot of paper work to check the flowers life cycle, replacement date, planted date, and etc. These paper works not only need a lot of labor to deal with but also take a lot of time to process these data. Therefore, our cloud inventory management system can help us to improve this issue.
At first, we will choose a block of flowers which has been embedded with RFID tags. With the handheld wireless RFID reader to do inventory management, we can get the data from the tags which were embedded inside after scanning this area. For example, we can get the data, including the name, life cycle, and amount of flowers. If we need to update or exchange the data, we can process synchronously via the rear-end cloud database service to inquire the stocking of flowers, or economic order quantity of flowers so that the concurrent engineering will be implemented to update and exchange the data simultaneously. If we do not have to update or exchange the data, we can make a record on the tags which were embedded in flowers so that it will help improve the effectiveness and efficiency of taking stock.
For this exposition, we will consider environment limitation as the following statements for our model assumption:
1. With the wide hinterland in this exposition, we need lots of labors to check stock for every batch of flower.
2. With the significant amount of flowers, it takes lots of labor cost and time to do inventory management.
3. With different life cycle for different flowers, it takes lots of time and labor cost to manage and integrate information.
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4. There are no path obstacles in this case.
5. Every route is smooth without altitude.
6. Operators work 8 hours every day.
7. Operators have same walk speed in this case.
8. Operators take 10 minutes break after 50 minutes work.
9. Operators need to count every batch of flowers one by one.
In the process of counting in Taipei Flora Exposition, flower supplier and tag supplier will keep products into warehouse. In the warehouse, flowers will be categorized and tags will be embedded with flower information so that the CIM system can integrate these information. When labors check stocks with RFID handheld reader, we can use the user platform, which is installed in handheld reader, to link the rear end clouding computing system. For instance, we find out the amount of flowers is under the safety stock, we can choose “place an order” to order flowers by labors. These actions will be recorded in CIM system for checking and updating simultaneously.
In this case, we divide Taipei Flora Exposition where planted with flower into 4 area and measure the route length and working time. In the table shows the route length comparison between each other, and the working time comparison between each other.
(1) By using cloud inventory management system, we can easily find out the reduction of route length and time reduction. As we can see from the table, by using the efficiency formula (see Eq. 1), we can acquire the data for the improvement of working efficiency. In the same route, we use less time to finish flowers checking and data processing.
This system provide us a simultaneously interaction between labor and their work. This system helps us to shorten 82.9792% walking route and time requirement to do flowers inventory counting. After applying the cloud inventory management system based on RFID technology, the efficiency of inventory counting gets greatly improved. According to the table, the inventory counting route lengths have been reduced 510563.7 meters after applying cloud inventory management system and inventory counting time reduced 9209.994 minutes after applying CIM system. The
CIM S
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labor cost reduced and the inventory management information is advanced processed.
In addition, the improved square measurement only has 17.044%. It indicates that applying cloud inventory management system has significantly improvement on working efficiency and time reduction in this case.
In this special case, we have special environmental limitation, including huge counting area. As a result, our efficiency improves dramatically by implementing cloud inventory management system base on RFID. RFID technology plays a very important role in the process to achieve rapid, real-time, standardize of collecting and processing of the logistics system. From the result we learn from the table, we have 82.955% efficiency improvement about route length and time reduction. In this special case, we actually lower down labor cost and time cost dramatically.
Fig. 6 CIM based on RFID technology in Taipei Flora Exposition case study structure.
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Table 2 Da-Chia River Side Area.
Da-Chia River Side Area
Route Length before applying CIM system(meter)
Route Length after applying CIM system(meter)
Area of the land
(m^2square meter) Efficiency (%) Square
measurement (%)
Labor working time before applying CIM system(minutes)
Labor working time after applying CIM system(minutes)
A01 3940.903 658.8402 63.8426 83.282 16.7179 59.1138 9.8826
A02 5175.284 934.947 103.0518 81.9348 18.0656 77.6292 14.0242
A03 1607.403 470.0121 29.8476 70.7595 29.2404 24.111 7.0501
A04 8222.643 1047.564 206.4901 87.26 12.7399 123.3396 15.7134
A05 10778.4 1255.903 302.8142 88.3479 11.652 161.676 18.8385
A06 5294.411 853.1872 91.317 83.8851 16.1148 79.4161 12.7978
A07 1835.63 384.2167 34.1495 79.0689 20.931 27.5344 5.7632
A08 7664.711 989.5853 132.0459 87.089 12.9109 114.9706 14.8437
A09 7911.412 855.9195 16.1416 89.1812 10.8187 118.6711 12.8387
A10 3982.843 712.0113 79.6199 82.123 17.8769 59.7426 10.6801
A11 3744.249 773.0146 82.8121 79.3546 20.6453 56.1637 11.5952
A12 8680.387 1068.686 181.1407 87.6884 12.3115 130.2058 16.0302
A13 6853.944 816.8189 136.691 88.0824 11.9175 102.8091 12.2522
A14 3252.416 856.9343 66.8357 73.6523 26.3476 48.7862 12.854
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Table 3 Yuan Shan Park Area
Yuan Shan
(m^2square meter) Efficiency (%) Square measurement (%)
B01 12169.54 426.9133 18.08859 92.9839 7.016095 225.1365 7.897896
B02 2409.152 262.2966 3.590423 78.22499 21.77501 44.56932 4.852487
B03 1069.9 108.9945 1.150621 79.62529 20.37471 19.79315 2.016398
B04 2595.406 203.8749 2.513508 84.28956 15.71044 48.01501 3.771685
B05 18836.67 1550.981 62.65032 83.53233 16.46767 348.4785 28.69315
B06 3998.797 380.2653 10.32807 80.98102 19.01898 73.97774 7.034907
B07 3575.66 278.6531 7.055343 84.41389 15.58611 66.1497 5.155081
B08 6547.034 379.8121 16.69997 88.39743 11.60257 121.1201 7.026523
B09 23217.42 1079.528 36.12394 90.70071 9.299289 429.5223 19.97126
B10 35392.13 1272.443 97.85925 92.80946 7.190543 654.7544 23.5402
B11 7995.363 485.6797 17.03218 87.85097 12.14903 147.9142 8.985074
B12 2180.671 246.3573 2.962851 77.40537 22.59463 40.34241 4.557609
B13 7731.758 508.6993 15.00455 86.8413 13.1587 143.0375 9.410936
B14 22742.78 949.8543 41.76847 91.64698 8.35302 420.7413 17.5723
B15 14446.74 1017.493 22.88035 85.91388 14.08612 267.2647 18.82361
B16 2684.69 269.0779 3.62806 79.95464 20.04536 49.66677 4.977941
B17 31975.51 1299.253 77.33523 91.87345 8.126548 591.5469 24.03617
B18 1903.704 198.3672 5.023012 79.15987 20.84013 35.21852 3.669792
B19 676.6737 118.1823 1.514046 65.06965 34.93035 12.51846 2.186372
B20 1429.723 139.0516 2.381998 80.54846 19.45154 26.44987 2.572454
B21 1846.666 181.8464 2.748333 80.30544 19.69456 34.16331 3.364157
B22 26925.45 1627.037 86.97962 87.91451 12.08549 498.1209 30.10018
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(m^2square meter) Efficiency (%) Square measurement (%)
C01 4577.266 534.161 30.57373 88.33013 11.66987 84.67942 9.881979
C02 77.8659 77.90015 0.473276 -0.04399 100.044 1.440519 1.441153
C03 3836.681 476.3209 21.9547 87.58508 12.41492 70.9786 8.811936
C04 1469.801 257.7587 6.390715 82.46302 17.53698 27.19132 4.768536
C05 1253.991 260.4822 0.260482 79.22775 20.77225 23.19884 4.818921
C06 1416.113 257.5403 6.37947 81.81357 18.18643 26.19808 4.764496
C07 2433.923 363.494 13.71083 85.06551 14.93449 45.02757 6.724638
C08 4472.468 4472.468 4472.468 4472.468 4472.468 4472.468 4472.468
C09 441.9323 207.6569 2.315365 53.01162 46.98838 8.175748 3.841652
C10 4811.198 428.6663 18.81885 91.09024 8.909762 89.00716 7.930326
C11 9181.827 624.3204 54.93141 93.20048 6.799523 169.8638 11.54993
C12 1628.233 310.6441 9.283048 80.92139 19.07861 30.1223 5.746916
C13 4876.303 527.8888 32.31657 89.1744 10.8256 90.2116 9.765943
C14 912.4883 252.7916 6.147173 72.29646 27.70354 16.88103 4.676645
C15 6722.174 535.2543 33.42894 92.03748 7.962517 124.3602 9.902204
C16 4020.192 372.0816 13.43296 90.74468 9.255317 74.37356 6.883509
C17 8442.085 508.1701 33.1373 93.98052 6.019485 156.1786 9.401146
C18 1786.56 242.1454 6.191669 86.44628 13.55372 33.05136 4.47969
C19 5990.852 973.7983 31.47739 83.74524 16.25476 110.8308 18.01527
C20 9542.399 1017.943 56.55631 89.33242 10.66758 176.5344 18.83195
C21 1488.838 243.0139 3.010107 83.67761 16.32239 27.54349 4.495756
C22 3681.218 342.7135 12.71386 90.69022 9.309785 68.10253 6.340199
C23 1135.043 220.1417 5.145734 80.60499 19.39501 20.99829 4.072621
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Table 5Xin Sheng Park Area.
Xin sheng Park
(m^2square meter) Efficiency (%) Square measurement (%)
D01 6761.611 606.3954 39.00266 91.03179 8.968209 125.0898 11.21831
D02 525.1202 182.0588 1.804927 65.33008 34.66992 9.714724 3.368087
D03 3048.417 401.4372 14.67264 86.83129 13.16871 56.39572 7.426587
D04 16906.61 1005.857 90.98501 94.05051 5.949492 312.7723 18.60836
D05 2683.241 333.2866 12.59659 87.57895 12.42105 49.63995 6.165801
D06 2724.463 344.3521 12.49358 87.36073 12.63927 50.40256 6.370514
D07 338.9069 90.01575 1.066137 73.43939 26.56061 6.269778 1.665291
D08 290.9362 88.50805 1.023671 69.57819 30.42181 5.382319 1.637399
D09 227.6171 87.5138 1.002672 61.55218 38.44782 4.210915 1.619005
D10 245.9674 94.38785 1.156985 61.62586 38.37414 4.550396 1.746175
D11 1610.493 229.593 6.32664 85.74393 14.25607 29.79412 4.247471
D12 2183.509 313.1373 10.04746 85.65898 14.34102 40.39491 5.79304
D13 2911.744 527.038 17.68695 81.89958 18.10042 53.86727 9.750202
D14 2833.854 408.3157 17.21031 85.59151 14.40849 52.4263 7.55384
D15 6009.477 596.7243 30.91839 90.07028 9.929722 111.1753 11.0394
D16 1889.47 258.8355 6.914981 86.30116 13.69884 34.9552 4.788457
D17 1195.139 248.5539 6.195701 79.20293 20.79707 22.11007 4.598246
D18 813.3464 162.5991 3.166236 80.00863 19.99137 15.04691 3.008082
D19 937.5333 173.7308 3.87324 81.46937 18.53063 17.34437 3.21402
D20 1155.75 174.0458 4.003168 84.94088 15.05912 21.38138 3.219846
D21 4446.965 533.1327 21.73097 88.01131 11.98869 82.26885 9.862955
D22 23573.22 1981.742 112.3928 91.59325 8.406753 436.1045 36.66223
D23 2012.744 270.435 9.453073 86.56387 13.43613 37.23577 5.003047
D24 3612.391 559.1193 12.7078 84.52219 15.47781 66.82924 10.34371
D25 18042.17 905.1582 88.66928 94.9831 5.016902 333.7802 16.74543
D26 8056.909 770.4201 38.10379 90.43777 9.56223 149.0528 14.25277
D27 16141.84 1836.603 67.01192 88.6221 11.3779 298.6241 33.97716
D28 3721.364 342.7155 12.1818 90.7906 9.209404 68.84523 6.340236
D29 276.2619 76.0457 0.68088 72.47333 27.52667 5.110844 1.406845
D30 1941.362 261.9626 8.573462 86.50625 13.49375 35.9152 4.846308
D31 1643.332 263.1389 7.259036 83.98748 16.01252 30.40164 4.86807
D32 2057.644 341.6943 9.011517 83.3939 16.6061 38.0664 6.321344
D33 3358.38 470.7821 13.96143 85.98187 14.01813 62.13002 8.709468
D34 789.7278 161.6621 3.194992 79.52939 20.47061 14.60996 2.990748
D35 6369.28 1054.422 22.88677 83.4452 16.5548 117.8317 19.5068
D36 9053.049 1391.306 33.78148 84.63163 15.36837 167.4814 25.73917
D37 3281.794 346.7986 13.94128 89.43265 10.56735 60.71319 6.415773
D38 3720.33 425.8429 11.00571 88.55363 11.44637 68.8261 7.878093
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