本研究的目的是要驗證即使在一個需求變化大的供應鏈環境下,使用拉式系統的運 作方式依然能有良好的管理績效且相對優於推式系統。為了證明此論點,本研究模擬一 個符合實務環境的供應鏈系統,邀請在庫存、採購、配銷管理…等相關領域具有實務經 驗的業界人士來進行實驗,透過 30 組(90 位實驗者)實驗結果的分析,可得以下幾點 結論:
1. 以低庫存滿足客戶需求是供應鏈上各成員的營運目標,但由實驗結果,目前的管理 者並沒有達成此目標,並且存在著備高低庫存的衝突。
2. 造成績效不佳的主因不是需求變化太大或預測不準,而是管理方式(推式系統或拉 式系統),且大部分的管理者採用推式系統。
3. 推式系統的問題在於當預測不準確時,容易發生庫存過多或缺貨情況,並沒有一套 機制來管控何時需要補貨,要補多少數量(防止過度補貨)。
4. 即使目標庫存水位設定不正確,但拉式系統的下單方式依然優於推式系統。
5. 拉式系統的管理方式(設定適當的目標庫存水位並以實際需求為下單數量)依然能 在市場需求變化大環境下,以低庫存滿足客戶需求。
6. 若所設定目標庫存水位足以應付需求變化,在此期間任何的需求波動都可視為雜音
(noise)。
7. 大多數的管理者的管理方式通常十分複雜卻沒有成效,雖然少數有經驗的管理者能 得到與拉式系統一樣的績效,但相較而言,拉式系統是一套相當簡單且有效的管理 機制。
8. 拉式系統的操作方式不需要對員工做長時間的教育訓練就能運作,尤其在產品種類 多的環境下更能顯現其簡單與有效性。
本研究證實造成績效不佳的主因是因為管理者使用以預測為基礎的管理方式,但這 並不代表預測不重要,而是要用在對的地方來預測(例如聚集點),若能妥善運用,預 測資訊可以輔助管理者做調整目標庫存的決策。雖然本研究的實驗結果證實拉式系統的 有效性,但不代表拉式系統能運用在任何環境下,一個有效的管理機制必須使用在符合 此機制的假設條件下,本研究已證實需求變化並不是拉式系統的限制,但現今許多產品 的生命週期都十分短暫,而拉式系統是否能有效運用在產品生命週期短的環境下是未來 值得研究的方向。
參考文獻
1. Chaman, L. J. (2006). Benchmarking Forecasting Errors. The Journal of Business Forecasting, 24(4), 13-15.
2. Chaman, L. J. (2007). Benchmarking Forecasting Errors. The Journal of Business Forecasting, 25(4), 18-21.
3. Chaman, L. J. (2008). Benchmarking Forecasting Errors. The Journal of Business Forecasting, 26(4), 19-23.
4. Chen, B., Ip, W. H., and Li, Y. (2006). The Study and Application of CPFR Model and Its Analysis in China, Service Systems and Service Management,2006 International Conference on, 1, 745-749.
5. Chen, F., Drezner, Z., Ryan, J. K., and Simchi-Levi, D. (2000). Quantify the Bullwhip Effect in a Simple Supply Chain. The Impact of Forecasting, Lead Times, and Information, Management Science, 46(3), 436-443.
6. Disney S.M., and Towill D.R. (2003). On the Bullwhip and Inventory Variance Produced by an Ordering Policy, Omega, 31(3), 157-167.
7. Dowling, G. R. (2004). The art and science of marketing (pp.266). UK: Oxford University Press.
8. Goldratt, E. M., and Goldratt, A. R. (2003). TOC Insights into Distribution and Supply-Chain.
9. Harrison, T. P., Lee, H. L., and Neale, J. J. (2003). The Practice of Supply Chain Management. Europe: Springer.
10. Hinkelman, E. G., and Putzi, S. (2005). Dictionary of International Trade - Handbook of the Global Trade Community. California USA: World Trade Press.
11. Lee H. L., So, K.C., and Tang, C.S. (2000). The Value of Information Sharing in a Two-Level Supply Chain, Management Science, 46(5), 626-643.
12. Lee H. L., Padmanabhan, V., and Whang, S. (1997a). Information Distortion in a Supply Chains, Management Science, 43(4), 546-558.
13. Lee, H. L., Padmanabhan, V., and Whang, S. (1997b). The Bullwhip Effect in Supply Chains, Sloan Management Review, 38(3), 93-102.
14. Lin, C. C., Shieh, S. C., Kao, Y. H., Chang, Y. T., and Chen, S. S. (2008). The simulation analysis of push and pull shelf replenishment policies for retail supply chain. Machine Learning and Cybernetics, 2008 International Conference on, 7, 3964 -3969.
15. Martin, Michael J.C. (1994). Managing Innovation and Entrepreneurship in
Technology-based Firms (pp.44). New York: Wiley-IEEE.
16. Masuchun, W., Davis, S., and Patterson, J. W. (2004). Comparison of push and pull control strategies for supply network management in a make-to-stock environment, Journal of Production Research, 42(20), 4401-4419.
17. Pfeifer, C., Hensolt, J., Wolfinger, K., Kornas, N., and Erath, S. (2008). Investigation of Opportunities that exist within the Automotive Supply Chain for Collaborative Planning Forecasting and Replenishment (VICS CPFR®).Retrieved Mar 2, 2009 from VICS database on the World Wide Web: http://www.vics.org
18. Voluntary Interindustry Commerce Standards (VICS) (2004). CPFR Overview.
Retrieved Mar 2, 2009 from VICS database on the World Wide Web:
http://www.vics.org
19. Zhang, X. H., and Lv, L. (2008). Performance Comparisons of Supply Chain between Push and Pull Models with Competing Retailers. Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on, 12-14, 1-4.
附錄一、預測與實際需求表
Scenario 1 & Scenario 2 Scenario 3
Scenario 1 & Scenario 2 Scenario 3
單期需求 6 期需求 6 期預測 單期需求 6 期需求 6 期預測 單期需求 6 期需求 6 期預測 單期需求 6 期需求 6 期預測
Week1 1344 4800 660 3523 4844 Week27 1369 4165 4822 572 5192 3761
Week2 1016 4256 1223 4458 5467 Week28 1688 5831 3543 1778 6690 4345
Week3 904 4040 1388 5036 4517 Week29 692 5740 3343 831 6516 4050
Week4 1098 4480 1210 6037 3789 Week30 1789 7116 4020 795 6615 3791
Week5 1087 4398 626 5841 3802 Week31 22 7048 3919 1022 6481 4774
Week6 1544 6993 4215 975 6082 4564 Week32 468 6028 4589 855 5853 4583
Week7 932 6581 3769 1285 6707 4799 Week33 0 4659 5910 1626 6907 4523
Week8 474 6039 3924 940 6424 4140 Week34 456 3427 5932 471 5600 3919
Week9 54 5189 4994 1726 6762 4175 Week35 1653 4388 5944 44 4813 4303
Week10 69 4160 5872 1542 7094 3734 Week36 538 3137 4291 1370 5388 5885
Week11 0 3073 6277 3 6471 3132 Week37 1346 4461 4209 208 4574 4986
Week12 1546 3075 6331 374 5870 4855 Week38 1002 4995 4516 271 3990 4822 Week13 1738 3881 4854 385 4970 6023 Week39 1408 6403 4052 1120 3484 5921 Week14 1123 4530 3116 235 4265 5641 Week40 1091 7038 3990 1658 4671 5009 Week15 954 5430 3539 499 3038 5780 Week41 1050 6435 3901 1192 5819 3622 Week16 528 5889 4323 1291 2787 5666 Week42 895 6792 4259 1398 5847 3550 Week17 1156 7045 4918 696 3480 4610 Week43 732 6178 4455 1459 7098 3810
Week18 21 5520 4716 219 3325 4413 Week44 853 6029 4773 139 6966 3543
Week19 597 4379 5223 461 3401 5485 Week45 169 4790 4815 784 6630 4802
Week20 11 3267 5782 643 3809 5720 Week46 1235 4934 5378 190 5162 5477
Week21 1676 3989 5792 453 3763 5296 Week47 13 3897 4996 1736 5706 5426
Week22 22 3483 4713 280 2752 5304 Week48 288 3290 5152 0 4308
Week23 783 3110 4702 1005 3061 5667 Week49 810 3368 6099 412 3261 Week24 413 3502 5595 696 3538 5115 Week50 209 2724 5302 224 3346
Week25 90 2995 5204 1156 4233 4699 Week51 1283 3838 5381 337 2899
Week26 1488 4472 5897 1483 5073 4548 Week52 273 2876 4908 54 2763