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Chuyên ngành: Kӻ TKXұWCông NJKLӋS 0mVӕ: 8.52.01.17
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73+Ӗ&+Ë0,1+WKiQJ08 QăP2021
Trang 2&Ð1*75Î1+ĈѬӦ&+2¬17+¬1+7Ҥ, 75ѬӠ1*ĈҤ,+Ӑ&%È&+.+2$ ±Ĉ+4*-HCM
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Trang 31Jj\WKiQJQăPVLQK 04/09/1996 1ѫLVLQK Bình 7KXұQ
Chuyên ngành: ӻ 7KXұW&{QJ1JKLӋS 0mVӕ : 8520117
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II 1+,ӊ09Ө9¬1Ӝ,DUNG :
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Trang 4LӠI &È0Ѫ1
ChҷQJPҩ\FKӕFPj KӑFNǤ FXӕLVҳSNӃWWK~c, nhuQOҥLTXmQJWKӡLJLDQÿmTXDHPFҧPWKҩ\UҩW WUkQWUӑQJYj ELӃWѫQ QăPNK{QJSKҧLFKһQJÿѭӡQJGjLQKѭQJQKӳQJÿLӅXÿmKӑFÿѭӧFQKӳQJQJѭӡLFyFѫKӝLJһSJӥVӁOX{QOjNӹQLӋPÿiQJNKҳFJKLQăPJҳQEyFQJQJ{LWUѭӡQJQj\%iFK.KRDVӁOjQLӅPWӵKjRYuOѭXJLӳQKLӅXNKyNKăQNLrQWUuYjFӕJҳQJ
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Tp HCM, ngày 31 tháng 7 QăP21
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Trang 5TÓM TҲT LUҰ19Ă1
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Trang 6ABSTRACT
This study would present an enhancement of the forecast process that has been used in a jewelry retailer Group is TSCZ in Ho Chi Minh area which is the highest proportion Thus, TSCZ is chosen in this study
Due to basic characteristics of historical time series, appropriate theoretical forecast models are used Comparing SARIMA and Holt-Exponential Winter's Smoothing techniques in order to provide high-accuracy customer transaction forecasts They would be ranked by comparing forecast accuracy and forecast bias to find out which one is the best forecast model for the case study After applying the solution, the forecast accuracy was increased by 10% The results of this study could be applied to other group with some necessary modifications The findings would assist in more accurate financial planning and budgeting when the demand forecast was done better
Trang 7LӠI &$0Ĉ2$1
Tôi [LQFDPÿRDQÿӅWjLOXұQYăQ³Phân WtFKNKҧRViWYj[iFÿӏQKP{KuQKGӵEiREiQKjQJFKRF{QJW\EiQOҿWUDQJVӭF´ OjF{QJWUuQKQJKLrQFӭXFiQKkQFӫDW{L
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Trang 12DANH SÁCH VIӂT TҲT ± THUҰT NGӲ
5 ARMA Autoregressive - Moving average 0{ KuQK Wӵ KӗL TX\ - trung bình ÿӝQJ
6 ARIMA Autoregressive Intergrated Moving Average
0{KuQKWӵKӗLTX\NӃWKӧSWUXQJEuQKÿӝQJ
7 SARIMA Seasonal Autoregressive Integrated Moving Average
Seasonal Autoregressive Integrated Moving Average 8 MAE Mean Absolute Error 6DLVӕWX\ӋWÿӕLWUXQJEuQK 9 MSE Mean Squared Error 6DLVӕEuQKSKѭѫQJWUXQJEuQK 10 MAPE Mean Absolute Percent Error 6DLVӕWѭѫQJÿӕLWUXQJEuQK
11 RMSE Root Mean Squared Error &ăQFӫDVDLVӕEuQKSKѭѫQJWUXQJbình
12 RSS Residual Sum of Squares 7әQJEuQKSKѭѫQJSKҫQGѭ 13 AIC Akaike Information Criterion 7LrXFKXҭQWK{QJWLQ$NDLNH 14 BIC Bayers Information Criterion 7LrXFKXҭQWK{QJWLQ%D\HUV 15 SKU Stock Keeping Unit ĈѫQYӏSKkQORҥLKjQJKRi 16 PNJ Phu Nhuan Jewelry Joint Stock
Company
&{QJW\FәSKҫQ9jQJEҥFĈiTXê3K~1KXұQ
17 KPI Key Performance Indicator ChӍ VӕÿiQKJLiKLӋXTXҧF{QJYLӋF
Trang 13&+ѬѪ1* 1: GIӞI THIӊU Ĉӄ TÀI
7URQJQӅQNLQKWӃSKiWWULӇQQKDQKYjFyQKLӅXP{KuQKNLQKGRDQKP{KuQKFiFFӱDKjQJEiQOҿÿDQJSKiWWULӇQQKDQKFKyQJWURQJUҩWQKLӅXOƭQKYӵF&yWKӇNӇ ÿӃQFiFFKXӛLFӱDKjQJ VLrXWKӏFӫD7KӃJLӟL'LÿӝQJ9LQPDUW« &iFFKXӛLFӱDKjQJEiQOҿWURQJOƭQKYӵFWKӡLWUDQJQKѭ+ 0&DUWLHU=DUD« [1] 9LӋW1DPÿmÿҥWÿѭӧFWӕFÿӝWăQJWUѭӣQJFDRWURQJOƭQKYӵFEiQ OҿQKӳQJQăPJҫQÿk\GRTX\P{GkQVӕOӟQYӟLKѫQ WULӋXQJѭӡL WKHRVӕOLӋXPӟLQKҩWQăP FѫFҩXGkQVӕWUҿ GkQVӕӣÿӝWXәL-50 ) [2] 7UrQWKӵFWӃFXӝFFiFKPҥQJF{QJQJKLӋSWURQJOƭQKYӵFEiQOҿÿDQJkPWKҫPGLӉQUDYjWҥRQKLӅXFѫKӝLSKiWWULӇQFKR9LӋW1DPYӟLQKӳQJWKD\ÿәLÿӝWSKiWӯQKӳQJWLrXFKXҭQGӏFKYөÿDGҥQJKLӋXTXҧYjQKDQKFKyQJGӵDWUrQQӅQWҧQJF{QJQJKӋVӕ [3]
ĈӇWKtFKQJKLYjSKiWWULӇQWURQJPӝWWKӏWUѭӡQJWKD\ÿәLQKDQKFKyQJYLӋFQҳPEҳWYjGӵEiRQKXFҫXÿӇWӗQWҥLOjPӝWYҩQÿӅUҩWÿѭӧFTXDQWkP1ӃXGӵEiRFKtQK[iFQKXFҫXFӫDNKiFKKjQJF{QJW\FyWKӇFKXҭQEӏÿѭӧFWLӅPOӵFYjSKkQEәNKҧQăQJÿӇYѭѫQOrQQҳPEҳW[XKѭӟQJYjGүQÿҫXWKӏWUѭӡQJ 9uWKӃYLӋFQJKLrQFӭXYjFҧLWKLӋQFiFP{KuQKGӵEiRYjÿiQKJLiNӃWTXҧFӫDFK~QJÿDQJUҩWÿѭӧFTXDQWkP [4]
.KRҧQJJҫQWKӏSKҫQWKӏWUѭӡQJWUDQJVӭFYjQJKLӋQYүQQҵPӣSKkQNK~FFӫDFiFFӱDWLӋPYjQJWUX\ӅQWKӕQJWX\QKLrQWKӏSKҫQFӫDFiFGRDQKQJKLӋSWUDQJVӭFOӟQ± kinh GRDQKGzQJKjQJFDRFҩSÿDQJWăQJOrQQKDQKTXDPӛLQăPKLӋQÿmӣPӭFKѫQ Vì Yұ\ GRDQKQJKLӋSFҫQFyNӃKRҥFKVҧQ[XҩWYjPXDKjQJÿ~QJÿӫNӏSWKӡLÿiSӭQJQKXFҫXNKiFKKjQJ
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1.1 ĈӕLWѭӧng nghiên cӭu
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Trang 14GӵEiR1JKLrQFӭXFiFYҩQÿӅOӵDFKӑQSKѭѫQJSKiSGӵEiRSKKӧSFKRF{QJW\EiQOҿWUDQJVӭFFyKѫQFӱDKjQJWUrQWRjQTXӕFYӟLUҩWQKLӅX\ӃXWӕҧQKKѭӣQJ
1.2 Mөc tiêu luұQYăQ
0өFWLrXFӫDÿӅWjLQJKLrQFӭXQj\Oj
- Chuҭn hoá quy trình các thao tác thӵc hiӋn dӵ báo
- Phát triӇn mô hình dӵ báo phù hӧp cho công ty bҧn lҿ trang sӭc vӟLKѫQFӱa hàng trên toàn quӕFÿӇ giҧm thiӇu forecast error
- Thao tác thӵc hiӋn giӳa các nhân viên chênh lӋch không quá 10% - Forecast accurancy ܨܣ ͺͷΨ
- Forecast bias ܨܤ ʹͲΨ
1.3 Phҥm vi và giӟi hҥn
- Phҥm vi thӡi gian: 6 tháng thӵc hiӋn
- Phҥm vi không gian: thӵc hiӋQÿӅ tài tҥi phòng cung ӭng cӫa mӝt công ty bán lҿ trang sӭc vӟLTX\P{KѫQFӱa hàng bán lҿ trên toàn quӕc
- Phҥm vi sӕ liӋu: thu thұp sӕ liӋXWURQJQăP
- Thӵc hiӋn sӕ liӋu dӵ báo cho 1 tháng vӟi lead time dӵ báo là 2 tháng - ĈiQKJLiÿӝ chính xác cӫa sӕ liӋu dӵ báo tháng hiӋn tҥi
- Bӣi vì tính bҧo mұt cӫa công ty, mӝt sӕ thông sӕ kӻ thuұt không thӇ chia sҿ
- Mӝt sӕ hình ҧnh vӅ máy móc công cөÿLӅu khoҧn bҧo mұt cӫa công ty sӁ ÿѭӧc miêu tҧ WѭӧQJWUѭQJ
1.4 Cҩu trúc cӫa luұQYăQ
/XұQYăQEDRJӗP5 FKѭѫQJQKѭVDX - &KѭѫQJ *Lӟi thiӋu
Giӟi thiӋu vӅ hiӋn trҥng ngành bán ӣ ViӋt Nam, nӝi dung, và mөFÿtFK cӫa cӫa nghiên cӭu trong luұQYăQQj\
- &KѭѫQJ &ѫVӣ lý thuyӃWYjSKѭѫQJSKiSOXұn
Trình bày các lê thuyӃWÿѭӧc nghiên cӭu, sӱ dөQJYjSKѭѫQJSKiSWKӵc hiӋn nghiên cӭu - &KѭѫQJ ĈӕLWѭӧng nghiên cӭu
Trang 15Giӟi thiӋXVѫOѭӧc vӅ ÿӕLWѭӧQJÿѭӧc nghiên cӭu; hoҥWÿӝng vұn hành, hiӋn trҥng và vҩn ÿӅ cҫQÿѭӧc giҧi quyӃt cӫDÿӕLWѭӧng 6DXNKL[iFÿӏQKÿѭӧc vҩQÿӅÿӅ tài sӁ ÿLVkXYjRviӋc phân tích và tìm hiӇu QJX\rQQKkQFNJQJQKѭÿӅ xuҩWSKѭѫQJiQNKҳc phөc, giҧi quyӃt cho vҩQÿӅ
- &KѭѫQJ ThiӃt kӃ và xây dӵng quy trình
Lӵa chӑn mô hình giҧi pháp cho vҩQÿӅ ÿѭӧFÿӅ ra, thiӃt kӃ chi tiӃt và xây dӵng mô hình áp dөng cho bài toán thӵc tiӉn
- &KѭѫQJ5: KӃt luұn và kiӃn nghӏ
Áp dөng mô hình vào mӝt sӕ bài toán cө thӇ YjÿiQKJLiVӵ vұn hành, hiӋu quҧ cӫa mô hình dӵDWUrQWLrXFKtÿӅ ra Ghi nhұn lҥi nhӳQJJuPjP{KuQKÿҥWÿѭӧc, sӵ thiӃu sót, bàn luұQYjÿӇ xuҩt nhӳng cҧi tiӃQWURQJWѭѫQJODL
&+ѬѪ1* &Ѫ SӢ LÝ THUYӂ73+ѬѪ1*3+È3/8ҰN 2.1 &ѫVӣ lý thuyӃt
2.1.1 ĈӏQKQJKƭD
'ӵEiROjPӝWNKRDKӑFYjQJKӋWKXұWWLrQÿRiQQKӳQJVӵYLӋFVӁ[ҧ\UDWURQJWѭѫQJODLWUrQFѫVӣSKkQWtFKNKRDKӑFYӅFiFGӳOLӋXÿmWKXWKұSÿѭӧF.KLWLӃQKjQKGӵEiRQJѭӡLWDWKѭӡQJFăQFӭYjRGӳOLӋXTXiNKӭYjKLӋQWҥLÿӇ[iFÿӏQK[XKѭӟQJYұQÿӝQJFӫDFiFKLӋQWѭӧQJWURQJWѭѫQJODLQKӡYjRPӝWVӕP{KuQKWRiQKӑF1JRjLUDQJѭӡLWDFzQGӵDWUrQNLQKQJKLӋPKRһFWUӵFJLiFYӅWѭѫQJODLÿӇWLӃQKjQKGӵEiRKRһFÿLӅXFKӍQKNӃWTXҧGӵEiRWӯP{KuQKWRiQ7KHRQKұQÿӏQKFӫD+DQNHYj:LFKHUQ WKuYDLWUzFӫDGӵEiRQJj\FjQJWăQJ YjKҫXKӃFiFOƭQKYӵFFӫDÿӡLVӕQJÿӅXVӱGөQJNӻWKXұWGӵEiR [5]
2.1.2 Phân loҥi
'ӵDWUrQWLrXFKtWKӡLJLDQQJѭӡLWDFKLDGӵEiRWKjQKEDORҥL
'jLK̩Q 7KӡLJLDQGӵEiRWӯQăPWUӣOrQ WKѭӡQJGùQJÿӇGӵEiRQKӳQJPөFtiêu,
FKLӃQOѭӧFYӅNLQKWӃFKtQKWUӏNKRDKӑFNӻWKXұWWURQJWKӡLJLDQGjLӣWҫPYƭP{7URQJNKXYӵFVҧQ[XҩWQJѭӡLWDVӱGөQJGӵEiRGjLKҥQÿӇTX\ӃWÿӏQK[k\GӵQJQKjPi\SKiWWULӇQVҧQSKҭP
7UXQJK̩Q 7KӡLJLDQGӵEiRWӯWKiQJÿӃQQăP SKөFYө FKRYLӋF[k\GӵQJNӃ
Trang 16KRҥFKWUXQJKҥQYӅNLQKWӃ[mKӝLNӃKRҥFKWKӏWUѭӡQJNӃKRҥFKVҧQ[XҩW
GӵEiRÿӇOұSFiFNӃKRҥFKNLQKWӃYăQKyD[mKӝLSKөFYөFKRF{QJWiFFKӍÿҥRNӏSWKӡLѭӟFOѭӧQJYLӋFWLӃQWULӇQFiFFKӍWLrXӢNKXYӵFVҧQ[XҩWVӱGөQJNӃWTXҧQJҳQKҥQÿӇGӵWUNLQKSKtÿһWKjQJÿLӅXÿӝ'ӵEiRQJҳQKҥQWKѭӡQJFKRNӃWTXҧFKtQK[iFKѫQGӵEiRGjL KҥQ
'ӵDWUrQWLrXFKtSKѭѫQJSKiSWLӃQKjQKGӵEiRQJѭӡL WDFKLDGӵEiRWKjQKKDLORҥL QKѭ sau:
Hunh 2.1 3KkQOR̩LFiFSK˱˯QJSKiSGEiR [5]
7URQJQJKLrQFӭXQj\WDVӱGөQJSKѭѫQJSKiSGӵEiRÿӏQKOѭӧQJÿӇQJKLrQFӭXXây GӵQJFiFP{KuQKGӵEiRGӵDYjRGӳOLӋXEiQKjQJOӏFKVӱ;k\GӵQJPӝWTX\WUuQKSKKӧSÿӇNKҧRViWYjOӵDFKӑQÿѭӧFP{KuQKGӵEiRQjRFyKLӋXTXҧQKҩWWҥLWKӡLÿLӇPGӵbáo
2.1.3 Mô hình tӵ hӗi quy (Autoregressive model -AR)
0{KuQKWӵKӗLTX\WәQJTXiWEұFS$5 S
Thông thѭӡQJJLiWUӏGӳOLӋXGm\VӕWKӡLJLDQWӏDÿLӇPFyJLiWUӏOLrQTXDQWӟLJLiWUӏӣWKӡLÿLӇPWUѭӟFÿy0{KuQKWӵKӗLTX\ÿѭӧFKLӇXOjWҥRUDJLiWUӏFӫDÿLӇPWKӡLJLDQWLӃS
ĈӏQKWtQK
3KѭѫQJSKiSGӵEiR
'ӵEiR thô Trung bình Hàm PNJ Phân tích ARIMA (p,d,q) ARCH/GARCH
+ӗLTXL ÿѫQ +ӗLTXL EӝL
&KXӛLWKӡLJLDQ +ӗLTXL
éNLӃQNKiFKKjQJ: 7әQJ
KӧSêNLӃQNKiFK hàng
3KѭѫQJSKiS'HOSKL: 6ӵ
ÿӗQJWKXұQFӫDErQ Lãnh ÿҥRTXҧQOêYjSKҧQ ELӋQ
Trang 17ܻ௧ ൌ ଵܻ௧ିଵ ଶܻ௧ିଶ ڮ ܻ௧ି ߝ௧ 7URQJÿy
ܻ௧ : Giá trӏ dӵ báo cӫa chuӛi thӡi gian tҥi thӡLÿLӇm t
ܻ௧ିଵǡ ܻ௧ିଶǡ ǥ ǡ ܻ௧ି : Chuӛi thӡi gian trӉ WUѭӧt) t-1, t-«W-p thӡLÿRҥn
ǡ ଵǡଶǡ ǥ ǡ : Các hӋ sӕ hӗi quy, giá trӏ các hӋ sӕ này giҧm dҫn vӅ mӭFNKLWăQJgiá trӏ trӉ WUѭӧt) k nӃu chuӛi dӯng
P: Mӭc tӵ hӗi quy
ߝ௧: Sai sӕ dӵ báo cӫa mô hình
2.1.4 0{KuQKWUXQJEuQKÿӝng ( Moving average model - MA)
0{KuQKWUXQJEuQKÿӝQJWUѭӧWTNêKLӋX0$ T GӵEiRJLiWUӏܻ௧ GӵDWUrQSKӕLKӧSWX\ӃQWtQKFӫDFiFVDLVӕFӫDKLӋQWҥLYjTXiNKӭ
ܻ௧ ൌ ߤ ߝ௧ െ ߱ଵߝ௧ିଵെ ߱ଶߝ௧ିଶെ ڮ െ ߱ߝ௧ି 7URQJÿy
ܻ௧ : Giá trӏ dӵ báo cӫa chuӛi thӡi gian tҥi thӡLÿLӇm t ߤ *LiWUӏWUXQJEuQKFӫDFKXӛLWKӡLJLDQ
ߝ௧ 6DLVӕGӵEiRWKӡLÿLrPW ߝ௧ିଵǡ ߝ௧ିଶ 6DLVӕGӵEiRTXiNKӭ ߱ଵǡ ߱ଶǡ ǥ ǡ ߱ +ӋVӕѭӟFOѭӧQJ
2.1.5 Mô hình ARMA ( Autoregressive - Moving average)
0{KuQKNӃWKӧSWӵKӗLTX\$5 S YjWUXQJEuQKÿӝQJ0$ T YӟLQKDXÿӇÿѭӧFP{KuQKKӛQKӧSWӵKӗLTX\± WUXQJEuQKÿӝQJNêKLӋX$50$ TS 7URQJÿy
S EұFFӫDSKҫQWӵKӗLTX\
T PӭFWUѭӧWWUXQJEuQKÿӝQJ0{KuQK$50$FyGҥQJQKѭVDX
ܻ௧ ൌ ଵܻ௧ିଵ ଶܻ௧ିଶ ڮ ܻ௧ି ߝ௧െ ߱ଵߝ௧ିଵെ ߱ଶߝ௧ିଶെ ڮ െ ߱ߝ௧ି7URQJÿy
ܻ௧ : Giá trӏ dӵ báo cӫa chuӛi thӡi gian tҥi thӡLÿLӇm t : Hҵng sӕ EDQÿҫu
: HӋ sӕ tӵ hӗi quy
߱: HӋ sӕ ѭӟFOѭӧQJOѭXêTX\WҳFÿһt dҩu trӯ
Trang 18ߝ௧: Sai sӕ dӵ báo thӡLÿLӇm t ߝ௧ିଵǡ ߝ௧ିଶ: Sai sӕ dӵ báo quá khӭ 2.1.6 Tӏnh hoá dӳ liӋu
ĈӇGӵEiRGӳOLӋXFKXӛLWKӡLJLDQWKHR$5 S