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ABSTRACT

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

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.KRҧQJJҫQWKӏSKҫQWKӏWUѭӡQJWUDQJVӭFYjQJKLӋQYүQQҵPӣSKkQNK~FFӫDFiFFӱDWLӋPYjQJWUX\ӅQWKӕQJWX\QKLrQWKӏSKҫQFӫDFiFGRDQKQJKLӋSWUDQJVӭFOӟQ± kinh GRDQKGzQJKjQJFDRFҩSÿDQJWăQJOrQQKDQKTXDPӛLQăPKLӋQÿmӣPӭFKѫQ Vì Yұ\ GRDQKQJKLӋSFҫQFyNӃKRҥFKVҧQ[XҩWYjPXDKjQJÿ~QJÿӫNӏSWKӡLÿiSӭQJQKXFҫXNKiFKKjQJ

HànJWӗQNKRKjQJEiQFKұPӣFiFFӱDKjQJEiQOҿYjӣNKRGӵWUӳӣWUXQJWkPFKLӃPKѫQWәQJVӕOѭӧQJKjQJKRi+jQJKRiFyJLiWUӏOӟQOjPWӗQYӕQҧQKKѭӟQJ[ҩXÿӃQWuQKKuQKWjLFKtQKĈӝFKtQK[iFFӫDGӵEiRWURQJWKiQJWKiQJWKiQJQăP FKӍFKLӃPKѫQ-  NK{QJÿiSӭQJQKXFҫXFӫDFӫDNKiFKKjQJYj.3,FӫDEӝSKұQ)RUHFDVWDFFXUDQF\GӵEiRSKҧLWUrQ

1.1 ĈӕLWѭӧng nghiên cӭu

ĈӅWjLVӁWKӵFKLӋQSKkQWtFKYjNKҧRViWFiFP{KuQKGӵEiRVӕOѭӧQJEiQKjQJFӫDPӝWF{QJW\EiQOҿWUDQJVӭF, cҧL WKLӋQÿӝFKtQK[iFFӫDNӃKRҥFKÿһWKjQJEҵQJSKѭѫQJSKiS

GӵEiR1JKLrQFӭXFiFYҩQÿӅOӵDFKӑQSKѭѫQJSKiSGӵEiRSKKӧSFKRF{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

Trình bày các lê thuyӃWÿѭӧc nghiên cӭu, sӱ dөQJYjSKѭѫQJSKiSWKӵc hiӋn nghiên cӭu

- &KѭѫQJĈӕLWѭӧng nghiên cӭu

Giӟi 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

- &KѭѫQJThiӃt 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

'jLK̩Q7KӡLJLDQGӵEiRWӯQăPWUӣOrQWKѭӡQJGùQJÿӇGӵEiRQKӳQJPөFtiêu,

FKLӃQOѭӧFYӅNLQKWӃFKtQKWUӏNKRDKӑFNӻWKXұWWURQJWKӡLJLDQGjLӣWҫPYƭP{7URQJNKXYӵFVҧQ[XҩWQJѭӡLWDVӱGөQJGӵEiRGjLKҥQÿӇTX\ӃWÿӏQK[k\GӵQJQKjPi\SKiWWULӇQVҧQSKҭP

7UXQJK̩Q7KӡLJLDQGӵEiRWӯWKiQJÿӃQQăPSKөFYө FKRYLӋF[k\GӵQJNӃ

KRҥFKWUXQJKҥQYӅNLQKWӃ[mKӝLNӃKRҥFKWKӏWUѭӡQJNӃKRҥFKVҧQ[XҩW

1J̷QK̩Q7KӡLJLDQGӵEiRWӕLÿDQăPWKѭӡQJQKӓKѫQWKiQJVӱGөQJNӃWTXҧ

GӵEiRÿӇOұSFiFNӃKRҥFKNLQKWӃYăQKyD[mKӝLSKөFYөFKRF{QJWiFFKӍÿҥRNӏSWKӡLѭӟFOѭӧQJYLӋFWLӃQWULӇQFiFFKӍWLrXӢNKXYӵFVҧQ[XҩWVӱGөQJNӃWTXҧQJҳQKҥQÿӇGӵWUNLQKSKtÿһWKjQJÿLӅXÿӝ'ӵEiRQJҳQKҥQWKѭӡQJFKRNӃWTXҧFKtQK[iFKѫQGӵEiRGjL KҥQ

'ӵDWUrQWLrXFKtSKѭѫQJSKiSWLӃQKjQKGӵEiRQJѭӡL WDFKLDGӵEiRWKjQKKDLORҥL QKѭ sau:

Hunh 2.13KkQOR̩LFiFSK˱˯QJSKiSG͹EiR [5]

7URQJQJKLrQFӭXQj\WDVӱGөQJSKѭѫQJSKiSGӵEiRÿӏQKOѭӧQJÿӇQJKLrQFӭXXây GӵQJFiFP{KuQKGӵEiRGӵDYjRGӳOLӋXEiQKjQJOӏFKVӱ;k\GӵQJPӝWTX\WUuQKSKKӧSÿӇNKҧRViWYjOӵDFKӑQÿѭӧFP{KuQKGӵEiRQjRFyKLӋXTXҧQKҩWWҥLWKӡLÿLӇPGӵbáo

2.1.3 Mô hình tӵ hӗi quy (Autoregressive model -AR)

0{KuQKWӵKӗLTX\WәQJTXiWEұFS$5S