Yalın Muhasebe Temellerine Dayanan, Regresyon Analiz Tabanlı

Yalın Muhasebe Temellerine Dayanan, Regresyon Analiz Tabanlı

Yalın Muhasebe Temeller ne Dayanan, Regresyon Anal z Tabanlı Mal yet Öngörme Model
Problem Tanımı
CMS
Yen s par şler fabr kaya geld ğ nde
mal yet öngörüler manuel-anal t k
prosedürlerle geçm ş ver ler anal z
ed lerek k ş sel deney mler sonucu
oluşturulmaktadır. Bunun sonucunda
mal yetlerde farklar değ ş kl k göstermekted r ve öngörme sürec uzun
sürmekted r.
Semptomlar
Üret m Alanı: 103,700 m
Üret m Kapas tes : 3,500,000 wheels/year
Çalışan Sayısı: 1,173
Avrupa ç n Pazar Payı ≅ 16%
Türk ye ç n Pazar Payı ≅ 85%
Uzun Zaman
Tutarsız
F yatlandırma
Problem
Makro & M kro S stem Anal z
S par ş
Paylaşımı
SEMPTOMLAR
·
Üret m Aşamaları
Düşük Kar
Marjı
2
Düşük Müşter
Memnun yet
Fabr kalar
Arası İl şk
Amaç
Gözlemler
Projede kullanılmak üzere firmadan anal t k ver ler toplandı.
F rmanın ERP s stem (SAP) üzer nden ver ler hazırlandı.
Bütçe Stratej ve Planlama departmanı le görüşmeler yapıldı.
İlk Talaş
ve Son Talaş
Boyahane&
Paketleme
Yüzey
Hazırlama
Dökümhane
Talaşlı İmalat (Mal yet)
Brüt B r m Ağırlık X1: Çap
X2: F re Oranı
Çap
X3: Brüt B r m Ağırlık
F re Oranı
X4: Doğrudan İşç l k
Doğrudan İşç l k
Mal yet
Mal yet
Talaşlı İmalat (Gel r)
X1: B r m Başına Talaş
M ktarı
X2: F re Oranı
Boyahane
X1: Boya T p
X2: F re Oranı
X3: Doğrudan İşç l k
Mal yet
Paketleme
Toz Boya
Fırçalama
D amond
İşleme
Toz Vern k
Döküm
Katma
Malzemeler
D rekt
İşç l k
Paketleme
Malzemeler
Boya
Malzemeler
Özet Tabloları
Multiple Regression for Actual Cost
Summary Report
Model Building Report
X1: Labor Hour P X2: Packaging Qu X3: ABJ
F nal Model
R
Model
adj
-36,3 - 1,403X 1 + 4,93 X 2 + 77,8 X 3 + 196 X 4
Dökümhane
98.93%
+ 101,5 X 3 2 + 1590 X 4 2 + 3,512X 1 *X 3
+ 25,12 X 1 *X 4 - 41,7 X 2 *X 4 - 985 X 3 *X 4
Incremental Impact of X Variables
Displays the order in which terms were added or removed.
Add X2
0,000
0,644
2
Add X1
0,000
0,000
Add X3
0,000
0,000
Add X1*X3
0,000
0,000
3
4 Add X2*X3
0,000
0,1
> 0,5
Yes
Long bars represent Xs that contribute the most new
information to the model.
Final P
The relationship between Y and the X variables in the model is
statistically significant (p < 0,10).
Labor Hour P
% of variation explained by the model
ABJ
0
15
30
25
50
75
R-sq = 90,25%
Each X Regressed on All Other Terms
97.08%
87.02%
Boyama
A t p boya = -1,129 + 84,01X 1 - 0,0194X 2
89.68%
Paketleme
B t p boya = 0,305 + 26,55X 1 + 0,2071X 2
R-Squared %
0,0
Final P
1
Add X1
0,000
0,000
2
Add X2
0,446
0,446
Add X3
0,624
0,624
C t p boya = 0,420 + 48,6X 1 - 0,006X 2
D t p boya = 0,221 + 49,68X 1 + 0,1367X 2
Add X2*X3
0
2
0,
0,1
Incremental Impact of X Variables
0,1
> 0,5
Yes
Long bars represent Xs that contribute the most new
information to the model.
The relationship between Y and the X variables in the model is
statistically significant (p < 0,10).
If the model fits the data well, this equation can be used to predict
Actual Cost for specific values of the X variables, or find the settings
for the X variables that correspond to a desired value or range of
values for Actual Cost.
% of variation explained by the model
2
4
0%
6
Increase in R-Squared %
0,337
75
100%
Low
Each X Regressed on All Other Terms
Long bars represent Xs that do not help explain
additional variation in Y.
Direct Labor
Öngörü doğruluğunu %10 arttırmak
Gerekl öngörü süres n 1 saate kadar düşürmek
2
D rençl b r model yaratmak (R adj ≥ 80%)
Doğruluk Tablosu
10 san yeden kısa
En düşük 85%
- Doğruluk %10'dan daha
fazla arttı.
- Öngörü süres 10 san yeden
daha kısa süreye düştü.
- R kare değerler nde en
düşük %85 değer elde ed ld .
Karar Destek S stem
High
R-sq = 87,69%
87,69% of the variation in Y can be explained by the regression
model.
100
R-Squared(adjusted) %
D
The following terms are in the fitted equation that models the
relationship between Y and the X variables:
X1: Direct Labor Hour Per Unit
X2: Scrap Rate
X3: Grup Boya Tipi
X2*X3
No
P < 0,001
Grup Boya Ti
50
C
Comments
Is there a relationship between Y and the X variables?
Scrap Rate
25
B
A
Summary Report
X1: Direct Labor X2: Scrap Rate X3: Grup Boya Ti
Direct Labor
0
Anahtar Performans Anal zler
30
15
Multiple Regression for Actual Cost
0
0,337
0
0,
Model Building Report
Model Building Sequence
Change Step P
A gray background
represents an X variable
not in the model.
Multiple Regression for Actual Cost
Displays the order in which terms were added or removed.
Step
ABJ
100
0
D amond = 0,07 + 54,21X 1 + 5,17 X 2
Packaging Qu
5,0
50
A gray bar represents an X variable not in the model.
Boyahane
Labor Hour P
10,0
Packaging Qu
1,040 + 3,058X 1 - 20,9 X 2 + 7,87 X 1 *X 2
CS = 0,823 + 54,21X 1 + 2,09 X 2
Actual Cost vs X Variables
Labor Hour P
+ 76,2 X 2 2 + 106,7X 4 2 + 25,12X 1 *X 4 7,52 X 2 *X 3
High
90,25% of the variation in Y can be explained by the regression
model.
100
R-Squared(adjusted) %
100%
Low
0,000
0
0%
45
Increase in R-Squared %
0
Talaşlı İmalat (Gel r)
If the model fits the data well, this equation can be used to predict
Actual Cost for specific values of the X variables, or find the settings
for the X variables that correspond to a desired value or range of
values for Actual Cost.
Packaging Qu
70,5 - 3,997X 1 + 124,6 X 2 + 0,226 X 3 - 403,6X 4
85.13%
The following terms are in the fitted equation that models the
relationship between Y and the X variables:
X1: Labor Hour Per Unit
X2: Packaging Quantity Per Unit
X3: ABJ
X1*X3; X2*X3
No
P < 0,001
Long bars represent Xs that do not help explain
additional variation in Y.
Talaşlı İmalat (Mal yet)
Comments
Is there a relationship between Y and the X variables?
0
1
Paketleme
Sab t
Mal yetler
Dolaylı
İşç l k
Genel İmalat
G derler
Multiple Regression for Actual Cost
Paketleme
2
Yüzey
Hazırlama
Sıvı Vern k
Toplam
Mal yet
Talaş
Alüm nyum
Change Step P
Manuel
Tesv ye
Helyum
Sızdırma Test
Sıvı Boya
Isıl İşlem
Göbek Delme
Havuç
Model Building Sequence
Dökümhane
Talaşlı İmalat (Maliyet)
Talaşlı İmalat (Gelir)
Boyahane
Paketleme
Balans Kontrol
D rekt
Malzeme
Step
Hata Yüzdesi Doğruluk Oranı
9%
91%
7%
93%
5%
95%
8%
92%
9%
91%
B jon Delme
Model Kurma Raporları
X1: Doğrudan İşç l k Mal yet
X2: B r m Başına Ambalaj Tutarı
X3: Ambalaj T p
- Çoklu regresyon uygulandı.
- Method olarak adım adım
(stepw se) regresyon seç ld .
- Dört varsayım kontrol ed ld :
* Doğrusallık
* Hataların bağımsızlığı
* Normal te
* Varyans Eş tl ğ
X-Ray
& Kal te
Kontrol
Döküm
Mevcut Mal yet S stem
- B rçok aday bağımsız değ şken
arasından bu tabloda yer alan
değ şkenler seç ld .
- Bu seç m, her değ şkene teker teker
bas t regresyon yapılmasıyla oluştu.
- Seç len bağımsız değ şkenler çoklu
regresyon anal z nde kullanıldı.
Bağımsız Değ şkenler
Erg tme
Talaşlı İmalat
·
“Yalın Muhasebe temeller ne dayanan, Regresyon Anal z tabanlı
mal yet öngörme model le öngörme süres n ve hatalarını
m n muma nd rerek değer akış karını opt m ze etmek
hedeflenm şt r.”
Model Formasyonu
X1:
X2:
X3:
X4:
Talaşlı
Erg tme
Kalıp Bakım
Yan Ürünler
··
·
Dökümhane
Actual Cost vs X Variables
Direct Labor
15,00 TL
Scrap Rate
Grup Boya Ti
10,00 TL
Scrap Rate
A gray background
represents an X variable
not in the model.
5,00 TL
0
50
100
R-Squared %
0,1
0
0,
0
0,
2
0,
2
0,
CS
4
0,
d
on
am
Di
A gray bar represents an X variable not in the model.
Multiple Regression for Revenue
Talaşlı İmalat
(Gel r)
0
Revenue = 1,040 + 3,058 X1 - 20,9 X2 + 7,87 X1*X2
1
Add X1
0,000
0,000
2
Add X2
0,000
0,000
3 Add X1*X2
0,000
0,000
0,1
> 0,5
Yes
The following terms are in the fitted equation that models the
relationship between Y and the X variables:
X1: Chip Quantity Per Unit
X2: Scrap Rate
X1*X2
No
P < 0,001
Incremental Impact of X Variables
The relationship between Y and the X variables in the model is
statistically significant (p < 0,10).
Long bars represent Xs that contribute the most new
information to the model.
Final P
Comments
Is there a relationship between Y and the X variables?
Final Model Equation
Displays the order in which terms were added or removed.
Change Step P
Summary Report
X1: Chip Quantit X2: Scrap Rate
Model Building Sequence
Step
Multiple Regression for Revenue
Model Building Report
If the model fits the data well, this equation can be used to predict
Revenue for specific values of the X variables, or find the settings for
the X variables that correspond to a desired value or range of values
for Revenue.
Chip Quantit
% of variation explained by the model
0%
Scrap Rate
0
0
25
50
75
20
40
Increase in R-Squared %
60
Low
High
R-sq = 97,19%
97,19% of the variation in Y can be explained by the regression
model.
100
R-Squared(adjusted) %
100%
Each X Regressed on All Other Terms
Long bars represent Xs that do not help explain
additional variation in Y.
Revenue vs X Variables
Chip Quantit
Chip Quantit
Scrap Rate
30,0 TL
Scrap Rate
0
50
R-Squared %
100
10,0 TL
A gray bar represents an X variable not in the model.
Talaşlı İmalat
(Mal yet)
A gray background
represents an X variable
not in the model.
20,0 TL
6
4
Multiple Regression for Actual Cost
1
0,
8
Comments
Is there a relationship between Y and the X variables?
Final Model Equation
0
Model Building Sequence
0,1
> 0,5
Yes
Final P
1
Add X4
0,000
0,000
2
Add X2
0,036
0,102
Add X2^2
0,005
0,004
Long bars represent Xs that contribute the most new
information to the model.
No
% of variation explained by the model
0%
Brüt Birim A
Isçilik
0,002
0,000
4
Add X1
0,420
0,037
Add X1*X4
0,000
0,000
100%
Low
0
Add X4^2
If the model fits the data well, this equation can be used to predict
Actual Cost for specific values of the X variables, or find the settings
for the X variables that correspond to a desired value or range of
values for Actual Cost.
Çap
Scrap Rate
3
The following terms are in the fitted equation that models the
relationship between Y and the X variables:
X1: Çap
X2: Scrap Rate
X3: Brüt Birim Agirlik
X4: Isçilik
X2^2; X4^2; X1*X4; X2*X3
The relationship between Y and the X variables in the model is
statistically significant (p < 0,10).
Incremental Impact of X Variables
Displays the order in which terms were added or removed.
3
Summary Report
X1: Çap X2: Scrap Rate X3: Brüt Birim A X4: Isçilik
P < 0,001
Change Step P
0,
Multiple Regression for Actual Cost
Model Building Report
Actual Cost = 70,5 - 3,997 X1 + 124,6 X2 + 0,226 X3 - 403,6 X4 + 76,2 X2^2 + 106,7 X4^2 + 25,12 X1*X4 - 7,52 X2*X3
Step
2
0,
15
30
Increase in R-Squared %
45
High
R-sq = 86,31%
86,31% of the variation in Y can be explained by the regression
model.
Each X Regressed on All Other Terms
Long bars represent Xs that do not help explain
additional variation in Y.
Actual Cost vs X Variables
Çap
Çap
80
Scrap Rate
Brüt Birim A
Isçilik
Scrap Rate
5
Add X3
0,003
0,003
Add X2*X3
0,002
0,002
Brüt Birim A
40
Isçilik
0
25
50
75
R-Squared(adjusted) %
0
100
50
0
100
R-Squared %
,0
15
,5
17
,0
20
0
Original = -36,3 - 1,403 X1 + 4,93 X2 + 77,8 X3 + 196 X4 + 101,5 X3^2 + 1590 X4^2 + 3,512 X1*X3 + 25,12 X1*X4 - 41,7 X2*X4 - 985 X3*X4
0,1
Model Building Sequence
Final P
1
Add X1
0,000
0,000
2
Add X3
0,000
0,000
3
Add X1*X3
0,000
Yes
No
The relationship between Y and the X variables in the model is
statistically significant (p < 0,10).
Incremental Impact of X Variables
Long bars represent Xs that contribute the most new
information to the model.
Çap
L teratür
4
Add X3^2
0,000
0,000
5
Add X4
0,005
0,000
6 Add X3*X4
0,000
0,000
7
Add X1*X4
0,007
0,002
8
Add X2
0,841
0,628
Add X2*X4
0,045
0,019
Scrap Rate
Add X4^2
0,073
0,073
Direct Labor
9
100%
Low
0
10
20
Increase in R-Squared %
30
High
R-sq = 99,04%
99,04% of the variation in Y can be explained by the regression
model.
Each X Regressed on All Other Terms
Long bars represent Xs that do not help explain
additional variation in Y.
Brüt Birim A
Brüt Birim A
0
25
50
75
R-Squared(adjusted) %
100
Çap
Original vs X Variables
Scrap Rate
45
0
50
R-Squared %
A gray bar represents an X variable not in the model.
100
20
10
20
30
,0
15
,5
17
,0
20
0
0,
2
0,
4
0,
10
0,
15
0,
0
0,2
A gray background represents an X variable not in the model.
CMS, 2012, http://www.cms.com.tr/about-us
Stenzel, J., 2007. “Lean Account ng: Best Pract ces for Susta nable Integrat on”. Hoboken, New Jersey:
John W ley & Sons, Inc.
Bates, D. M., Watts, D. G., 2007. “Nonl near Regress on Analys s and Its Appl cat ons”.
John W ley & Sons, Inc.
Campbell, D. and S., 2008. “Introduct on to Regress on and Data Analys s”. StatLab Workshop Ser es.
PROJE TAKIM ÜYELERİ
BEGÜM KAHRAMAN
CANSU EBRU KOYLAN
ÇAĞLAR ÇAKIR
MERVE OĞUZ
MUAMMER ÖNEL
CMS: ERKUT SOKAK NO: 11 EGE SERBEST BÖLGE GAZİEMİR, İZMİR - TÜRKİYE
Direct Labor
70
Çap
Datar, S., 2012, “Management and Cost Account ng”, Harvard Un vers ty
Ohno, T., 1988. “Toyota Product on System Beyond Large-scale Product on: Product v ty”.
Katko, N. S., September 16, 2013, “Lean CFO”.
Maskell, B., Baggaley, B., 2003. “Pract cal lean account ng: a proven system for measur ng
and manag ng the lean enterpr se”. New York: Product v ty Press.
DANIŞMANLAR
Dr. EFTHIMIA STAIOU
SEL ÖZCAN TATARİ
ŞİRKET DANIŞMANLARI
ERDEM TORUN
EMRE ERSEN
UĞUR İPEK
50
0,
If the model fits the data well, this equation can be used to predict
Original for specific values of the X variables, or find the settings for
the X variables that correspond to a desired value or range of values
for Original.
% of variation explained by the model
0%
Scrap Rate
Direct Labor
Karar Destek S stem
- Toplam b r m mal yet hesaplar.
- Yen ver lerle güncellenmeye
açıktır.
- Sürdürüleb l r ve uygulanab l r
b r s stemd r.
25
0,
The following terms are in the fitted equation that models the
relationship between Y and the X variables:
X1: Brüt Birim Agirlik
X2: Çap
X3: Scrap Rate
X4: Direct Labor Per Hour
X3^2; X4^2; X1*X3; X1*X4; X2*X4; X3*X4
Brüt Birim A
0,000
00
0,
Comments
> 0,5
P < 0,001
Displays the order in which terms were added or removed.
30
Is there a relationship between Y and the X variables?
Final Model Equation
Change Step P
20
Summary Report
Model Building Report
X1: Brüt Birim A X2: Çap X3: Scrap Rate X4: Direct Labor
Step
10
0
0,5
Multiple Regression for Original
Multiple Regression for Original
Dökümhane
25
0,
00
0,
A gray background represents an X variable not in the model.
A gray bar represents an X variable not in the model.
YAŞAR ÜNİVERSİTESİ: ÜNİVERSİTE CADDESİ NO: 35-37 BORNOVA,İZMİR