Yalın Muhasebe Temellerine Dayanan, Regresyon Analiz Tabanlı
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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