FACILITY DESIGN NURUL UMMI, ST MT

PERENCANAAN FASILITAS Beberapa pertanyaan yang harus dijawab sebelum rencana alternative fasilitas dikembangkan : Apa yang akan diproduksi ? Desain produk Bagaimana produk diproduksi ? Desain Proses Kapan produk diproduksi ? Schedule desain Berapa banyak masing-masing produk akan diproduksi ? Schedule desain (peramalan permintaan produk) Berapa lama produk tersebut akan diproduksi ? Dimana akan diproduksi ? Penentuan lokasi pabrik

Hubungan Desain produk, Desain proses & Schedule desain Desain proses, desain produk, dan schedule design tidak dilakukan terpisah tetapi dilakukan dengan melihat permasalahan secara menyeluruh. Product Design Process Design Facility Design Schedule Design Hubungan Product, Process, dan Schedle Design dengan Perencanaan Fasilitas Sumber : Thompkins , 2003

Desain Produk Kegiatan ini menentukan produk yang akan diproduksi dan desain detil dari produk tersebut.

Desain Proses Kegiatan ini menentukan bagaimana produk dan masing-masing komponennya diproduksi, dibeli atau sub kontrak.

Bill of Material (BOM) Pada tahap ini akan dihasilkan Struktur Produk dan Bill of Material (BOM) yang berisi informasi tentang level perakitan produk, komponen yang dibutuhkan dan jumlahnya serta sumber dari setiap komponen dibuat atau dibeli Definisikan elemen operasi Identifikasi alternative proses untuk setiap operasi Analisis alternative operasi Standarisasi proses Evaluasi alternative proses Pilih proses Prosedur Menyeleksi Proses Sumber : Thompkins : Facilities Planning, 2003

Schedule Design Schedule Design untuk menjawab pertanyaan kapan harus dibuat dan berapa banyak. Berapa banyak yang harus diproduksi berdasarkan peramalan terhadap permintaan. Dalam merencanakan fasilitas pabrik sebaiknya kapasitas produksi yang akan dibangun dilakukan berdasarkan peramalan jangka panjang yaitu lima sampai sepuluh tahun, karena dengan segera kelebihan fasilitas akan dipakai lebih cepat dari perkiraan

Desain Fasilitas Prosedur pengembangan tata letak dikembangkan oleh Muther (Tompkins, 2003) yang dikenal sebagai Systematic Layout Planning (SLP). Langkah-langkah dalam SLP dapat diterjemahkan dalam 10 langkah tugas besar perencanaan lay out pabrik sebagai berikut : Forecasting Merencanakan urutan proses (OPC, MPPC) Membuat Routing Sheet Merencanakan luas lantai produksi Menentukan luas gudang, organisasi perusahaan dan luas lantai penunjang produksi Membuat From to Chart Menghitung ongkos material handling Membuat ARC Membuat ARD dan AAD Membuat Templete

Input data dan kegiatan 1. Analisis aliran Operasi 2.An. Keterkaitan Kegiatan 3. Diagram Keterk. Kegiatan 4. Luas lantai yg dibutuhkan 5. Luas lantai yg tersedia 6. Diagram keterk. ruangan 7. Pertimbangan modifikasi 8. Pembatasan praktis 9. Pengembangan alternative lay out 10. Evaluasi

Peramalan :“If we can predict what the future will be like we can modify our behaviour now to be in a better position, than we otherwise would have been, when the future arrives.” Artinya, jika kita dapat memprediksi apa yang terjadi di masa depan maka kita dapat mengubah kebiasaan kita saat ini menjadi lebih baik dan akan jauh lebih berbeda di masa yang akan datang

Metode Peramalan Rentang Waktu Tipe Keputusan Contoh Jangka Pendek ( 3 – 6 bulan) Operasional Perencanaan Produksi, Distribusi Jangka Menengah ( 2 tahun) Taktis Penyewaan Lokasi dan Peralatan Jangka Panjang (Lebih dari 2 tahun) Strategis Penelitian dan Pengembangan untuk akuisisi dan merger Atau pembuatan produk baru

Model Peramalan Kuantitatif Deret Berkala (Time Series)Metode ini menggunakan riwayat permintaan masa lalu dalam membuat ramalan untuk masa depan Metode Rata-rata Bergerak (Moving Average Method) CONTOH

Time-Series Methods Moving Averages Week 450 — 430 — 410 — 390 — 370 — | | | | | | 0 5 10 15 20 25 30 Patient arrivals Actual patient arrivals This is the actual data as shown in Figure12.5. 31

Time-Series Methods Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week | | | | | | 0 5 10 15 20 25 30 Patient arrivals This slide advances automatically. 32

Time-Series Methods Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week | | | | | | 0 5 10 15 20 25 30 Patient Week Arrivals 1 400 2 380 3 411 Patient arrivals This slide advances automatically. 33

Time-Series Methods Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week | | | | | | 0 5 10 15 20 25 30 Patient Week Arrivals 1 400 2 380 3 411 Patient arrivals 33

Time-Series Methods Moving Averages Actual patient arrivals Week 450 — 430 — 410 — 390 — 370 — | | | | | | 0 5 10 15 20 25 30 Patient Week Arrivals 1 400 2 380 3 411 F4 = 411 + 380 + 400 3 Patient arrivals Using the frost three months data it is possible to create a forecast for month 4. 34

Time-Series Methods Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week | | | | | | 0 5 10 15 20 25 30 Patient Week Arrivals 1 400 2 380 3 411 F4 = 397.0 Patient arrivals This slide advances automatically. 35

Time-Series Methods Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week | | | | | | 0 5 10 15 20 25 30 Patient Week Arrivals 1 400 2 380 3 411 F4 = 397.0 Patient arrivals 36

Time-Series Methods Moving Averages Actual patient arrivals Week 450 — 430 — 410 — 390 — 370 — | | | | | | 0 5 10 15 20 25 30 Patient Week Arrivals 2 380 3 411 4 415 F5 = 415 + 411 + 380 3 Patient arrivals This slide advances automatically. 37

Time-Series Methods Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week | | | | | | 0 5 10 15 20 25 30 Patient Week Arrivals 2 380 3 411 4 415 F5 = 402.0 Patient arrivals Similarly month 5 can be forecast. 38

Time-Series Methods Moving Averages Week 450 — 430 — 410 — 390 — 370 — | | | | | | 0 5 10 15 20 25 30 Patient arrivals 3-week MA forecast Actual patient arrivals And this adds the 6-week moving average which would be derived in the same fashion. 40

Metode Pemulusan Exponensial (Exponential Smoothing Method) Keterangan : Ft = nilai ramalan untuk periode waktu ke-t Ft-1 = nilai ramalan untuk satu periode waktu yang lalu, t-1 At-1 = nilai aktual untuk satu periode waktu yang lalu, t-1 α = konstanta pemulusan (Smoothing Constant) (0 <  < 1)

Exponential Smoothing Equations Ft = Ft-1 + (At-1 - Ft-1) Ft = Forecast value At = Actual value  = Smoothing constant You may wish to discuss several points: - this is just a moving average wherein every point in included in the forecast, but the weights of the points continuously decrease as they extend further back in time. - the equation actually used to calculate the forecast is convenient for programming on the computer since it requires as data only the actual and forecast values from the previous time point. - we need a formal process and criteria for choosing the “best” smoothing constant. CONTOH

Exponential Smoothing Example IF  = .10. and The first period forecast was 175.. Period Actual 1 180 2 168 3 159 4 175 5 190 6 205 7 180 8 182 9 ? Find the forecast for the 9th Period. This slide begins an exponential smoothing example.

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) 175.00 + Forecast, F t Period Actual ( α = .10) 1 180 175.00 (Given) 2 168 3 159 4 175 5 190 6 205

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F Period Actual t ( α = .10) 1 180 175.00 (Given) 2 168 175.00 + .10( 3 159 4 175 5 190 6 205

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F t Period Actual ( α = .10) 1 180 175.00 (Given) 2 168 175.00 + .10(180 - 3 159 4 175 5 190 6 205

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F t Period Actual ( α = .10) 1 180 175.00 (Given) 2 168 175.00 + .10(180 - 175.00) 3 159 4 175 5 190 6 205

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F Period Actual t ( α = .10) 1 180 175.00 (Given) 2 168 175.00 + .10(180 - 175.00) = 175.50 3 159 4 175 5 190 6 205

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F Period Actual t ( α = .10) 1 180 175.00 (Given) 2 168 175.00 + .10(180 - 175.00) = 175.50 3 159 175.50 + .10(168 - 175.50) = 174.75 4 175 5 190 6 205

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F t Period Actual ( α = .10) 1995 180 175.00 (Given) 1996 168 175.00 + .10(180 - 175.00) = 175.50 1997 159 175.50 + .10(168 - 175.50) = 174.75 1998 175 174.75 + .10(159 - 174.75)= 173.18 1999 190 2000 205

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F Actual t Period ( α = .10) 1 180 175.00 (Given) 2 168 175.00 + .10(180 - 175.00) = 175.50 3 159 175.50 + .10(168 - 175.50) = 174.75 4 175 174.75 + .10(159 - 174.75) = 173.18 5 190 173.18 + .10(175 - 173.18) = 173.36 6 205

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F Period t Actual ( α = .10) 1 180 175.00 (Given) 2 168 175.00 + .10(180 - 175.00) = 175.50 3 159 175.50 + .10(168 - 175.50) = 174.75 This slide illustrates the result of the steps used to make the forecast desired in the example. In the PowerPoint presentation, there are additional slides to illustrate the individual steps. 4 175 174.75 + .10(159 - 174.75) = 173.18 5 190 173.18 + .10(175 - 173.18) = 173.36 6 205 173.36 + .10(190 - 173.36) = 175.02

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F Period t Actual ( α = .10) 4 175 174.75 + .10(159 - 174.75) = 173.18 5 190 173.18 + .10(175 - 173.18) = 173.36 6 205 173.36 + .10(190 - 173.36) = 175.02 This slide illustrates the result of the steps used to make the forecast desired in the example. In the PowerPoint presentation, there are additional slides to illustrate the individual steps. 7 180 175.02 + .10(205 - 175.02) = 178.02 8 9

Exponential Smoothing Ft = Ft-1 + 0.1(At-1 - Ft-1) Forecast, F t Period Actual ( α = .10) 4 175 174.75 + .10(159 - 174.75) = 173.18 5 190 173.18 + .10(175 - 173.18) = 173.36 6 205 173.36 + .10(190 - 173.36) = 175.02 7 This slide illustrates the result of the steps used to make the forecast desired in the example. In the PowerPoint presentation, there are additional slides to illustrate the individual steps. 180 175.02 + .10(205 - 175.02) = 178.02 8 182 178.02 + .10(180 - 178.02) = 178.22 ? 178.22 + .10(182 - 178.22) = 178.58 9

Metode Analisis Garis Kecenderungan (Trend Line Analysis Method) Keterangan : F t = Nilai Ramalan untuk Periode Waktu Ke-t a = Intercep b = Slope dari Garis Kecenderungan (Trend Line) t = Indeks Waktu (t = 1, 2, 3,....., n)

Slope dan Intersep dari persamaan garis lurus dihitung dengan menggunakan formula sebagai berikut : Keterangan : b = Slope dari Persamaan Garis Lurus a = Intercep dari Persamaan Garis Lurus t = Indeks Waktu (t = 1, 2, 3,....., n) A = Data Aktual Permintaan tA = Indeks Waktu x Data Aktual Permintaan n = Jumlah Data t_bar = Nilai rata-rata dari t A_bar = Nilai Rata-Rata Permintaan Per Periode Waktu (Rata-Rata dari A)

VALLIDASI PERAMALAN Perhitungan Akurasi Peramalan 1. MAD (Mean Absolute Deviation = Rata- rata Penyimpangan Absolut). Keterangan : |E | = Absolute Error n = Jumlah Data

2. MSE (Mean Square Error = Rata-rata Kuadrat Kesalahan) Keterangan : E2 = Nilai Error yang Dikuadratkan n = Jumlah Data

3. MAPE ( Mean Absolute Procentage Error = Rata-rata Persentase kesalahan Absolut) Keterangan : |PE | = Persentase Absolute Error n = Jumlah Data CONTOH

Forecast Error Equations Mean Square Error (MSE) Mean Absolute Deviation (MAD) Mean Absolute Percent Error (MAPE) This slide illustrates the equations for two measures of forecast error. Students might be asked if there is an occasion when one method might be preferred over the other.

Selecting Forecasting Model Example How to calculate the accuracy of forecast? Example Actual Exponential Smoothing Year Sales Forecast (.9) 1998 1 1.0 1999 1 1.0 2000 2 1.9 2001 2 2.0 2002 4 3.8 This slide begins an example of choosing a model.

Exponential Smoothing Methode Evaluation ^ |Error| Year Y Y Error Error2 |Error| i i Actual 1998 1 1.0 0.0 0.00 0.0 0.00 1999 1 1.0 0.0 0.00 0.0 0.00 2000 2 1.9 0.1 0.01 0.1 0.05 2001 2 2.0 0.0 0.00 0.0 0.00 2002 4 3.8 0.2 0.04 0.2 0.05 Total 0.3 0.05 0.3 0.10 MSE = Σ Error2 / n = 0.05 / 5 = 0.01 MAD = Σ |Error| / n = 0.3 / 5 = 0.06 MAPE = 100 Σ |Absolute percent errors|/n = 0.10/5 = 0.02

Exponential Smoothing Methode Evaluation Exponential Smoothing Model: MSE = Σ Error2 / n = 0.05 / 5 = 0.01 MAD = Σ |Error| / n = 0.3 / 5 = 0.06 MAPE = 100 Σ |Absolute percent errors|/n = 0.10/5 = 0.02 This slide presents the result of the calculations of MSE and MAD for the Linear and Exponential Smoothing models. Students should be asked to choose the “better” model. Students should also be asked to consider the differences between the values calculated for the error measures for a given model, and between the two models. Do these differences tell us more than simply that one model is preferable to the other? (For example, is the exponential smoothing model 22 times better than the linear model?)

Tracking Signal merupakan suatu ukuran untuk menentukan seberapa baiknya suatu ramalan dalam memperkirakan nilai-nilai aktual Keterangan : RSFE = Jumlah Berjalan dari Nilai kesalahan Peramalan (Nilai Kumulatif Error) MAD = Rata-rata Kesalahan Absolut CONTOH

Tracking Signal Equation

Tracking Signal Computation No Fcst Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 Error = Actual - Forecast = 90 - 100 = -10 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 RSFE =  Errors = NA + (-10) = -10 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 Abs Error = |Error| = |-10| = 10 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 Cum |Error| =  |Errors| = NA + 10 = 10 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum |Error| MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 MAD =  |Errors|/n = 10/1 = 10

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 TS = RSFE/MAD = -10/10 = -1 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 Error = Actual - Forecast = 95 - 100 = -5 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 RSFE =  Errors = (-10) + (-5) = -15 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 Abs Error = |Error| = |-5| = 5 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 15 Cum Error =  |Errors| = 10 + 5 = 15 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 15 7.5 MAD =  |Errors|/n = 15/2 = 7.5 |Error|

Tracking Signal Computation No Forc Act Error RSFE Abs Cum MAD TS 1 100 90 2 95 3 115 4 5 125 6 140 -10 10 10.0 -1 -5 -15 15 7.5 -2 |Error| TS = RSFE/MAD = -15/7.5 = -2 This slide illustrates the last step in the calculation of a tracking signal for a simple example problem. The PowerPoint slide presentation contains this as the last of a sequence of slides - the others stepping through the actual calculation process.

Plot of a Tracking Signal Signal exceeded limit Tracking signal Upper control limit + MAD Acceptable range - This slide illustrates a graph of a tracking signal form a “practical” problem. Lower control limit Time

Tugas -1 Buat desain produk untuk produk manufaktur. Gambarkan secara detail masing-masing komponen Buat desain proses OPC beserta BOM Ramalkan kebutuhan produk tersebut “asumsi terjadi pertumbuhan per tahun sebesar 7%.