Tujuan Pengajaran Mengindentifikasikan atau mendefinisikan : Setelah mempelajari bab ini, diharapkan anda mampu : Mengindentifikasikan atau mendefinisikan : Peramalan Jenis-jenis peramalan Horison waktu pendekatan untuk meramal Mendeskripsikan dan menjelaskan : Rata-rata bergerak Penghalusan Eksponensial Proyeksi trend Analisa Regresi

Forecasting di Tupperware Masing-masing 50 profit centers diseluruh dunia milik Tupperware mempertanggung jawabkan secara komputerisasi laporan proyeksi pemasaran untuk periode bulanan, kuartal, dan 12 bulan. Laporan proyeksi ini secara agregat berdasarkan kawasan, kemudian diakumulasi secara global di kantor pusat Tupperware’s World Headquarters Tupperware menggunakan seluruh metode peramalan yg akan dibahas pd bab ini. This slide introduces the topic of forecasting at Tupperware. The next several slides elaborate. One might ask students several questions: - “What does a useful forecast consist of at Tupperware? - What problems might a company such as Tupperware experience in developing a useful forecast. As you move through the following two slides, you could point out the application of multiple forecasting techniques to help solve some of the problems identified.

Tiga Faktor kunci bagi Tupperware Jumlah konsultan dan sales representatives yang terdaftar. Persentase agen yang aktif (jumlah ini berubah setiap minggu dan bulan) Jumlah salesman per agen yang aktif, setiap minggunya. Ask students: “Why does the number of ‘active’ dealers change so often?” and “If the number of ‘active’ dealers changes so often, should not this problem be addressed before attempting to forecast sales?” This question raises the issue of the impact of the distribution chain on one’s ability to forecast.

Tupperware – Peramalan berdasarkan Konsensus Meskipun data masukan dari para salesman, merketing, keuangan, dan bagian produksi, peramalan akhir datang dari kesepakatan dari semua manager yg berpartisipasi. Tahap akhir dalam menentukan peramalan di Tupperware disebut sebagai versi “jury of executive opinion” You might take the notion of “problems” one step further and ask students why Tupperware uses a “jury of executive opinion” as part of its forecasting process.

Apa itu Peramalan ? Suatu proses untuk memprediksi masa yang akan datang. Menjadi basis bagi semua keputusan bisnis spt ; Production Inventory Personnel Facilities Sales will be $200 Million!

Jenis Peramalan berdasarkan Seri Waktu Peramalan jangka pendek Jangka hingga 1 tahun; umumnya kurang dari 3 bulan Job scheduling, worker assignments Jangka menengah 3 bulan hingga 3 tahun Sales & production planning, budgeting Jangka panjang 3+ years New product planning, facility location At this point, it may be useful to point out the “time horizons” considered by different industries. For example, some colleges and universities look 30 to fifty years ahead, industries engaged in long distance transportation (steam ship, railroad) or provision of basic power (electrical and gas utilities, etc.) also look far ahead (20 to 100 years). Ask them to give examples of industries having much shorter long-range horizons.

Jangka Pendek vs. Jangka Panjang Jangka Menengah/panjang Peramalan berkenaan dengan isu-isu yg lebih komprehensif dan kebijakan-kebijakan mendukung manajemen berkenaan dgn planning and products, plants and processes. Jangka Pendek Peramalan yg biasanya menggunakan berbagai macam metodologi dibandingkan peramalan jangka panjang. Jangka Pendek Peramalan cenderung lebih akurat dibandingkan peramalan jangka panjang. At this point it may be helpful to discuss the actual variables one might wish to forecast in the various time periods.

Pengaruh dari Product Life Cycle Introduction, Growth, Maturity, Decline Tahapan introduction dan growth membutuhkan peramalan jangka panjang dibandingkan periode maturity dan decline Peramalan berguna dalam memproyeksikan staffing levels, inventory levels, and factory capacity ketika produk melalui tahapan-tahapan siklus kehidupan/ life cycle stages This slide introduces the impact of product life cycle on forecasting The following slide, reproduced from chapter 2, summarizes the changing issues over the product’s lifetime for those faculty who wish to treat the issue in greater depth.

Strategi dan isu-isu selama Siklus kehidupan Introduction Growth Maturity Decline Standardization Less rapid product changes - more minor changes Optimum capacity Increasing stability of process Long production runs Product improvement and cost cutting Little product differentiation Cost minimization Over capacity in the industry Prune line to eliminate items not returning good margin Reduce capacity Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focused Enhance distribution Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Best period to increase market share R&D product engineering critical Practical to change price or quality image Strengthen niche Cost control critical Poor time to change image, price, or quality Competitive costs become critical Defend market position OM Strategy/Issues Company Strategy/Issues HDTV CD-ROM Color copiers Drive-thru restaurants Fax machines Station wagons Sales 3 1/2” Floppy disks Internet

Jenis-jenis Peramalan Economic forecasts Ditujukan untuk siklus bisnis , e.g., inflation rate, money supply etc. Technological forecasts Meramalkan laju perkembangan teknologi Meramalkan penerimaan pasar terhadap produk baru Demand forecasts Meramalkan penjualan existing product One can use an example based upon one’s college or university. Students can be asked why each of these forecast types is important to the college. Once they begin to appreciate the importance, one can then begin to discuss the problems. For example, is predicting “demand” merely as simple as predicting the number of students who will graduate from high school next year (i.e., a simple counting exercise)?

Tujuh tahap Peramalan Menentukan penggunaan peramalan itu Memilih hal-hal yg akan diramalkan Menentukan horison waktunya Memilih model peramalan Mengumpulkan data Membuat ramalan Menerapkan hasilnya A point to be made here is that one requires a forecasting “plan,” not merely the selection of a particular forecasting methodology.

Permintaan Produk digambar untuk periode 4 tahun dengan Trend and Seasonality Year 1 2 3 4 Seasonal peaks Trend component Actual demand line Average demand over four years Permintaan produk atau jasa Random variation This slide illustrates a typical demand curve. You might ask students why it is important to know more than simply the actual demand over time. Why, for example, would one wish to be able to break out a “seasonality” factor?

Permintaan aktual, Rata-rata bergerak, Rata-rata bergerak tertimbang This slide illustrates one of the simplest forecasting techniques - the moving average. It may be useful to point out the lag introduced by exponential smoothing - and ask how one can actually make use of the forecast.

Kenyataan Peramalan Permalan jarang sekali sempurna Kebanyakan permalan didasarkan pada asumsi bahwa sistem stabil Both product family and aggregated product forecasts are more accurate than individual product forecasts This slide provides a framework for discussing some of the inherent difficulties in developing reliable forecasts. You may wish to include in this discussion the difficulties posed by attempting forecast in a continuously, and rapidly changing environment where product life-times are measured less often in years and more often in months than ever before. One might wish to emphasize the inherent difficulties in developing reliable forecasts.

Pendekatan Peramalan Metode Kualitatif Metode kuantitatif Used when situation is vague & little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet Used when situation is ‘stable’ & historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of color televisions This slide distinguishes between Quantitative and Qualitative forecasting. If you accept the argument that the future is one of perpetual, and perhaps significant change, you may wish to ask students to consider whether quantitative forecasting will ever be sufficient in the future - or will we always need to employ qualitative forecasting also. (Consider Tupperware’s ‘jury of executive opinion.’)

Metode Kualitatif secara umum Juri dan opini eksekutif Pengumpulan opini dari sebagian kecil eksekutif level atas, kadang-kadang diperkuat dengan model statistik Metode Delphi Panel of experts, queried iteratively Gabungan armada penjualan Estimates from individual salespersons are reviewed for reasonableness, then aggregated Survey pasar konsumen Ask the customer This slide outlines several qualitative methods of forecasting. Ask students to give examples of occasions when each might be appropriate. The next several slides elaborate on these qualitative methods.

Pendekatan Kuantitatif Secara umum Naïve approach Moving averages Exponential smoothing Trend projection Linear regression Time-series Models Associative models

Metode Peramalan Kuantitatif (Non-Naive) Quantitative Forecasting Time Series Associative Models Models A point you may wish to make here is that only in the case of linear regression are we assuming that we know “why” something happened. General time-series models are based exclusively on “what” happened in the past; not at all on “why.” Does operating in a time of drastic change imply limitations on our ability to use time series models? Moving Exponential Trend Linear Average Smoothing Projection Regression

Model Seri Waktu ? Set of evenly spaced numerical data Diperoleh berdasarkan hasil obervasi terhadap respon dari suatu variable pada periode waktu tertentu. Forecast based only on past values Dengan asumsi bahwa faktor-faktor yang mempengaruhi masa lalu dan sekarang akan terus berpengaruh dimasa yang akan datang. Example Year: 1998 1999 2000 2001 2002 Sales: 78.7 63.5 89.7 93.2 92.1 This and subsequent slide frame a discussion on time series - and introduce the various components.

Komponen-komponen Seri Waktu Trend Musim Siklus Variasi acak

Komponen Trend Persistent, Secara umum polanya naik atau turun. Diakibatkan oleh populasi, technology dll. Durasi beberapa tahun Mo., Qtr., Yr. Response © 1984-1994 T/Maker Co.

Komponen musim Pola reguler, fluktuasi naik dan turun Diakibatkan oleh cuaca, musim, dll. Terjadi dalam 1 tahun Mo., Qtr. Response Summer © 1984-1994 T/Maker Co.

Jumlah musim dalam pola Pola Musim secara umum Periode pola Lamanya Jumlah musim dalam pola Minggu Hari 7 Bulan 4 – 4 ½ 28 – 31 Tahun Kuartal 4 12 52

 Komponen siklus Pergerakan naik dan turun berulang Diakibatkan interaksi diantara faktor-faktor yg mempengaruhi ekonomi Durasi 2-10 tahun Mo., Qtr., Yr. Response Cycle 

Komponen Variasi Acak Erratic, unsystematic, ‘residual’ fluctuations Diakibatkan oleh variasi acak atau situasi yang tidak biasa Pemogokan buruh Tornado Durasi pendek & tidak berulang © 1984-1994 T/Maker Co.

Pendekatan Naive Mengasumsikan permintaan pada periode akan datang adalah sama dengan permintaaan sekarang. e.g., Jika penjualan Mei adalah 48, maka penjualan Juni akan 48 Kadang-kadang efektif dan efesien secara biaya © 1995 Corel Corp. This slide introduces the naïve approach. Subsequent slides introduce other methodologies.