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"Krisis ekonomi dan moneter yang melanda Indonesia sejak medio 1997 telah merontokkan sejumlah perusahaan di Indonesia, endemi kebangkrutan ini telah memberikan kerugian yang besar bagi para stakeholders perusahaan-perusahaan tersebut, mulai dari pemegang saham yang rugi karena nilai saham perusahaannya jatuh bebas, kreditur yang tidak dapat menagih piutangnya, sampai pegawai yang terpaksa di PHK-kan.Kebangkrutan massal tersebut menyadarkan masyarakat tentang pentingnya prediksi keadaan perusahaan di masa yang akan datang. Karena prediksi ini dilakukan oleh masyarakat umum maka data yang dipergunakan dalam prediksi tersebut haruslah data yang mudah diperoleh, seperti laporan keuangan yang dipublikasikan setiap tahunnya.Sebenarnya telah ada beberapa pemndelan prediksi kebangkrutan perusahaan yang umum dipakai, namun model-model tersebut dibuat berdasarkan data-data di negara lain dan pada tahun yang berbeda, sehingga sedikit banyak kurang sesuai dengan keadaan di Indonesia.Penelitian untuk memodelkan prediksi kebangkrutan ini dibatasi pada perusahaan-perusahaan dalam industri barang konsumsi, namun tidak tertutup kemungkinan dipergunakan pada industri lainnya. Sampel yang dipakai adalah dari 40 pemsahaan dalam kurun waktu 1999 sampai 2004, dan menggunakan 22 rasio-rasio keuangan sebagai indikator atas keadaan likuiditas, efisiensi, leverage, dan profitabilitas perusahaan.Penelitan ini menggunakan data tahun 2000 sebagai tahun dasar dan menghasilkan model:Z = 0.948X, +0.697X5 -1.195X5 +1.319X15 +4.599X17 dimana:Xa total liability/ total shareholder's equityX5 : long term liability/ long term liability + total shareholder's equityX6 : total liability/ total assetX15 net sales/ working capitalX17 net income/net salesModel diskriminan ini menunjukkan akurasi yang cukup balk jika diuji menggunakan sampel data tahun 2000 sampai 2004, dengan akurasi rata-ratanya berturut-turut adalah 100.00%, 97.44%, 97.44%, 97.37%, dan 91.89%.Penelitian ini menyimpulkan bahwa persamaan dengan data tahun 2000 sebagai tahun dasar merupakan prediksi kebangkrutan yang cukup akurat untuk perssaaan dalam industri barang konsumsi di Indonesia dengan menggunakan data tahun 1999-2004.

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Economic and monetary crises which came to Indonesia since mid 1997 have pulled several companies into bankruptcy. This bankruptcy endemic forced those companies' stakeholder to suffer a major loss, shareholders who suffer loss because their stock prices are free-falling, creditors who can't redeem their debt, and last but not least, employees who loss their job because their companies are bankrupt.

This massive bankruptcy endemic notices people about the importance of company future predictions. Since everybody should be able to do this prediction, therefore every data which required doing the prediction should be easily found, for example, financial report published on the internet.

There are some company bankruptcy models commonly used, but those models are built based on data from different countries and in different year. Therefore those models might not be suitable for Indonesia.

This research to model bankruptcy prediction is limited to companies under consumer goods industry, nonetheless it can be built using data from other industries. Samples to be used are from 40 companies from 1999 to 2004, also there are 22 financial ratios as indicator for liquidity, efficiency, leverage, and company profitability.

This research uses data from financial year 2000 as base year, and the research results:

Z = 0.948X4 +0.697X5 -1.195X5 +1.319X15 + 0.599X17 where:

X4 : total liability/ total shareholder's equity

X5 : long term liability/ long term liability + total shareholder's equity

A 6 : total liability) total asset

X15 : net sales/ working capital

X17 : net income/ net sales

This discriminate model shows a high accuracy when tested using data from year 2000 to 2004, with average accuracy consecutively, 100.00%, 97.44%, 97.44%, 97.37% and 91.89%.

This research concludes that model with year 2000 as base year is an accurate bankruptcy prediction for companies in consumer goods industry in Indonesia using data from year 1999-2004."

"Tesis ini membahas penerapan metode autoregressive integrated moving average (ARIMA) dalam menentukan cash forecasting. Metode ARIMA dapat mendukung keberhasilan sistem automatic cash pooling yang saat ini digunakan oleh perusahaan. Metode cash forecasting yang saat ini digunakan perusahaan menimbulkan kelebihan dan kekurangan kas. Hal ini menyebabkan inefisiensi bagi perusahaan dengan adanya opportunity cost dan transaction cost. Penelitian ini menyarankan kepada perusahaan untuk menggunakan metode ARIMA dalam menentukan cash forecasting cabang dan dikombinasikan dengan sistem cash pooling yang sudah diimplementasikan saat ini.

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This thesis discusses application of autoregressive integrated moving average (ARIMA) method in determining cash forecasting. ARIMA method is able to support the success of the automatic cash pooling system that currently used by the company. Cash forecasting method used by the company currently has excess cash and cash shortage. It makes inefficiency for the company by raising the opportunity cost and transaction cost. This study recommend to the company to use ARIMA method in determining branches cash forecasting and combines with cash pooling system that has been implemented."

"he first book to be published on the Theta method, outlining under what conditions the method outperforms other forecasting methods This book is the first to detail the Theta method of forecasting - one of the most difficult-to-beat forecasting benchmarks, which topped the biggest forecasting competition in the world in 2000: the M3 competition. Written by two of the leading experts in the forecasting field, it illuminates the exact replication of the method and under what conditions the method outperforms other forecasting methods. Recent developments such as multivariate models are also included, as are a series of practical applications in finance, economics, and healthcare. The book also offers practical tools in MS Excel and guidance, as well as provisional access, for the use of R source code and respective packages. Forecasting with the Theta Method: Theory and Applications includes three main parts. The first part, titled Theory, Methods, Models & Applications details the new theory about the method. The second part, Applications & Performance in Forecasting Competitions, describes empirical results and simulations on the method. The last part roadmaps future research and also include contributions from another leading scholar of the method - Dr. Fotios Petropoulos. First ever book to be published on the Theta Method Explores new theory and exact conditions under which methods would outperform most forecasting benchmarks Clearly written with practical applications Employs R - open source code with all included implementations Forecasting with the Theta Method: Theory and Applications is a valuable tool for both academics and practitioners involved in forecasting and respective software development"

"PT ABC merupakan distributor suku cadang alat berat produk Komatsu, salah satunya adalah komoditas GET (Ground engaging tools). Dalam penerapan pengelolaan Inventory serta supply chain management salah satu hal yang sangat penting yaitu forecasting, dimana dalam hal ini di PT ABC melakukan forecasting untuk suku cadang yang sangat dibutuhkan ketersediaannya dalam pemeliharaan alat berat dan dukungan layanan purna jual. Inventory merupakan suatu working capital yg cukup vital bagi PT ABC, sehingga diperlukan peningkatan efektifitas forecasting atau peramalan suku cadang yg sangat baik untuk mencapai inventory yang sehat dengan output ketersediaan suku cadang yang tinggi. Metode yang digunakan pada forecasting komoditas GET, yaitu metode Trend analysis dengan nilai safety stock dan lead time fix adjustment, dan Moving Average dengan menggunakan data aktual lead time serta safety stock dengan pertimbangan data historis fluktuasi demand. Analisis efektifitias forecasting dilakukan dengan melihat hasil dari akurasi, kontribusi, serta nilai error forecasting terhadap aktual demand yang terjadi, Berdasarkan hasil analisis menunjukkan bahwa metode forcasting moving average memiliki nilai error forecasting quantity lebih kecil, yaitu 35% dibandingkan metode trend analysis yang menghasilkan error forecasting quantity 98%.

......PT ABC as Distributor of Heavy equipment parts of Komatsu Product, such as GET (Ground engaging tools). In inventory controlling and supply chain managing, one of important thing is forecasting, while in this situation at PT ABC forecasting parts that is very use for Heavy equipment maintenance and also for supporting after sales services. Inventory is a vital working capital for PT ABC, it’s necessary to increase forecasting effectiveness to achieve health inventory with high Parts availability output. Forecasting method that use in GET commodity, are Trend analysis with safety stock and lead time number is fix adjustment, and moving average with lead time number is actual historical data and safety stock number considering fluctuating demand, this analysis carried out by analyze the result of accuracy, contribution, and also error number of forecasting quantity to the actual demand that occurs. Based on the results of the analysis, it shows that the forecasting moving average method has a smaller forecasting quantity error value, which is 35% compared to the trend analysis method which produces a forecasting quantity error of 98%."