Forecasting Sales

This chapter deals with forecasting sales (in units or money), where an explicit distinction is made between sales of durable goods (computers, cars, books) and sales of utilitarian products (SKU level in supermarkets). Invariably, sales forecasting amounts to a combination of statistical modeling and an expert’s touch. Models for durable goods sales are usually based on (variants of) the Bass model, while SKU sales forecasts are typically based on simple extrapolation methods. Forecast evaluation is not standard due to the interaction of model and expert.diffusion;SKU-level sales;durable goods;human judgment;sales forecasting

Forecasting Chilean Industrial Production and Sales with Automated Procedures

This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate linear models perform about as well for almost every forecasting horizon.

Evaluasi Penerapan Sales Forecasting dalam Authoritative Budgeting sebagai Acuan Penilaian Kinerja Karyawan dan Divisi Penjualan di PT. Xyz Cabang Surabaya

Tujuan penulisan skripsi ini termasuk explanatory research karena fokus dari penulisan ini mengevaluasi penerapan sales forecasting dalam authoritative budgeting sebagai acuan penilaian kinerja karyawan dan divisi penjualan di PT XYZ cabang Surabaya. Hasil penelitian di Perusahaan adalah peramalan penjualan dilakukan oleh kantor pusat (China) dalam melakukan penyusunan anggaran penjualan kantor-kantor cabang di Indonesia termasuk cabang Surabaya menggunakan sistem penganggaran authoritavtive dan kantor pusat (China) menentukan dasar peramalan penjualan untuk kantor-kantor cabang di Indonesia termasuk cabang Surabaya secara simple yang hanya didasarkan pada pencapaian anggaran penjualan selama 3 bulan sebelumnya. Dampak yang terjadi atas anggaran penjualan Perusahaan yang disusun dengan menggunakan sisitem penganggaran authoritavtive dan peramalan penjualan yang ditentukan secara simple tersebut adalah menurunnya motivasi kinerja karyawan penjualan (SPM dan SPG) dalam memenuhi anggaran penjualan yang telah ditetapkan oleh Perusahaan

Forecasting Chilean Industrial Production with Automated Procedures

This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate linear models perform about as well for almost every forecasting horizonForecasting, Time Series, Threshold, Artificial Neural Networks, Reality Check, Bootstrap

How Important of the Accuracy Sales Forecasting Method Against Sales Strategy Decision Making of the Mobile Phone Network Operator Company: a Case Study

This paper examines the accuracy of sales forecasting method that are important for decision making of sales strategy the mobile phone network operator company. Sales forecasting used to estimate company products sales for next year, so the company could make sales strategy plans and estimation an amount to be produced next year. Mobile phone network operator is one of un-seasonable products. The purpose of the paper is to compare, analyze, and giving a recommend of the best sales forecasting method to improve the mobile phone network operator company. Quantitative data were collected through published companies finnacial reports, articles, and journals which related with Supply Chain Management theory. The contribution this paper is providing the mobile phone network operator company to make successful sales strategy based from good sales forecasting method. Keywords – Sales Forecasting, Mobile Phone Network Operator Company, Sales Strategy, Forecasting Metho

Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach

Developments in the global electronics industry are typically monitored by tracking indicators that span a whole spectrum of activities in the sector. However, these indicators invariably give mixed signals at each point in time, thereby hampering efforts at prediction. In this paper, we present a unified framework for forecasting the global electronics cycle by constructing a VAR model that captures the economic interactions between leading indicators representing expectations, investments, orders, inventories and prices. The ability of the indicators to presage world semiconductor sales is assessed by Granger causality tests. The VAR model is also used to derive the dynamic paths of adjustment of global chip sales in response to shocks in each of the leading variables. These impulse response functions conform to our theoretical priors and confirm the leading quality of the selected indicators. Finally, out-of-sample forecasts of global chip sales are generated from the VAR model and compared with predictions from a univariate model as well as a model which uses a composite index of the leading indicators. An evaluation of their relative accuracy suggests that the VAR model's forecasting performance is superior to that of the univariate model and comparable to that of the composite index modelLeading indicators; Global electronics cycle; VAR; Forecasting

Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach

Developments in the global electronics industry are typically monitored by tracking indicators that span a whole spectrum of activities in the sector. However, these indicators invariably give mixed signals at each point in time, thereby hampering efforts at prediction. In this paper, we propose a unified framework for forecasting the global electronics cycle by constructing a VAR model that captures the economic interactions between leading indicators representing expectations, orders, inventories and prices. The ability of the indicators to presage world semiconductor sales is first demonstrated by Granger causality tests. The VAR model is then used to derive the dynamic paths of adjustment of global chip sales in response to orthogonalized shocks in each of the leading variables. These impulse response functions confirm the leading qualities of the selected indicators. Finally, out-of-sample forecasts of global chip sales are generated from a parsimonious variant of the model viz., the Bayesian VAR (BVAR), and compared with predictions from a univariate benchmark model and a bivariate model which uses a composite index of the leading indicators. An evaluation of their relative accuracy suggests that the BVAR's forecasting performance is superior to both the univariate and composite index models.Leading indicators; Global electronics cycle; VAR; Forecasting

Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach

Developments in the global electronics industry are typically monitored by tracking indicators that span a whole spectrum of activities in the sector. However, these indicators invariably give mixed signals at each point in time, thereby hampering efforts at prediction. In this paper, we propose a unified framework for forecasting the global electronics cycle by constructing a VAR model that captures the economic interactions between leading indicators representing expectations, orders, inventories and prices. The ability of the indicators to presage world semiconductor sales is first demonstrated by Granger causality tests. The VAR model is then used to derive the dynamic paths of adjustment of global chip sales in response to orthogonalized shocks in each of the leading variables. These impulse response functions confirm the leading qualities of the selected indicators. Finally, out-of-sample forecasts of global chip sales are generated from a parsimonious variant of the model viz., the Bayesian VAR (BVAR), and compared with predictions from a univariate benchmark model and a bivariate model which uses a composite index of the leading indicators. An evaluation of their relative accuracy suggests that the BVAR’s forecasting performance is superior to both the univariate and composite index models.Leading indicators; Global electronics cycle; VAR; Forecasting