The use of analogies in forecasting the annual sales of new electronics products

Mathematical models are often used to describe the sales and adoption patterns of products in the years following their launch and one of the most popular of these models is the Bass model. However, using this model to forecast sales time series for new products is problematical because there is no historic time series data with which to estimate the model’s parameters. One possible solution is to fit the model to the sales time series of analogous products that have been launched in an earlier time period and to assume that the parameter values identified for the analogy are applicable to the new product. In this paper we investigate the effectiveness of this approach by applying four forecasting methods based on analogies (and variants of these methods) to the sales of consumer electronics products marketed in the USA. We found that all of the methods tended to lead to forecasts with high absolute percentage errors, which is consistent with other studies of new product sales forecasting. The use of the means of published parameter values for analogies led to higher errors than the parameters we estimated from our own data. When using this data averaging the parameter values of multiple analogies, rather than relying on a single most-similar, product led to improved accuracy. However, there was little to be gained by using more than 5 or 6 analogies

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Active force control applied to a rigid robot arm

The paper presents the implementation of Active Force Control (AFC) strategy to control a rigid robot arm. The robustness and effectiveness of AFC as 'disturbance rejector' is demonstrated through a simulation study using MATLAB(R) and SIMULINK(r)* softare packages. The work is carried out on a rigid two link disturbances. The results are directly compared to an equivalent system which employs the conventional model-based Proportional-Derivative (PD) control method

A State-Of-The-Art Review Of Job-Shop Scheduling Techniques

A great deal of research has been focused on solving the job-shop problem (P J ), over the last forty years, resulting in a wide variety of approaches. Recently, much effort has been concentrated on hybrid methods to solve P J as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that combine myopic problem specific methods and a meta-strategy which guides the search out of local optima. These approaches currently provide the best results. Such hybrid techniques are known as iterated local search algorithms or meta-heuristics. In this paper we seek to assess the work done in the job-shop domain by providing a review of many of the techniques used. The impact of the major contributions is indicated by applying these techniques to a set of standard benchmark problems. It is established that methods such as Tabu Search, Genetic Algorithms, Simulated Annealing should be considered complementary rather than competitive..

Scheduling A Job-Shop Using A Modified Back-Error Propagation Neural Network

General job-shop scheduling is a difficult problem to be solved. The complexity of the problem is highlighted by the fact that in a general job-shop having m machines and n jobs the total number of schedules can be as high as (n!) m , hence if "n=20 m=10" the number of possible solutions is 7.2651x10 183 . Many approaches such as Branch and Bound, Simulated Annealing, Tabu Search and others have been tried but with limited success. Recent advances in neural technology led the way to use neural networks to solve this problem. Here we present a work based on training a modified back-error propagation network to solve the job-shop scheduling problem after reviewing major neural network based job-shop scheduling systems. Key Words: neural network, modified back-error propagation, job-shop scheduling, NP-Hard problem 1. Introduction Scheduling is one of the most vital activities in a productive system. Accurate scheduling is a pre-requisite for effective utilisation of scarce resource..