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The work of Scott, Bruce and Cooper on small firm growth and development is reviewed. It is shown that by adapting exponential smoothing forecasting procedures it is possible to monitor the commercial health of a small firm. This is achieved by ‘tracking’ key indicators and producing an exception message when a signal exceeds certain predetermined control limits. The procedure is equally effective for either a step or ramp change in the underlying input data. This suggested approach requires little sophistication in either data or technique and has a practical application to small firm management, while adding to our understanding of the process of growth of small businesses.

Demand forecasting has long been an imperative tenet in production planning especially in a make-to-order environment where a typical manufacturer has to balance the issues of holding excessive safety stocks and experiencing possible stockout. Many studies provide pragmatic paradigms to generate demand forecasts (mainly based on smoothing forecasting models.) At the same time, artificial neural networks (ANNs) have been emerging as alternatives. In this chapter, we propose a two-stage forecasting approach, which combines the strengths of a neural network with a more conventional exponential smoothing model. In the first stage of this approach, a smoothing model estimates the series of demand forecasts. In the second stage, general regression neural network (GRNN) is applied to learn and then correct the errors of estimates. Our empirical study evaluates the use of different static and dynamic smoothing models and calibrates their synergies with GRNN. Various statistical tests are performed to compare the performances of the two-stage models (with error correction by neural network) and those of the original single-stage models (without error-correction by neural network). Comparisons with the single-stage GRNN are also included. Statistical results show that neural network correction leads to improvements to the forecasts made by all examined smoothing models and can outperform the single-stage GRNN in most cases. Relative performances at different levels of demand lumpiness are also examined.

Applies time‐series forecasting, a traditional operations analysis methodology, to develop a forecasting procedure and ordering policy for a natural‐gas customer of Columbia Gas of Ohio, USA. Evaluates six time‐series methods and four operating policies against four commonly used measures of error and the cost consequences of error to the customer. Demonstrates that time‐series forecasting and decision theory developed by operations and applied in an actual industrial situation can become a powerful marketing technique. Provides further insights into evaluating forecasting models and ordering policies, demonstrating that introducing optimal planned bias is a robust decision‐making/forecasting approach within services. There are three parts to the study. The first is a straightforward testing of forecasting methods, using the forecasts as the natural‐gas ordering policy. Results vary depending upon how well forecasts are fitted to the data. For example, one inaccurate forecast with a poor fit incurs a penalty cost of $179,270, while the best forecast results in a penalty cost of $27,081. The second part evaluates two additional complex ordering rules with the same forecasting methods, further reducing the lowest cost to $17,709. The third part is a technical analysis reflecting a redesign of the study, demonstrating the difficulty of generalizing when characteristics of the underlying demand change. Concludes that the best forecasting model/operating policy is to use the very basic forecasting model of simple moving average (or the equivalent, first‐order exponential smoothing) combined with an optimal planned bias ordering policy, i.e. with the planned introduction of bias.

This study examines forecast accuracy associated with the forecast of 55 revenue data series of 18 local governments. The last 18 months (6 quarters; or 2 years) of the data are held-out for accuracy evaluation. Results show that forecast software, damped trend methods, and simple exponential smoothing methods perform best with monthly and quarterly data; and use of monthly or quarterly data is marginally better than annualized data. For monthly data, there is no advantage to converting dollar values to real dollars before forecasting and reconverting using a forecasted index. With annual data, naïve methods can outperform exponential smoothing methods for some types of data; and real dollar conversion generally outperforms nominal dollars. The study suggests benchmark forecast errors and recommends a process for selecting a forecast method.

Product success is contingent on forecasting when a product is needed and how it should be offered. Forecasting accuracy is contingent on the correct forecasting technique. Using supermarket data across two product categories, this chapter shows that using a bevy of forecasting methods improves forecasting accuracy. Accuracy is measured by the mean absolute percentage error. The optimal methods for one consumer goods product may be different than for another. The best model varied from sophisticated, most such as autoregressive integrated moving average (ARIMA) and Holt–Winters to a random walk model. Forecasters must be proficient in multiple statistical techniques since the best technique varies within a categories, variety, and product size.

This article considers that one way to help the small‐ and medium‐sized enterprise (SME) to survive is to offer it a robust but simple monitoring and control technique that would help it manage the business effectively and this, in turn, should help to increase its chances of survival. This technique should also be of interest to all people involved with monitoring or advising a large number of small enterprises or business units within a larger organization. For example, a bank manager or a small business consultant responsible for a portfolio of firms. The authors utilize process control techniques more often used in production and inventory control systems to demonstrate how one might monitor the marketing “health” of small firms.

When forecasting intermittent demand the method derived by Croston (1972) is often cited. Previous research favorably compared Croston's forecasting method for demand with simple exponential smoothing assuming a nonzero demand occurs as a Bernoulli process with a constant probability. In practice, however, the assumption of a constant probability for the occurrence of nonzero demand is often violated. This research investigates Croston's method under violation of the assumption of a constant probability of nonzero demand. In a simulation study, forecasts derived using single exponential smoothing (SES) are compared to forecasts using a modification of Croston's method utilizing double exponential smoothing to forecast the time between nonzero demands assuming a normal distribution for demand size with different standard deviation levels. This methodology may be applicable to forecasting intermittent demand at the beginning or end of a product's life cycle.

Focuses on a computerized sales forecasting system for the control of automotive spare parts. Outlines the logic of the forecasting method, a refinement of exponential smoothing, together with a method for monitoring forecast errors. Describes experiences in developing, implementing and operating the system.

Quantitative forecasting techniques see their greatest application as part of production and inventory systems. Perhaps unfortunately, the problem has been left to systems analysts while the professional societies have contented themselves with exhortations to improve forecasting, neglecting recent developments from forecasting research. However, improvements in accuracy have a direct and often substantial financial impact. This article shows how the production and inventory control forecasting problem differs from other forecasting applications in its use of information and goes on to consider the characteristics of inventory type data. No single forecasting method is suited to all data series and the article then discusses how recent developments in forecasting methodology can improve accuracy. Considers approaches to extending the database beyond just the time‐series history of the data series. Concludes with a discussion of an “ideal” forecasting system and how far removed many popular programs used in production and inventory control are from this ideal.

Users are the key players in an online social network (OSN), so the behavior of the OSN is strongly related to their behavior. User weight refers to the influence of the users on the OSN. The purpose of this paper is to propose a method to identify the user weight based on a new metric for defining the time intervals.

The behavior of an OSN changes over time, thus the user weight in the OSN is different in each time frame. Therefore, a good metric for estimating the user weight in an OSN depends on the accuracy of the metric used to define the time interval. New metric for defining the time intervals is based on the standard deviation and identifies that the user weight is based on a simple exponential smoothing model.

The results show that the proposed method covers the maximum behavioral changes of the OSN and is able to identify the influential users in the OSN more accurately than existing methods.

In event detection, when a terrorist attack occurs as an event, knowing the influential users help us to know the leader of the attack. Knowing the influential user in each time interval based on this study can help us to detect communities which formed around these people. Finally, in marketing, this issue helps us to have a targeted advertising.

User effect is a significant issue in many OSN domain problems, such as community detection, event detection and recommender systems.

Previous studies do not give priority to the recent time intervals in identifying the relative importance of users. Thus, defining a metric to compute a time interval that covers the maximum changes in the network is a major shortcoming of earlier studies. Some experiments were conducted on six different data sets to test the performance of the proposed model in terms of the computed time intervals and user weights.