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Reports the results of a survey of manufacturing managers to assesstheir perception of the changing manufacturing task and the role of themanufacturing manager. Within…
Reports the results of a survey of manufacturing managers to assess their perception of the changing manufacturing task and the role of the manufacturing manager. Within this context investigates the contribution of new manufacturing techniques and approaches, the involvement of manufacturing staff in service factory roles and the steps to increase integration across the value chain on manufacturing performance.
Describes how the increasing emphasis on customer service‐basedperformance measures changes the way in which we need to manageoperations along the total supply chain from…
Describes how the increasing emphasis on customer service‐based performance measures changes the way in which we need to manage operations along the total supply chain from raw materials to end user. Senior managers at Caterpillar, General Motors, ICL, Philips and Rank Xerox were interviewed about the steps that they have taken to achieve greater supply chain integration and the problems to be overcome before further progress can be achieved. Describes the changes that have been necessary to achieve greater supply chain integration and discusses the likely impact of these changes on the future role of the operations manager.
When a production system is operating at close to capacity then,after a period of high demand, it may take some time to restore stocksto the level necessary to provide a…
When a production system is operating at close to capacity then, after a period of high demand, it may take some time to restore stocks to the level necessary to provide a given level of stockout risk. During this period the risk of a stockout will be higher than intended. Uses simulation to show how customer service levels fall dramatically as average production levels approach available capacity and to determine the increases in levels of safety stock necessary to maintain desired customer service levels when capacity is limited.
This report presents the preliminary findings of a research study to determine the factors which enable a manufacturing plant to simultaneously achieve high labour…
This report presents the preliminary findings of a research study to determine the factors which enable a manufacturing plant to simultaneously achieve high labour productivity, fast, reliable delivery and high quality consistency. The conclusions are based on analysis of a database containing details of 953 manufacturing plants in the UK. Based on the performance measures mentioned above, a composite performance measure was calculated for each plant in the database. The plants were then divided into groups of high performers, medium performers and low performers. Using statistical analysis, those differences between the high and low‐performing plants that were significant were identified. The main factors differentiating high‐performing plants from the rest were those associated with low process variability, high schedule stability and more reliable deliveries by suppliers.
A sample of 782 manufacturing plants drawn from the UK Best Factor Awards database was used to investigate the nature of trade‐offs between different measures of…
A sample of 782 manufacturing plants drawn from the UK Best Factor Awards database was used to investigate the nature of trade‐offs between different measures of manufacturing performance. Each plant was ranked within its industry on each performance measure, a high ranking indicating good performance on that measure and a low ranking indicating poor performance. By comparing the ranking of each plant within its industry on each performance measure it was possible to determine the extent to which good performance on one measure was correlated with good performance on other measures. Rankings on added value per employee £, quality consistency, delivery reliability, speed of delivery and the rate of new product introduction were positively correlated, suggesting that good performance on each of these factors is associated with good performance on the rest. Only the extent to which a plant exhibited product variety showed conventional trade‐off characteristics, being negatively correlated with rankings on added value per employee £ and the rate of new product introduction. This implies that, provided that individual operating units can be organized so that each is focused on a relatively narrow product range, trade‐offs can be avoided.
Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector…
Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.
A method is developed for evaluating forecasting models withrespect to both error and complexity in forecasting. Several types offorecasting accuracy measures (MSE, MPE…
A method is developed for evaluating forecasting models with respect to both error and complexity in forecasting. Several types of forecasting accuracy measures (MSE, MPE, MAPE, Theil′s U‐Statistic and a loss cost function) are examined and the approach is illustrated using short‐term forecasting methods, and weekly and four‐weekly data. The approach can, however, be applied equally to immediate, medium‐ and long‐term forecasting.
Motivated by the growing importance of the expected shortfall in banking and finance, this study aims to compare the performance of popular non-parametric estimators of…
Motivated by the growing importance of the expected shortfall in banking and finance, this study aims to compare the performance of popular non-parametric estimators of the expected shortfall (i.e. different variants of historical, outlier-adjusted and kernel methods) to each other, selected parametric benchmarks and estimates based on the idea of forecast combination.
Within a multidimensional simulation setup (spanned by different distributional settings, sample sizes and confidence levels), the authors rank the estimators based on classic error measures, as well as an innovative performance profile technique, which the authors adapt from the mathematical programming literature.
The rich set of results supports academics and practitioners in the search for an answer to the question of which estimators are preferable under which circumstances. This is because no estimator or combination of estimators ranks first in all considered settings.
To the best of their knowledge, the authors are the first to provide a structured simulation-based comparison of non-parametric expected shortfall estimators, study the effects of estimator averaging and apply the mentioned profiling technique in risk management.
The purpose of this paper is to investigate the impact of demand uncertainty on inventory turnover performance through empirical modeling. In particular the authors use…
The purpose of this paper is to investigate the impact of demand uncertainty on inventory turnover performance through empirical modeling. In particular the authors use the inaccuracy of quarterly sales forecasts as a proxy for demand uncertainty and study its impact on firm-level inventory turnover ratios.
The authors use regression analysis to study the effect of various measures on inventory performance. The authors use a sample financial data for 304 publicly listed US retail firms for the 25-year period from 1985 to 2009.
Controlling for the effects of retail segments and year, it is found that inventory turnover is negatively correlated with mean absolute percentage error of quarterly sales forecasts and gross margin and positively correlated with capital intensity and sales surprise. These four variables explain 73.7 percent of the variation across firms and over time and 93.4 percent of the within-firm variation in the data.
In addition to conducting an empirical investigation for the sources of variation in a major operational metric, the results in this study can also be used to benchmark a retailer’s inventory performance against its competitors.
The authors develop a new proxy to measure the demand uncertainty that a firm faces and show that this measure may help to explain the variation in inventory performance.
Over the last decade or so there has been an increased interest in combining the forecasts from different models. Pooling the forecast outcomes from different models has…
Over the last decade or so there has been an increased interest in combining the forecasts from different models. Pooling the forecast outcomes from different models has been shown to improve out‐of‐sample forecast test statistics beyond any of the individual component techniques. The discussion and practice of forecast combination has revolved around the pooling of results from individual forecasting methodologies. A different approach to forecast combination is followed in this paper. A method is used in which negatively correlated forecasts are combined to see if this offers improved out‐of‐sample forecasting performance in property markets. This is compared with the outcome from both the original model and with benchmark naïve forecasts over three 12‐month out‐of‐sample periods. The study will look at securitised property in three international property markets – the USA, the UK and Australia.