Operations Research
Seife Ebeyedengel Tekletsadik
Abstract
Productivity improvement is important for the sustainability of the business. However, before improvement it is important to measure the existing system productivity level. In this sense, the productivity of the case company has been measured by using the PO-P approach. Using this approach, the overall ...
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Productivity improvement is important for the sustainability of the business. However, before improvement it is important to measure the existing system productivity level. In this sense, the productivity of the case company has been measured by using the PO-P approach. Using this approach, the overall productivity of the case company has become 0.652. By having the ergonomic sub system and strategic goal of the case company, goal programing has been formulated to show by how much percent does the ergonomics subsystem alone will improve the overall productivity level. For this, it is required to have optimal solution of performance value of the performance objectives of the ergonomics sub-system. The optimal solution of the excel solver has given the suggested performance values of x3=1.035, x4= 0.82, x5=0.7, x6=0.8, x7=2.76, x8=0.75, x9=0.8, x10=0.5, and x11=0.4. Having these values, the productivity of the ergonomics subsystem became 1.492, in effect increased the overall productivity from 0.652 to 0.776.
Engineering Optimization
C. L. Karmaker
Abstract
Exponential smoothing is a sophisticated forecasting method that works based on previous forecast plus a percentage of the forecast error. A key issue of this technique is the proper choice of exponential smoothing constant. In order to minimize forecasting errors, choosing an appropriate value of smoothing ...
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Exponential smoothing is a sophisticated forecasting method that works based on previous forecast plus a percentage of the forecast error. A key issue of this technique is the proper choice of exponential smoothing constant. In order to minimize forecasting errors, choosing an appropriate value of smoothing constant is very crucial. In this study, a framework is developed for the selection of optimal value of smoothing constant that minimizes a measure of forecast errors like mean square error (MSE) and mean absolute deviation (MAD). Both “trial & error” and Excel based non-linear optimizer (“Excel Solver”) are used for this purpose. To validate the proposed model, necessary demand data of Ruchi Jhal Muri from years 2010-2016 from Square Food & Beverage Ltd. in Mohakhali, Dhaka were collected. The optimum values of smoothing constant under trial & error method are 0.31 and 0.14 for minimum MAD and MSE respectively whereas for excel solver, values are 0.314 and 0.143 with respect to minimum MAD and MSE. Although both methods provide approximately the same results but excel solver is much easier & requires less time for deriving optimum solution. This study will provide an outline for the forecast planners as well as manufacturers to improve the accuracy of exponential forecasting through using Excel Solver for determining the optimum value of smoothing constant.