221 research outputs found
A Metamodel-Based Monte Carlo Simulation Approach for Responsive Production Planning of Manufacturing Systems
Production planning is concerned with finding a release plan of jobs into the manufacturing system so that its actual outputs over time match the customer demand with the least cost. The biggest challenge of production planning lies in the difficulty to quantify the performance of a release plan, which is the necessary basis for plan optimization. Triggered by an input plan over a time horizon, the system outputs, work in process (WIP) and job departures, are non-stationary bivariate time series that interact with customer demand (another time series), resulting in the fulfillment/non-fulfillment of demand and in the holding cost of both WIP and finished-goods inventory. The relationship between a release plan and its resulting performance metrics (typically, mean/variance of the total cost and the demand fulfill rate is far from being adequately quantified in the existing literature of production planning. In this dissertation, a metamodel-based Monte Carlo simulation (MCS) method is developed to accurately capture the dynamic and stochastic behavior of a manufacturing system, and to allow for real-time evaluation of a release plan in terms of its performance metrics. This evaluation capability is embedded in a multi-objective optimization framework to enable the quick search of good (or optimum) release plans. The developed method has been applied to a scaled-down semiconductor fabrication system to demonstrate the quality of the metamodel-based MCS evaluation and the plan optimization results
Technology sourcing by large incumbents through acquisition of small firms
Innovation activities in high technology industries provide considerable challenges for technology and innovation management. In particular, since these industries have a long history of radical innovations taking place through distinct industry cycles of higher and lower demand, firms frequently consider the option to use acquisitions as a means for technology sourcing. The paper investigates this behaviour for three high technology industries, namely semiconductor manufacturing, biotechnology and electronic design automation which is a specific sub-segment of the semiconductor industry. It analyses the association of firm characteristics with different aspects of acquisition behaviour with a particular focus being put on innovation-related firm characteristics. The paper confirms a substitutive relationship between acquisitions and own research activities as well as between own and acquired firm patenting, but also finds that firm size, financial conditions and geographical origin of the firm matter for acquisition behaviour.Acquisition, innovation, high technology, quantitative methods, research, R&D
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Impact of Forecasting Method Selection and Information Sharing on Supply Chain Performance.
Effective supply chain management gains much attention from industry and academia because it helps firms across a supply chain to reduce cost and improve customer service level efficiently. Focusing on one of the key challenges of the supply chains, namely, demand uncertainty, this dissertation extends the work of Zhao, Xie, and Leung so as to examine the effects of forecasting method selection coupled with information sharing on supply chain performance in a dynamic business environment. The results of this study showed that under various scenarios, advanced forecasting methods such as neural network and GARCH models play a more significant role when capacity tightness increases and is more important to the retailers than to the supplier under certain circumstances in terms of supply chain costs. Thus, advanced forecasting models should be promoted in supply chain management. However, this study also demonstrated that forecasting methods not capable of modeling features of certain demand patterns significantly impact a supply chain's performance. That is, a forecasting method misspecified for characteristics of the demand pattern usually results in higher supply chain costs. Thus, in practice, supply chain managers should be cognizant of the cost impact of selecting commonly used traditional forecasting methods, such as moving average and exponential smoothing, in conjunction with various operational and environmental factors, to keep supply chain cost under control. This study demonstrated that when capacity tightness is high for the supplier, information sharing plays a more important role in effective supply chain management. In addition, this study also showed that retailers benefit directly from information sharing when advanced forecasting methods are employed under certain conditions
The doctoral research abstracts Vol:2 2012 / Institute of Graduate Studies, UiTM
Foreword:
Congratulations to Institute of Graduate
studies on the conscientious efforts to publish
yet another issue of the doctoral research
abstracts. The second issue, I believe reflects IGS
continuity in the pursuit of academic excellence
in research following the inaugural publication
during the 76th convocation ceremony.
The publication epitomizes knowledge par
excellence and marks UiTM acknowledgment and
tribute to the 27 doctorates whose achievements
we proudly celebrate. Doctoral researches
transcend beyond academic achievements
which launch doctorates wherever they want to
go or whatever they want to do. It is indeed the
beginning of a lifelong learning and merely not
a milestone, but a stepping stone in the lives of
doctorates.
This issue features the PhD abstracts from across
the faculties from the disciplines of science and
technology, social science and humanities; and
business and administration.
May the Almighty guides us to the straight path,
in our endeavor for academic excellence and
grant us success in this world and the next.
Datoâ Sri Prof Ir Dr Sahol Hamid Bin Abu Bakar , FASc
Vice Chancellor
Universiti Teknologi MAR
QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum - Classical Neural Network
Supply chain management relies on accurate backorder prediction for
optimizing inventory control, reducing costs, and enhancing customer
satisfaction. However, traditional machine-learning models struggle with
large-scale datasets and complex relationships, hindering real-world data
collection. This research introduces a novel methodological framework for
supply chain backorder prediction, addressing the challenge of handling large
datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques
within a quantum-classical neural network to predict backorders effectively on
short and imbalanced datasets. Experimental evaluations on a benchmark dataset
demonstrate QAmplifyNet's superiority over classical models, quantum ensembles,
quantum neural networks, and deep reinforcement learning. Its proficiency in
handling short, imbalanced datasets makes it an ideal solution for supply chain
management. To enhance model interpretability, we use Explainable Artificial
Intelligence techniques. Practical implications include improved inventory
control, reduced backorders, and enhanced operational efficiency. QAmplifyNet
seamlessly integrates into real-world supply chain management systems, enabling
proactive decision-making and efficient resource allocation. Future work
involves exploring additional quantum-inspired techniques, expanding the
dataset, and investigating other supply chain applications. This research
unlocks the potential of quantum computing in supply chain optimization and
paves the way for further exploration of quantum-inspired machine learning
models in supply chain management. Our framework and QAmplifyNet model offer a
breakthrough approach to supply chain backorder prediction, providing superior
performance and opening new avenues for leveraging quantum-inspired techniques
in supply chain management
Inventory planning with dynamic demand. A state of art review
Proper inventory planning should incorporate factors changing over time, since static factors are not robust to this apparent variability. In models of inventories is necessary to recognize the great demand uncertainty. This paper reviews the state of the art of the most significant developments related to inventory models, especially those who consider dynamic demands in time. In addition, demand forecasting models and some techniques for optimizing inventories are analyzed, considering costs and service levels, among others. In the literature review, gaps have been identified related to the treatment of multivariate inventories as well as the use of Bayesian statistics for the purpose of optimization and the development of demand forecasts
Demand forecasting using a hybrid model based on artificial neural networks: A study case on electrical products
Purpose: This work aims to evaluate demand forecasting models to determine if using exogenous factors and machine learning techniques helps improve performance compared to univariate statistical models, allowing manufacturing companies to manage demand better.
Design/methodology/approach: We implemented a multivariate Auto-Regressive Moving Average with eXogenous input (ARMAX) statistical model and a Neural Network-ARMAX (NN-ARMAX) hybrid model for forecasting. Later, we compared both to a standard univariate statistical model to forecast the demand for electrical products in a Colombian manufacturing company.
Findings: The outcomes demonstrated that the NN-ARMAX model outperformed the other two. Indeed, demand management improved with the reduction of overstock and out-of-stock products.
Research limitations/implications: The findings and conclusions in this work are limited to Colombian manufacturing companies that sell electrical products to the construction industry. Moreover, the experts from the company that provided us with the data also selected the external factors based on their own experiences, i.e., we might have disregarded potential factors.
Practical implications: This work suggests that a model using neural networks and including exogenous variables can improve demand forecasting accuracy, promoting this approach in manufacturing companies dealing with demand planning issues.
Originality/value: The findings in this work demonstrate the convenience of using the proposed hybrid model to improve demand forecasting accuracy and thus provide a reliable basis for its implementation in supply chain planning for the electrical/construction sector in Colombian manufacturing companies
Forecasting for Nonlinear and Nonstationary Systems Using Intrinsic Functional Decomposition Models
The purpose of this study is to develop nonlinear and nonstationary time series forecasting methods to address modeling and prediction of real-world, complex systems. Particular emphasis has been placed on nonlinear and nonstationary time series forecasting in systems and processes that are of interest to IE researchers. Two new advanced prediction methods are developed using nonlinear decomposition techniques and a battery of advanced statistical methods. The research methodologies include empirical mode decomposition (EMD)-based prediction, structural relationship identification (SRI) methodology, and intrinsic time-scale decomposition (ITD)-based prediction. The advantages of using these prediction methods are local characteristic time scales and the use of an adaptive basis that does not require a parametric functional form (during the decomposition process). The utilization of SRI methodology in ITD-based prediction also provides a relationship identification advantage that can be used to capture the interrelationships of variables in the system for prediction application. The empirical results of using these new prediction methods have shown a significant improvement in the accuracy for customer willingness-to-pay and automobile demand prediction applications.Industrial Engineering & Managemen
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