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Author: Meenakshi Prajapati
Publisher:
ISBN:
Category : Production planning
Languages : en
Pages : 66
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Book Description
Production planning plays a vital role in the management of manufacturing facilities. The problem is to determine the production loading plan consisting of the quantity of production and the workforce level - to fulfill a future demand. Although the deterministic version of the problem has been widely studied in the literature, the stochastic production planning problem has not. The application of production planning models could be limited if the stochastic nature of the problem, for example, uncertainty in future demand, is not addressed. This study addresses such a stochastic production planning problem under uncertain demand and its application in an enclosure manufacturing facility. The thesis first addresses the forecast of the demand where seasonal fluctuation is present. A decomposition model is utilized in the forecast and compared with other forecasting methods. Although forecast models could be used to improve the accuracy of forecast, error and uncertainty still exists. To deal with this uncertainty, a two stage stochastic scenario based production planning model is developed to minimize the total cost consisting of production cost, labor cost, inventory cost and overtime cost under uncertain demand. The model is solved with data from a local manufacturing facility and the results are compared with various deterministic production models to show the effectiveness of the developed stochastic model. Parametric analysis are performed to derive managerial insights related to issues such as overtime usage and inventory holding cost and the proper selection of scenarios under pessimist, neutral and optimist forecasts. An extension of the stochastic model, i.e., a robust model is also solved in an effort to minimize changes in the solutions under various scenarios. The stochastic production planning model has been implemented in the manufacturing facility, provided guidance for material acquisition and production plans and has dramatically increased the company’s bottom line. As a result, it’s estimated an approximately annual savings of $340,000 in inventory cost can be achieved for the company in the next few years.
Author: Meenakshi Prajapati
Publisher:
ISBN:
Category : Production planning
Languages : en
Pages : 66
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Book Description
Production planning plays a vital role in the management of manufacturing facilities. The problem is to determine the production loading plan consisting of the quantity of production and the workforce level - to fulfill a future demand. Although the deterministic version of the problem has been widely studied in the literature, the stochastic production planning problem has not. The application of production planning models could be limited if the stochastic nature of the problem, for example, uncertainty in future demand, is not addressed. This study addresses such a stochastic production planning problem under uncertain demand and its application in an enclosure manufacturing facility. The thesis first addresses the forecast of the demand where seasonal fluctuation is present. A decomposition model is utilized in the forecast and compared with other forecasting methods. Although forecast models could be used to improve the accuracy of forecast, error and uncertainty still exists. To deal with this uncertainty, a two stage stochastic scenario based production planning model is developed to minimize the total cost consisting of production cost, labor cost, inventory cost and overtime cost under uncertain demand. The model is solved with data from a local manufacturing facility and the results are compared with various deterministic production models to show the effectiveness of the developed stochastic model. Parametric analysis are performed to derive managerial insights related to issues such as overtime usage and inventory holding cost and the proper selection of scenarios under pessimist, neutral and optimist forecasts. An extension of the stochastic model, i.e., a robust model is also solved in an effort to minimize changes in the solutions under various scenarios. The stochastic production planning model has been implemented in the manufacturing facility, provided guidance for material acquisition and production plans and has dramatically increased the company’s bottom line. As a result, it’s estimated an approximately annual savings of $340,000 in inventory cost can be achieved for the company in the next few years.
Author: Vibhor Vineet
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Effective planning strategies are essential to minimize high costs of production and inventory. Uncertainty and seasonal variation in product demand is a major issue that contributes to a substantial share of production planning costs. Hence, it is important to consider the uncertain information while designing a production planning model. This thesis is aimed at presenting a comparative analysis of deterministic and stochastic approaches towards finding optimal solutions for demand uncertainty problems. The first model is a generic mixed-integer programming model to maximize total profit. Decision variables are identified and random values are substituted by their expected values considering uncertainty to obtain the expected value solutions. Second model is formulated as a stochastic programming model by adding scenarios and probabilities in the deterministic model to explicitly account for the uncertainties in the product demand. The models are programmed and solved by LINGO optimization solver based on data collected from a brewing company. Several test problems are solved by varying the input parameters, product demand and probability of existence of scenarios to study the sensitivity of the models. A statistical comparative analysis is conducted on all the example problems by measuring the Expected Value of Perfect Information (EVPI), Value of Stochastic Solution (VSS) and the results are discussed.
Author: Alexander Hübl
Publisher: Springer
ISBN: 3658191201
Category : Business & Economics
Languages : en
Pages : 147
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Book Description
Alexander Hübl develops models for production planning and analyzes performance indicators to investigate production system behaviour. He extends existing literature by considering the uncertainty of customer required lead time and processing times as well as by increasing the complexity of multi-machine multi-items production models. Results are on the one hand a decision support system for determining capacity and the further development of the production planning method Conwip. On the other hand, the author develops the JIT intensity and analytically proves the effects of dispatching rules on production lead time.
Author: Hu Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 308
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Book Description
Author: Hercules Vladimirou
Publisher:
ISBN:
Category : Mathematical optimization
Languages : en
Pages : 302
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Book Description
Author: Robert Lincoln Clay
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Michael Kirste
Publisher: BoD – Books on Demand
ISBN: 3744838056
Category : Business & Economics
Languages : en
Pages : 250
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Book Description
In the real world, production systems are affected by external and internal uncertainties. Stochastic demand - an external uncertainty - arises mainly due to forecast errors and unknown behavior of customers in future. Internal uncertainties occur in situations where random yield, random production capacity, or stochastic processing times affect the productivity of a manufacturing system. The resulting stochastic production output is especially present in industries with modern and complex technologies as the semiconductor industry. This thesis provides model formulations and solution methods for capacitated dynamic lot sizing problems with stochastic demand and stochastic production output that can be used by practitioners within Manufacturing Resource Planning Systems (MRP), Capacitated Production Planning Systems (CPPS), and Advanced Planning Systems (APS). In all models, backordered demand is controlled with service levels. Numerical studies compare the solution methods and give managerial implications in presence of stochastic production output. This book addresses practitioners, consultants, and developers as well as students, lecturers, and researchers with focus on lot sizing, production planning, and supply chain management.
Author: Marc Salomon
Publisher: Springer Science & Business Media
ISBN: 3642516491
Category : Business & Economics
Languages : en
Pages : 162
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Book Description
This thesis deals with timing and sizing decisions for production lots, and more precisely, with mathematical models to support optimal tim ing and sizing decisions. These models are called lotsizing models. They are characterized by the fact that production lots are determined based on a trade-offbetween production costs and customer service. Production costs can be categorized as basic production costs, which consist of material costs, labour costs, machine startup costs and over head costs, and inventory related costs, which include costs of capital tied up in inventory, insurances and taxes. Customer service is the capability of the firm to deliver to their clients the products in the quantity they ordered at the agreed upon time and place. The costs of realizing a certain service level are usuaIly very dif ficult to convert into money. They include costs of expediting, loss of customer goodwill, and loss of sales revenues resulting from the short age situation.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: George Liberopoulos
Publisher: Springer Science & Business Media
ISBN: 3540290575
Category : Business & Economics
Languages : en
Pages : 363
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Book Description
Manufacturing systems rarely perform exactly as expected and predicted. Unexpected events, such as order changes, equipment failures and product defects, affect the performance of the system and complicate decision-making. This volume is devoted to the development of analytical methods aiming at responding to variability in a way that limits its corrupting effects on system performance. The book includes fifteen novel chapters that mostly focus on the development and analysis of performance evaluation models of manufacturing systems using decomposition-based methods, Markovian and queuing analysis, simulation, and inventory control approaches. They are organized into four distinct sections to reflect their shared viewpoints: factory design, unreliable production lines, queuing network models, production planning and assembly.
