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Author: Paul J. Kuzdrall Publisher: Praeger ISBN: Category : Business & Economics Languages : en Pages : 288
Book Description
Discount Schedule Analysis (DSA), a technique illustrated and explained in this management book, provides analytical tools for increasing profits by linking prices and discounts to management decision making. According to the authors, the most direct way for many manufacturers to affect the bottom line is to reduce the prices paid for goods and services. It is possible to achieve this profit leverage effect without switching suppliers or even requiring suppliers to reduce prices. The authors show how to analyze published quantity discount schedules to find out how the seller sets prices and how to break schedules down into fixed and variable (cost) components. With this information, corporate buyers can exploit vendors' price and quantity discount schedules to maximum advantage. Conversely, corporate vendors can forestall revenue losses and undesirable side effects of discount schedules by constructing schedules according to examples in the book. Using actual price schedules, the authors analyze the ideas, theory, hazards, and advantages inherent in the schedules. They examine how quantity discounts are set, whether the price-setting practices are consistent, and whether the discounts meet their stated objectives. The book explains the gray market phenomenon and how to profit from it. It shows how to evaluate the true costs of such common sweeteners as free delivery, installation, and delayed payment. The authors also analyze purchasing requirements for Just in Time (JIT) inventory systems. The book offers valuable methodologies for both the buyer and the seller. Purchasing managers as well as managers responsible for cost accounting, marketing, sales, finance, and legal areas will benefit.
Author: Paul J. Kuzdrall Publisher: Praeger ISBN: Category : Business & Economics Languages : en Pages : 288
Book Description
Discount Schedule Analysis (DSA), a technique illustrated and explained in this management book, provides analytical tools for increasing profits by linking prices and discounts to management decision making. According to the authors, the most direct way for many manufacturers to affect the bottom line is to reduce the prices paid for goods and services. It is possible to achieve this profit leverage effect without switching suppliers or even requiring suppliers to reduce prices. The authors show how to analyze published quantity discount schedules to find out how the seller sets prices and how to break schedules down into fixed and variable (cost) components. With this information, corporate buyers can exploit vendors' price and quantity discount schedules to maximum advantage. Conversely, corporate vendors can forestall revenue losses and undesirable side effects of discount schedules by constructing schedules according to examples in the book. Using actual price schedules, the authors analyze the ideas, theory, hazards, and advantages inherent in the schedules. They examine how quantity discounts are set, whether the price-setting practices are consistent, and whether the discounts meet their stated objectives. The book explains the gray market phenomenon and how to profit from it. It shows how to evaluate the true costs of such common sweeteners as free delivery, installation, and delayed payment. The authors also analyze purchasing requirements for Just in Time (JIT) inventory systems. The book offers valuable methodologies for both the buyer and the seller. Purchasing managers as well as managers responsible for cost accounting, marketing, sales, finance, and legal areas will benefit.
Author: Stanford University Publisher: ISBN: Category : Languages : en Pages : 810
Book Description
1913/15 contains reports of chancellor and treasurer; 1919/24, reports of treasurer and comptroller; 1924- reports of treasurer, comptroller, departments, committees and the publications of the faculty.
Author: Barry Render Publisher: Prentice Hall ISBN: Category : Business & Economics Languages : en Pages : 760
Book Description
Fully integrated with the personal computer, this easy-to-use book provides readers with the skills to necessary to apply the techniques of quantitative analysis in all kinds of organizational decision-making situations. It covers every major topic in the quantitative analysis/management science field, showing how each technique works, discussing the assumptions and limitations of the models, and illustrating the real-world usefulness of each technique with many applications and case studies in both profit-making and nonprofit organizations. A FREE CD-ROM readers can use to solve the examples presented in the book is conveniently packaged with the book providing Excel QM, Crystal Ball, TreePlan, QM for Windows and data files for examples. Probability Concepts and Applications, Decision Theory, Decision Trees with Utility Theory, Forecasting, Inventory Control Models, Linear Programming Models, Linear Programming: The Simplex Method. Transportation and Assignment Models, Integer Programming, Goal Programming, Non Linear Programming, and Branch and Bound Models, Analytic Hierarchy Process, Network Models, Project Management, Waiting Lines and Queuing Theory Models, Simulation Modeling, Markov Analysis, Using QM for Windows, Using Excel OM. Appropriate for business managers and analysts.
Author: Vivian Siahaan Publisher: BALIGE PUBLISHING ISBN: Category : Computers Languages : en Pages : 300
Book Description
In this comprehensive data science project focusing on sales analysis, forecasting, clustering, and prediction with Python, we embarked on an enlightening journey of data exploration and analysis. Our primary objective was to gain valuable insights from the dataset and leverage the power of machine learning to make accurate predictions and informed decisions. We began by meticulously exploring the dataset, examining its structure, and identifying any missing or inconsistent data. By visualizing features' distributions and conducting statistical analyses, we gained a better understanding of the data's characteristics and potential challenges. The first key aspect of the project was weekly sales forecasting. We employed various machine learning regression models, including Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, KNN Regression, Catboost Regression, Naïve Bayes Regression, and Multi-Layer Perceptron Regression. These models enabled us to predict weekly sales based on relevant features, allowing us to uncover patterns and relationships between different factors and sales performance. To optimize the performance of our regression models, we employed grid search with cross-validation. This technique systematically explored hyperparameter combinations to find the optimal configuration, maximizing the models' accuracy and predictive capabilities. Moving on to data segmentation, we adopted the widely-used K-means clustering technique, an unsupervised learning method. The goal was to divide data into distinct segments. By determining the optimal number of clusters through grid search with cross-validation, we ensured that the clustering accurately captured the underlying patterns in the data. The next phase of the project focused on predicting the cluster of new customers using machine learning classifiers. We employed powerful classifiers such as Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP) to make accurate predictions. Grid search with cross-validation was again applied to fine-tune the classifiers' hyperparameters, enhancing their performance. Throughout the project, we emphasized the significance of feature scaling techniques, such as Min-Max scaling and Standardization. These preprocessing steps played a crucial role in ensuring that all features were on the same scale, contributing equally during model training, and improving the models' interpretability. Evaluation of our models was conducted using various metrics. For regression tasks, we utilized mean squared error, while classification tasks employed accuracy, precision, recall, and F1-score. The use of cross-validation helped validate the models' robustness, providing comprehensive assessments of their effectiveness. Visualization played a vital role in presenting our findings effectively. Utilizing libraries such as Matplotlib and Seaborn, we created informative visualizations that facilitated the communication of complex insights to stakeholders and decision-makers. Throughout the project, we followed an iterative approach, refining our strategies through data preprocessing, model training, and hyperparameter tuning. The grid search technique proved to be an invaluable tool in identifying the best parameter combinations, resulting in more accurate predictions and meaningful customer segmentation. In conclusion, this data science project demonstrated the power of machine learning techniques in sales analysis, forecasting, and customer segmentation. The insights and recommendations generated from the models can provide valuable guidance for businesses seeking to optimize sales strategies, target marketing efforts, and make data-driven decisions to achieve growth and success. The project showcases the importance of leveraging advanced analytical methods to unlock hidden patterns and unleash the full potential of data for business success.
Author: Frederick W. Mueller Publisher: Springer Science & Business Media ISBN: 1461597501 Category : Science Languages : en Pages : 517
Book Description
Management and administrative processes within the construction industry have been undergoing major changes in the last several decades. These changes have involved significant adjustments in management science and manage ment techniques, brought about by the need for contemporary valid informa tion with which to manage the construction process. In short, management in the construction industry is changing significantly; change will continue at an accelerated pace at least through the next decade. The responses required of construction industry management are now resulting in a movement away from an entrepreneurial management style to professional management tech niques and procedures. THE COMPELLING ECONOMIC ISSUES The issues forcing these changes are economic. The rising costs of construction and of money are forcing the buyers of construction services to be more demanding. Their demands are for more construction economies, more pro duction, and more productivity than at any time in the past. Nowhere has this been more evident than in the Business Roundtable on construction and in the response of the construction industry to it.· To be successfully responsive, management in the construction industry will be required to use the best project management methods available for cost control, schedule control, and for financial and accounting controls. But responsive professional management can survive and will flourish within this more demanding eco nomic environment.
Author: Paul J. Kuzdrall
Author: Paul J. Kuzdrall
Author: 
Author: Stanford University
Author: 
Author: Barry Render
Author: Hiram C. Barksdale
Author: Vivian Siahaan
Author: Frederick W. Mueller
Author: 
Author: