Download Free Demand Fulfillment In Customer Hierarchies With Stochastic Demand Book in PDF and EPUB Free Download. You can read online Demand Fulfillment In Customer Hierarchies With Stochastic Demand and write the review.

​This book extends the existing demand fulfillment research by considering multi-stage customer hierarchies. Basis is a two-step allocation and consumption planning procedure. In the existing literature, it is assumed that the customer segments are ‘flat’. This means they can be sorted easily during the allocation planning step by a single central planner in decreasing order of profitability. In the subsequent consumption planning phase, if order requests differ in terms of profit margins, companies can render prioritized service in real time to their most profitable customers by consuming the reserved quotas.
Up to now, demand fulfillment in make-to-stock manufacturing is usually handled by advanced planning systems. Orders are fulfilled on the basis of simple rules or deterministic planning approaches not taking into account demand fluctuations. The consideration of different customer classes as it is often done today requires more sophisticated approaches explicitly considering stochastic influences. This book reviews current literature, presents a framework that addresses revenue management and demand fulfillment at once and introduces new stochastic approaches for demand fulfillment in make-to-stock manufacturing based on the ideas of the revenue management literature.
Supply Chain Management, Enterprise Resources Planning (ERP), and Advanced Planning Systems (APS) are important concepts in order to organize and optimize the flow of materials, information and financial funds. This book, already in its fifth edition, gives a broad and up-to-date overview of the concepts underlying APS. Special emphasis is given to modeling supply chains and implementing APS successfully in industry. Understanding is enhanced by several case studies covering APS from various software vendors. The fifth edition contains updated material, rewritten chapters and an additional case study.
In 2012, a Forestry Special Interest Group (FSIG) was founded within the Canadian Operational Research Society (CORS). Besides a general commitment to promoting the application of operational research (OR) to forest management and forest products industry problems, the FSIG has two concrete mandates: organizing the forestry cluster at the annual CORS conference, and managing the editorial process for forestry-themed special issues of INFOR. The FSIG has been very successful in the first of these two mandates, with record attendance at the forestry cluster over the last four years, hosting of several special sessions, financial and in-kind support from the NSERC Strategic Network on Value Chain Optimization (VCO), and the inauguration of the David Martell Student Paper Prize in Forestry (DMSPPF). This is the first compilation of forestry-themed papers since the inauguration of the CORS FSIG. The six pieces selected for the special issue, now published as a book, feature applications of OR to a wide range of forest management and forest products industry contexts, including supply-chain planning, lumber production planning, demand-driven harvest and transportation planning, and fire-aware wood supply planning. This book was originally published as a special issue of the INFOR: Information Systems and Operational Research journal.
This book deals with stochastic combinatorial optimization problems in supply chain disruption management, with a particular focus on management of disrupted flows in customer-driven supply chains. The problems are modeled using a scenario based stochastic mixed integer programming to address risk-neutral, risk-averse and mean-risk decision-making in the presence of supply chain disruption risks. The book focuses on innovative, computationally efficient portfolio approaches to supply chain disruption management, e.g., selection of primary and recovery supply portfolios, demand portfolios, capacity portfolios, etc. Numerous computational examples throughout the book, modeled in part on real-world supply chain disruption management problems, illustrate the material presented and provide managerial insights. In the computational examples, the proposed mathematical programming models are solved using an advanced algebraic modeling language such as AMPL and CPLEX, GUROBI and XPRESS solvers. The knowledge and tools provided in the book allow the reader to model and solve supply chain disruption management problems using commercially available software for mixed integer programming. Using the end-of chapter problems and exercises, the monograph can also be used as a textbook for an advanced course in supply chain risk management. After an introductory chapter, the book is then divided into five main parts. Part I addresses selection of a supply portfolio; Part II considers integrated selection of supply portfolio and scheduling; Part III looks at integrated, equitably efficient selection of supply portfolio and scheduling; Part IV examines integrated selection of primary and recovery supply (and demand) portfolios and scheduling; and Part V addresses disruption management of information flows in supply chains.
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.
Companies frequently operate in an uncertain environment and many real life production planning problems imply volatility and stochastics of the customer demands. Thereby, the determination of the lot-sizes and the production periods significantly affects the profitability of a manufacturing company and the service offered to the customers. This thesis provides practice-oriented formulations and variants of dynamic lot-sizing problems in presence of restricted production resources and demand uncertainty. The demand fulfillment is regulated by service level constraints. Additionally, integrated production and remanufacturing planning under demand and return uncertainty in closed-loop supply chains is addressed. This book offers introductions to these problems and presents approximation models that can be applied under uncertainty. Comprehensive numerical studies provide managerial implications. The book is written for practitioners interested in supply chain management and production as well as for lecturers and students in business studies with a focus on supply chain management and operations management.
In recent years, supply chain planning has emerged as one of the most challenging problems in the industry. As a consequence, the planning focus is shifting from the management of plant-speci?c operations to a holistic view of the various logistics and productionstages, that is an approach in which suppliers, productionplants and customers are considered as constituents of an integrated network. A major dr- ing force behind this development lies in the globalization of the world economy, which has facilitated the co-operation between different partners working together in world-wide logistics networks. Hence, considerable cost savings can be gained from optimizing the structure and the operations of complex supply networks li- ing plants, suppliers, distribution centres and customers. Consequently, to improve the performance of the entire logistic chain, more sophisticated planning systems and more effective decision support are needed. Clearly, successful applications of supply chain management have driven the development of advanced planning systems (APS), which are concerned with s- porting decision-making activities at the strategic, tactical and operational decision level. These software packages basically rely on the application of quantitative methods, which are used to model the underlying complex decision problems c- sidering the limited availability of resources and the need to react on time to customer orders. The core module at the mid-term level of APS comprises op- ational supply chain planning. In many industries, productionstages are assigned to differentplantsand distribution centreshave been established at geographicallyd- persed locations.
Advanced Planning Systems (APS) are a key enabler of the supply chain management. However, APS are highly complex and difficult to comprehend. This book provides students with valuable insights into the capabilities of state-of-the-art APS and bridges the gap between theory (model building and solution algorithms), software implementation, and adaptation to a specific business case. Our business case – named Frutado – provides a unifying framework for illustrating the different planning tasks that arise in a company – from demand planning to the distribution of goods – that are addressed by APS. In addition, the book guides through interactive learning units which have been created and recorded for each module of SAP ́s APS. Learning units can be downloaded free of charge ready to be displayed in a web browser. Together, the textbook and the learning units provide the required skills to better understand the concepts, models, and algorithms underlying today ́s APS.