Material Requirements Planning (MRP) is one of the earliest production scheduling approaches that utilizes computers. MRP is still regarded as one of the most widely used systems for production scheduling. Even though MRP has made contributions, there are some fundamental problems (i.e. the assumption of infinite capacity and fixed lead times) which make the MRP system vulnerable to effects of uncertainty. To overcome this fundamental flaw, there was a trend towards the development of detailed finite-capacity scheduling systems (i.e. MRP II, ERP, and APO). All these MRP-based systems still ignore variability and randomness and are inherently push systems. Instead of creating a detailed schedule based on forecast, Factory Physics Inc. developed Dynamic Risk-Based Scheduling (DRS), which creates a set of policy parameters (e.g. WIP level, lot sizes, reorder point, and reorder quantity) that work for a range of situations to calculate the production schedule. This thesis compares the key performance measures of DRS and MRP-based scheduling systems. We begin with a single-machine problem and develop simulation models for varying levels of uncertainty in forecast demand (i.e. base demand scenario, under-estimated scenario and over-estimated scenario) and two levels of variability in the system (i.e. moderate variability and no variability). Then the experiment is extended to multiple-machine problems. We also introduce more constraints into the DRS and MRP models to improve their performance. We also test the performance of MRP models for different planning horizons. We find that the DRS strategy is more robust to forecast error than MRP-based strategies. DRS also usually obtains better performance than MRP-based models in terms of higher fill rate and lower inventory.

"This book opens up the world of simulation to you by providing the basics of general simulation techonology, identifying the skills needed for successful simulation projects, and introducting a state-of-the-art simulation package." --

"An intuitive proven planning and execution method for today's complex and volatile supply chains"--Cover.

Our economy and future way of life depend on how well American manufacturing managers adapt to the dynamic, globally competitive landscape and evolve their firms to keep pace. A major challenge is how to structure the firms environment so that it attains the speed and low cost of high-volume flow lines while retaining the flexibility and customization potential of a low-volume job shop. The books three parts are organized according to three categories of skills required by managers and engineers: basics, intuition, and synthesis. Part I reviews traditional operations management techniques and identifies the necessary components of the science of manufacturing. Part II presents the core concepts of the book, beginning with the structure of the science of manufacturing and a discussion of the systems approach to problem solving. Other topics include behavioral tendencies of manufacturing plants, push and pull production systems, the human element in operations management, and the relationship between quality and operations. Chapter conclusions include main points and observations framed as manufacturing laws. In Part III, the lessons of Part I and the laws of Part II are applied to address specific manufacturing management issues in detail. The authors compare and contrast common problems, including shop floor control, long-range aggregate planning, workforce planning and capacity management. A main focus in Part III is to help readers visualize how general concepts in Part II can be applied to specific problems. Written for both engineering and management students, the authors demonstrate the effectiveness of a rule-based and data driven approach to operations planning and control. They advance an organized framework from which to evaluate management practices and develop useful intuition about manufacturing systems.