Manufacturing Systems Foundations of World-Class Practice (1992) / Chapter Skim
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Measuring, Describing, and Predicting System Performance
Measuring, Describing, and Predicting System Performance
Pages 43-60
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From page 43... ...
One is seeking to maximize or minimize multiobjective functions within a specified set of constraints; for example, maximizing profits, or minimizing capital expenditures, defects, or material use per unit of product, within such constraints as fixed total resources, equipment configuration, or product mix. Determining an optimal strategy for a complex manufacturing system, whether it is for control, investment, or processing, is seldom straightforward.
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From page 44... ...
They are commonly recognized as appropriate measures of some aspects of the manufacturing system, usually within a single discipline; for instance, metal forming. And finally there are metrics that are developed by individual companies reflecting their special circumstances.
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From page 45... ...
For the subsystems there are additional metrics, such as yield from a series of processes, that reflect the performance of an integrated subsystem of production equipment and its accompanying labor force; once again these are likely to be aggregations of many variables that "indicate" the performance of the subsystems. However, at the component and unit operation level of
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From page 46... ...
notes the importance of formally developing and using statistical metrics for each step of the production process, and describing the range of upper and lower bounds within which the machines and equipment are expected to perform. Then, when the performance exceeds previously defined limits, corrective action can be taken at the operator level by modifying machine settings rather than at the aggregated process level, which is less well understood.
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From page 47... ...
Cycle time focuses attention on a characteristic that remains consistent throughout successive product generations, and it can incorporate changes in process and product technologies. Therefore, it does not penalize design and production engineers for furthering their understanding of the fundamentals underlying the product and developing mechanisms for managing the idiosyncrasies of its manufacture.
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From page 48... ...
For some time, engineering managers have expressed concern with the difficulty of available methods for justifying capital investments in such areas as flexible manufacturing equipment, increasing product quality through better production process controls, and promoting greater work force involvement through employee education programs. The management accounting community has finally recognized these problems and begun to question the metrics incorporated in the manufacturing operating policies, control parameters, and performance-evaluation criteria that are used to evaluate the return/viability of new projects (Johnson and Kaplan, 1987~.
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From page 49... ...
For example, it may not be easy to quantify the financial returns expected from investment in computerized flexible machining systems or training the work force in quality function deployNonfinancial Indicators and Long-Term Profitability In an important sense, a call for more extensive use of nonfinancial indicators is a call for a return to the operations-based measures that were the origin of management accounting systems. The initial goal of management accounting systems in the nineteenth-century textile firms and railroads was to provide information on the operating efficiency of these organizations.
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From page 50... ...
emphasizes the importance of developing the proper metrics for identifying, measuring, and evaluating these characteristics to ensure the future viability of the company. His metrics for gauging the capabilities of the organization to evaluate technological developments include the level of support for internal research and development; the portion of the R&D budget devoted to long-term projects, exploratory activities, new concepts, and technological innovations; the level of encouragement and support personnel receive to participate in worldwide technical meetings and activities; and the level of investment in technical libraries and information resources.
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From page 51... ...
Edmondson (in this volume) discusses the importance of using metrics that reflect whether the product definition, captured by designers and marketing staff, matches that articulated by customers.
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From page 52... ...
Little notes that we are more likely to find a taxonomy or hierarchy of "manufacturing models" that provide various degrees of generic applicability. The bases available for constructing descriptions of phenomena are limited to mathematical expressions that have no necessary relationship to the real world, physical laws, which require observation of the world and induction about the relationships among observable variables, and empirical descriptions of the world in which there are fewer simple formulas and only approximate representations for phenomena.
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From page 53... ...
These models for projecting quality improvement share a similar form with the models developed in the late 1930s to explain the significant decreases in product unit costs as a consequence of accumulated production volume. Several possible reasons can be proposed for their comparable configurations: Although the specific actions taken to improve quality differ from those taken to reduce unit costs, a striking similarity exists between the two....
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From page 54... ...
result in longer than anticipated execution consequences and, thus, strongly influence feedback loops in the manufacturing system (Bower, in this volume, p.
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From page 55... ...
218~. And when there are several alternative representations of a particular system, Compton too advises that although determining the most appropriate model depends upon many factors, such as the data sampling protocol, it is probably best to use "the simplest formulation possible." But often the simple representation can be discovered only after constructing and examining more complex models to gain additional insight into the problem; when constructing a first model it may be difficult to determine which variables and relationships define or constrain the performance of the problem.
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From page 56... ...
The following table is a sample of the variety of material conversion technologies available for changing the physical properties or appearance of materials or combining them. Processes for Changing Physical Properties Chemical reactions Hot working Heat treatment Refining/extraction Cold working Shot peening Processes for Changing the Shape of Materials Casting Piercing Torch cutting Forging Swaging Explosive forming Extruding Bending Electrohydraulic forming Rolling Shearing Magnetic forming Drawing Spinning Electroforming Squeezing Stretch forming Powder metal forming Crushing Roll forming Plastics molding Processes for Machining Parts to a Fixed Dimension Traditional chip removal processes Turning Sawing Boring Planing Broaching Reaming Shaping Milling Hobbing Dri 11 i ng Grinding Routing Nontraditional machining processes Ultrasonic Chem-milling Optical laser Electrical discharge Abrasive jet cutting Electrochemical Electro-arc Electron beam Plasma arc Processes for Obtaining a Surface Finish Pol ishing Superfinish ing Honing Abrasive belt grinding Metal spraying Lapping Barrel tumbling Inorganic coatings Anodizing Electroplating Parkerizing Sheradizing Processes for Joining Parts or Materials Welding Pressing Sintering Soldering Riveting Plugging Brazing Screw fastening Adhesive joining SOU RCE: Amstead et al.
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From page 57... ...
Theoretical limits provide "both an outer bound for forecasts of potential process performance and a framework for clarifying the principles that govern the process." Based on fundamental principles and reasoning, they are numerical estimates of process performance. Engineering limits, on the other hand, "provide numerical estimates of the levels process variables could attain, using known technologies." The engineering limit for a specific indicator of process performance is intended as a practical estimate of what is achievable without regard to possible adverse effects on other indicators.
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From page 58... ...
Mize goes on to characterize the challenge of working backward from a desired future state to the present in a way that clearly shows a path of action. He suggests that models will be needed to help most people to deal with the interdependent variables and dynamic changes affecting the necessary day-to-day control to achieve their organization's strategic visions.
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From page 59... ...
It ignores continuing developments in technology, and it encourages debate about the desirability of specific renderings of technological possibilities, forms unlikely to appear in any event, far less to be influenced by the debate. However, if we focus on the process of the design of future factories, a topic far more significant than any specific technological possibility, such as the robot, or for that matter any specific picture, such as the totally automated factory, three issues must be considered: 1.
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From page 60... ...
For example, it can be used to explain operating procedures, often through animations of the manufacturing system being modeled; to present graphical summaries of large volumes of data generated by the system, including tabulations, statistical estimators, statistical graphs, and sensitivity plots for analysis of manufacturing systems; to rank and select from among design alternatives; to schedule production; to dispatch resources; and to train machine operators, schedulers, and process .
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