QUANTIFYING THE ECONOMIC VALUE ADDED OF ON-LINE QUALITY CONTROL SYSTEMS Roland Thomas, John Rowland, and David Kazmer V.P. Technology Chief Operating Officer Assistant Professor Moldflow Inc. Phoneware Limited University of Massachusetts Amherst 91 Hartwell Ave. 303 Burwood Hwy Eng. Lab. Bldg. Lexington, MA 02173 Burwood East, VIC 3131 Amherst, Massachusetts 01003 AUSTRALIA The subject of this paper is the efficient achievement and assurance of molded part quality, a primary competitive measure for modern commercial molders. The term Quality is generally defined as the sum total of all properties relating to the suitability of a component to meet a given specification. In layman's terms it is often said that a quality component is “fit for purpose” . To deliver the required product quality, modern injection molding machines are utilizing increasingly sophisticated systems for control of machine parameters and plastic process variables. In theory, these systems promise to deliver significantly increased production yields, improved product quality, and reduced down-time. This paper will discuss a systematic approach for development and evaluation of on-line quality monitoring systems using an increasing common performance metric, economic value added. Economic Added Value In today's competitive, market-driven world, a company's survival rests in its ability to create added value for its customers. At the same time, manufacturers are under continuous pressure by stakeholders to increase operating profits while coping with possible market erosion by fierce competition. This has created an expectation for engineers, project managers, and suppliers to directly impact the bottom line. The very existence of all non-value added positions and technologies are in jeopardy. But what metric can capture the added value from so many varied functions? Standard accounting measures such as sales growth, reduced costs, and cash flow are not adequate to the task. Economic value added (EVA) has become a common metric for estimating performance of business operations and technologies. EVA evaluates how much value is added to a business without all the accounting distortions. “It is a tool for evaluating the performance of almost any business operation by focusing on its use of capital.”  Simply put, EVA is a company's net operating profit after taxes and after deducting the cost of capital. The capital (used to generate the operating profits) includes non- depreciated investment tied up in things such as real estate, equipment, machinery, and computers. It also includes working capital: mainly cash, inventories, and receivables . To calculate the cost of capital, an estimated rate of return (demanded by lenders and the shareholders in compensation for their investment risk) is multiplied by the total value of capital. To increase EVA, a company has to do one of three things: 1) divert or liquidate capital from business activities that do not provide adequate returns, 2) invest new capital in projects that earn more than the cost of capital, or 3) increase operating profits without tying up more capital. This paper will use EVA to estimate the value that added investment in on-line quality control systems could bring to molding operations. On-line Quality Control Systems To achieve on-line quality control, the system outlined in this paper is comprised of three major components : an injection molding machine, MF/OPTIM, and SmartMold. The design intent of this system was to function for most combinations of polymer, machine, and part design. The only requirement for the molding machine is that it have a process controller with control of the velocity and pressure phases. It is also an advantage if the machine has a serial interface for on-line downloading of machine set points both in Die Set and Production modes as this eliminates the need for direct interfacing with the control valve. MF/OPTIM calculates theoretical optimum injection parameters for a given mold, machine and material combination. These conditions are transferred electronically to SmartMold and are used for initial machine set-up. Provided with information on the actual molding machine, grade specific material data and geometry of the part, MF/OPTIM calculates: • A ram velocity profile that minimizes the variation in the melt front velocity. The machine response time is taken into account as the inertia and valve response times can be significant. The flow front temperature is kept uniform to minimize part warpage and surface defects. • A holding pressure profile that minimizes volumetric and linear shrinkage variation through the part. This minimizes warpage and enables control of dimensional tolerances for parts where shrinkage is the prime cause of warpage. • The stroke length, based on one shot per cycle, allowing for material compressibility and cushion. • The cooling time. SmartMold is a quality controller that complements the existing machine process controller. SmartMold-DieSet provides the link between the predicted optimum processing conditions and actual molded part quality. SmartMold-DieSet starts with initial processing conditions, downloaded directly onto the machine from MF/OPTIM. The quality is automatically tuned by interaction with the operator, who provides yes/no feedback to queries from SmartMold-DieSet about the part. After running SmartMold- DieSet, a set of processing conditions that produce a good part have been established. SmartMold-Production ensures that consistent quality parts are manufactured, despite normal perturbations such as changes to ambient temperature, and batch to batch changes in material. Using the final processing conditions defined by SmartMold-DieSet, SmartMold-Production produces several more parts to establish the tolerance of the process to environmental, material and machine variables. This enables a “process window” to be defined, within which quality parts are produced. SmartMold- Production then monitors the production process, automatically adjusting processing conditions from shot to shot, where necessary, to ensure each part produced is acceptable. If a major or unusual disturbance to the process is detected, SmartMold- Production alerts the operator to ensure preventative action can be quickly taken. VALUE ADDED OF ON-LINE QUALITY CONTROL SYSTEMS The marginal profit of a molded part is calculated as the quoted price per part minus three primary components: amortized tooling costs, per part material costs, and machine costs. The magnitude and ratios of these components to the total part cost will vary with each molding application and its size, complexity, specifications, etc. For many engineering applications, a general rule of thumb is to multiply the part’s material costs by two. A Java-based cost estimator has been developed to explore these cost drivers and is freely available via the World Wide Web at http://www.ecs. umass.edu/mie/faculty/kazmer/IMCost.html . Several case studies have been performed to estimate the value added of quality control systems. One typical case study involved taking an existing production tool and applying the on-line quality system to optimize part quality and reduce cycle time. The system comprised an Ube PZ III-350 a 350 ton injection molding machine equipped with the SmartMold on-line quality controller, a single cavity optical laser disc tool and Ube ABS VW 10 polymer. The part (shown in Figure 1) was difficult to mold due to critical dimensional and aesthetic specifications. It was also difficult to fill with long thin ribs on the underside of the tray while thicker ribs resulted in persistent sink marks. As the part is fundamentally a flat tray it has a strong tendency to warp and this was used as one of the quality comparitors. Also, as the tool had been in production for over three years there was some wear around the gate which meant that there was a tendency to flash in that region under faster injection velocities and higher pressures. Figure 1: The Optical Laser Disc Tray The procedure was to model the part and perform an MF/OPTIM analysis to establish the initial processing conditions. The results were then fed to SmartMold via a floppy disc. Using operator feedback on part quality, the initial MF/OPTIM results were fine-tuned after which a procedure for cycle time minimization was implemented. . The existing process conditions were then compared with the initial MF/OPTIM results and the results fine-tuned using SmartMold, as shown in Table 1. Table 1: Quantified Savings in Cycle Time (seconds) Existing Initial Fine-tuned Process MF/OPTIM MF/OPTIM using Conditions Results SmartMold Injection 2.7 2.6 2.6 Packing 5.0 10.2 3.0 Cooling 26.0 26.0 14.0* TOTAL 33.7 38.2 19.6 *Note: Cooling time could not be reduced any further as this represented the minimum plastication time The final part quality was superior to the existing production results. This was primarily due to the improved filling profile that minimized the frozen layer thickness thus allowing the polymer melt to fill all the thin ribs and the packing profile could then be more effective in compensating for the volumetric shrinkage. As the tool was no longer being used as a jig to hold the part flat as it cooled, the cooling time could be significantly reduced to the plastication time - the practical minimum. The end result was a part with less visible sink marks and reduced warpage and a significant reduction in cycle time from 33.7 to 19.6 seconds. To summarize these results, the cycle time, t, was reduced by approximately 30% while simultaneously improving the part quality to gain a 5% price premium. (Note: if improved quality levels do not directly command price premiums, then costs may be further removed from the part at the expense of quality to obtain similar profit increases.) The machine rate, did increase 4% due to the added capital investment of the on-line quality control system. However, this added expense was greatly off-set by the process improvements in quality, cycle-time, and increased unit price. The impact of the on-line quality control system on the economic value added in this case study are shown in Table 2. As a result of this implementation, the net increase in EVA was approximately 15% of the total part cost, resulting in increased operating profits of $10,500 for one batch of 50,000 molded parts. This estimate, moreover, does not include additional savings relating to improved yield or machine commissioning times that were not estimated in this study. Table 2: Impact of SmartMold on EVA for Laser Disk Tray Parameter Quantity Increased Part Price Premium $0.05 Reduction in Processing Costs $0.16 Total EVA per Part $0.21 CONCLUSIONS This paper has examined the impact of on-line quality control systems using the concept of economic value added. Case studies have shown that such on-line quality control systems can generate added value through four mechanisms, by: • improving product quality to command a price premium or remove cost from the product, • improving the production yields of acceptable parts, • reducing the cycle time without adversely affecting part quality, and • reducing mold commissioning and set-up times. The magnitude and ratio of economic value added from on-line quality control systems will vary with each application due to different product quality requirements and process operating procedures. Productivity improvements of 10% seem to be representative of large segments of the industry as confirmed with discussions of major manufacturers. However, the goals and expectations of implementing such an on-line quality control system must be considered within each molding operation prior to adoption of the technology. REFERENCES 1. DeVor, R. E, Chang, T., Sutherland, J., W., Statistical Quality Design and Control: Contemporary Concepts and Methods, Macmillan Publishing Company, New York, 1992. 2. Hubbell, W. W., “A Case Study in Economic Value Added and Activity-Based Management,” Journal of Cost Management for Manufacturing, v. 10, n. 2, p. 21, 1996. 3. Tully, S., “The Real Key to Creating Wealth,” Fortune, p. 30, September 20, 1993. 4. Kazmer, D. O., Rowland, J. C., Sherbelis, G. S., “Foundations of Intelligent Process Control for Injection Molding,” Journal of Injection Molding Technology, v. 1, n. 1, p. 44, 1997. 5. Deitz, D., Java: A New Tool for Engineering, Mechanical Engineering, p. 68, v. 118, n. 4, 1996.
Pages to are hidden for
"Copy of 1998_MP_EVA_IPC"Please download to view full document