strategy for optimizing manufacturing operations. full report
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A manufacturing optimization strategy which combines Asset Utilization and Process Optimization framework is discussed in this topic. Although this strategy is applicable, the approach is discussed here with respect to polymer sheet manufacturing operations. The Au model demonstrates the efficient equipment utilization along with identification of improvement opportunities so that greater benefit can be obtained. The process optimization framework consists of three activities and a designed experiment which establishes a process-product relationship. The overall strategy of predictive model development is to synthesize a model based on existing data both qualitatively and quantitatively, to identify the main effect variables affecting the principal efficiency constraints identified by AU and process optimization framework and calculations employed to refine this model using the designed experiments to facilitate the development of a proactive optimization strategy for eliminating the constraints. A brief of the tagucchi method is also dealt along with this topic.
A basic and first level definition of manufacturing excellence is making product to customer specification in the most cost effective manner with efficient use of resources (equipment and people) and delivering to the customer on time. Numerous articles and books have been published on manufacturing excellence, which typically encompasses just-in-time manufacturing, total quality management, total productive maintenance and employee involvement.
Five basic objectives of manufacturing excellence are:
¢ match throughput demand----- make only what is needed
¢ reduce inventory
¢ maintain high quality throughout the operation
¢ reduce lead times
¢ reduce operating expenses
Achieving these objectives will maximize efficient operation in a cost effective manner while fulfilling customerâ„¢s demands for high quality, short lead times, and flexibility.
Operational judgment is key in achieving these objectives various trade-offs exist in reducing inventory versus reducing lead times versus reducing operating expenses. The goals and approaches selected to accomplish these objectives must be grounded through an integrated production and inventory strategy suited for the businessâ„¢s product customization and delivery performance expectations on a make-to-stock, make-to-order, or assemble-to-order basis.
Driving continual and rapid improvements in these objectives results in continuing improvements in quality, deliver performance expectations, all of which will contribute to the profitability objectives of the enterprise. And additionally
Improvement may vary considerably with the type of industry and from operation to operation within an industry.
In various types of manufacturing operations, opportunities for process improvement are often missed or given incomplete attention because of a lack of discipline in collecting data, analyzing data, and executing a quantitative systematic plan for improvement. The best strategy for capturing improvement opportunities offered by the manufacturing excellence are
¢ Identifying and quantifying the opportunities for achieving efficient operations through use of asset utilization (AU) process.
¢ Focusing on these opportunities
The process for polymer sheet manufacturing is based largely on technology developed many decades ago. The polymer sheet forming process is a continuous casting operation. A schematic example of a typical continuous casting process is shown in the figure. A viscous polymer stream is cast onto a wheel and conveyed through an oven system to create a sheet of specific thickness and characteristics. This sheet is wound onto large rolls, which are then sent to other operations within the company, and the critical features of the sheet include thickness uniformity, absence of defects, and sheet modulus (rheology).

Teams of operators in the polymer operation are responsible for operating a group of machines and performing basic maintenance. Individual process engineers are involved with day-to-day process improvement activities for specific groups of machines. In addition to the machine teams, process improvement teams also drive improvement activities by machine functions. These cross-functional teams are composed of engineers, working within the polymer operations, who cover machine functions such as casting, coating, and conveyance.
Normally we observe that the same product, produced on different machines, exhibited different performance characteristics, and hence a strong held belief was that manufacturing process is as art and not a science.
This case study focuses on a set of machines in the polymer manufacturing operations, and illustrates the application of AU to identify and quantify improvement opportunities through root cause analysis and the application of a process optimization framework to understand and quantify key process-product relationships as a mechanism for capturing the quality improvement opportunities identified by AU.
A process for identifying and quantifying opportunities for improvement is AU. The AU process looks at how we can efficiently match demand requirements with equipment utilization and efficient operation.
The goal is not to drive each piece of equipment to 100%AU as it would result in excess inventory or work in process. The Au process that employed in the polymer sheet manufacturing (dealt as a case study) focuses on specific aspects, such as scheduled maintenance, unscheduled maintenance, material flow through the operation, feed stock issues, throughput inefficiencies, production rates, product quality issues, and waste. There are various other approaches similar to AU and these include the overall equipment effectiveness approach described as a part of TPM.
Improvement opportunities are identified by measuring an overall AU number and four key manufacturing productivity parameters: Availability, Run time efficiency, Run speed efficiency, and yield.
Availability determines the percent of time that the equipment is available to run product. Downtime, which is time spent on scheduled and unscheduled maintenance, no operation, and idle time caused by lack of customer orders, are tracked by this metric. The no operation category is time that the equipment is down because of situations beyond its control such as equipment being down in other parts of the operation, material flow problems or incoming material, and supplies that are not available or are of poor quality.
Run speed determines the percentage of time that the equipment ran at maximum speed. Time spent running at actual operating speed is compared with the maximum equipment speed. Run speed efficiency is calculated by determining how the actual amount of material produced compares with what amount of material should have been produced at maximum speed or standard rate.
Yield is the percent of time that quality product is produced on the equipment. To calculate yield, the amount of time spent running waste or running substandard product must be assessed.
1. The AU process should be employed to drive toward predictable equipment and operations. Unscheduled maintenance and quality loss events marked by AU denote that equipment and processes are not predictable or reliable. Events or conditions leading to unscheduled maintenance and quality losses should be eliminated.
2. Improvement activities should focus on increasing the AU of any capacity-constrained equipment, or in the case of unconstrained equipment, the slowest producing piece of equipment versus across all equipment with a given function.
3. The goal of the AU process is to increase efficient equipment utilization as a way to reduce costs. AU should not be driven to 100%, as it would increase the inventory costs. It is important that each operation make the product mix required in the most efficient manner and in the minimum amount of time needed to meet the demand or make only what is needed. To achieve all these objectives operations must be predictable and reliable and material flow must be synchronized across the operation.
A process optimization framework was developed through this project and implimentation for reducing process variability and increasing product quality because of a %yield improvement opportunity identified through AU.
The process optimization framework is comprised of two parts. The first part strives to link the knowledge and experience of personnel within the operation with fundamental theory and statistical techniques, by using multivariate canonical discriminant analysis to quantify the relationship between key process conditions and product attributes, based on existing process and product attribute data. The second part uses the learnings from the first part for developing a designed experiment, which quantifies the magnitude of process effects on product attributes by changing process conditions in a controlled manner. The learnings from both parts are then employed to develop a real time predictive model for the casting process signals based on the polymer sheet thickness profile attributes.
A schematic diagram of the casting zone was shown earlier in the figure. The viscous polymer stream flows into the casting hopper reservoir at a specific temperature and viscosity. The polymer flows from the casting hopper reservoir through a slot of fixed dimensionality, forming a catenary between the hopper slot and the wheel surface. In the casting process flow diagram shown, two functions of the casting are highlighted a critical to the casting process. These functions distribute flow of polymer in the hopper and shape the catenary between the hopper and the wheel surface, are the first steps in creating the polymer sheet. The process conditions associated with these two functions directly and dramatically affect the final polymer sheet profile and edge shape quality.

A framework was established for understanding and quantifying the process-
product relationship. This framework examines the cause and effect relationships between casting process conditions and the resulting sheet product attributes by using these parallel activities to select key casting process parameters and determining their effect on polymer sheet metrics. These parallel activities serve to better characterize the casting functions from three points of reference: knowledge and experience of operations personnel, process data analysis with multivariate statistical tools, and order of magnitude calculations.
Valuable information about any process resides with the engineers, operators, and maintenance personnel working in the operation. It is critical that the knowledge, opinions, and experience of these people be captured in a systematic format for driving an focusing the casting process improvement activities .tools such as fault tree diagrams are appropriate for this purpose.
The second critical activity is the evaluation of casting process data with valid and appropriate statistical techniques. Multivariate statistical tools such as principal components analysis, canonical discriminant analysis¦ etc can be employed successfully to evaluate large populations of attribute data to identify the main process parameters as well as codependent sources of variability.
The third activity serves to link the first and second activities to the fundamental theory of the casting process. Order of magnitude of calculations can be used to determine the magnitude of change anticipated on the cast sheet attributes with changing process conditions.
The learnings and output of these three parallel activities were incorporated coherently into a designed experiment as the next step in the process optimization framework. The final step in the process-optimizing framework the development of a predictive model for the real-time detection of process conditions leading to out of spec product.
The AU calculations were performed for a set of eight polymer machines within the sheet manufacturing operations using nine consecutive months of process data. The dataâ„¢s are shown in the table. Most of the down time was caused by unscheduled maintenances and scheduled maintenances activities. There was little idle time across the set of polymer machines evaluated. Run time efficiency values approached 100%, ranging from polymer 95 to 99%. High values for this parameter were expected, because this is a continuous operation with a large number of dedicated machines and minimal product changes.
Availability(%) Run time efficiency(%) Run speed efficiency(%) Yield(%) Asset utilization(%)
C 78 96 75 68 38
D 95 99 88 80 66
F 64 95 74 69 31
G 95 98 84 85 66
H 81 96 91 72 51
I 93 98 85 79 61
M 86 97 85 81 57
O 97 99 79 85 64
The run speed efficiency values ranged from 74 to 91%. A major root cause of running at lower speeds was the occurrence of quality problems at the higher operating speeds. Yield values ranged from 68 to 85%. Time spent running any product that does not meet customer quality satisfaction affects this metrics. The resulting AU numbers ranged from 31 to 66%. This shows a difference in utilization of approximately 35%across the machine evaluated, examining the root causes of quality losses further pinpointed specific yield improvement opportunities by quantifying the types of waste and reject generated across the machines.
The engineers, operators, and maintenance personnel working on the polymer machines were extremely valuable resources for information about the casting process. Two undesirable product conditions were downselected as most frequently occurring in the sheet. These are widthwise thickness variability and edge condition
variability. Fault tree diagrams were developed to organize this process information , obtained from brainstorming sessions conducted over a 4-month period. These diagrams help to understand the relationship between the casting process conditions and undesirable product quality. These diagrams and the process by which they are generated are critically important for capturing the knowledge, opinions, and learnings of experienced personnel, which is often lost.

The second of the three parallel activities is to examine historical data from the casting process areas. The goal of this work is to determine if a predictive model, using inputs from existing process signals, could be developed from historically recorded qualitative product attribute metrics. This process is important in determining if adequate process data (or attributes) are being monitored or new sensor inputs are required.
The third activity, conducted in parallel to the other two discussed above, examines the theoretical calculations of the casting process to determine the magnitude of change anticipated on the casting conditions. Order-of-magnitude calculations were used to examine the effects of changing polymer temperature and viscosity. One of the principal goals of the order-of-magnitude calculations was to preclude meaningless experimental design scenarios and offer yet another opportunity to discover potential main effect variables that could affect observed process performance.
When examining the commonality of these three parallel activities, it is crucial to note that the casting process signals cited in the knowledge and experience activity corroborated the key casting signals determined by the multivariate statistical analysis of historical data and the order-of-magnitude theoretical calculations. This was a significant step toward demonstrating that the process is a science and not an art.
Based on the data obtained through the three parallel activities, a screening experiment was employed for the next step in the process optimization framework. The screening experiment was designed to examine quantitatively the casting process sheet thickness profile relationships as a mechanism to verify the casting functions. Distribute flow, and shape catenary. It was hypothesized that casting conditions would affect sheet thickness profile directly or through interactions with one another. Because of the constraints of the time and lost sheet production over the testing period, the screening experiment was limited to the evaluation of individual casting parameters as main-effect terms. Production losses caused by experimentation can be considerable when, as in certain case, changes to certain main effect production line conditions, like polymer temperature, require much time to attain thermal equilibrium.

Experimental results are discussed for the four experiments in which the polymer temperature and the casting hopper temperature were varied. Examples of the thickness profile data for these four experiments, labeled as 1, 2, 11, 12 as shown in the figure. Each trace is vertically offset to separate the profiles for ease of viewing. Temperature conditions were observed to affect the resultant thickness profiles in a dramatic manner. The thickness traces for experiments 1 and 12 are for casting conditions in which the polymer temperature is greater than the hopper temperature. The thickness traces for experiments 2 and 11 are for casting conditions in which the polymer temperature is less than the hopper temperature. These profiles have the largest edge-to-centre difference.
It is being increasingly recognized that the high quality of a product or service and the associated customer satisfaction are the key for enterprise survival. Also recognized is the fact that pre-production experiments, assuming properly designed and analyzed, can contribute significantly towards quality improvements of a product. A traditional (but still very popular) method of improving the quality of a product is the method of adjusting one factor at a time during pre-production experimentation. In this method, the engineer observes the result of an experiment after changing the setting of only one factor (parameter). This method has the major disadvantages of being very costly and unreliable. The Japanese were the first to realise the potential of another method using statistical design of experiments (SDE) - originally developed by R. Fisher. SDE, in contrast to the one factor method, advocates the changing of many factors simultaneously in a systematic way (ensuring an independent study of the product factors). In either method, once factors have been adequately characterised, steps are taken to control the production process so that causes of poor quality in a product are minimised.
In the manufacturing industry, one area of current development is concerned with the application of modern off-line quality control techniques (pre-production experimentation and analysis) to product and process engineering. Most of the ideas for these quality control techniques are derived from W. E. Deming . These ideas were built upon by Professor Genichi Taguchi. While Deming's main achievements was to convince companies to shift quality improvements to statistical control of the production process , Taguchi makes a further step back from production to design, to make a design robust against variability in both production and user environments.
Five major points of the Taguchi quality philosophy are :
1.In a competitive market environment, continual quality improvements and cost reductions are necessary for business survival.
2.An important measurement of the quality of a manufactured product is the total loss generated by that product to the society.
3.Change the pre-production experimental procedure from varying one factor at a time to varying many factors simultaneously (SDE) , so that quality can be built into the product and the process.
4.The customer's loss due to poor quality is approximately proportional to the square of the deviation of the performance characteristic from its target or nominal value. Taguchi changes the objectives of the experiments and the definition of quality from "achieving conformance to specifications" to "achieving the target and minimising the variability.
5.A product (or service) performance variation can be reduced by examining the non-linear effects of factors (parameters) on the performance characteristics. Any deviation from a target leads to poor quality.
Taguchi's main objectives are to improve process and product design through the identification of controllable factors and their settings, which minimise the variation of a product around a target response. By setting factors to their optimal levels, a product can be manufactured more robust to changes in operation and environmental conditions. Taguchi removes the bad effect of the cause rather than the cause of a bad effect, thus obtaining a higher quality product.
A manufacturing optimization strategy with a unique combination of tools has been presented and is comprised of an AU model and a Process optimization framework using multivariate statistical analysis. The AU model demonstrates that efficient equipment utilization can be assessed and serves as the principle identification metric by which improvement activities can be focused on areas where the greatest benefit to the operation can be accomplished. The Process optimization framework, made up of three parallel activities and a designed experiment, established the process-product relationship. This framework also served to quantify the effect of process conditions on product attributes and selected key process parameters for the verification strategy. One of the most significant results from the parallel activities in this work was the development of a prediction model, from pre-existing data that capably established the relationship between process conditions and qualitative product attribute data. Fourier analysis was employed for the quantitative evaluation of thickness profile and dramatically improved the diagnostic utility of thickness profile for data monitoring. Most importantly, because of this manufacturing optimization strategy, the polymer sheet manufacturing operation can be said to be a process based on quantifiable science instead of a process that is based as an art.
The AU process and the four productivity parameters act as drivers for identifying and quantifying opportunities for increasing capacity and for reducing operational cost with existing equipment by improving the overall efficiencies of equipment utilization. Examination of the % yield values for the eight polymer machines studied in this work shows that % yield for the eight machine listed in Table, range from 68 to 85%. Six machines have values less that 0.85. If the quality losers could be reduced so that the % yield values across all eight machines could be improved to 85%or greater, the benefit to the operation would be equivalent to a net capacity gain of an additional machine. Similarly, % yield improvements to 85% or greater on all low-efficiency machines across the operation would result in a net capacity gain of one additional machine, which is an important zero(or low) capital opportunity for activities that increase % yield to 85%,which is a realizable goal as benchmarked on in-plant machines. Additional net capacity gains can be achieved with improvement activities that focus on increasing the other productivity parameters and the Overall AU number, as discussed below. A schematic of how the AU process helps to identify and drive improvement activities is show in Figure.
Polymer sheet capacity gain provides two opportunities for the polymer operations. First, if additional capacity is needed, a capacity increase can be realized without additional capital expenditures. Second, if there is no need for additional capacity, the overall number of machines in operation can be reduced, providing savings in environmental and operating costs.
Current Yield (%)
Yield Improvement
Capacity Gain
C 68 85 17
D 80 85 5
F 69 85 16
G 85
H 72 85 13
I 79 85 6
M 81 85 4
O 85
Although the project and implimentation deadlines constraints the nature and magnitude of improvements realized in the polymer sheet manufacturing, a large scope of additional improvement opportunities remained. The AU process is used to assess quantitatively these opportunities and provide a framework for root cause analysis to define process optimization activities.
As listed in the tables, the run time efficiencies are high, averaging around 97%, as might be expected for continuous, specific product dedicated machines where set up times and product changes have been minimized. Availability numbers are next highest, averaging 87%. The principal production controlled factors contributing to lost availability are unscheduled maintenance and scheduled maintenance. Key activities for minimizing unscheduled maintenance are the implementation of preventive maintenance and equipment reliability programs. Increasing run speed efficiency would increase throughput but would adversely affect product quality, contributing to even lower yield numbers. Owing to the apparent dependency often observed between these two metrics, optimizing %yield allows for the development of a better understanding of the key process parameters contributing to yield losses. The next step in improving %yield would be to perform a designed experiment, focusing on the major process factors or casting parameters identified in the screening experiment. The knowledge gained from this phase of the optimization process then could be employed to reexamine increasing run speeds under deliberately controlled process conditions wherein yield losses are minimized.
The manufacturing optimization strategy established through this work is comprised of the AU process and the process optimization framework. The AU process a be adapted readily across different operations, which are set up as continuous, batch, or job shop operations. Batch or job operations typically would have lower run time efficiency numbers than a continuous operation because of the setup time and product change times required for each batch or piece to be produced. The AU process has been applied successfully to continuous polymer sheet manufacturing, batch and semi continuous chemical operations, batch aluminum rolling.
The process optimization framework can be applied across different operations, wherever there is a need to reduce process variability and product quality. The strengths and unique features of this framework are the qualitative linkage of knowledge and experience of operations personnel with theoretical foundations and multivariate statistical tools to quantify the relationships of more than one key process signal to product quality attributes.

The AU model demonstrates that efficient equipment utilization can be assessed and serves as the principle identification metric by which improvement activities can be focused on areas where the greatest benefit to the operation can be accomplished. The Process optimization framework, made up of three parallel activities and a designed experiment, established the process-product relationship. This framework also served to quantify the effect of process conditions on product attributes and selected key process parameters for the verification strategy. One of the most significant results from the parallel activities in this work was the development of a prediction model, from pre-existing data that capably established the relationship between process conditions and qualitative product attribute data
Most importantly, because of this manufacturing optimization strategy, the polymer sheet manufacturing operation can be said to be a process based on quantifiable science instead of a process that is based as an art.
From the capacity gained through the Au process for the polymer sheet manufacturing operations we could infer that, Polymer sheet capacity gain provides two opportunities for the polymer operations. First, if additional capacity is needed, a capacity increase can be realized without additional capital expenditures. Second, if there is no need for additional capacity, the overall number of machines in operation can be reduced, providing savings in environmental and operating costs. This manufacturing operation could be applied to other manufacturing operations, which are set up as continuous, batch, and job match operations.
Taguchi Methods is a system of cost-driven quality engineering that emphasizes the effective application of engineering strategies rather than advanced statistical techniques. It includes both upstream and shop-floor quality engineering.
Upstream methods efficiently use small-scale experiments to reduce variability and find cost-effective, robust designs for large-scale production and the marketplace. Shop-floor techniques provide cost-based, real-time methods for monitoring and maintaining quality in production.
Taguchi Methods allow a company to rapidly and accurately acquire technical information to design and produce low-cost, highly reliable products and process. Taguchi Methods require a new way of thinking about product development. These methods differ from others in that the methods for dealing with quality problems center on the design stage of product development, and express quality and cost improvement in monetary terms.

1. Journal paper on A Systematic Strategy for Jonell kerkhoff,
Optimizing Manufacturing Operations Thomas.W.Eager
Production and operations management, vol.7, spring 1998 James Utterback
2. An overview of Tagucchi method Ravi Mathur
3.Production Technology R.K.Jain
4. Introduction to manufacturing process John.A.Schey

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