Mathematical application in textiles

Mathematical application in textiles

It is not surprising that the various regions developed their own systems of textile measurement and textile vocabulary. In a world in which the pace of life was relatively slow, regional variations in systems of units were tolerable, but to-day communications are rapid, and commerce and technology need a uniform system of measurement that is universally accepted and understood. Errors of conversion are automatically eliminated, but, of course, during the transitional stage, there will be misunderstandings and arithmetical errors when old units are converted into new, even when prepared conversion tables are used. For textile calculations, it may be found that the usual sets of conversion tables do not include quantities peculiar to the textile industry. For these quantities, a conversion system has to be devised by using first principles and then published as a table or graph or left just as a conversion factor. Most of the calculations made by a textile technologist consist of a series of relatively simple steps, mainly arithmetical and at times using elementary aspects of trigonometry, geometry Algebra. The calculation is generally straightforward; it is the local thinking required that often presents most difficulty. It is usually worth spending a few minutes in considering various approaches to a problem before setting down the first line of calculation. An engineer or research scientist may employ more complex mathematics, a thorough training in pure and applied mathematics being required. The objective of any experiment or measurement should be to produce an answer that is as accurate as the instruments available and the skill of operator will allow. For many calculations, the person doing the necessary numerical work has a rough idea of the order of magnitude he should obtain. Scientific sampling, design of experiments, the analysis, presentation and interpretation of data through statistical techniques-all these created the concept of specification, production and inspection as a dynamic cycle. Inspection now is the source of data which, analyzed and interpreted through statistical methods, is continuously feed back to production people for corrective and preventive action. Inspection, that is, the act of screening out defectives before they reached the customer.

Need for Mathematics in Textiles

In any manufactured product no two articles are perfectly alike, For example, it is impossible to find two knots of yarn having exactly the same count, strength, evenness, length etc. this is because the raw material i.e. cotton itself varies from fibre to fibre within a bale, bale to bale, and season to season. The quality of the product in each process, therefore, varies according to the variation in the raw material used and degree of technical and refinement attained during processing. Further, machines and tools wear and tear due to long use it is neither possible nor economical to replace the machine. Superimposed on this is the variation arising from lack of fibre control during drafting and that from chance causes. Further, it is impossible to eliminate the effect of human factor entirely. Changes in atmospheric conditions also contribute towards an increase in overall variation in the quality of the product. These variations in various regions are often occurring problems in textile. Using various mathematical calculations can solve these variations.

Various Mathematical Calculation Methods Involved in Textile

Statistical Tests	Graphical Analysis
Distributions	Vectors
Trigonometric Functions	Matrix
Fourier series	Z and Laplace Transformation
Conversions and Formulas	General Mathematical Applications

Statistical Techniques

Statistics is defined as scientific method, which deals with collection, compilation, analysis and presentation of data. It is also defined as the science of average and the study of variability. They enable us to take corrective and preventive actions in case of variability and certainty. Some of the statistical techniques are:

• Chi-square Test: This method is used when there is no prior knowledge of the distribution of test values. They are also used to identify the goodness of he fit of the given samples. End breakages in spinning, roving, carding, nep generation in blow room and carding are assessed using this test. The results are compared with the confidence limits and the performances are determined.

 

• 'F' Test: This distribution is used to test the equality of variance of the populations from which two small samples have been drawn. Auto-leveller performance, c.v. of sliver hank, twist variability, etc. are determined using this test.
• 'T' Test: They are used to assess the performance of the same specimen produced from different sectors, machines and to compare the result to improve the status. E.g. comparing hariness between two samples produced from the different machines.
• Critical Difference: It is a measure of the difference between two values that arises solely due to natural or unavoidable causes. This determines the number of samples to be taken for each test and the tolerance limit for the results. E.g. for 2.5% span length testing 4 combs per sample are to be tested and the tolerance value is 4% of mean.
• Six Sigma: Used to designate the distribution spread about the m ean of any processes or procedure or product that indicates how well the process is performing. The performance sigma measures the 3.4 defectives per million which is virtually defect free. As sigma increases costs go down, cycle time goes down and customer satisfaction goes up.

Q * A = E ; Q=quality A=acceptance E=effectiveness

• Linear Programming: In this method the individual properties are combined to give the resultant property of the mixing in spinning.
• Snap Study: A round inside the department to list the number of machines stopped due to various causes is known as snap study. It helps in calculating the production accurately by avoiding the machines stoppages. 

Graphical Analysis

Charts are used represent the data in graphical form so that we can get relative variations between two or more variables. Some of the various graphical representations are:

• Control Chart: It shows when the job is running satisfactorily, shows a needing corrective action when something went wrong and it provides a measure for improving the process. It as a powerful tool for monitoring variations in process. It is applicable mainly in spinning to have control over the various process and variables such as hank, degree of opening and cleaning. Variations in production and quality in various sectors like spinning, weaving, knitting, etc. can be analyzed.
• Histogram: Histogram is a simple graph compiling measured data such as GSM, Dia., Garment measurement, etc. It serves to estimate the extend of variation in the group and to determine either the non-conformance is due to setting or variability.
• Nomogram: The variables of the calculation are indicated on scales of separate graphs and the answer is arrived at with the aid of a straight edge. Nomogram are used in ring frame production, spindle speed calculation, twist/cm, yarn delivery, etc.

Nomogram for Ring Frame Production

Coefficient of Variation

When we refer to "average" of something, we are talking about its arithmetic mean. For ungrouped population, population arithmetic mean is given by

Standard Deviation: This measure of dispersion is probably the most widely used method of indicating scatter, together with the associated coefficient of variation. 

Standard deviation =

In the view of above inherent variations, the frequency, i.e. the number of times each characteristic will occur in a sample, would also vary when a large number of readings are taken. The variation in count, yarn strength, yarn twist, roving stretch, effective length of fibres, between and with-in bobbin count variations, fibre length can be measured by the "Co-efficient of variation" (C.V) which is merely the standard deviation expressed as a percentage of the average.

Sampling by Distribution Function

Distributions are used to calculate the number of trials to be taken for testing and to get the accurate results. The following distributions are used in textile for testing, sampling etc.

• Binomial Distribution: We consider n trials made in an experiment, p as probability of getting a success and q as probability of getting a failure. If the number of samples is less than 30 binomial distribution is used. It is used to determine the displacement of driving pin with the crank angle in the weaving looms by this we can also calculate the speed of the machine.
• Binomial theorem of probability: let there be n independent trials of an experiment with p as probability of success and q=1-p as the probability of failure.

Then, P(r successes) =

• Poisson distribution: In a Poisson distribution with mean m, the probability is Poisson distribution is when the number of samples is more than 30.It is used for nep counting, i.e. to determine the number of neps present in the blowroom lap, carding slivers, etc.
• Normal Distributions: A sample is called large or small according as n≥30 or n<30 .="">

Using confidence limits, critical regions in normal distributions we can determine the productivity as well as quality are with in control or not.

• Probability Distribution: Probability distribution can be thought to be a theoretical frequency distribution that describes how outcomes are expected to vary. Since these distributions deals with expectations, they are useful models in making inferences and decisions under conditions of uncertainly. There are two distributions namely, addition theorem and multiplication theorem of probability.

These probabilities are used for sampling in textile.

• Baye's Theorem: If H1, H2..Hn form a set of mutually exclusive and exhaustive events of a random experiment and E is an event.

Vectors

In general vectors represent those quantities, which have both magnitude and direction. Resultant vectors are available to calculate the net effect of the two vectors.

• They are used to analyze the path taken by the shuttle where it has traverse motion, lateral motion, and vertical movement.
• Used to calculate the net winding rate and the angle at what the traverse path will the yarn be wound onto the package
• In many testing instruments vectors are helpful in analyzing the load to be applied, force acting on the specimen, to calculate breaking load, controlling the movement of the pointer, determining the forces involved in the inclined plane testing devices, etc.
• To calculate the force acting on the backrest of a loom due to warp tension.
• As simple harmonic equations it is used in the calculation of velocity, acceleration, speed and displacement of shuttle at various crank positions.

Trigonometric Functions

• Pythagoras Theorem: Actual winding rate in cone winding machine can be calculated with the given suitable data.
• Angle of Inclination: The angle at which a particular object is inclined with reference to the given object plays a vital important role in assessing the performance. Further they are used in determining the coil angle of cop, crank angle positions in weaving, winding angle, traverse ratio, angle of wind, chase angle from which the shape and content of the package of the package are calculated
• Frictional Drives: Friction which is calculated from angle of contact with the surface of the moving and stationery object is used to analyze the tension present in the yarn, tension required and the tensioner weight needed in warping and many other processes.

Conversions and General Mathematics

Mathematics is interlinked to each and every processes involved in any field. Some of the important general applications of mathematics in textile are as follows,

• Conversions from one unit to other as different countries have different set of units and to convert to common unit. E.g. denier, tex, count, etc.
• To arrive at a relationship between two or more variables so that by knowing one variable we can find the other. E.g. Tpi-count-twist multiplier, stitch length-wales-spacing, etc.
• By using the area, volume and density of the shapes, cross section of the fiber, density, volume and geometry of the structure can be analyzed.
• Production, Efficiency, Cover factor, Speed of the machines from gearing, weft preparation calculations in weaving, beam requirement in warping and in so many other applications in various department.

In Computer Color Matching

The main aim of the computer color matching is not only to obtain the desired shade but also to analyze the various possibilities to get the shades at minimum cost.

• Matrix: In this method the various dye compositions, their intensity, proportion, concentration and cost are treated as variables in matrix and solved by trisimulus method to get the required datas.
• Factorials: Factorials are used to explore how many combinations can match the shade, which of them are economical or how close they are when viewed in different light (meteamerism).

Partial Differentiation: It is used to predict the accuracy of the color, alignment of dyes, reflectance measurement, saturation limit, compatibility and differences in the strength and tone of the dye used.

Conclusion

In textile from cotton to apparel manufacturing every process is carried out by calculations. In order to get the required quality and production mathematical knowledge is essential especially for the management peoples. Instead of going for testing the samples for identifying many numbers of variables to arrive at the result, mathematical conversions and formulas are used for easy calculations and time saving. These mathematical formulas are mostly applied in textile sampling and testing. In order to for a new process in the industry apart from the regular process, mathematical applications are involved to obtain the optimum standards and settings.

References:

• "Textile Mathematics" Volume I, II, III By: J.E. Booth.
• "Textile Calculations" By: E.A. Posselt.
• "Computer Color Analysis" By: A.D. Sule.
• "Quality Control In Spinning" By SITRA.

Relative short fibre content and its influence on yarn Quality

ABSTRACT

This paper deals with the determination of the appropriate reference length for determining Short Fibre Content (SFC). For several reasons, the fibre length distribution of a cotton variety changes through the season from lot to lot. While spinners manage this variation to a certain extent by bale management and other methods, there is still considerable variation in the SFC levels of the mixing issued over a period. Since the process is generally optimized only at the start of a season when there is a major change in raw material quality, the fluctuation in SFC, results in a corresponding change in the yarn quality. It has been shown earlier that a relative measure of the Short Fibre Content is more appropriate and useful for raw material selection than an absolute measure such as the 12.7 mm (½ inch) conventionally used. The present study compares the influences in process optimization. It was observed that the sliver quality and the yarn quality are best correlated when the SFC is estimated at about 30% of the 5% aQura ™ Length. Therefore, optimizing the various process stages to minimise short fibres at this level is recommended for optimum yarn quality. 

Introduction

The conventional method of determining SFC is by measuring the amount of fibres by weight or number shorter than 12.7 mm (½ inch). Such an absolute measure of the SFC is not adequate for either raw material purchase or for process control. Inherently, short staple cotton tends to have more fibres below 12.7 mm (½ inch) than a longer staple cotton assuming a given level of fibre damage at the ginning processes. Decisions based on the short fibre content without regard to the specific variety would prove to be improper since it is not possible to distinguish between short staple cotton which inherently has more short fibres and a long staple cotton which has higher short fibres due to more breakages during harvesting and ginning. A relative measure based on the staple length of the cotton provides more meaningful results.

Fig. 1 provides a diagrammatical explanation of the Relative Short Fibre Content using the 5% aQura ™ length as the reference measure for the staple length.

It is seen that while the absolute short fibre content is lower for longer staple cottons, the Relative Short Fibre Content does not show a significant change.

Several relative measures of determining short fibre content have been proposed over the years. 

The concept had been used in very early years when SFC was assessed by using geometrical constructions on the staple diagram from Baersorter. A measure close to this classical method was used by Mr. Allan Heap in his paper at the last conference (2004). The relative SFC parameter he used, was the percentage of fibres shorter than one half of the Upper Half Mean Length.

There have also been other relative measures experimented like the Lower Half Mean Length and others.

By definition, such relative measures are better than the absolute measure of the short fibre content for taking care staple length differences between different cotton varieties. The utility of such a measure in taking care of variances within a variety over a period is yet to be studied. 

The present study attempts to find possible relationships between the Relative Short Fibre Content and the corresponding yarn quality. Further, the study also attempts to arrive at a specific reference point or definition for the Relative Short Fibre Content. 

Relative Short Fibre Estimates from Premier aQura™

The 5% aQuraTM Length from the fibre length distribution provided by aQura™ is taken as the base length for arriving at several relative levels of Short Fibre Content. The different levels are shown in Figure 3

Thus, the SFC can be determined at several percentages of the 5% Length such as 10%, 20%, 30% etc.

The current paper aims at determining the most appropriate relative short fibre content influencing the yarn quality results. 

Materials and Experiments

The studies were conducted by analysing the Cotton and yarn quality data from a spinning mill running medium to fine hosiery yarn. The quality data were analysed for a medium staple cotton processed and spun to 14.76 tex (40s Ne) Combed Hosiery yarn. The cotton mix issued at the blow room, the comber lap and the combed sliver quality in terms of the length distribution was measured using the aQura™.

The range in short fibre content over the entire period was assessed. For comparisons of the yarn quality with the fibre quality, several discrete mix issues with differing levels of short fibre content was considered. Corresponding to each test result, from the length distribution diagram, the SFC at different reference points was determined (Different percentages of 5% length). The reference points covered ranged from 5% of the 5% aQura length to 50% of the 5% aQura length. 

The yarn quality characteristics analysed were evenness and imperfections (U%, thick and thin places).

Results

Figure 3 showed different Relative Short Fibre Content levels based on the 5% aQura length as the reference. It is also possible to derive different Relative SFC levels based on a different reference length such as 3% length, 7% length and so on. 

Figure 4 gives plots of the correlations (Y-axis) between the yarn quality characteristics (Yarn Thick places) and Relative SFC at different levels with 3%, 5% and 7% as the base reference length.

From Figure 4, it is clear that, the correlation coefficient values are found to be relatively high for RSFC based on 5% Reference length when compared to 3% and 7% Reference lengths. Based on this initial observation, the various yarn quality parameters were correlated with the different Relative SFC levels based on 5% length as the basis. 

These correlations are provided in Figure 5.

It is seen from Figure 5 that, at a Relative SFC level corresponding to the 30% of 5% aQura length, the correlation is maximum between the SFC and the various yarn quality characteristics. 

The correlation is very good at above 0.7 for the thick places at the normal and higher sensitivity level. For some of the other yarn quality parameters, the correlation values are relatively lower but the pattern is maintained with the maximum correlations achieved at the 30% level.

The Relative SFC at this level varied from 4.9% to 8.7%.

Representation of the Relative Short Fibre Content in aQura™ 

For further research on this parameter, and to provide the possibility of additional experience being gained, the Premier aQura™ is now incorporated with a new parameter called the ‘Premier Relative Short Fibre Content’ which provides an estimate of the Short Fibre Content below 30% of the 5% aQura length. The user also has the option to set this reference point at a different level for his experiments.

Conclusions

The studies on Relative Short Fibre Content indicate that, while the Relative Short Fibre Content is a better parameter than the absolute short fibre content, the specific reference level at which the relative short fibre content is measured also has an influence on the yarn quality results. The study reported here indicates this would be at about 30% of the 5% Length. This observation is worth exploring further.

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Optimization of process parameters in spinning mills to delight customers

Introduction

Today spinning mills in India are being watched by their peers across the globe. Indeed it is a golden chance given to Indian spinners and many leading garment players are trying to have tie up in some form or other with leading Indian Industries. At least one leading global player ties up with an Indian Industry every two months. This is a crucial period for Indian spinners and it is high time for them to optimize their processes towards achieving excellence in manufacturing.

Reference point / Benchmarking

When we start benchmarking, we should know our point of reference. For example, in a family there will be achievers in studies, sports, good practices etc. We used to refer the achievers as our point of reference. Similarly, achievers in each business sector become a point of reference to those in that line of business. For example, when we say Polyester Sewing Threads the immediate point of reference is a well known company in India. When we say gassed, mercerized and dyed yarns, the leader is the most reputed export house of India.

They should be taken as the leaders and we should benchmark ourselves against them to excel in business through process optimization.

Customer Classification

The customers of spinning mills can be classified into four major groups.

• Weavers
• Knitters
• Garment manufacturers
• Value addition group

a) Weavers

Weavers can be further divided into two major groups. One is grey fabric weaver and the other section is yarn dyed fabric weaver.

Grey fabric weaving

Spinners yarn is directly taken to warping to produce warping beams. After sizing, weavers beam is produced for weaving grey fabric .This grey fabric is fully bleached/scoured & dyed/printed.

Grey fabric weaver runs the warping machines at the highest speed up to 1000 meters per minute and his expectation on warping breaks is below 0.5 breaks per million meters in single yarn and zero in the case of double yarn.

Yarn Dyed Fabric Weaving

Yarn dyed fabric weavers runs the warping machines at 600 meters per minute only. However the expectations on warping breaks are the same as that of grey fabric weaver. Average RKM and the 10 percentile RKM from the tensile testing instruments need attention for achieving this level of performance.

Another important factor worth mentioning for warping is length consistency of the packages (cones). If the length of the packages differs between cones beyond 1%, certainly this is going to affect their cost of warping operations.

In weft packages, weavers expect higher size packages with tail ends because high speed looms operate at an insertion rate of 2000 meters per minute. The package defects should be NIL as the loss of utilization of high speed jet looms is going to add manufacturing cost at weavers end.

Todays preferred packages for coarse and medium counts is 2.4 kg and fine counts is 1.85 kg without any defects like crushed cones, cut ends and rib boning/entanglement.

Yarn dyed fabric weaver demands a very high quality of yarn in terms of yarn hairiness, yarn strength and yarn elongation. This is mainly because yarn undergoes many mechanical / chemical processing before reaching warping machines. Elongation and Single yarn strength of the yarn can be totally spoilt in warping machines if enough care is not taken at the time of warping process optimization.

b) Knitters

Knitters also can be broadly classified into two major groups. One is grey fabricators and another is yarn dyed fabric knitters (Autostripe fabricators). Requirements for both groups are different.

Grey fabric knitters focus on lesser long term faults and optimum twist level. Dyed knitted fabric manufacturers require stronger yarn with a slightly higher twist level. They also demand less hairy yarn with optimum level of lubricity for smooth running of knitting machines.

c) Garment Manufacturers

This group procures Polyester or Cotton Sewing Threads directly or indirectly from spinning mills. The expectations of these customers are,

• Less splice
• Zero knots
• Near zero short thick places
• Zero breakage of yarn at 6500 stitches per minute speed with relevant needle gauge for that count

We should also note high speed stitching machines are now capable of working at 9000 stitches per minute and the expectations could become more stringent in the days to come.

d) Value Addition Group

This class of customers buys single yarn and adds value to the same through,

• Doubling
• Gassing(singeing)
• Scouring, Bleaching ,Dyeing
• Mercerisation

Requirements of this customer base are very stringent and any deviation leads to severe loss to the yarn supplier in the form of compensation. The requirements are,

• Uniform shade through homogenous mixing
• Less long term faults
• Less fluff like faults
• Good single yarn strength to meet the stress during continued operations.

Normal customer complaints

We have seen elaborately our customer group and their requirements. Let us have a quick look at the nature of customer complaints we receive from Knitters.

• Shade variation
• Needle breaks
• Fabric holes
• weight shortage
• Fluff liberation
• Contamination

Normal customer complaints from weavers

• More warping breaks(most of the time due to package defect)
• Entanglements due to sizing(poor splice quality)
• Warp and weft breaks in loomshed(reasons are manifold)

Contamination levels

Spinners have very limited role in controlling the contamination level. Practices in the Indian Ginneries are going in for a tremendous change thanks to the efforts taken by Government of India and few reputed private sector mills in India. Some of the Ginners have even installed Contamination clearers in their post cleaning line after ginning.

A point must be noted here even the cotton from US has contaminations whereas US demands contamination free fabrics from India! If the efforts taken by Indian Ginners continue with still more support from the spinning industry we can definitely become leaders in supplying contamination free cotton/fabrics in the world.

Optimisation of the process

By seeing the customer base, requirements and the type of complaints let us start working on optimizing the process. To start with let us optimize our goals:

• What type of customer is going to use our yarn?
• What are the yarn quality requirements?
• What are the end use performance characteristics?
• What are the implied needs?
• Who are the current suppliers to this customer and what is their quality and performance level?

Secondly, let us examine our current quality level of our product against these requirements. The gap between the requirements and our level has to be identified.

Through process optimization we have to bridge the gap. Once we achieve the goals, let us try to reset our goals for continuous improvement. Only then we can excel others in the race. It alone helps us to establish our Brand.

Guiding sources for the Spinners

When we like to benchmark ourselves, we should have source of information for benchmarking ourselves. What are all the guiding sources of information for the textile industry today?

• SITRAS CPQ findings
• PREMIER/USTER Statistics Issued periodically
• Competitors product quality.

This information helps us to benchmark ourselves and improve continuously thro process optimization. Quality statistics by Uster/Premier Statistics is a voluminous data collection and interpretation of data. These become norms for the industries to excel in Quality. When we say that we have achieved Uster/Premier 10% standards, it means that we have achieved the quality level of the top ten percent of the particular product manufactures across the globe participated in that survey.

Analyzing competitors quality and its performance should be a disciplined continuous affair without which we cannot remain at the top continuously. For assessing performance level of our product against competitors at customers sites, we need to establish a good rapport with our customers.

Product Quality

It can be classified into three:

• Measurable - U%, Imperfections ,RKM etc
• Discrete - Package Quality like entanglement, ribboning etc
• Performance characteristics Warping breaks, Fluff liberation etc.

Cost of Quality

Quality achievement involves cost. Financial implications of process optimization should be understood very clearly. Cost of a product is decided by,

• Raw material
• Wages
• Power
• Stores
• Administration and selling expenses

To achieve better quality, if we extract higher waste levels or use a richer mix, the immediate effect will be on raw material cost. If we use finer hanks in process or reduce production level it is going to affect both power and wages cost.

If we replace critical parts like top combs/card wires prematurely it will have an impact on our Repairs & Maintenance cost.

Hence it is essential to identify our CTQ-critical to quality factors. We have to focus our attention to those factors on top priority for that particular product. While focusing on that, we should benchmark our norms for each level of our process. It is necessary to have the flowchart of our operations covering,

• Blow room and carding
• Combing
• Drawing
• Roving
• Spinning
• Autocone winding

Let us earmark our decision points and its alarm limits. For example, Nep removal efficiency is one of the decision point in Carding and alarm limit could be 60%.

Rework loops

When we set alarm limits we should also specify rework loops for each stage of processing, in case of any non conformers. Non conformers cannot be treated as waste as we have added value up to that particular point of production.

If the above system is implemented, we will come across many internal complaints and this will help us to fine tune the process continuously.

External complaints / Feedbacks

External complaints and feedbacks should not be ignored since they are vital in our proposed system to continuously improve our product quality. Based on the review of our failures and the corrective actions, we should install preventive measures to achieve consistency.

There is a paradigm shift in our todays manufacturing process. In the earlier days:

• We will act on problems to solve. Today we foresee problem areas and take proactive steps to eliminate occurrence itself
• In the earlier days, we used to set norms for waste and monitor the deviation. Today we are planning to optimize the waste itself by controlling the lint loss.
• Similarly today we focus on only unreliability and not reliability levels. For example, 0.1% unreliability in US means 12 babies given to wrong parents and 8,80,000 credit cards with wrong information. Hence it is essential to focus on unreliability and not on reliability levels. Because it is the unreliability level which is going to affect our brand image in the minds of customers.

As a customer, he has every right to get what they want and certainly not what we produce. He has to receive value for the money he pays to the product. They also have the right to demand Right First Time-RFT.

Different approaches of meeting the customer requirements thro` process optimization

• Tailor made products
• Co creation

Tailor made products

When we buy shirts, we expect good handcuffs, matching buttons and threads. We delete certain features like extra pockets etc. That is, it is a Taylor made product for us. Likewise, certain quality characteristics will be given more importance by the customers. As a manufacturer, we should be ready to optimize our process by Taylor making the products to suit his requirements.

Co-creation

In todays market scenario Co creation is wide spread in much industrial activity. It is nothing but creation of the products, building up the quality into the products along with the customers themselves. We should allow him to decide the in-process parameters itself to get the final product to meet his requirements.

However, we have certain issues to be sorted out in Co creation:

• Customers requirements become dynamic
• We should reorient our relationship with the customers
• Our managers fear of insecurity or interference

In a transparent system of management these problems can be easily sorted out to make these systems to work successfully.

Conclusion

We have discussed at length on optimization of process parameters to meet customer requirements. Most important of all is to take decisions based on facts. Hence we should have a system in place to document our decisions and the rational behind our decisions which alone can help the organization to move forward continuously.