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FREE PROJECT: STATISTICAL QUALITY CONTROL FOR THE PRODUCTION OF YUGURT (A CASE STUDY OF YUGURT COMPANY)

FREE PROJECT: STATISTICAL QUALITY CONTROL FOR THE PRODUCTION OF YUGURT (A CASE STUDY OF YUGURT COMPANY)

FREE PROJECT: STATISTICAL QUALITY CONTROL FOR THE PRODUCTION OF YUGURT (A CASE STUDY OF YUGURT COMPANY)



CHAPTER ONE

1.0 BACKGROUND OF THE STUDY

In any production the quality of the product, the statistical quality control is an approach to management in which the quality of the organization output is given first and foremost attention.

Statistical quality control involves sampling techniques for quality inspection. It dated back to 1930, these include the development of statistical base samplings plan an alternative to 100% inspection and the associated control charts.

However, it was not until 1970 when this industry reacts seriously to higher quality of product imported from Japan, that the application of statistical quality control became wide spread, the management by control approaches has been commonly practical in united state since the early part of nineteen century.

Variation in manufacturing is that no two objects are ever made alike and its law in nature in that no two items in any category are the same. Some variations may be large space and easily identified (assignable causes).

Correlated and abnormal ever may affect more than one variable at the same time, which such situation occurs, it’s difficult for the operator to isolate and determine the source of problem, which may be with only one of the money correlated alarming variable or as non-measurable variable (impurities purged, pipe blockages of sensor) that causes several other measured variable to go out of control.

Every producer be it private or public more especially profit making has objective, some of which are to sue a process/method that is convenient and hygienic for himself, workers and to the final consumers of his product.

This research aims to investigate the extent at which the Lack of research has landed many producers of yoghurt into confusion on the method and the process of producing yoghurt to be up to standard, there by producing a substandard yoghurt which may not meet the specification. So that the producers will have some profit after calculating the cost and the products retail price will not be too much for the average consumer to afford.      

QUALITY CONTORL

This is a process employed to ensure a certain level at quality in a product or service. It may include whatever actions a business deems necessary to provide for the control and verification of certain characteristics of a product or service. The basic goal of quality control is to ensure that the product meet specific requirements and are dependable, satisfactory and fiscally sound.

Essentially, quality control involves the examination of a product, service or process for certain minimum levels of quality. The goal of a quality control team is to identify product or service that do not meet a company’s specified standard of quality usually, it is not the job of a quality control team or professional to correct quality issue. Typically other individuals are involved in the process of discovering the cause of quality issues and fixing them.

AIM AND OBJECTIVES

The aims of this project is to examine and accept the quality of yoghurt produced by Nura yoghurt Company Tudun Wada Gusau. With the following objective:

To test whether the process is under control or out of control.

To know whether the product is meeting the standard 

To determine the factors responsible for poor production of yoghurt

To make suggestion on how to improve efficiency

SCOPE OF THE STUDY

This research work is limited to the study of statistical quality control on the production of Nura Yoghurt Company Gusau Zamfara State.

1.4 LIMITATION OF THE STUDY

This research is limited to Nura Yoghurt Company Gusau Zamfara State.

STATEMENT OF THE PROBLEMSThis research work will look at the problems inherited with the existing level of quality control for the production of Yoghurt. The problems include:-

How quality control in the production of yoghurt

Yoghurt is very expensive and mostbe method are tedious 

Most yoghurt produced, cannot keep for long time due to the inability of the producers to get the right materials from the production of yoghurt

ABBREVIATIONS

UCL: Upper Control Limit   2. LCL: Lower Control Limit                         

3. CCL: Center Control Limit     4. n = is the sample size    

5. B = is the standard deviation    6. U = is the sample mean 

7. NYC = Nura Yoghurt Company 







CHAPTER TWO

2.0 REVIEW OF RELEVANT LITERATURE

Quality control being one of the prominent tools employed to ensure a certain level of quality in a product or service, has emerged as a prime tool and an important factor required by any successful industry operating in today’s highly competitive business environment to incorporate in order to ensure quality of standard. Quality may be defined as conformance to requirementsor expectations and need of the customer. It may also be defined as fitness to purpose. On the other hand, control is a device for operating, regulating, testing (or limiting) and keeping an activity/ process in order. Peters and Waterman (2013) found quality to be an important element in the pursuit of excellence. Thus, quality control is simply defined as the use of techniques and activities towards achieving, sustaining and improving the quality of products or services. It may also be defined as all the features and characteristics of the product or service that contributes to the satisfaction of customer’s needs; (price, safety, availability, reliability and usability, etc.). According to Alford and Beatty (2016), quality control is an industrial management technique by means of which products of uniform and acceptable quality are manufactured or rendered. It answers the question of ‘What to produce?’ What is required (or expected)? When and How to carry out the inspection? And what measures to take to ensure zero defective item(s) in a production process? It can also be viewed as the technique of applying statistical methods based on theory of probability to establish quality standards and maintain it in the most economical manner. Quality control therefore entails, integrating the following related techniques and activities such as:

• Specification of what is needed,

• Design of the product or service to meet the specification,

• Production installation to meet the full intent of specification,

• Carrying out inspection to determine conformance to the specification and to

• Make review of usage to provide information for the revision of the specifications when necessary.

On that note, every organization is therefore expected to make use of the following steps in ensuring that quality is inculcated into their process:

Setting Standards: This means determining the required cost, performance quality, safety quality, reliability quality and standard for a product.

Appraising Conformance: This involves comparing the conformance of the manufactured product or service offered to the earlier set standards.

Acting When Necessary: This involves correcting problems and their cost through the full range of maintenance of factors influencing customer’s satisfaction and,

Planning for Improvements: This requires developing continuous efforts towards improving on; the costs of performance, reliability standards and the adopted safety measures.

According to Montgomery (2017), the origin of Statistical Quality Control techniques could be traced to Dr. Walter A. Shewhart of Bell Telephone Laboratories in (1924), when he developed a statistical chart for the control of product variables. Later in the same decade Dodge and Rooming, both of Bell Telephone in 1939 developed the area of acceptance sampling as a substitute of 100% inspection. Shahian, et al. (2001), applied statistical quality control to cardiac surgery, he used quality control charts to analyze perioperative morbidity and mortality as well as the length of stay in 1131 non-emergent population. The result which shows that common adverse outcomes appear to follow the laws of statistical fluctuation was in statistical control. He however, concluded that statistical quality control may be a valuable method to analyze the variability of these adverse postoperative events over time. Appalasamy, et al. (2012), undertook a research on the application of statistical quality control in accessing the quality of wine production using the physio-chemical quality characteristics (Alcohol percentage, haze, original gravity, etc.).

The philosophy underlying the implementation of SQC strategy requires the company or an organization to see customers as the vital key to their company’s success Shama and Kodali (2008). This implies that companies with quality control concepts rather see their corporate performance and productivity through the eyes of their customers/clients and measure them against customer/client expectation Denise, et al. (2010). The predominant notion of such company therefore should not be how to make initial profit, but to give quality service to her customer(s). It should however be borne in mind that implementing quality control concept and techniques requires substantial measurement and considerable survey plan and research.

Manufacturing organization applies various quality control techniques to improve the quality of the process by reducing its variability. A range of techniques are available to control product or process quality. These include

Antony et al (1998) classified the technique as advanced. Nevertheless, the content is more important than the classification.’

Among the basic techniques are SPC. SPC is a statistical approach for assisting operators, supervisors and managers to manage quality and to eliminate special causes of variability in a process (Oakland, 2003). The initial role of SPC is to prevent rather than identify product or process deterioration, but Xie and Goh (1999) suggest for its new role to actively identifying opportunities for process improvement. The main tools in SPC are control charts. The basic idea of control charts is to test the hypothesis that there are only common causes of variability versus the alternative that there are special causes by continuously monitoring the process, the manufacturing organization could prevent defect items to be processed in the next stage and to take immediate corrective action once a process is found to be out of control (Hairulliza et al., 2005).

DoE and Taguchi methods are powerful tools for product and process development. Taguchi methods, for instance, aim at making product or process that robust to undesirable disturbances such as environmental and manufacturing variations. However, the application of these two methods by industries is limited (Antony and Kaye, 1995). Antony et al (1998) explore the difficulties in the application including improper understanding and fear of statistical concepts in the methods, thus propose a methodology for the implementation.

Process capability study is an efficient method to examine the capability of a process to produce items that meet specifications. The method gains rapid growing interest due to increased use of quality system QS9000, where use of process capability studies is requested (Deleryd et al, 1999). The findings from capability study might require adjustment of process using other statistical technique such as SPC or DoE. Capability studies conducted by Motorcu and Gullu (2004) and Srikaeo et al (2005) show that the machine tool and process capability and production stability was evaluated and necessary steps to reduce poor quality production was carried out using other statistical techniques.

FMEA is a powerful method to detect where exactly problems can occur and to prioritize possible problems in the order of their severity (Dale et al., 2003).

 The tool is useful to identify problems in product, i.e. design FMEA, as well as to trouble shoot problems in process, i.e. process FMEA (Xie and Goh, 1999). Six sigma is also a statistical tool for ensuring defect free products through process continuous improvement. The term six sigma originated at

 Motorola and many inspired worldwide organizations have set goal towards a six sigma level of performance (Breyfogle and Cupello, 2011). The application of six sigma has been mainly used in manufacturing industry. An example of the use of six sigma in non-manufacturing industry is in software development (Mahanti and Antony, 2015).





CHAPTER THREE

3.0 METHODOLOGY

 INTRODUCTION

This segment of the project work is concerned with the nature of data collection, type of the data collected, where the data is collected and the method of data collection used in the research.

3.1 METHOD OF DATA COLLECTION

The data was collected in primary form, and the data were quantitative data because the weights of the yoghurt product in Nura yoghurt company and were measured using scale and record down the result. The project also limited the research to sample by sampling out of the products at particular period of Production process, using simple random sampling as the sampling type respectively.

The can of each litre of the product were measured in gram and the producer’s specification, expectation oriented output is 1.00kg of each can of the yoghurt.

3.2   METHOD OF DATA ANALYSIS

3.2.1 CONTROL CHART

A control chart is chart that includes the lower and upper control limits that identify the range of variation that can be described to common causes.

The purpose of control charts is to detect the occurrence of assignable causes affecting the quality of process output.

We have different method of control chat such as:-

 –chart   2. S – chart   3. R – chart

Put the most appropriate chart for this research is R – chart there are two stages in the use of control chart in quality control.

Process parameters: this serves as centre line e.g. process mean () process range (R), standard deviation and sample number (N).

The second stage involve selection of sample at regular intervals of the sample are estimated and flouted on the control chart. Would be evaluated from the prospective of:

3.2.2 – BAR – CHART 

 – Control chart if the sample size is relatively small (say equal or less than (10) we can use the range instead of the standard deviation of a sample to construct chart on and the range.

The range of the sample is simply the difference between the largest and smallest observation.

There is statistical relationship (patnaik) (1946) between the mean range for data from a normal distribution and the standard deviation of that distribution. This relationship depend on the sample size in the mean of R is d2 where the value of d2 is also a function of n. an estimator is therefore.

Aimed with this background we can now develop the R control Chart.

Let R1, R2, - - - Rkbe the range of K sample, the average range is 


Then an estimate can be computed as 

3.2.3 – CHART

In the preceding section we showed that the upper and lower control limits for the x chart are

Hence to construct the control limit for the  chart we need to estimate and. The mean and standard deviation of the process. An estimated of   is given by . An estimated of   can be developed by using the range data. It can be shown that an estimator of the process standard deviation   is the average range divided by d2, a constant that depends, on the sample size n that is estimate of 


Average range sample is computed as follows:




k = number of samples

The American society for testing and materials manual on presentation of data and control chart analysis provides values for d2 as shown in appendix B for instance, when n is 4 d2 = 2.059 and the estimate of   is the average range divided by 2.059 and if we substitute x and for   for in equation (3.1) we can write the control limit for the  chart


Note that A2 = is a constant that depends only on the sample size, value for A2, are also provided in appendix B for n = 4 A2 = 0.7729

3.2.4 R – CHART

It can be shown that an estimate of the standard deviation of the range denoted


Where d2 and d3 are constants that depend on the sample size. Values of d2 and d3 are provided in appendix B thus, the UCL for the R – chart is given by:


And the LCL is given by



We can write the control limits for the R – chart as 

LCL = D4, LCL = D3

D3 and D4 are also provided in Appendix B.

3.2.4.   S - CHART

The s chart are used to monitor the mean and variation of a process based on samples taken from the process at given times (hours, shifts, days, weeks, months, etc.). The measurements of the samples at a given time constitute a subgroup. Typically, an initial series of siubgroups is used to estimate the standard deviation of a process. The standard deviation are then used to produce control limits for the standard deviation of each subgroup. During this initial phase, the process should be in control. If points are out of control during the initial (estimation) phase, the assignable cause should be determined and the subgroup should be removed from estimation. 

Once the control limits have been established of the s charts, these limits may be used to monitor the variation of the process going forward. When a point is outside these established control limits it indicates that the variation of the process is out of control. An assignable cause is suspected whenever the control chart indicates an out of control process.

3.3 VARIATION

Variation in manufacturing is that no two objects are made alike and it’s how in nature that no two items in and category are the same, some variation may be large and easily identified and (assignable cause). Variation can be small which is difficult to detect (chance variation).

Variation in product or service can be participated into two, these are:-

Variation due to chance causes

Variation due to assignable causes

3.3.1 CHANCE VARIATION

This is a causes of variation resulting from many usually unknown causes, none of which is investable and not a cause for concern, the causes may be due to operations, physical and emotional well – being skill of natural operation, the working environment and time.

3.3.2 ASSIGNABLE CAUSES

This is variation attributed to one or very few, rather important causes and its large magnitude, therefore readily identify, the causes may now operator improper machine, setting different skills among workers, bad raw materials and poor design of production etc.

The variation in quality of product may be attributed to both or one of the causes of the variation mentioned above. If there is variation in quality that is attributed to chance causes alone, the production process is assumed to be under statistical control.

However, if the assignable causes are also present we suggest that the process is out of control.

Then, can we know that the production process is under control or not? The solution to those problems is by using control chart.

3.4 NORMALITY TEST

Statistical analysis may rely on your data being “NORMAL” (i.e. bell-shaped).

The two tests most commonly used are; Normal probability plot and p-value or critical value method. There are other methods of normality test: Chi-square goodness-of-fit, K - S test and Histograms. For this research, probability plot and histograms would be considered for normality testing.

3.4.1 HYPOTHESIS TEST   

: The process is under control i.e

 The process is not under control i.e

3.4.2    TEST STATISTICS: If P-value<at 0.1% level of significance reject  and conclude that the process is not under control otherwise accept  and conclude that the process is under control

DATA ANALYSIS

4.0 DATA PRESENTATION

Base on this research work, the weight of yoghurt product in NURA yoghurt using 25 different samples measured in gram scale was collected and will be analyzed using different statistical quality control tool.

Table 4.1 Volume of yoghurt product measured in grm

sample

obs1

obs2

obs3

Obs4

obs5

X bar

R


1

1.00

1.010

1.010

1.000

1.000

1.004

0.01


2

0.99

1.000

0.990

0.990

1.000

0.994

0.01


3

1.01

1.010

1.010

1.000

1.000

1.006

0.01


4

1.02

1.000

1.000

1.010

1.010

1.010

0.02


5

1.01

1.000

1.000

1.010

1.010

1.008

0.01


6

1.00

1.005

1.010

1.010

1.010

1.007

0.01


7

0.99

1.010

1.000

1.010

1.010

1.004

0.02


8

1.01

1.000

1.000

1.010

1.010

1.006

0.01


9

1.02

1.010

1.000

1.000

0.990

1.004

0.02


10

1.00

1.000

1.000

1.010

1.000

1.002

0.01


11

1.00

0.990

1.000

1.000

1.000

0.998

0.01


12

1.00

1.000

1.010

1.000

1.010

1.004

0.01


13

1.00

1.000

1.000

1.000

1.010

1.002

0.01


14

1.00

0.990

1.000

1.000

1.010

1.000

0.02


15

1.01

1.000

1.000

0.990

1.010

1.002

0.02


16

1.00

1.000

1.010

1.010

1.020

1.008

0.02


17

1.00

1.005

1.005

1.010

1.010

1.006

0.01


18

1.00

1.000

1.000

1.005

1.000

1.001

0.005


19

1.02

1.000

1.010

1.000

1.000

1.005

0.02


20

1.00

1.000

1.000

1.000

1.005

1.001

0.005


21

1.00

1.010

1.010

1.000

1.000

1.004

0.01


22

1.02

1.000

1.000

1.000

1.000

1.004

0.02


23

1.00

1.005

1.005

1.000

1.010

1.004

0.01


24

1.00

1.000

1.000

1.010

1.000

1.002

0.01


25

1.00

1.000

1.000

1.005

1.000

1.001

0.005



4.1 









By using three significance (38) approach them 



And  can be obtain from the appendix when n = 5 = 2.326







Standard error (SE) = 









Range Chart (R-Chart)















Figure 4.1 (X bar chart) for the yoghurt production

4.1.1 DISCUSSION OF RESULT

The figure above shows the mean chart (Xbar chart) for the yoghurt production base on their weight and samples taken in NURA yoghurt company which deduced that the yoghurt production are almost in statistical control, only the sample number two is out of control since it goes below the lower control limits (LCL), that is to say its concluded that production of yoghurt drinks in NURA yoghurtwith respect to their weight base on mean chart is in statistical control and it was justified from the numerical calculation above. 


Figure 4.2 (R chart) for the yoghurt production

4.1.2 DISCUSSION OF RESULT

The figure above shows the Range chart (R chart) for the yoghurt production base on their weight and samples taken in NURA yoghurt which deduced that the yoghurt production are all in statistical control, no any sample number is out of control since it does not beyond or  below the control limits (UCL and  LCL), that is to say its testified that production of yoghurt drinks in NURA yoghurt with respect to their weight base on mean chart is in statistical control and it was justified from the numerical calculation above. 

4.1.3 S CHART FOR YOGHURT DRINK


Figure 4.3: (S chart ) for the yoghurt production

4.2 DISCUSSION OF RESULT

The figure above shows Sample standard deviation chart (S chart) for the yoghurt production base on their weight and samples taken in NURA yoghurt which revealed that the yoghurt production are all in statistical control considering the 25 samples taken since no any sample goes beyond or below the control limits (UCL and LCL), that is to say its concluded that production of yoghurt drinks in NURA yoghurt with respect to their weight base on S chart is in statistical control.

4.3 CONCLUSION

Based on the results obtained so far, the mean chart (X-bar chart) of suggest that only the observation in sample two is below the LCL, but there are no samples go beyond the UCL, while the R chart of yoghurt drinks revealed that no any single observation is above or beneath the UCL or LCL respectively, similarly we have the same result from the S chart. Therefore we concluded that the process is out of control since observations are observed outside the control area. 

4.4 RECOMMENDATION

Our findings are relevant for policy makers, based on the conclusion drawn, thus the following recommendation are to be considered

The machines in which the products were been measured should be properly checked and set the measurement accuracy before the production process begin.

The techniques of control chart should be persistently used in the company, for the products to be always in statistical control, so as to examine any assignable source of variation.





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