Factors affecting WIP

A dissertation submitted in partial Fulfillmentof the requirement for the award of Degree

In

Master of Fashion Technology (Apparel Production)

Submitted By

AJAD KUMAR PANDEY

Under the Guidance of

Ms. Girija Jha(Asst. professor)

Department of Fashion TechnologyNational Institute of Fashion Technology, New Delhi

May, 2010

Abstract

An effective cost reduction and higher productivity should be the main objective of every

Indian garment manufacturer to get the maximum price advantage to compete

successfully in the world market. It is very much necessary to balance the available

resources including workforce and machine with the technological developments

affordable within the reach of our own Indian conditions. One of the main reasons

behind low productivity of Indian apparel manufacturers is ‘poor line efficiency’. This is

mainly because the utilization of available time is very less, which may be due to

number of reasons i.e. unavailability of accessories or cut parts, or may be due to poor

balancing of workflow between the operators. To maintain even workflow it is essential

to determine the optimum WIP.

WIP is related to the line efficiency and throughput time. Due to improper WIP the

Company can face the problems of low productivity which also led to overtime working.

The bottleneck caused in the critical operations affects the productivity. It has been

observed that a buffer of garments between the operations i.e. Work In Progress (WIP)

helps to overcome these delays. Work In Progress (WIP) helps to overcome these

delays but then question arises that to what extent WIP and line efficiency, operator

absenteeism, Input and output etc. are co-related to each other? This study aims to

“Factors affecting WIP”.

ii

Certificate

“This is to certify that this Project Report titled “Factors

affecting WIP” is based on my AJAD KUMAR PANDEY

original research work, conducted under the guidance of

Ms. Girija Jha towards partial fulfillment of the

requirement for award of the Master’s Degree in Fashion

Technology (Apparel Production), of the National Institute

of Fashion Technology, New Delhi.

No part of this work has been copied from any other

source. Material, wherever borrowed has been duly

acknowledged.”

Signature of Author/Researchers

iii

ACKNOWLEDGEMENT

The subject matter of my Research Project covers a wide

range and an attempt is made to study the various aspects. Even

though the scope of my Research Project is of an extensive

nature, in a brief span of time, I have tried to assimilate some

relevant and pertinent information.

I am highly thankful to the management and staff of the

Silver Spark Apparel Ltd, Bangalore for their appreciable

support during my Research project. I would like to give my

special thanks to Mr. Ashish grover (Vice-president), Mr.

Kaushalendra Narayan (Plant Manager), Mr.Sunil (I.E. head)

and Mr. Hiral Lakdawala (Ass. Production Manager) for their

invaluable support and guidance.

I sincerely thank Prof. Prabir Jana (Head-Industry Linkage),

Ms. Girija Jha (Asst. professor), Mr. N.A. Khan (Centre Co-

ordinator M.F.Tech) and Ms. Bhawna kapoor (Centre Co-

ordinator B.F.Tech) for providing me opportunity & guidelines to

execute my Research Project.

iv

These people have been a constant source of

encouragement and have provided me with the means and the

opportunity to pursue my Research Project.

v

Table of ContentsAbstract.......................................................................................................ii

Certificate...................................................................................................iii

ACKNOWLEDGEMENT.............................................................................iv

1 Introduction...........................................................................................4

2 Review of literature...............................................................................7

2.1 Determination of Work In Process..................................................................7

2.2 Efficiency Measurement.................................................................................12

2.3 Line balancing.................................................................................................16

2.3.2 Control Parameters in Line Balancing...........................................................................33

Work in progress......................................................................................................37

2.4 Throughput time.............................................................................................38

2.5 SAM..................................................................................................................39

2.6 Absenteeism....................................................................................................41

2.7 Time study.......................................................................................................42

3 Research methodology......................................................................45

3.1 Steps taken to establish the Correlation......................................................49

4 Data Collection....................................................................................54

4.1 SAM OF JACKET.............................................................................................62

5 Results.................................................................................................67

5.1 Relation between WIP and output.................................................................67

5.2 Relation between WIP and Line efficiency...................................................69

5.3 Relation between WIP and Input....................................................................71

5.4 Relation between WIP and Operator absenteeism.......................................73

5.5 Relation between output and Line efficiency...............................................75

5.6 Relation between Line efficiency and operator absenteeism.....................77

5.7 Relation between Output and Operator Absenteeism.................................79

5.8 Relation between Input and Output...............................................................82

6 Calculation for optimum WIP.............................................................86

7 Limitations and scope of further study............................................90

8 Conclusion..........................................................................................92

9 Bibliography..........................................................................................93

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Chapter-I

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1 IntroductionAn effective cost reduction and higher productivity should be the main objective of every

Indian manufacturer to get the maximum price advantage to compete successfully in the

world market. Considering the heavy investment in importing the specialized

components and machine manufacturer should effectively plan with the available

resources keeping the same as a dominating factor behind sharp productivity gain.

The apparel manufacturing industry cannot afford to loose sight of the critically

important measure of manufacturing performance. Every garment manufacturer must

achieve higher levels of productivity to survive in an increasingly competitive business

environment.

Technological achievement will not achieve productivity unless the manufacture

couples it with a skilled work force and experienced efficient production executives. The

job of the production executive is very important and pivotal in improving productivity.

There is no point in augmenting the machine alone since it will not increase productivity

unless appropriate training is being given to the labour force.

There must be a good team of production executives who can take up the task of

the problem solving, counseling and efficient training of labour to achieve the expected

target of production.

It is very difficult for India to buy all the technologically advanced machinery due

to the heavy investment cost to minimize the outflow of the foreign exchange. The

payback period for some of the new machine may even be 15-16 years and this will be

considered very high capital investment.

It is very much necessary to balance the available resources including work force

and machine with the technological developments affordable within the reach of our

own Indian condition. A careful and thorough analysis is required in the long-term

investment a return on investment philosophies in mind the pricing of the product to

compete with the world market.

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The Indian apparel industry is facing savior competition from its neighborhood

countries, which are manufacturing similar clothing items for the same price at lower

cost. The reason being low productivity due to “Poor line Efficiency” this is mainly

because the utilization of available time is very less, which may be due to number of

reasons i.e. unavailability of accessories or cut parts, or may be due to poor balancing

of workflow between the operators. To maintain even workflow it is essential to

determine the optimum WIP. Work In Progress (WIP) helps to overcome these delays

but then question arises that to what extent WIP and line efficiency, operator

absenteeism, Input and output etc. are co-related to each other? This study aims to

“Factors affecting WIP”.

Objective

To determine the factors responsible for inconsistent WIP.

Sub objective

To determine the improvement potential in the manufacturing process and

identifying improvement parameters.

To suggest the necessary steps to optimize WIP.

To implement the suggestions and provide the required solution.

Scope

To maintain the desired WIP is a major problem in the garment industry and it lead to

many problems like productivity loss and longer lead times. Huge improvement potential

lies in increasing production by increasing line efficiency by optimizing WIP. Clever and

scientific management of WIP can result in reasonable improvement in the line

efficiency. The project aims to get solutions to optimize WIP in the manufacturing

process of a garment factory.

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Chapter-II

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2 Review of literature

2.1 Determination of Work In ProcessIn a balanced production line Work-In-Process (WIP) plays the important role of

balancing out production variance between operations as well acts as buffer stock in

case of emergency. In a balanced line, each operator should produce the same number

of product units per unit time. But as the time value for each operation varies, so it

needs to assign different number of operators for different operations i.e. if operation no.

1 needs 2 operators then next operation 2 might need 5 operators to produce the same

number of product in the same time. Therefore, the first step in formulation an efficient

production schedule is to determine the number of workers per operation necessary to

yield a balanced production line for the required output. The next step in making the

production schedule is to determine the proper amount of backlog time, which will

prevent bottlenecking with a minimum "in process" inventory. Suppose those 2

operators in operation 1 produces 2 bundles (1 each) in 10 minutes time. But can we

pass those bundles to operation 2? No, as there are 5 operators waiting. So, there has

to be at least 5 bundles produced in operation 1 before we pass them to operation 2.

Now to produce 5 bundles of operation 1, it will take 30 minutes (though actually 6

bundles will be produced as each operator works on a whole bundle).

There is a simple graph methods that can be used to Calculate the minimum

backlog time required for minimum inventory-in-process without bottlenecking.

The steps for the graph method are as follows:

1. Determine the operating time per bundle for each operation.

2. Compute the number of operators needed for each operation in order to yield the

balanced production required per unit time (preferably in hour).

3. Construct a graph with the X-axis, the abscissa, marked off in time units and the Y-

axis, the ordinate, marked off in bundle units.

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4. From the information from step 1 and 2, plot the operating time required to produce

groups of whole bundles for each operation. Plot this in a block format as illustrated

in fig. 1

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Pre assumptions:

WIP is generally maintained in any assembly line manufacturing process. While

allocating operators for a new style, old WIP is gradually phased out and new style WIP

builds in. We generally don't build WIP from zero every time. This module will explain

minimum starting lag time between operations for avoiding bottleneck. You have to

allow multiple of minimum lag time to build pre-requisite WIP between process.

Let's explain this with an example

In a production schedule the targeted production is 6 bundles per hour. And let us take

4 successive operations whose work contents per bundles are 20, 50, 30, and 80 SM

(Standard Minutes) respectively. Hence to produce 6 bundles per hour the number of

operator required in each operation and the number of bundle produced by each

operator in those operations are determined and shown in the following table

Plot the block graph structure for each job in the sequence in which the operations are

performed in the line. While plotting the operations keep the 1st operation at the X, Y

zero juncture of the graph. The block graph structure for each job will have a rising step

formation. The slopes of the block graph structures will be alike for all the operations

because the total amount being produced per unit time in each successive operation is

alike for each operation (as the line is balanced). See fig 1

The stairway dimensions rise and tread, of each “stairway” graph line will vary

according to the relative graph space measurements assigned to bundles and time

values. The block graph structure for a successive operation will begin at the time value

at which sufficient bundles have been produced in the preceding operation to permit all

of the operators of the successive operations to begin working simultaneously.

If blocks from two successive block structure overlap, this signifies a bottleneck

between the two operators. If means a lag time is required between the time at which

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sufficient bundles are produced and the time at which successive operators can begin

without overtaking the production of the operators in the preceding operation. The

minimum lag, needed to present such bottlenecks, can be determined easily by

measuring the greatest abscissa overlap in two overlapping blocks.

Pre assumptions:

Components of 10, 15 or 20 numbers and tied (or bundled) together and treated as unit

for material movement between operations. Bundles can't be broken while material is

moved from one operation to another. For example if two bundles and three operators

are available, we can't break the bundle and distribute the work. We need minimum of

three bundles to give to three operators.

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Let's explain the graph in details:

In operation 1, as there are two operators and the work content of each bundle is 20

min, hence here the 1st two bundles will be produced on 20th min., let us plot the block.

We cannot start operation 2 at this stage, as there are 5 operator who needs 5 bundles.

So, next two bundles of operation 1 will be produced on 40th min. We still can not start

operation 2 as there are now only 4 bundles and we require 5. So, next two bundles of

operation 1 will be produced on 60th min. Now at 60th min when in total 6 bundles are

ready, we can start operation 2.

As in operation 2, there are 5 operator and the work content of each bundle is 50

minutes. Hence 5 bundles of operation 2 will be produced at 110 th min (we started this

operation on 60th min and it took 50 min). Plot this block. At this stage can we start

operation 3? Yes, as there are 3 operators and 5 bundles are ready for them. So,

continue the plotting blocks for operation 2 and start plotting blocks for operation 3

starting at 110th min.

Similarly plot operation 3 and 4.

From the graph we can see that at a later stage blocks from operation 2 and 3 as well

as blocks from operations 3 and 4 are overlapping. That means there going to be

bottlenecks at that stage (explanation below as footnote). To prevent that we need to

measure the greatest abscissa overlap. In case of 2 and 3 it is 20 minutes. Hence that

starting lag time between 2 & 3 should be 20 min. to prevent bottleneck. Hence

operation 3 should be start at 130th min instead of 110th min. Similarly, operation 4

should have another 10 min of starting lag time over 60 min with operation 3 to avoid

bottleneck. So, operation 4 should start at 230th min.

See Fig 2 where graph is drawn with required lag time between respective operations

and no overlap and thus no bottleneck in starting operations.

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Overlap Explanation:

Five operators at operation 2 complete first bundle after 50 mins, i.e. total 5

bundle is ready for operation 3 at 110th minute. At same speed next 5 bundle will be

ready for operation 3 at 160th min. Operation 3 can starts at 110th minute with 3 bundle

and completes work by 140th minute. Operators at operation 3 needs another 3 bundle

now, but only 2 (5-3 =2) bundle is available and next 5 bundle from operation 2 will be

out only on 160th minute. So operators at Operation 3 has to wait for 20 minutes (160-

140 = 20). Hence that starting lag time between 2 & 3 should be 20 min. to prevent

bottleneck.

(SOURCE: -Determination of In-Process Inventory Requirements, Developed by Siddhartha Sankar Ray & Edited by Prabir Jana)

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2.2 Efficiency MeasurementMeasurement of line utilization or line efficiency is a common measurement in

sewing floor of apparel manufacturing organizations. This is calculated by “minutes

produced”/ “minutes attended” expressed as percentage.

In a sewing line of 20 operators a style of 20 SMV is produced in 8 hour shift. If

the Average daily production of the style is 400 pcs/shift then the Line Utilization or line

Efficiency can be calculated as under:

Line efficiency = (output * SAM/SMV) / (Actual hrs worked * 60 * No. of operators)

Minutes Utilized” = SMV X pcs/shift = 20x400

Minutes attended = number of operator X number of minutes per shift = 20 x 480 =

9600

So, line utilization or line efficiency is 8000x100/9600= 83.33%

This measure is also known as balance efficiency (used commonly in Japanese

literature) as the primary reason behind time loss is attributed to poor balancing. The

other factors that may be responsible for time loss are:

[1] No or improper feeding of cut parts

[2] Non-availability of accessories (thread, zipper, cord, etc.)

[3] Shifting of operators resulting start-up loss

In macro measure the above may be sufficient but while analysing the cause of

inefficiency or underutilization in micro terms we need to take care of various factors

thus various measures. Although above measure tells us 1600 minutes (9600 – 8000)

was lost by the line during shift hours but we do not know the break-up. How much time

was lost due to balancing problem (waiting for work), how much time was lost due to

machine breakdown, how much time lost in repair, etc. Micro measures are required for

in-depth analysis of the line and finer control. Here we will list out some commonly used

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measures, their definitions, formulae to measure, and how those influences other

measures and parameters.

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Contracted time

As it says it is time in contract of employment. It is measured usually hours x by

number of operators to get total hours potentially available to factory or department.

Used in capacity planning and calculation of absence as %

.(Contracted hours – absence) x expected efficiency = standard hours to be

Produced as capacity

Usually calculated for sections, departments or factories as it is used to calculate

absence levels, and capacity and you would not normally do this for individuals.

Attended time

Time needed for calculation of efficiency and utilization. It is usually the time

operator spent in factory not forgetting to take away any lunch break. (Contracted hours

– absence) = (shift hours – lunch break – tea break) = Attended time. Bit more

complicated. Usually lunch break is excluded as per your calculation.

Tea break is totally dependent upon company policy i.e. do they remove it from the

working day. In UK it is usually included in attended time.

It is usually calculated for both individuals and lines. Individuals for calculating

utilization and performance (attended – off standard). Line for calculating section

efficiency and utilization.

Off-standard time

Off standard time is that time utilized on performing tasks to which SMVs are not

allocated. Alternatively an operator earning SMVs is working ‘on-standard’. Utilization is

a measure at how well the manager and supervisor control the section and keep

operators working i.e. “on standard”. In most apparel manufacturing factories most

operators are on some form of incentive payment whilst they are doing the job for which

they are trained and equipped. They are then said to be ‘on standard’. If they come off

incentive or ‘Off standard’ then they will have no financial incentive to work hard (Chuter

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1998). It is calculated as a percentage of operator attended time (utilisation) and

reported in different categories, although the exact categorization varies but principal

categories are wait for work, machine breakdown, unmeasured work, repairs and

rejects, sample making, undergoing re-training, work study.

Key off standard time categories are usually waiting for work, machine

breakdown, repairs, unmeasured work, samples, retraining or “new job” (many

companies do not class retrain as off standard but separate from other production

activities and pay make up pay to worker so they earn minimum pay). Although

dependent on style changes, not more than 3% of total attended time is generally time

for ‘retraining’. Off standard time obviously links to operator utilisation measures and

efficiency. Most companies’ show ‘off standard’ by categories on a daily cost control

report by section/line, department and factory with management and supervisory staff

targeted to achieve budgeted limits.

It is usually calculated for both individuals and lines. Individuals for calculating

Utilization and performance (attended – off standard). Line for calculating section

efficiency and utilization

On-standard time

This is the actual time worker spent on productive work. This is calculated as the

difference between attended time and off-standard time. This should be obvious and is

used in calculating operator performance

On standard time = (Attended time – Off-standard time)

It is usually calculated for individuals as performance calculation is an individual

measure but can be averaged into section performance as extra KPI

Utilisation

It is the time spent on productive time out of total attended time. It is the

percentage of attended time working earning Standard Minute Values [SMV].

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Utilisation = (On standard time / Attended time) X 100

It is usually calculated for a line as it is a measure of how well the supervisor is

running the line.

SMV Earned

It is the measure of work done by operator. It is calculated from output from an

operation generated in attended minutes and SMV of the operation.

SMV earned = (SMV X Output in number of units)

Used in both performance and efficiency calculations.

It is usually calculated for individuals as well as for a line. As it is needed for

individuals for incentive payment calculation and performance and needed for lines for

efficiency calculation. Also sometimes aggregated for departments and factories to get

factory efficiency. This measure or KPI can be called “direct (and indirect) labour cost

per standard minute produced.

Key component in incentive schemes of all types. Can be converted straight into

money in “piecework system”, e.g. one smv is worth x rupees therefore total incentive

payment is smv’s earned x by conversion factor. Easy to calculate and understand

especially as most operators have access to cheap calculators, even mobile phones.

A variant on incentive is SMVs can also be used to calculate performance and

different performances are paid at different salary levels. This is very common as it can

be weighted to encourage people to achieve particular performances. Increments on

hourly rates are higher between 80 – 95 % to encourage people to strive to achieve

from a low base but levels off at high performances where workers may be striving to do

too much and quality issues may arise.

Individual Operator performance

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It is a measure of both the skills and motivation of the operator and calculated

daily. Performance = (SMVs earned on standard / On standard time) x 100

It is usually calculated for individuals and sometimes averaged for a section as a

supervisor KPI.

As previously stated it is a key for incentive payments. An experienced manager

or supervisor will compare performance and efficiency and note variation. A big gap can

only mean high off standard time. Is this justified? If the operator trying to work the

system to achieve higher salaries or conversely if there is no gap but the supervisor

knows the section has problems then is the operator slowing to work supply. It is

particularly useful with new or trainee operators whose progress has to be monitored.

Also useful to compare this performance calculation with the “potential performance”

measured by cycle timing. Why are there differences?

(Source:-Chuter, A.J., 1998b, Introduction to Clothing Production Management, second Ed, 1998, Blackwell Science Ltd, pp 19-20)

2.3 Line balancing

The line has to be balanced taking into consideration the heavy absenteeism,

balancing the operations with the available operators for work and balancing the line

based on the various styles and operation involved in that with the available machine.

The most important is the flexibility of the operator who should be trained for more than

two operations to simplify the process of line balancing. The line balancing will not be

effective unless there is a scientific management building up the records of the workers

performance and categorizing them in distinctive grades based on efficiencies.

Arrangement of machine capacity to secure relatively uniform flow at capacity

operation in a layout is called line balancing product layout requires line balancing and

if any production line remains unbalanced, machinery utilization may be poor. The

machine in this will operate for only say half of the time.

A balanced line eliminates bottleneck operation as well as prevents the

necessary duplication of equipment of capacity. Line balancing is a major consideration

in layout because the line it is not essential that output of each operation should be the 18

same but the essential to see that the output of fastest machine should be multiple of

the remaining machine.

Line balancing will not be made effective unless there is a scientific management

building up of the records of the workers performance and categorizing team in

distinctive grades based on efficiencies.

1. An efficient history record of each employee is to be maintained by a professional

personnel department indicating clearly the number of operation the person did

along with the proficient level in a particular operation with the categorization of

their grade.

2. A through and systematic statistics of absenteeism each day to be maintained to

arrive at a average percentage of absenteeism. There must a buffer general pool

of operators to be kept always available to the line as leave vacancies matching

to the statistics. The operator of the general pool should be trained in all the

operations to fit into place where regular operator is absent.

3. A thorough record to be maintained on the late comers to arrive at an average so

that there is no need to panic early in the morning so long as the number of late

comers are within that average. This should be computed from the daily figures

and give the distinctive reason for the highest late coming i. e. whether it is due

to seasonal or transportation problem or festivals etc. the lowest percentage is an

ideal situation and this should be analyzed with reason as to how it has achieved

to maintain it further list of dependable operators should be made out by the

supervise and they should mark and train these people on pivotal operations to

avoid major problems on the line balancing on any single day the supervisor

should also train the operators into the discipline of availing only after prior

intimation.

4. The production executive must be highly disciplined who are regular in

attendance and along with the line supervisors and masters their entry in the

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factory. They should evaluate this production achieved for the day and also

analyze the problems if any with the line supervisor and master to achieve better

strategic plans for the future.

Condition are being satisfied by a company, than 70% line balancing/work load

balancing a through planning has to be made by the production executive describing the

operation of a particular style and listing it out clearly with the expected of production.

The Basic Concept

Assembly line balancing can be defined as the step by step process of:

Optimum sectionalisation (operation breakdown) of a garment

Calculate time values for each operation (using either time study or PMTS)

Calculate total workforce requirement based on target, utilisation factor,

attendance and avg. efficiency (rating) of operator

Allocate available resources (operators who are present) to requirements

(operations), so that

I. Target is fulfilled

II. Priorities of operations are met

III. Operators are assigned to operations they do best

IV. Idle time for operators are minimized

In short balancing a line means trying to bring equilibrium in output among operations

in a sectionalized working scenario.

A small example will illustrate the principle further; Let us consider there are 5

operations in a garment. B & D operations are dependent on A & C respectively; E is

dependent on both B & D. A & C can start simultaneously. Time value (to be precise

SAM value) for operation A is 3 minutes, so operation A will complete 20 pcs/hr.

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Similarly capacities for other operations are like C-30pcs/hr, B-30pcs/hr, D-40pcs/hr and

E-30pcs/hr.

Minimum how many operators do we need for every operation to have a balanced

production?

A simple LCM will solve the problem like A need 6, B, C and E need 4 each and

D needs 3 operators to balance the line and give a output of 120 pcs/hr. Here no

operator will be idle at any point of time.

Now imagine E can produce only 25 pcs/hr instead of 30pcs/hr. To balance the

same line perfectly now we need minimum A-30 operators, B&C -20 operators each, D-

15 and E-24 operators and output will be 600pcs/hr. But we may not need such high

production or we don’t have so many m/cs to use. If we need to balance the same line

for a output target of 120 pcs/hr only, then we have to give 5 operators for operation E.

Now E has a capacity to produce 125 pcs/hr but will be producing only 120 pcs/hr (as

he is getting only 120 pcs components from B & D). Please note that in this case E is

under utilised (idle for 4% of time)

In actual shopfloor condition the situation is much more complex than above

because:

• Numbers of operations in a garment are quite high; (for a simple shirt it may be 25 and

for a fully lined ski jacket it might be 140!).

• All operators don’t work at same efficiency

• Operation timings are not in round figures (i.e. as per time study capacities may be 23

pcs/hr, 37pcs/hr, 41pcs/hr etc.). To solve this type of cases using only LCM principle

will lead to unrealistic solutions.

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So we need some practical ways out:

First step: We try to round off the operation timings as far as possible (i.e. 23 will be

treated as 20 pcs/hr and 37 will be treated as 40 pcs/hr and so on). Now some

operators are under-utilised (e.g. when 23pcs/hr capacity operator was asked to

produce only 20pcs/hr) and some are overburdened (e.g. 37 pcs/hr was asked to

produce 40pcs/hr). Rounding off operation timings can help in theoretical balancing of

line, but in practical cases it creates one bunch of idle operators (under-utilised ones)

and another bunch of bottleneck operators (overburdened ones). That is why in shop

floor always we have some under-utilised operators and some overburdened operators

however balanced the line might be.

Second step: While rounding off we also must build up work-in-process (WIP) between

operations so that operators are not working with hand to mouth situation rather working

with process stock. For example if a 27pcs/hr operator feeds another 30pcs/hr operator,

then the later operator will be idle for 6 minutes for every hour of operation. This 6

minutes of idle time is absorbed in the operation in such a way that it is not easily visible

but makes the operator slow paced and relaxed. If 30 pcs of WIP is provided between

the same two operators then both can work at their own pace achieving respective

targets continuously for 10 hours before the total WIP runs out. Popular practice is to

use extra hands or non-productive times (break-hours, overtime) to replenish the WIP.

Apart from covering for such imbalances WIP is also used as a buffer in case of

machine breakdown or operator fall ill etc.

Third step: Sometimes the actual operation SAM values are so diverse (e.g. 17 pcs/hr,

37pcs/hr, 20pcs/hr and so on) that only rounding off does not lead to realistic line

balance. Then we need to club or split operations before rounding off based on required

output or available machinery. Like for example 37pcs/hr operation can be split to two

operations of 17pcs/hr and 20pcs/hr (if required target is around 20pcs/hr) or A and C

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can be clubbed to one operation of 37pcs/hr (if required target is around 40 pcs/hr).

Please note that clubbing and splitting of operations are dependent on type of

operation, type of m/c and sequence of operation and often not practically possible even

required for the sake of balancing. Clubbing of operations are comparatively easier and

operations that require similar machines (similar attachment also) can easily be clubbed

even though the material flow in the floor becomes jigsaw. Splitting of a time consuming

operation may require operation re-engineering and require reasonably good command

over garment construction techniques.

After completing above steps the supervisor calculate operator efficiency required for

every operation and then operators are assigned jobs (operations) based on what they

can do best.

Initial Balance

Initial worker allocation for different operations in an assembly line is called “initial

balance”. There are two ways allocation can happen. Either based on a target we

decide upon the number of workers required and then assigning each worker different

operations or based on available machine and operator we calculate target production.

Calculating number of workers required to achieve a given target is generally done in

macro scale while planning for whole factory. But when we are referring manpower

planning for a new style in the running factory we prefer the second method.

Traditionally sewing department in a factory is broken into different lines, each line

consists of equal or variable number of sewing machines. Different products require

different types of machine (sewing as well as non sewing) and in required number to

achieve maximum utilization of the line. The number of machines is generally decided

based on experience and industry benchmark and not necessarily through any scientific

calculation. Sometimes factories decide machine mix in a line based on generic product

categories, like one line for tops and one line for bottoms, etc. Here blouse, shirt,

camisoles all can be manufactured in the top line, but utilization and production in

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number of garments vary based on standard minute value (SMV) of the product. There

are some factories set up separate specialized lines for ladies blouse, camisoles, shirts

etc. and obviously can achieve better utilization of the line capacity. Whatever may be

the approach the number of machine in a line is generally fixed in any factory and

achievable target is being calculated based on that, not deciding on number of machine

and operator that will be required for achieving target for a given style. I will first discuss

how to calculate the number of machine and operator that will be required for achieving

target for a given style (Chuter 2002), and then how to calculate the achievable target

for a given line and workforce.

SOURCE:-Assembly Line Balancing Made Easy, Prabir Jana, Associate Professor, Garment Manufacturing Technology Department, NIFT,

New Delhi

Calculating the number of machine and operator requirement for achieving target

for a given style

Let’s say we have received an order for a nightdress whose estimated work content is

10.00 sm. This is a uncomplicated garments and all operations are in sequence, since it

is made on a transporter. 1920 nightdresses must be made in one 40 hours week.

Therefore, required target hour= 1,920 = 48 garments per hour

40

Now if we convert the target from number of garments to standard minutes then

required output =48X10=480 standard minute/hour

Table 5.1 Operation breakdown for Case Study 30.

24

Now we have to calculate number of people required to achieve a target output

of 480 sm per hour. Theoritically any person can work 60 minutes per hour at maximum,

so we would require 480/60 = 8 people theoretically to meet the target. At this point lot

of you are thinking that practically no human being can work for 60 minutes per hour,

because they require allowances for personal needs, fatigue and delay. But we must

recollect that all these allowance factors are already been considered while calculating

standard minute value. That means in this example the 10 min SMV for the nightdress

already includes the allowances and we need not count that again.

Even then 8 operator is not realistic and there are other factors that need to be

considered. Firstly we are planning number of people that would be required in the line

but not necessarily everyone would be present everyday. Secondly it is assumed that all

8 people should be able to perform all operations at 100 BSI percent rating, which is not

true. Thirdly it is assumed that apart from personal fatigue and delay allowances all

operators would work non-stop while working in a line.

25

Lets analyze the actual situation, firstly absenteeism is a common phenomenon,

to have 8 people present and work in the line we must have more than 8 people in the

pay role. For example if any factory has 20% daily absenteeism, they should have 10

people in pay role to ensure 8 people present in a day.

Any factory consists of operators with variable rating in different operations. On

average an old factory may have 90% BSI operators, while a new factory may have 70

BSI operators. In above example 480 SM per is achievable by 8 operators of 100 BSI

each, but in actual case all 8 might not be of 100 BSI standard. If average all 8

operators is 80% BSI rating, then they will be able to achieve a target of 480 x 80% =

384 minutes per hour. In other words we require more than 8 operators to achieve the

target.

Thirdly we have already discussed that in any assembly line balancing is never

100%, resulting in certain operators are underutilized. In above example if all 8

operators are 100% utilized then we might achieve the target, but because some of

them will be underutilized, they will under produce. In other words we have to increase

the operator from 8 to achieve the target. Based on the number of underutilized

operator and extent of utilization “utilization factor“ for each line is calculated. This

“utilization factor“ is also known as “balance efficiency” (as underutilization is due to

poor balancing) and expressed in percentage.

Now we take from the factory records the current figures for attendance,

utilization factor, and average performance of the line. These may be modified because

of other information (Like record might say average absenteeism for last 3 months is

10% but you are planning for the month of March and due to festival HOLI the

absenteeism is likely to increase further) and then become the predicted figures.

Predicted attendance 90%

Predicted utilization 80%

Predicted average 'piecework' performance on standards 95 BSI

26

Labour required = 480 X 100 X 100 x 100=11.7=12 People

60 90 80 95

So, realistically 12 workers should be available on roll to achieve the target. Now

generally there are two types of workers are there, namely operators and floaters in a

line. While operators are assigned specific operations, floaters are kept standby to

cover for actual absenteeism and the problems of balancing. While the operators are

specialized in one or more operations and have relatively higher rating in those

operations, floaters can “perform all operations but specialized in none” and has

relatively low rating in those operations.

It is important to mention at this point that irrespective of whatever rating the

operator and/or floater performs any operation, it is assumed they deliver acceptable

quality standard. Rating in any operation is only a reflection of time taken in comparison

to SMV of that operation and have no relation with quality standard of the operation.

In above example out of 12 people some will be operators and few will be

floaters. Now let us assume average rating of floater is 60% (based on Chuter 2002).

Calculations of this sort are not exact and it is usually good enough to decide upon the

number needed purely on the basis of the absenteeism. Roughly due to 10%

absenteeism 1.2 people out of 12 should be floater type. Average rating of 12 people

was 95%, whereas average rating for floater is 60 BSI, so. If we calculate more

accurately there should be 2 floaters and rest 10 people are operators. Calculations for

the floater as under.

12X (100-90) X 100 X 95= 1.9 ~ 2 floaters

100 60 100

we now have 10 operators to be assigned specific operations in the style, so, we

must have 10 machines in the line. Next step would be to assign 10 operators to 13

27

different operations (see from the operations breakdown of the nightdress) based on

each operators’ competency in different operations while achieving the target.

Now we have to calculate Pitch time, which is the theoretical operation time,

each operator should take for a planned balanced line. This means if the garment can

be broken up into number of operations same as no. of operator required to meet the

target and also work content of all operation is equal. It is calculated as:

= (SMV of the nightdress) / (No. of operator required to meet the target)

= 10 / 10

= 1 minute

As per pitch time every operation should take 1 minute, which means 60 pieces per

hour from every operation in the line. So, our planned target is 60 nightdresses per

hour.

NOTE: - At this stage it is important to note that our original target was 48 nightdresses

per hour. Ideally (in utopian condition) we needed only 8 operators. After forecasting all

the factors that can disrupt or reduce the production we have decided to allocate 10

operators to carry out the production. Actual production will be lower than the planned

one due to poor utilization (80% in this example). So we have to plan for higher target

with more number of operators to actually achieve our lesser target (original target of 48

nightdresses per hour) with lesser number of operator.

Planned target = original target / utilization factor

= 48/80%

= 60 nightdresses per hour.

Which means 60 pieces per hour from every operation in the line.

Assigning operation to operator

According to the operation breakdown of the nightdress there are 13 operations in

sequence and we have only 10 operators to assign. Obviously some operations need to

be combined to make it to 10. Rules to follow while combining operations are

28

1. Operations require same machine (same stitch type, bed type and feed type for a

sewing machine) and attachments, if any.

2. Sequence of operations.

3. After combining more than one operation the SMV for the combined operation should

be closer to the pitch time.

Even though this example require combining of operations, sometimes (if the

existing operation breakdown has less number of operations than the number to

operators) we may need to split the operations. There are obvious difficulties with

splitting of operations and it is sometimes worthwhile to make minor changes in design

in order to permit this. While a planner can combine operations following above three

rules and with little knowledge of construction of the garment, splitting of operation

require in-depth knowledge of sewing and not always possible.

There does not need to be one operative per operation ('operatives in series'). A

long element could be covered by two or more people ('Operatives in parallel') On the

other hand, one or more elements can be combined together, provided that they require

the same machinery and that sequence of manufacture permits this. Sometimes an

operative can be provided with two workplace next to each other. In this case work in

process will still be needed between them.

An operation which is too big will become a bottleneck in production and the

installation of work aids or special machinery can sometimes be justified more on these

grounds than because of the labour saving. An example of this was when 'integrated

sewing units' were being introduced and were in short supply. Their installation at a

bottleneck operation could cut the work content by up to 15 per cent.

Table 5.2 sets out the various alternative arrangements.

When a theoretical operation balance is completed, then the management must

check that the garment can be produced with the operations as selected.

29

30

Table 5.2 Alternative arrangements.

Table 5.3 Theoretical operation balance after combining operations

Date: Size: Wms. & WX Style: 280

Work station

Operation number Operation description sm content

1 1 Elasticate, att. lace & elastic to sleeves

0.70

2 2 Join two back seams 1.0

3 3 & 4 Att. front yoke. Att. back yoke 1.2

4 6 Att. lace to front yoke 1.0

5 13 Yokes & top stitch each side to hold

1.0

6 5, 8 & 9 Join both shoulders and join sleeve seams

1.1

7 7 Att. binding & lace to neck & tab

0.7

31

8 10 Att. sleeves on round 1.4

9 11 Whip hem 0.9

10 12 Top stitch (3) neck & sleeves 1.0

Total 10.0

After our best effort theoretical operation balance shows that out of 10 operations

4 operation’s work content is 1 minute, which is equal to pitch time. 2 operations with

10% variations, 1 operation with 20% variation, 2 operations with 30% variations and 1

operation with as high as 40% variation from pitch time. In fact the work content per

operation varies from minimum 0.70 sm to double that figure at 1.4sm. These variations

actually lead to poor utilization of the line.

Perhaps the most important aspect of the training of supervisors in balancing is that it

develops their pride in the skill recorded for their operatives. They can use the

inventory to target improvements in output for low performers and an increase in

versatility for others. Only the supervisor can arrange things so that potential floaters

have a chance to build up their skills.

Example of a skills inventory for twelve people (10 operators and 2 floaters) is

available in table 5.4. It shows that Ann and Gail cannot do very many operations and

none well. As far as possible they should be kept on one type of work, in order to train

them up until they can achieve a consistent 100 performance or 100 BSI. Kate and Lyn,

on the other hand, are likely to be floaters. A skills inventory will seldom be able to

predict performance to a greater accuracy than +10 percent.

32

Table 5.4

Date

Ope

ratio

n

s SN

Lock

stitc

h

Ove

rlock

Ove

rlock

Fril

ler

SN

Lock

stitc

h

2N

Lock

stitc

h

SN

Lock

stitc

h

Wh

ippe

r

Wh

ippe

r

Ela

stic

ate

Operators

Ann 50 50 75 75

Bel 100 100

Cher 120 120

Dot 75 75 25 100 50 25

Eve 75 50 100

Fran 100 100

Gail 75 75 50

Hon 120 120

33

Iris 50 50 100 75

Jill 75 50 50 100

Kate 50 75 75 50 50 50 25 25

Lyn 75 50 50 75 50 75 50 50

Enter BSI PERFORMANCE on Top Line

Enter date & style then Garments /hr on Bottom Line for Initial Balance.

Now we have to allocate resources to the requirement. At this stage we can try to match

the work content of the operations to the skill of the operatives, in order to reduce the

variation in output.

2.3.1.1 Allocating operatives

At each operation the work content in sm multiplied by the planned output in

garments per minute will give the performance required for a perfectly smooth output.

For example, the work content of the first operation is 0.7 SM, target for this operation is

60 pieces per hour. Performance needed (expresses in %) in this operation to achieve

the target is

= (SM of the operation) X (Target in pieces per hour) X 100 / (Minutes in an hour)

34

or

= (SM of the operation) X (Target in pieces per Minute) X100

= 0.7 x 60 X 100 / 60

= 70

So, we require a operator who can perform Whipper Elasticate at 70 BSI

performance. From the skill inventory table we see that both Ann and Iris can perform

Whipper Elasticate at 75 BSI. We have to decide between Ann and Iris, once any

operator is allocated for an operation she can’t be utilized for other. While Ann’s best

performance in any operation is 75 BSI, Iris can perform whip hem operation at 100 BSI

performance. We should try to allocate operator to operation what they can do best, so,

logically we allocate Ann for the first operation and keep Iris free for next allocation.

Similarly the work content of the second operation is 1.0 SM, target for this

operation again 60 pieces per hour. Performance needed (expresses in %) in this

operation to achieve the target is 1.0 X 60 X 100 /60 = 100

So, we require a operator who can perform overlock at 100 BSI performance.

From the skill inventory table we see that both Bell and Fran can perform overlock at

100 BSI. We have to decide between Bell and Fran, As both Bell and Fran can perform

two operations each (incidentally same operations) and both at 100 BSI, both Bell and

Fran are equally good choice.

One by one all operations were allocated an operator. It is important to note that

allocations of operators are based on trial and error and first come first allocate basis.

This will vary from person to person. There is no right or wrong answer; your solution

may be one of many possible solutions.

(Source:-Chuter, A.J., 1998b, Introduction to Clothing Production Management, second Ed, 1998, Blackwell Science Ltd, pp 19-20)

35

2.3.2 Control Parameters in Line Balancing

There are different parameters, which control balancing of assembly line in

garment manufacturing. Some of the important parameters are defined and explained

here.

Operator’s Skill Inventory:

This database maintains the record of each operator, who can do what operation

and at what rating. It is very important to keep this database updated as over the time

operator acquires skills for new operations as well as improve performance in existing

operations.

Allocation:

Appropriate allocation of operators to operations is the key to any balancing.

Allocation also depends on type of balance required. One approach of allocation is to

find the closest match between operator performance required and operator

performance available. This type of allocation results intrinsic balance of line.

Another approach of allocation is to utilise the operators in operations they can do

36

best. This approach results dynamic balance of line. Let's understand this from an

example:

For intrinsic balance we have allocated total three operators with closest

performance available. For operation A we need 100% operator, and closest available

is Urmila (105%) and so on. Also interesting to note that Savita is not utilized for

operations, what she can do best. For dynamic balance we have allocated only two

operators, total performance needed is 246% (100+80+66). Savita and Rita together

(140+100)% can match the requirement. Here operators are allocated to operations,

what they can do best. This dynamic balance results better operator utilisation but

comparatively difficult to maintain. Some of the characteristics of these systems are

mentioned below.

Balance:

The aim of balancing is to maximise operator and machine utilisation, while

ensuring least operator shuffling. (Movement between operations)

37

Attendance:

Record of attendance is very important prerequisite for balancing a line. Only the

present operators are allocated to different operations. While planning for balancing a

line, average absenteeism figure is used to calculate the no. of expected present

operators, whom operation can be allocated.

Movement of operators:

Output is lost every time an operator is moved to a new job. Anticipation of

problems is the best way to keep this lost output to a minimum and line operators

should never be moved for less than an hour except in an emergency. Even floaters

should be given a sufficiently long run to work up their speed. On occasions it is more

efficient to arrange the manning.

Floater:

Floater generally can perform all operations but at much lower efficiency.

Floaters should not be allocated to any specific operation in any line plan. Floaters are

there to handle unforeseeable circumstances, once operators are absent, performing

below standard, sick, or any such emergencies, then floaters are allocated to the

problematic operation.

Rating:

Rating is the assessment of a workers rate of working. What the observer is

concerned with is therefore the speed with which the operator carries out the work, in

relation to observers' concept of normal speed. Thus rating is very subjective exercise.

It is necessary to have a numerical scale of rating to make the assessment effective. In

a 0-100 scale, 0 represents zero activity and 100 the normal rate of working of a

motivated qualified worker - i.e. the standard rate. Rating is used as factor by which the

38

observed time can be multiplied to give the basic time. In Operator’s Skill Inventory

database the rating should be updated regularly for near accurate balancing.

39

Pitch Time:

Pitch time is the theoretical operation time, each operator should take for a

planned balanced line. It is calculated as:

= (SAM value of a style) / (No. of operator required to meet the target)

Clubbing and Splitting of operations are done to match every operation timings with

pitch time. Lesser the deviation of operation timings from pitch time, better the balancing

efficiency can be achieved.

WIP:

[1] Balance between the speed of workers

[2] To take care of absenteeism

[3] To take care machine breakdown

(Source :- Control Parameters in Line Balancing, Prabir Jana, Associate Professor, Garment Manufacturing Technology Department, NIFT,

New Delhi)

40

Bottleneck operation

Often excessive WIP will build up ahead of bottleneck ones the bottleneck is determined

an engineer need to study the operation to determine whether it can be feasibly be

improved.

Calculation of the optimum WIP in a line

In order to attain the minimum total processing time or total inventory in process

time it is necessary has a balanced production system. A balanced production is one

which each grid station contains sufficient workers to produce the same amount per unit

time all the grid station produce the same amount per unit time. Therefore the first step

in formulating an efficient production schedule is determine the number of workers per

grid station necessary to yield a balanced production line for the required volume per

unit time the next step in making the production schedule is to determine the proper

amount of back log time which will prevent bottlenecking with a minimum in process

inventory the idea is to have just enough back log between two successive level to

ensure against anybody waiting for the work and at the same time have the minimum of

work in progress between the two levels, necessary to accomplish such coordination.

Work in progress (WIP)

“WIP refers to the number of garments that are under production at any given

time.” Throughout conversion, garments are at different stages of completion, but all are

considered work in progress. When work accumulates as backlog ahead of operations

to keep the plant operating smoothly, but excessive amounts also affect productivity,

reduce flexibility, and restrict cash flow.

41

Need of WIP

A buffer of garments between the operations helps to overcome short- term

problems. Like machine breakdown, operator absenteeism, bottleneck operations

(constraint to throughput that limits the volume of work that can be completed in a

workday) it also helps in balancing the workflow.

How to create WIP

To build WIP in between the operations the first operator has to complete his/ her

task before the second operator timing.

More the WIP more the throughput time and vice versa.

Throughput

Throughput is the volume of work that can be completed in a specific amount of

time. The time block may be an hour, day, or week. Throughput provides a means of

evaluating performance of a manufacturing unit, and is a measure of a plant’s

production capability. Production of goods to fill orders can be planned and based on

throughput.

2.4 Throughput timeIt is the amount of time it takes for a style to go through the production process,

cutting to shipping. This includes the actual processing time and the time a style waits to

be processed ahead of each operation. Manufacturers plan production schedules to

meet delivery dates based on projected throughput time. Throughput time also imparts

lead-time and the total costs and decreasing cash flow.

Throughput time may be in minutes, days or weeks depending on the firm’s

priorities and commitments to flexible manufacturing and market responsiveness. If

throughput time is in minute’s orders can ship to customers the same day they go into

production. A very large order may not be ready to ship the same day, but a significant

42

portion could be shipped if the customer desired the style. If throughput time is in weeks

filing end shipping an order could take several months. This requires much more lead-

time on orders.

The theoretical Throughput time is calculated by using the following formula

Throughput time= {SAM of critical path (1+WIP in critical path)}

2.5 SAM

The SAM will be calculated by using the formula

SAM = Basic time + Allowances

To know the observe time. Time study should be done i.e. “Time study is used to

determine the time required by qualified and well trained person working at a normal

pace to do a specified task”.

Source: - Pratima J.Naik & Chandresh Raj (2000-2002) batch diploma project “Effect of work in progress on Throughput time and line

efficiency”.

43

Standard time of an operation

(Source: -Juki manual)

Standard time of an operation

Standard rating Qualified

Operator1. Physique2. Intelligence3. Skill

Defined method

motivation

Standard time of an operation

Basic time for each element

M/C attention allowance

Relaxation allowance

Defined method

Defined quality stds.

Occurrence per garment

Observed time (stop watch)

Rating Speed effectiveness

Thread breaks

Thread change

Bobbin change

Recovery from effort

Personnel needs

Male/Female standing/sitting,Weight lifted etc.

44

2.6 AbsenteeismEmployees’ presence at the work place during the schedule time is highly

essential for the smooth running of the production process in particular and the

organization in general. Despite the significance of presence, employees sometime fail

to report to the work place during the scheduled time, which is known as “Absenteeism”.

According to PICOARS AND MAYERS: Unexpected absence disturbs the

efficiency of the group as the jobs are inter connected, if one single man remains absent

without prior notice the whole operation process is distributed. This Absenteeism results

in production losses because, due to Absenteeism, workers cost increases and thus

efficiency of operations is affected.

Types of Absenteeism

Absenteeism is of four types viz….

1. Authorized Absenteeism.

2. Unauthorized Absenteeism.

3. Willful Absenteeism.

4. Absenteeism caused by circumstances beyond one’s control.

1) Authorized Absenteeism

If an employee is absent from work by taking permission from superior and applying for

leave, such Absenteeism is called authorized Absenteeism.

2) Unauthorized Absenteeism

If any employee absents himself from work without informing or taking permission and

without applying for leave, such absenteeism is called Unauthorized Absenteeism.

45

3) Willful Absenteeism

If any employee absents himself from duty willfully, such Absenteeism is called Willful

Absenteeism.

4) Absenteeism caused by circumstances beyond one’s control

If any employee absent himself from duty owing to the circumstances beyond his

control like involvement in accidents (or) sudden sickness, such absenteeism is called

Absenteeism caused by circumstances beyond one’s control.

(Source: - Study on Absenteeism of Workers at Nutrine chocolate in year 2005.)

2.7 Time studyDetermine the elements to be studied

Writes detailed element description and indicate clear, precise, well-defined

breakpoints.

There are three general rules to be considered when separating jobs into elements.

The element should be as short in duration as can be timed accurately

(usually between 6 and 30 seconds duration).

Handling or manual time should be separated from machine running time.

Constant elements, that are those elements that are repeated identically

throughout the study, should be separated from variable elements.

Rate and time the elements

Rating of the speed and effectiveness of an operators work during each

element is carried out simultaneously with the timing of each element. Ideally the rating

should be recorded on the study sheet before the observed time is written down.

A representative number of work cycles should be studied in order to ensure

that conditions are reflected in the times observed.

46

Take a final time check

The stopwatch is left running until an accurate time check has been observed.

Calculation of frequencies

In order to calculate an accurate basic time for a work cycle, its frequency must

be taken into consideration. For example certain elements only occur at the beginning

or the end of an operation and their total time must be calculated as a ratio of the overall

time. A closed once, therefore any elements relating to the opening and closing of this

bundle will be at a frequency of 1 to 3.

(Source: - Barnes, Ralph M ninth edition, ‘motion and time study – design and measurement of work’ john Wiley & sons Inc.)

47

Chapter-III

48

3 Research methodology

Defining the problem

Exploratory research (literature survey)

Develop the hypothesis

Define the objective

Data collection

Data analysis

Data interpretation

Conclusion

49

To arrive at the final result the steps that were carried out by me are as follows.

Defining the problem

One of the many strength of the Indian garment industry is believed to be its “low

wage rate and availability of traditionally skilled workforce”. There are countries in our

neighborhood with lower labour cost, which would manufacture the similar clothing

items for the same price at lower cost. The solution for this is to upgrade its production

techniques to increase productivity.

The RMG industry is marked by technological backwards in almost all areas of its

operation and lags far behind in comparison to the prevalent modern technology

nevertheless there is perceived a good potential of the growth of the industry if suitable

steps are taken. The solution to the problem is to upgrade the techniques to the level

that is most congeal to condition and improve the existing technology available through

innovation. The option can suit any manufacturer exporter who does not need to go in

for the importing technology but with the help of the re engineered process can increase

productivity.

One of the main reasons behind low productivity of Indian apparel manufacturers

is poor “Line Efficiency”. This is mainly because the utilization of available time is very

less, which may be due to number of reasons i.e. unavailability of accessories or cut

parts, or may be due to poor balancing of workflow the operators. To maintain even

workflow it is essential to determine the optimum WIP.

Exploratory research (literature survey)

Once project’s aim and objectives are decided, an extensive literature survey on

Work In Progress, line efficiency, operator absenteeism, Input, output and throughput

time was required to move ahead with actual execution.

50

Develop the hypothesis

To carry out the project I had set some hypothesis based on our assumption

WIP have a positive correlation with Line Input, Line efficiency, Line output and

Operator absenteeism.

Operator absenteeism has a negative correlation with Line efficiency and output.

Data collection

Method of data collection

Primary sources- collected from the factory

Secondary sources- Resource centre, Internet, Manuals

Data analysis and interpretation

The data collected has been used to study in detail for determination and analysis of

factors affecting WIP and suggest possible way to optimize WIP.

The data was analyzed and interpreted to get the answers for the questions.

Establish correlation between WIP and line efficiency.

Establish correlation between WIP and operator absenteeism.

Establish correlation between WIP and output.

Establish correlation between WIP and Input

Establish correlation between output and Line efficiency

Establish correlation between Input and output.

Establish correlation between output and operator absenteeism.

Establish correlation between line efficiency and operator absenteeism.

Conclusion

Remarkable improvement is probable in production by maintaining the optimum WIP in

the line and getting same output and line efficiency.

51

Chapter-IV

52

3.1 Steps taken to establish the CorrelationStep-I

Time Study

First of all it is very necessary to do the time study to know the SAM of a particular style

Operational breakdown of a particular style

Observe the timing taken for completing different operations.

SAM of the particular style will be calculated.

Step-II

Develop the hypothesis

WIP have a positive correlation with Line Input, Line efficiency, Line output and

Operator absenteeism.

Operator absenteeism has a negative correlation with Line efficiency and output.

Step-III

Data collection

In the beginning counted the WIP, Input, output and Operator absenteeism as

maintained by them.

Analyzed the data to find out what extent correlation between Line efficiency,

Input, Output and operator absenteeism, with Work In Progress.

Then try to find out the solution for optimum WIP in the line.

53

Step-IV

Establish the correlation between WIP and output, WIP and Input, WIP and

Line efficiency and WIP and operator absenteeism using correlation coefficient test.

Correlation coefficient Test

The main result of a correlation is called the correlation coefficient (or "r"). It

ranges from -1.0 to +1.0. The closer r is to +1 or -1, the more closely the two variables

are related.

If r is close to 0, it means there is no relationship between the variables. If r is

positive, it means that as one variable gets larger the other gets larger. If r is negative it

means that as one gets larger, the other gets smaller (often called an "inverse"

correlation).

Techniques in Determining Correlation

There are several different correlation techniques. The Survey System's optional

Statistics Module includes the most common type, called the Pearson or product-

moment correlation. The module also includes a variation on this type called partial

correlation. The latter is useful when you want to look at the relationship between two

variables while removing the effect of one or two other variables.

Like all statistical techniques, correlation is only appropriate for certain kinds of data.

Correlation works for quantifiable data in which numbers are meaningful, usually

quantities of some sort. It cannot be used for purely categorical data, such as gender,

brands purchased, or favorite color.

Correlation Coefficient, r

The quantity r, called the linear correlation coefficient, measures the strength and

 the direction of a linear relationship between two variables. The linear correlation

54

 coefficient is sometimes referred to as the Pearson product moment correlation

coefficient in honor of its developer Karl Pearson.

The mathematical formula for computing r is:

Where n is the number of pairs of data.

The value of r is such that -1 < r < +1.  The + and – signs are used for positive linear

correlations and negative linear correlations, respectively.

Positive correlation:    If x and y have a strong positive linear correlation, r is close to

+1.  An r value of exactly +1 indicates a perfect positive fit. Positive values indicate a

relationship between x and y variables such that as values for x increases, values for  y

also increase.

Negative correlation:   If x and y have a strong negative linear correlation, r is close to

-1.  An r value of exactly -1 indicates a perfect negative fit.   Negative values indicate a

relationship between x and y such that as values for x increase, values

for y decrease.

No correlation:  If there is no linear correlation or a weak linear correlation, r is close to

0.  A value near zero means that there is a random, nonlinear relationship between the

two variables.

Note that r is a dimensionless quantity; that is; it does not depend on the units

employed.

    A perfect correlation of ± 1 occurs only when the data points all lie exactly on a

55

     straight line.  If r = +1, the slope of this line is positive.  If r = -1, the slope of this

     line is negative. 

    A correlation greater than 0.8 is generally described as strong, whereas a

correlation

      less than 0.5 is generally described as weak. 

These values can vary based upon the "type" of data being examined.  A study utilizing

scientific data may require a stronger correlation than a study using social science data.

56

Chapter-V

57

4 Data CollectionData has been collected for WIP in the line, input, output, Line efficiency

and operator absenteeism. It has been taken 11 month data for try to find out correlation

between WIP and Input, Output, Operator absenteeism and line efficiency.

MONTH INPUT OUTPUT WIP T.PRES %T.ABS. LINE EFF%MAY 09 207 250 780 91 9.90099 43.13334

237 275 742 93 7.920792 46.42631308 275 775 94 7.843137 45.93242775 210 1340 94 8.737864 35.07566100 225 1215 98 6.666667 36.04715168 400 983 99 5.714286 63.43651508 425 1066 100 5.660377 66.72728600 350 1066 101 5.607477 54.4078600 425 1419 103 3.738318 64.78376605 400 1696 99 9.174312 63.43651607 475 1828 102 6.422018 73.11524500 500 1828 103 5.504587 76.21619600 425 2003 100 8.256881 66.72728400 400 2003 101 8.181818 62.18034500 425 2078 94 14.54545 70.98646450 430 2008 96 12.72727 70.32531500 450 2148 96 12.72727 73.59626500 450 2198 101 8.181818 69.95288410 450 2158 104 5.454545 67.93501448 550 2056 104 5.454545 83.03168516 525 2048 102 7.272727 80.81158541 500 2089 101 8.181818 77.72542496 500 2058 104 5.454545 75.48334

0 510 2076 104 5.454545 76.99301JUNE 09 502 400 2177 110 5.172414 57.09286

436 400 2213 110 5.172414 57.09286450 425 2238 111 4.310345 60.11466520 550 2208 108 6.896552 79.95643498 575 2131 109 6.034483 82.82393520 600 2051 108 6.896552 87.2252511 500 2026 104 10.34483 75.48334

58

450 500 2012 106 8.62069 74.05913471 500 1953 105 9.482759 74.76445

0 425 1558 105 9.482759 63.549791052 475 2135 105 9.482759 71.02623816 450 2296 101 12.93103 69.95288632 400 3034 97 16.37931 64.74448490 500 2723 102 12.82051 76.96341504 525 2702 100 14.52991 82.42781567 510 2759 104 11.11111 76.99301416 550 2625 105 10.25641 82.2409543 600 2025 108 7.692308 87.2252553 450 2676 105 10.25641 67.28801502 500 2673 104 11.11111 75.48334536 575 2634 107 8.547009 84.37204608 650 2592 108 7.692308 94.49396560 610 2542 108 7.692308 88.67895600 630 2512 107 8.547009 92.4424614 600 2526 106 9.401709 88.87096610 600 2536 105 10.25641 89.71735

JULY 09 410 525 2421 108 3.571429 76.32205506 530 2397 110 1.785714 75.64803566 560 2403 110 1.785714 79.93462 535 2330 109 2.678571 77.06226506 500 2336 107 4.464286 73.36699506 525 2317 110 2.654867 74.93437484 500 2301 112 0.884956 70.09168506 425 2382 109 3.539823 61.21768547 500 2429 107 5.309735 73.36699498 530 2397 108 4.424779 77.04892445 525 2317 108 4.424779 76.32205213 525 2005 109 3.539823 75.62184413 500 1918 107 5.309735 73.36699418 425 1911 109 3.539823 61.21768509 475 1945 107 5.309735 69.69864419 250 1791 108 4.424779 36.34383222 350 1711 109 3.539823 50.41456528 425 1816 105 7.079646 63.54979408 425 1799 105 7.079646 63.54979152 500 1451 105 7.079646 74.76445189 375 1265 107 5.309735 55.02524

59

357 450 1172 106 6.19469 66.65322246 400 1080 105 7.079646 59.81156581 325 1274 105 7.079646 48.5969391 325 1340 101 10.61947 50.5215227 400 967 103 8.849558 60.97295

AUG 09 12 300 679 100 7.407407 47.10161276 275 680 97 10.18519 44.51183329 350 659 100 7.407407 54.9518789 325 423 96 11.11111 53.1528590 400 386 99 8.333333 63.43651

199 100 486 98 9.259259 16.02095151 70 567 99 8.333333 11.10139384 125 828 98 9.259259 20.02619266 335 759 99 8.333333 53.12807

0 220 545 101 6.481481 34.199190 150 449 101 6.481481 23.31763

824 230 1043 102 5.555556 35.40317196 125 1117 102 5.555556 19.24085306 140 1283 101 6.481481 21.76312

0 375 908 105 7.079646 56.07334498 200 1206 104 7.964602 30.19334113 225 1094 107 5.309735 33.01514

8 250 852 107 5.309735 36.683490 200 652 107 5.309735 29.3468

116 275 493 107 5.309735 40.35184504 250 747 107 5.309735 36.68349542 175 1114 104 7.964602 26.4191732 150 996 102 9.734513 23.08902

546 200 1342 102 9.734513 30.78536SEP 09 104 175 1198 104 5.454545 26.41917

515 250 1463 105 4.545455 37.38223416 270 1609 103 7.207207 41.15674508 170 1947 101 8.181818 26.42664243 400 1790 102 7.272727 61.57073507 375 1922 102 7.272727 57.72256662 400 2184 103 6.363636 60.97295542 325 2401 101 8.181818 50.52152386 250 2537 103 6.363636 38.108135 385 2187 100 9.090909 60.44706

378 350 2216 99 10 55.50694

60

0 450 1766 99 10 71.366070 435 1331 99 10 68.9872

756 425 1662 99 10 67.401290 410 1252 100 9.090909 64.3722

248 330 1170 100 9.090909 51.81177320 175 1315 100 9.090909 27.47594144 240 1219 99 10 38.0619312 285 1246 99 10 45.19851340 330 1256 103 6.363636 50.30269412 335 1333 102 7.272727 51.56548350 340 1343 107 5.309735 49.88955

0 230 1113 107 5.309735 33.74881392 325 1180 105 7.079646 48.5969484 500 1164 107 5.309735 73.36699

OCT 09 495 500 1159 102 8.928571 76.96341318 400 1077 103 8.035714 60.97295387 350 1114 101 9.821429 54.4078

0 425 689 100 10.71429 66.72728510 350 849 102 8.928571 53.87439509 375 983 102 8.928571 57.72256198 200 981 100 10.71429 31.4010787 425 643 99 11.60714 67.40129

450 400 693 102 8.928571 61.57073537 400 830 101 9.821429 62.18034487 450 867 100 10.71429 70.65241415 455 827 99 11.60714 72.15903520 400 947 98 12.5 64.08382342 400 889 102 8.928571 61.57073504 475 918 104 7.142857 71.70918383 300 1002 102 8.928571 46.17805506 450 1058 100 10.71429 70.65241509 500 1067 103 8.035714 76.21619485 350 1202 101 9.821429 54.4078500 450 1252 102 8.928571 69.26707500 410 1342 104 7.142857 61.89634

0 400 942 101 9.821429 62.18034500 475 967 100 10.71429 74.57754554 425 1096 99 11.60714 67.40129608 410 1294 99 11.60714 65.02242

NOV 09 492 425 1361 102 10.52632 65.4189

61

510 400 1471 104 8.77193 60.38667528 480 1519 104 8.77193 72.46401520 400 1639 99 13.15789 63.43651493 450 1682 102 10.52632 69.26707459 450 1691 104 8.77193 67.93501366 400 1657 102 10.52632 61.57073481 475 1418 105 8.695652 71.02623506 450 1719 103 10.43478 68.59457497 450 1766 102 11.30435 69.26707500 450 1816 107 9.322034 66.03029500 200 2116 109 9.917355 28.80832500 350 2266 105 13.22314 52.33512475 375 2366 108 10.7438 54.51575600 450 2482 106 12.39669 66.65322520 475 2561 107 11.57025 69.69864505 400 2666 103 14.87603 60.9729580 425 2321 104 14.04959 64.160840 350 1971 104 14.04959 52.838340 300 1671 105 13.22314 44.85867

818 350 2139 105 13.22314 52.33512753 330 2562 106 12.39669 48.87903

0 425 2137 107 11.57025 62.361940 425 1712 107 11.57025 62.36194

765 350 2127 107 11.57025 51.35689DEC 09 518 350 2295 106 5.357143 51.84139

704 400 2599 106 5.357143 59.2473510 400 2709 106 5.357143 59.2473508 375 2842 100 10.71429 58.87701169 350 1661 99 11.60714 55.50694375 300 2718 102 8.928571 46.17805225 360 2583 101 9.821429 55.9623458 400 2641 101 9.821429 62.18034351 400 2592 100 10.71429 62.80214376 415 2553 101 9.821429 64.5121392 425 2520 100 10.71429 66.72728204 400 2324 98 12.5 64.08382282 375 2231 96 14.28571 61.33022520 410 2341 98 12.5 65.68591494 400 2435 100 10.71429 62.80214

1302 350 3387 100 10.71429 54.95187

62

156 450 3093 100 10.71429 70.65241156 175 3074 99 11.60714 27.75347

0 325 2749 99 11.60714 51.542160 330 2419 98 12.5 52.86915

350 385 3409 99 11.60714 61.057641216 400 3261 105 9.482759 59.811561206 380 4087 105 9.482759 56.82099575 325 4337 104 10.34483 49.0641794 400 3631 104 10.34483 60.386670 400 3631 104 10.34483 60.386670 475 3156 104 10.34483 71.70918

JAN 10 0 400 2756 107 2.727273 58.693590 400 2356 105 4.545455 59.81156

100 400 2056 104 5.454545 60.38667400 400 2056 104 5.454545 60.38667500 400 2156 104 5.454545 60.38667200 425 1831 104 5.454545 64.16084

0 425 1306 104 5.454545 64.160840 260 1046 104 5.454545 39.25134

790 150 1786 107 4.464286 22.0101790 300 2276 103 8.035714 45.72971575 380 1896 105 6.25 56.82099725 325 2871 105 6.25 48.5969

0 375 2496 104 7.142857 56.61251300 260 2536 106 5.357143 38.51075240 300 2501 104 7.142857 45.29001260 300 2436 102 8.928571 46.17805400 350 2486 100 10.71429 54.95187100 300 2286 101 9.821429 46.63525

0 375 1910 102 8.928571 57.722560 300 1610 100 10.71429 47.10161

775 360 2025 102 8.928571 55.41365375 350 2050 102 8.928571 53.87439

0 250 1800 102 8.928571 38.4817FEB 10 500 300 2000 100 6.542056 47.10161

350 300 2050 96 10.28037 49.06417300 375 1675 96 10.28037 61.33022

0 300 1675 94 12.14953 50.108090 425 1250 103 11.2069 64.78376

300 430 1120 104 10.34483 64.91568

63

0 410 710 102 12.06897 63.11159 400 469 106 8.62069 59.2473

0 25 444 108 6.896552 3.634383394 150 688 108 6.896552 21.8063400 225 863 110 7.563025 32.11473244 90 1017 104 12.60504 13.587586 125 1478 108 9.243697 18.17192392 150 1720 105 11.76471 22.42934336 225 1831 106 10.92437 33.32661

0 375 1456 107 10.08403 55.025240 450 1006 107 10.08403 66.030290 480 526 103 13.44538 73.16754

217 350 393 107 10.08403 51.35689768 200 961 104 12.60504 30.19334114 275 800 105 11.76471 41.12045300 300 800 106 10.92437 44.43548519 330 989 106 10.92437 48.87903403 310 1082 106 10.92437 45.91666

MARCH 10 0 375 707 118 6.349206 49.89577217 330 594 117 7.142857 44.28356

0 365 229 118 6.349206 48.56522432 200 461 116 7.936508 27.06989450 225 686 114 9.52381 30.9879550 232 1004 115 8.730159 31.67412550 225 1329 112 11.11111 31.54125575 325 1579 118 9.923664 43.243400 350 1629 114 9.52381 48.2034625 325 1929 113 10.31746 45.15641600 375 2154 111 11.90476 53.04235416 350 2220 111 11.90476 49.50619471 350 2341 105 16.66667 52.33512142 300 2183 103 18.25397 45.72971912 425 2670 109 13.49206 61.21768475 275 2870 110 12.69841 39.25134510 400 2980 109 13.49206 57.61664435 300 3115 110 12.69841 42.81964615 375 3355 108 14.28571 54.51575504 400 3459 109 13.49206 57.61664377 450 3386 111 11.90476 63.65082896 500 3782 112 11.11111 70.09168

64

120 450 3452 113 10.31746 62.524260 425 3027 113 10.31746 59.050690 220 2807 113 10.31746 30.56741

65

4.1 SAM OF JACKETCalculate SAM with the help of stop watch of each and every operation. Take five

reading of each operation and average out which is written in the table.

SL. NO.

FRONT SECTION OPERATIONBASIC

TIME(MIN) WITH

ALLOWANCESAM(MIN)

SAM IN SEC.

1 front and bottom edge marking 0.71 0.8378 0.67024 40.2144

2 dart marking 0.53 0.6254 0.50032 30.0192

3 front dart sewing 0.56 0.6608 0.52864 31.7184

4 dart seam press 0.95 1.121 0.8968 53.808

5 front side panel attach 1.03 1.2154 0.97232 58.3392

6 centre back stitching 0.6 0.708 0.5664 33.984

7 armhole tape attach 0.83 0.9794 0.78352 47.0112

8 side panel press 1.7 2.006 1.6048 96.288

9 breast pocket attach 0.4 0.472 0.3776 22.656

10 breast pocket press 0.86 1.0148 0.81184 48.7104

11 breast pkt zig-zag 0.71 0.8378 0.67024 40.2144

12 welt pocket attach 0.65 0.767 0.6136 36.816

13 welt pocket press 0.96 1.1328 0.90624 54.3744

14 welt pkt. Backtack and pocket bag attach 1 1.18 0.944 56.64

15 pocket bag close 0.88 1.0384 0.83072 49.8432

16 canvas attach with press 1.06 1.2508 1.00064 60.0384

17 canvas close/ blind stitch 0.75 0.885 0.708 42.48

18 front body press 0.96 1.1328 0.90624 54.3744

19 armhole canvas cutting 1.2 1.416 1.1328 67.968

20 side seam 0.91 1.0738 0.85904 51.5424

21 press side seam 1 1.18 0.944 56.64

22 bottom press 0.73 0.8614 0.68912 41.3472

23 shoulder attach 0.95 1.121 0.8968 53.808

24 shoulder press 0.96 1.1328 0.90624 54.3744

25 shoulder pad attach 0.71 0.8378 0.67024 40.2144

       20.3904

1223.424

SR. NO.

LINING SECTIONBASIC

TIME(MIN) WITH

ALLOWANCESAM(MIN)

SAM IN SEC.

1 center back attach 0.86 1.0148 0.81184 48.7104

2 side panel attach with back 0.86 1.0148 0.81184 48.7104

3 front facing attach with lining 1.45 1.711 1.3688 82.128

4 press front facing /marking of pockets 0.95 1.121 0.8968 53.808

66

5 saddle stitch 0.6 0.708 0.5664 33.984

6 lining welt pocket 1.33 1.5694 1.25552 75.3312

7 press welt pocket 0.9 1.062 0.8496 50.976

8 brand label attachment with pocket bag 0.76 0.8968 0.71744 43.0464

9 pocket bag attach and close 1 1.18 0.944 56.64

10 front side panel attach with back 1.03 1.2154 0.97232 58.3392

11 shoulder attach 0.8 0.944 0.7552 45.312

13 sweat shield 0.7 0.826 0.6608 39.648

14 sweat shield attach 0.75 0.885 0.708 42.48

15 collar attach 0.9 1.062 0.8496 50.976

16 lining press 0.83 0.9794 0.78352 47.0112

17 neckband stitch 0.86 1.0148 0.81184 48.7104

18 collar press 0.42 0.4956 0.39648 23.7888

19 canvas attach/blind hem 0.52 0.6136 0.49088 29.4528

        14.6509 879.0528SR. NO.

SLEEVE SECTIONBASIC

TIME(MIN) WITH

ALLOWANCESAM(MIN)

SAM IN SEC.

1 sleeve head ready 0.6 0.708 0.5664 33.984

2 elbow seam lining stitch 0.85 1.003 0.8024 48.144

3 elbow seam shell stitch 0.65 0.767 0.6136 36.816

3 shell elbow seam press 0.7 0.826 0.6608 39.648

4 sleeve vent 0.63 0.7434 0.59472 35.6832

5 bottom press 0.85 1.003 0.8024 48.144

6 sleeve vent tack 0.88 1.0384 0.83072 49.8432

7 button marking 0.35 0.413 0.3304 19.824

8 button attach to sleeve 0.88 1.0384 0.83072 49.8432

9 sleeve head attach 0.73 0.8614 0.68912 41.3472

10 shell and lining attach 0.93 1.0974 0.87792 52.6752

11 inseam close (shell & lining) 1.2 1.416 1.1328 67.968

12 inseam split press 0.75 0.885 0.708 42.48

13 sleeve close 0.66 0.7788 0.62304 37.3824

14 turn and press 0.48 0.5664 0.45312 27.1872

        10.5162 630.9696SR. NO.

SMALL PART SECTIONBASIC

TIME(MIN) WITH

ALLOWANCESAM(MIN)

SAM IN SEC.

1 pocket facing attach 0.26 0.3068 0.24544 14.7264

2 lining pocket facing attach with label 0.4 0.472 0.3776 22.656

5 flap run stitch 0.3 0.354 0.2832 16.992

6 turn and press of flap 0.5 0.59 0.472 28.32

7 breast pocket flap press 0.34 0.4012 0.32096 19.2576

67

8 canvas zig zag 0.7 0.826 0.6608 39.648

9 canvas felt attach 0.73 0.8614 0.68912 41.3472

10 canvas tape attach 0.83 0.9794 0.78352 47.0112

11 breast pocket marking 0.2 0.236 0.1888 11.328

        4.02144 241.2864SR. NO.

COLLAR SECTIONBASIC

TIME(MIN) WITH

ALLOWANCESAM(MIN)

SAM IN SEC.

1 collar marking 0.1 0.118 0.0944 5.664

2 Collar notch marking 0.1 0.118 0.0944 5.664

3 chain stitch 0.216 0.25488 0.2039 12.23424

4 collar zig-zag 0.26 0.3068 0.24544 14.7264

5 collar stand attach 0.31 0.3658 0.29264 17.5584

6 press and fuse collar 0.25 0.295 0.236 14.16

7 collar corner lock 0.216 0.25488 0.2039 12.23424

        1.37069 82.24128SR. NO.

ASSEMBLY-1 OPERATIONBASIC

TIME(MIN) WITH

ALLOWANCESAM(MIN)

SAM IN SEC.

1 matching of parts 0.9 1.062 0.8496 50.976

2 lapel peak 1.4 1.652 1.3216 79.296

3 collar zig-zag 0.8 0.944 0.7552 45.312

4 front edge stitch 1.35 1.593 1.2744 76.464

5 sticker removal and manual cutting 0.9 1.062 0.8496 50.976

6 front edge press open 0.88 1.0384 0.83072 49.8432

7 bottom closing stitch 2.5 3.2 2.56 153.6

8 piping pressing 3 3.54 2.832 169.92

9 lapel pressing 2 2.36 1.888 113.28

10 button hole marking 0.7 0.826 0.6608 39.648

11 buttonhole + flowe4rhole 0.75 0.885 0.708 42.48

12 hanger loop+ bar tack 0.55 0.649 0.5192 31.152

        15.0491 902.9472SR. NO.

ASSEMBLY-2 OPERATIONBASIC

TIME(MIN) WITH

ALLOWANCESAM(MIN)

SAM IN SEC.

1 sleeve attach 2.16 2.5488 2.03904 122.3424

2 sleeve crown pad pressing 0.95 1.121 0.8968 53.808

3 shoulder pad stitching 0.8 1.06 0.849 50.94

4 armhole basting 1.15 1.357 1.0856 65.136

5 lining close 3.16 3.7288 2.98304 178.9824

6 vent top stitch1 1.18 0.944 56.64

7 sleeve lock

8 thread trimming 0.6 0.708 0.5664 33.984

9.36388 561.8328

68

Summary

SECTIONWISE SAM IN SEC. IN MINLINING 879.0528 14.65088SLEEVE 630.9696 10.51616FRONT 1223.424 20.3904ASS.-1 902.9472 15.04912ASS.-2 561.8328 9.36388COLLAR & SMALL PARTS 323.5277 5.392128TOTAL SAM 4521.754 75.36257

69

Chapter-VI

70

5 Results

5.1 Relation between WIP and outputGeneral assumption is greater the WIP in a production line, greater is the output.

WIP is calculated by subtracting input from output. So output is one of the factors

influencing WIP. With help of 11 month data the study aims at finding out correlation

between WIP and output.

As reflected in the graph WIP varies from 229 to 4337 where as output ranges

from 25 to 650. The correlation between the two couldn’t be established. Using

“correlation coefficient test” relation of strength between WIP and output was

established. For this purpose 11 month data was collected. To start with individual

71

month data was analyzed and lastly cumulative data (11 month) was analyzed to

establish relation between output and WIP.

RELATION BETWEEN WIP & OUTPUT CORRELATION COEFFICIENTFOR MONTH OF MAY 2009 0.761481124FOR MONTH OF JUNE 2009 0.140470155FOR MONTH OF JULY 2009 0.661726143FOR MONTH OF AUG 2009 -0.283061582FOR MONTH OF SEP 2009 0.092156771FOR MONTH OF OCT 2009 0.011654819FOR MONTH OF NOV 2009 -0.256482795FOR MONTH OF DEC 2009 -0.036840554FOR MONTH OF JAN 2010 0.096791828FOR MONTH OF FEB 2010 -0.057707101FOR MONTH OF MARCH 2010 0.574960779FOR MONTH OF MAY 09 TO MARCH 10 0.366719609

Calculated data shown that in month of May 2009 strength of relation between

WIP and output is quite significant (0.76), which shown that 76% dependency on each

other. Positive value shown that WIP and output they are directly correlated with each

other. Means when WIP increases output also increases. Similarly month of July 2009

and March 2010, this is quite significant positive correlation.

Correlation coefficient value of month of June 2009, Sep 2009, Oct 2009, and

Jan 2010 had very less; this is 0.14, 0.09, 0.01, and 0.09 respectively. These values are

nearer to 0. This means there is no significant relation between them. Values are

positive means they are positively correlated but only 14%, 9%, 1% and 9% respectively

dependent on WIP.

Similarly correlation coefficient value of month of Aug 2009, Nov 2009, Dec 2009,

and Feb 2010 had -0.28, -0.25, -0.04 and -0.06 respectively. These values are nearer to

0. This means there is no significant relation between them. Values are negative means

they are negatively correlated but only 28%, 25%, 4% and 6% respectively indirectly

dependent on WIP.

72

Lastly calculated correlation coefficient of whole 11 month data (for month of May

2009 to March 2010) .This was 0.37, this is also less than 0.5, means there is no

significant correlation between them. Only output is 37% dependent on WIP. Positive

value shown that positively correlated and directly dependent on WIP. Strength of

relation is only 37%, this study couldn’t establish any direct & significant correlation

between output and WIP.

5.2 Relation between WIP and Line efficiencyGeneral assumption, more WIP in line less the line efficiency and vice-versa.

WIP is calculated by subtraction of Input and output in the line. Output is directly

proportional to line efficiency. So Line efficiency may be a factor influences WIP. With

the help of 11 month data try to find out correlation between WIP and line efficiency.

From graph WIP had varied from 229 to 4337 whereas line efficiency had varied

from 3.6% to 94.5%. This data can’t say anything about any relation between them. So

with the help correlation coefficient test try to find out the relation of strength between

73

them. For this purpose taken 11 month data and try to find out the correlation between

them. First of all analyzed individual month data and lastly whole data (11 month) to

establish relation between line efficiency and WIP.

RELATION BETWEEN WIP & LINE EFFICIENCY CORRELATION COEFFICIENTFOR MONTH OF MAY 2009 0.764553697FOR MONTH OF JUNE 2009 0.23492695FOR MONTH OF JULY 2009 0.605890894FOR MONTH OF AUG 2009 -0.309851794FOR MONTH OF SEP 2009 0.115252285FOR MONTH OF OCT 2009 -0.015632589FOR MONTH OF NOV 2009 -0.308470194FOR MONTH OF DEC 2009 -0.126594918FOR MONTH OF JAN 2010 0.08003724FOR MONTH OF FEB 2010 -0.002627631FOR MONTH OF MARCH 2010 0.641701775FOR MONTH OF MAY 09 TO MARCH 10 0.344653741

Calculated data shown that in month of May 2009 strength of relation between

WIP and line efficiency is quite significant (0.76), which shown that 76% dependency on

each other. Positive value shown that WIP and line efficiency they are directly

correlated with each other. Means when WIP increases line efficiency also increases.

Similarly month of July 2009 and March 2010, this is quite significant positive

correlation.

Correlation coefficient value of month of June 2009, Sep 2009 and Jan 2010 had

very less; this is 0.23, 0.11, and 0.08 respectively. These values are nearer to 0. This

means there is no significant relation between them. Values are positive means they are

positively correlated but only 23%, 11% and 8% respectively dependent on WIP.

Similarly correlation coefficient value of month of Aug 2009, Oct 2009, Nov 2009

, Dec 2009, and Feb 2010 had -0.30, -0.01, -0.30, -0.12 and -0.002 respectively. These

values are nearer to 0. This means there is no significant relation between them. Values

are negative means they are negatively correlated but only 30%, 1%, 30%, 12% and

0.2%% respectively indirectly dependent on WIP.

74

Lastly calculated correlation coefficient of whole 11 month data (for month of May

2009 to March 2010) .This was 0.34, this is also less than 0.5, means there is no

significant correlation between them. Only line efficiency is 34% dependent on WIP.

Positive value showed that positively correlated and dependent on WIP. Strength of

relation is only 34%, this study couldn’t establish any direct & significant correlation

between line efficiency and WIP.

5.3 Relation between WIP and InputGeneral assumption, more the input in line more the WIP. WIP is calculated by

subtraction of Input and output in the line. So input may be a factor influences WIP. With

the help of 11 month data try to find out correlation between WIP and input.

From graph WIP had varied from 229 to 4337 where as Input had varied

from 0 to 1302. This data can’t say anything about any relation between them. So with

the help correlation coefficient test try to find out the relation of strength between them.

For this purpose taken 11 month data and try to find out the correlation. First of all

analyzed individual month data and lastly whole data (11 month) to establish relation

between Input and WIP.

75

RELATION BETWEEN WIP & INPUT CORRELATION COEFFICIENTFOR MONTH OF MAY 2009 0.292189972FOR MONTH OF JUNE 2009 0.334102464FOR MONTH OF JULY 2009 0.652938758FOR MONTH OF AUG 2009 0.524325978FOR MONTH OF SEP 2009 0.338959162FOR MONTH OF OCT 2009 0.481261269FOR MONTH OF NOV 2009 0.165364201FOR MONTH OF DEC 2009 0.308694194FOR MONTH OF JAN 2010 0.225481434FOR MONTH OF FEB 2010 0.272219284FOR MONTH OF MARCH 2010 0.254592034FOR MONTH OF MAY 09 TO MARCH 10 0.329156073

Calculated data shown that in month of July 2009 strength of relation between

WIP and Input is quite significant (0.65), which shown that 65% dependency on each

other. Positive value shown that WIP and Input they are directly correlated with each

other. Means when Input increases WIP also increases. Similarly month of Aug 2009

had quite significant positive correlation.

Correlation coefficient value of month of June 2009, Sep 2009, Oct 2009, Nov

2009, Dec 2009, Jan 2010, Feb2010 and March 2010 had very less. These values are

nearer to 0. This means there is no significant relation between them. Values are

positive means they are positively correlated.

Lastly calculated correlation coefficient of whole 11 month data (for month

of May 2009 to March 2010) .This was 0.32, this is also less than 0.5, means there is no

significant correlation between them. Only Input is 32% dependent on WIP. Positive

valued shown that positively correlated and directly dependent on WIP. Strength of

relation is only 32%, this study couldn’t establish any direct & significant correlation

between Input and WIP.

76

5.4 Relation between WIP and Operator absenteeismGeneral assumption, more WIP in line more the absenteeism and vice-versa.

WIP is calculated by subtraction of Input and output in the line. Absenteeism directly

influenced output of the line. So Absenteeism may be a factor influences WIP. With the

help of 11 month data try to find out correlation between WIP and operator

absenteeism.

From graph WIP had varied from 229 to 4337 whereas absenteeism had varied

from 0.88% to 18.25%. This data can’t say anything about any relation between them.

So with the help correlation coefficient test try to find out the relation of strength

between them. For this purpose taken 11 month data and try to find out the correlation

between them. First of all analyzed individual month data and lastly whole data (11

month) to establish relation between absenteeism and WIP.

77

RELATION BETWEEN WIP & OPERATOR ABSENTEEISM CORRELATION COEFFICIENTFOR MONTH OF MAY 2009 0.189404273FOR MONTH OF JUNE 2009 0.526949846FOR MONTH OF JULY 2009 -0.753468821FOR MONTH OF AUG 2009 -0.11807085FOR MONTH OF SEP 2009 0.118727027FOR MONTH OF OCT 2009 -0.24991573FOR MONTH OF NOV 2009 0.593978361FOR MONTH OF DEC 2009 -0.072957388FOR MONTH OF JAN 2010 -0.026321122FOR MONTH OF FEB 2010 0.020629655FOR MONTH OF MARCH 2010 0.622341054FOR MONTH OF MAY 09 TO MARCH 10 0.344653741

Calculated data show that month of June 2009, Nov 2009, and March 2010 had

value greater than 0.5, which shown relation of strength is quite good. These months of

absenteeism are directly proportional to WIP. Whereas month of July 2009 had shown

strongly negative correlation.

Similarly value of r of month of May 2009, Sep 2009 and Feb 2010 had very less,

which is below than 0.5. This means there is no significant correlation between them.

But value of r of month of Aug 2010, Oct 2009, Dec 2009 and Jan 2010 had

negative value, which is below than -0.5. These also show that there is no significant

correlation between them.

Lastly calculated correlation coefficient of whole 11 month data (for month of May

2009 to March 2010) .This was 0.34, this is also less than 0.5, means there is no

significant correlation between them. Only absenteeism is 34% dependent on WIP.

Positive value shown that positively correlated and directly dependent on WIP. Strength

of relation is only 34%, this study couldn’t establish any direct & significant correlation

between absenteeism and WIP.

78

5.5 Relation between output and Line efficiencyMore line efficiency more the output and vice-versa. Both factors output and line

efficiency may affect WIP in line. Study tries to find out strength of relation between

output and line efficiency. For this purpose taken 11 month data and calculate r value of

output and line efficiency.

From graph OUTPUT had varied from 25 to 650 whereas line efficiency had

varied from 3.63% to 94.5%. This data can’t say anything about any relation between

them. So with the help correlation coefficient test try to find out the relation of strength

between them. For this purpose taken 11 month data and try to find out correlation. First

of all analyzed individual month data and lastly whole data (11 month) to establish

relation between line efficiency and OUTPUT.

79

RELATION BETWEEN OUTPUT & LINE EFFICIENCY CORRELATION COEFFICIENTFOR MONTH OF MAY 2009 0.991983293FOR MONTH OF JUNE 2009 0.981713704FOR MONTH OF JULY 2009 0.993757125FOR MONTH OF AUG 2009 0.995563806FOR MONTH OF SEP 2009 0.994922953FOR MONTH OF OCT 2009 0.994406774FOR MONTH OF NOV 2009 0.991792106FOR MONTH OF DEC 2009 0.981445136FOR MONTH OF JAN 2010 0.996199027FOR MONTH OF FEB 2010 0.994941806FOR MONTH OF MARCH 2010 0.990257632FOR MONTH OF MAY 09 TO MARCH 10 0.989977859

Calculated value of r shows that strength of correlation is very good. Value of r is

nearer to +1. Positive value shown that output and line efficiency are directly

proportional to each other, means output increases line efficiency also increases and

vice-versa.

80

5.6 Relation between Line efficiency and operator absenteeism

More operator absenteeism less the line efficiency and vice-versa. Both factors

absenteeism and line efficiency may affect WIP in line. Study tries to find out strength of

relation between absenteeism and line efficiency. For this purpose taken 11 month data

and calculate r value of absenteeism and line efficiency.

From graph line efficiency had varied from 3.63% to 94.5% whereas

absenteeism had varied from 0.88% to 18.25%. This data can’t say anything about any

relation between them. So with the help correlation coefficient test try to find out the

relation of strength between them. For this purpose taken 11 month data and try to find

out correlation. First of all analyzed individual month data and lastly whole data (11

month) to establish relation between line efficiency and operator absenteeism.

81

RELATION BETWEEN ABSENTEEISM & LINE EFFICIENCY CORRELATION COEFFICIENTFOR MONTH OF MAY 2009 -0.066264098FOR MONTH OF JUNE 2009 0.04656705FOR MONTH OF JULY 2009 -0.432196224FOR MONTH OF AUG 2009 0.085693367FOR MONTH OF SEP 2009 0.285116387FOR MONTH OF OCT 2009 0.028487673FOR MONTH OF NOV 2009 -0.291471493FOR MONTH OF DEC 2009 0.057923127FOR MONTH OF JAN 2010 -0.107834459FOR MONTH OF FEB 2010 0.298585317FOR MONTH OF MARCH 2010 0.294223791FOR MONTH OF MAY 09 TO MARCH 10 -0.007436478

Calculated value of r shows that strength of correlation is very poor. Value of r is

nearer to 0.Value shown that absenteeism and line efficiency had no significant

correlation; means absenteeism and line efficiency both are independent. There had no

dependency on each other.

82

5.7 Relation between Output and Operator AbsenteeismMore operator absenteeism less the Output. Both factors absenteeism

and Output may affect WIP in line. Study tries to find out strength of relation between

absenteeism and Output. For this purpose taken 11 month data and calculate r value of

absenteeism and Output.

From graph Output had varied from 25 to 650 whereas absenteeism had varied

from 0.88% to 18.25%. This data can’t say anything about any relation between them.

So with the help correlation coefficient test try to find out the relation of strength

between them. For this purpose taken 11 month data and try to find out correlation. First

of all analyzed individual month data and lastly whole data (11 month) to establish

relation between Output and operator absenteeism.

83

RELATION BETWEEN OUTPUT & OPERATOR ABSENTEEISM CORRELATION COEFFICIENTFOR MONTH OF MAY 2009 -0.179389509FOR MONTH OF JUNE 2009 -0.1428189FOR MONTH OF JULY 2009 -0.52832003FOR MONTH OF AUG 2009 0.015688849FOR MONTH OF SEP 2009 0.197514149FOR MONTH OF OCT 2009 -0.076630635FOR MONTH OF NOV 2009 -0.31548758FOR MONTH OF DEC 2009 -0.102758056FOR MONTH OF JAN 2010 -0.189672391FOR MONTH OF FEB 2010 0.283865983FOR MONTH OF MARCH 2010 0.162987123FOR MONTH OF MAY 09 TO MARCH 10 -0.05792246

Calculated value of r shows that strength of correlation is very poor except month

of July 2009. Value of r is nearer to 0.Valued shown that absenteeism and Output had

no significant correlation; means absenteeism and Output both are independent. There

had no dependency on each other.

Logically if operator absent today its affected tomorrow, day after tomorrow or

next to next day output. So study tried to find correlation If absenteeism today what was

the correlation of output after one day, 2nd, 3rd day, 4th day, 5th day. For this purpose

taken 11 month data and try to find out correlation.

RELATION BETWEEN OUTPUT & OPERATOR ABSENTEEISMCORRELATION COEFFICIENT

CORRELATION BETWEEN OPERATOR ABSENTEEISM ON OUTPUT AFTER 1 DAY 0.00017493CORRELATION BETWEEN OPERATOR ABSENTEEISM ON OUTPUT AFTER 2 DAY -0.015011756CORRELATION BETWEEN OPERATOR ABSENTEEISM ON OUTPUT AFTER 3 DAY -0.00753055CORRELATION BETWEEN OPERATOR ABSENTEEISM ON OUTPUT AFTER 4 DAY -0.004821064CORRELATION BETWEEN OPERATOR ABSENTEEISM ON OUTPUT AFTER 5 DAY -0.016229764

Calculated value of r shows that strength of correlation is very poor. Value of r is

nearer to 0.Valued shown that absenteeism and Output had no significant correlation;

84

means absenteeism and Output both are independent. There had no dependency on

each other.

Company had taken 10% extra operator so try to find out correlation if

absenteeism is 10% and more than 10%, 11% and more than 11%, 12% and more

than12%, and so on. For this purpose taken 11 month data and try to find out

correlation.

RELATION BETWEEN OUTPUT & OPERATOR ABSENTEEISMCORRELATION COEFFICIENT

CORRELATION BETWEEN OPERATOR ABSENTEEISM 10%/ MORE THAN 10% ON OUTPUT -0.0528CORRELATION BETWEEN OPERATOR ABSENTEEISM 11%/ MORE THAN 11% ON OUTPUT -0.01808CORRELATION BETWEEN OPERATOR ABSENTEEISM 12%/ MORE THAN 12% ON OUTPUT 0.055644CORRELATION BETWEEN OPERATOR ABSENTEEISM 13%/ MORE THAN 13% ON OUTPUT -0.24865CORRELATION BETWEEN OPERATOR ABSENTEEISM 14%/ MORE THAN 14% ON OUTPUT -0.52649CORRELATION BETWEEN OPERATOR ABSENTEEISM 15%/ MORE THAN 15% ON OUTPUT -0.92836CORRELATION BETWEEN OPERATOR ABSENTEEISM 16%/ MORE THAN 16% ON OUTPUT -0.92836

Calculated value shown that up to 12% and more than 12% absenteeism there is

no significant correlation between output and absenteeism. Means both (output and

absenteeism) had independent variable. But if absenteeism 14% and more than 14%

There is 52% dependency on each other indirectly.

Similarly if absenteeism 15% and more than 15%. There is 92%

dependency on each other indirectly. Strength of relation had quite well.

This study established indirect & significant correlation between absenteeism and

Output if absenteeism more than 14% and more than 14%.

85

5.8 Relation between Input and OutputWIP is calculated by subtraction of Input and output in the line. So Input and

output might be a factor influences WIP. Study tried to find out strength of relation

between Input and Output. For this purpose taken 11 month data and calculate r value

of Input and Output.

From graph Input had varied from 0 to 1302 whereas output had varied from 25

to 650. This data can’t say anything about any relation between them. So with the help

correlation coefficient test try to find out the relation of strength between them. For this

purpose taken 11 month data and tried to find out correlation. First of all analyzed

individual month data and lastly whole data (11 month) to established correlation

between Input and output.

86

RELATION BETWEEN OUTPUT & INPUT CORRELATION COEFFICIENTFOR MONTH OF MAY 2009 0.186892668FOR MONTH OF JUNE 2009 0.149097703FOR MONTH OF JULY 2009 0.254392421FOR MONTH OF AUG 2009 -0.141672259FOR MONTH OF SEP 2009 0.018530435FOR MONTH OF OCT 2009 0.261382969FOR MONTH OF NOV 2009 0.001130583FOR MONTH OF DEC 2009 0.041030203FOR MONTH OF JAN 2010 -0.247759148FOR MONTH OF FEB 2010 -0.404775021FOR MONTH OF MARCH 2010 0.128012509FOR MONTH OF MAY 09 TO MARCH 10 0.215719965

Calculated value of r shows that strength of correlation is very poor. Value of r

had below than +/-0.5.Valued shown that Input and Output had no significant

correlation; means Input and Output both are independent. There had no dependency

on each other.

Logically if Input has putted in the line its production come on throughput time or

after that. So study tried to find correlation If today Input has putted in the line what was

the correlation of output after one day, 2nd, 3rd day, 4th day, 5th day and so on. For this

purpose taken 11 month data and tried to find out correlation.

RELATION BETWEEN OUTPUT & INPUT CORRELATION COEFFICIENT(r)CORRELATION BETWEEN INPUT ON OUTPUT AFTER 1 DAY 0.256468336CORRELATION BETWEEN INPUT ON OUTPUT AFTER 2 DAY 0.278511342CORRELATION BETWEEN INPUT ON OUTPUT AFTER 3 DAY 0.311368696CORRELATION BETWEEN INPUT ON OUTPUT AFTER 4 DAY 0.305845937CORRELATION BETWEEN INPUT ON OUTPUT AFTER 5 DAY 0.35813177CORRELATION BETWEEN INPUT ON OUTPUT AFTER 6 DAY 0.326608779CORRELATION BETWEEN INPUT ON OUTPUT AFTER 7 DAY 0.36829406CORRELATION BETWEEN INPUT ON OUTPUT AFTER 8 DAY 0.287189447CORRELATION BETWEEN INPUT ON OUTPUT AFTER 9 DAY 0.286961104

87

Calculated value shown that strength of relation between input and output gone

increases after 1st, 2nd, 3rd … and 7th day but after 8th day and so on its gone decreases.

Value of r is below than 0.5. So there is no significant correlation between output and

Input. Means both (output and Input) had independent variable

This study couldn’t establish any direct & significant correlation between output

and Input.

88

Chapter-VII

89

6 Calculation for optimum WIP

During my research work the finding was that there is no direct and significant

correlation between the WIP and factors taken into account. Also the WIP varied from

229 to 4337. So For Calculating Optimum WIP in the line following formula was

devised:-

Throughput time= {SAM of critical path (1+WIP in critical path)}

For the calculation of optimum throughput time harmonic mean was applied on

the data collected from the industry during the research project.

Mar-10 po/num order sizeThroughput

time(Multiple of 8 hrs)

(Throughput time) time in min.

JCP 124 2 960 JCP 93 2 960 JCP 174 1 480 JCP 258 1 480 158041 1825 2 960 158040 1325 4 1920 B60 1429 3 1440 B60 1472 5 2400 B60 615 4 1920 G60 404 5 2400

Feb-10 157389 925 5 2400 TBS2 150 4 1920 TBS4 150 4 1920 TBS3 159 3 1440 TBJ1 194 2 960 TBJ2 200 4 1920 JCP 1958 4 1920 JCP 124 1 480 JCP 93 2 960 JCP 882 1 480 JCP 1222 2 960

90

Jan-10 jcp 1790 6 2880 jcp 2200 5 2400 B60 790 1 480

Dec-09 156633 1884 6 2880 JCP 6484 7 3360

Nov-09 154039 300 3 1440 154042 1224 3 1440 154061 376 4 1920 154038 88 3 1440 154032 338 4 1920 154031 108 3 1440 154045 823 4 1920 155390 925 4 1920 15D 1175 4 1920 B03 2005 5 2400 156634 4576 4 1920

Oct-09 154034 340 3 1440 154035 147 3 1440 154044 980 2 960 154036 285 2 960 154046 1435 1 480 154048 340 2 960 154060 520 2 960 153128 1200 2 960 153132 1300 2 960 154415 1350 3 1440 154416 800 2 960

Sep-09 153472 2219 5 2400 250 1930 3 1440 153408 632 4 1920 153866 1100 3 1440

Harmonic mean of the throughput time which were collected from the industry is

= 1183.339 min.

91

SAM of the critical path is (Here front, Ass-1 and Ass-II are in critical path)

critical path SAM

SAM in SEC

SAM in min

front 1223.424ass1 902.9472ass2 561.8328

critical path SAM 2688.204 44.8034

Now putting the value of throughput time and SAM of critical path in above formula and calculate the WIP in critical path is

WIP in critical path = (Throughput time/SAM of critical path)-1

= (1183.339/44.8034)-1

= 25.4

This means for one operation 25 WIP should be maintained, where the total operation in the critical path is 45.

So total WIP should be maintained in the line will be = 45* 25 = 1125.

92

Chapter-VIII

93

7 Limitations and scope of further study

The study aimed at establishing correlation between WIP and factors affecting it.

In a production line inconsistent WIP can be attributed to inconsistent Input and output.

The study couldn’t establish remarkably distinct correlation between WIP and factors

affecting it m/c break down, production system, line balanced%, order size, frequency of

style change. Correlation between WIP and absenteeism couldn’t be established till

absenteeism was above 13%. To overcome the problem the unit had taken 10% extra

operators due to which the correlation between WIP and absenteeism couldn’t be

established in a distinct way.

To summarize inconsistent Input, inconsistent output and extra number of

operators can be accounted for non establishment of clearly defined and significant

correlation between WIP and various factors affecting it.

As there are myriad factors affecting WIP the study focused on establishing

correlation between WIP and Input, output, absenteeism and Line efficiency. There is

scope of further study to precisely establish correlation between WIP and machine

break down, Production system, line balanced%, order size, frequency of style change

which were not a part of this study.

94

Chapter-IX

95

8 ConclusionThe objective of the study was to establish correlation between WIP and factors

affecting it. Out of the myriad factors affecting WIP the ones which were a part of the study were input, output, absenteeism, and Line efficiency. Data was collected on a daily basis (11 month data) and correlation was established between WIP and these factors. The “relation of strength” established between WIP and these factors are as such:

Relation between WIP and Output: - 0.37

Relation between WIP and line efficiency: - 0.34

Relation between WIP and Input: - 0.33

Relation between WIP and operator absenteeism: - 0.34

Relation between Output and Line efficiency: - 0.99, which indicates that there is direct and significant correlation between Output and line efficiency.

Relation between Line efficiency and operator absenteeism: - -0.007

Relation between Output and operator absenteeism: - -0.06

Relation between Input and Output: - 0.22

To conclude the study established that no direct and significant correlation exist between WIP and various factors affecting it (Input, output, operator absenteeism and line efficiency). Absenteeism only beyond 14% found to have adverse affect on output.

To summarize inconsistent Input, inconsistent output and extra number of operators can be accounted for non establishment of clearly defined and significant correlation between WIP and various factors affecting it.

96

9 Bibliography

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Sankar Ray & Edited by Prabir Jana

Chuter, A.J., 1998b, Introduction to Clothing Production Management, second

Ed, 1998, Blackwell Science Ltd, pp 19-20)

Assembly Line Balancing Made Easy, Prabir Jana, Associate Professor,

Garment Manufacturing Technology Department, NIFT, New Delhi

Control Parameters in Line Balancing, Prabir Jana, Associate Professor,

Garment Manufacturing Technology Department, NIFT, New Delhi

Pratima J.Naik & Chandresh Raj (2000-2002) batch diploma project “Effect of

work in progress on Throughput time and line efficiency”.

Juki manual

Study on Absenteeism of Workers at Nutrine chocolate in year 2005.

Barnes, Ralph M ninth edition, ‘motion and time study – design and

measurement of work’ john Wiley & sons Inc.

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2010)

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