improving productivity of manufacturing division using lean concepts

International Journal of Lean Thinking Volume 3, Issue 2 (December 2012)

Lean Thinkingjournal homepage: www.thinkinglean.com/ijlt

IMPROVING PRODUCTIVITY OF MANUFACTURING DIVISION USING LEAN CONCEPTS AND DEVELOPMENT OF MATERIAL GRAVITY FEEDER

–A CASE STUDY

K.Hemanand Department of Mechanical Engineering, PSG College of

Technology,Coimbatore – 641 004, India.

D.Amuthuselvan Department of Mechanical Engineering, PSG College of

Technology,Coimbatore – 641 004, India.

A B S T R A C T K E Y W O R D S

A R T I C L E I N F O

Layout,

Simulation,

Material, Time,

Transportation

Received 30 July 2012

Accepted 07 September 2012

Available online 01 October 2012

The automotive industry has been experiencing a competitive

environment and striving hard to find methods to reduce

manufacturing cost, waste and improve quality. Lean

manufacturing concepts are used by the industries to reduce work

in progress inventories and also to reduce the waste for competing

in the global market. The ultimate goal is to speed up the process

there by increasing productivity through a proper utilization of

man and machine. In a manufacturing industry, the layout and

material flow in the shop floor decides its productivity. Material

handling system also plays a key role in influencing productivity,

throughput time and cost of the product.

This research work has been carried out as a case study in an

automotive industry with the objective of waste reduction. Efforts

are made to reduce the motion waste in the shop floor. The

problems in the current layout are identified and analyzed through

simulation. Then the layout is modified, simulated and the results

are compared with the current layout. The results revealed an

improvement of 11.95% in productivity. A new material handling

system has been designed and developed to reduce the motion

wastes and unwanted transportation.

________________________________

* Corresponding Author

1. Introduction

Lean manufacturing is “A systematic approach for identifying and eliminating waste

through continuous improvement by flowing the product at the pull of customer in pursuit of

perfection”.

Lean manufacturing concepts are mostly applied in industries where more repetitive

human resources are used. In these industries productivity is highly influenced by the efficiency

working people with tools or operating equipments. To eliminate waste, it is important to

understand exactly what it is and where it exists. The processes add either value or waste to the

production of goods. The seven manufacturing wastes originated in Japan, where waste knows

as “muda" as demonstrated by Rotaru (2008).

S.Chidambara Raja* Department of Mechanical

Engineering, PSG College of Technology,Coimbatore –

641 004, India.

[email protected]

G.Sundararaja Department of Mechanical

Engineering, PSG College of Technology,Coimbatore –

641 004, India.

HEMANAND,AMUTHUSELVAN, CHIDAMBARA RAJA, SUNDARARAJA / International Journal of Lean Thinking Volume 3, Issue 2(December 2012)

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as “muda" as demonstrated by Rotaru (2008).

The concept of Lean manufacturing first came to be more widely known with the book ‘The Machine

That Changed the World’ published by Womack et al.(1990) and later through the book ‘Lean Thinking’.

The Key points of emphasis in Lean appear to be reducing process variability, reducing system cycle times,

and above all, eliminating wastes in the manufacturing cycle as stated by Womack and Jones (1986).

Paul and Rabindra (2006) used subjective assessment through questionnaire, direct observation

method, and archival data to improve productivity, quality, increasing revenue and reducing rejection cost

of the Manual Component

Insertion (MCI) lines in a printed circuit assembly (PCA) factory. Live experiments were conducted

on production lines. The drawback of this work is that an experimental design could not be performed to

find the best insertion sequence and component bin arrangements as there was a hindrance in

conducting experiments in real-life line, i.e. the study itself might reduce line output and affect quality.

Brown and Mitchell (1988) did an investigation into operators, engineers, and managers of PCA

factories to determine the work environment parameters that inhibited their performance and they

recommended opportunities to improve productivity & quality.

Lim and Hoffmann (1997) found that improved layout of the workplace increased productivity of the

workers, through more economical use of hand movements by conducting an experiment on hacksaws

assembly.

Christopher (1998) stated that the success in any competitive context depends on having either a cost

advantage or a value advantage, or, ideally, both.

Imad Alsyouf (2007) illustrated how an effective maintenance policy could influence the productivity

and profitability of a manufacturing process and showed how changes in the productivity affect profit,

separately from the effects of changes in the uncontrollable factors, i.e. price recovery

Browning and Heath (2009) developed a revised framework that reconceptualises the effect of lean

on production costs and used it to develop eleven propositions to direct further research and illuminated

how operations managers might control key variables to draw greater benefits from lean implementation.

White et al. (1999) found that plant size had a significant effect on the implementation of lean

practices. This shows that, regardless of establishing what lean is, it remains important to establish how

best to become lean in varied contexts.

Shah and Ward (2003) empirically validated four “bundles” of inter-related and internally consistent

practices; these are just-in-time (JIT), total quality management (TQM), total preventive maintenance

(TPM), and human resource management and investigated their effects on operational performance.

Flynn et al.(1995) and McKone et al.(2001) have explored the implementation and performance

relationship with two aspects of lean.

Crute et al.(2003) discussed the key drivers for Lean in aerospace and did a Lean implementation

case comparison which examines how difficulties that arise may have more to do with individual plant

context and management than with sector specific factors.

Womack et al.(1990) developed from the massively successful Toyota Production System, focusing on

the removal of all forms of waste from a system.

Krafcik, (1998) describes the Japanese-style manufacturing process pioneered by Toyota, which uses

a range of techniques including just-in-time inventory systems, continuous improvement, and quality

circles.


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In the cited literatures, Researchers revealed the importance of lean concepts in the competitive

industries especially for improving the productivity, quality of products, profitability, and for reducing

lean wastes and inventories.

The current research aims at improving productivity, increasing revenue of the plant by the reduction

of motion wastes. In addition, the design and development of the gravity feeder for the material handling

have been done as a value addition to this research work. The methodology of the current case study is

presented in the block diagram and depicted in Figure 1.The collection of data is done by direct

observation method. The time study is conducted using stop watch. From the collected data, the

problems in the current layout are identified and rectified by redesigning the layout in two aspects. One

aspect is reducing the material handling by the design and development of new material gravity feeder

which is designed in solid edge V19 and structural analysis is done in Ansys 12. Another aspect is

conducting the simulation study to reveal the performance of current layout using WITNESS simulating

software. Based on the results, a new layout is proposed and implemented.

Figure 1: Block diagram of methodology followed in the case study


120

Abbreviations

Opn - Operation

SPM - Special purpose machine

VMC - Vertical machining centre

HMC - Horizontal machining centre

RM - Raw material

FG - Finished goods

MHS - Material Handling System

M/C -Machine

No. - Number

Nomenclature

M - Bending Moment (N mm)

I - Moment of Inertia (mm4)

σ - Bending stress (N/mm2)

Y - Distance from neutral axis (mm)

2. Case study – Layout of CNC Machine shop

2.1 Current layout

A case study on machining of bearing cap has been chosen to find the working of current layout and

its performance. The Bearing cap for engine is machined using CNC and Special purpose machines. The

surfaces of bearing cap to be machined are marked by arrows in Figure 2.

Figure 2: Bearing cap machining details

The part flow and sequence of the machining processes carried out on the bearing cap from raw

material to final finished goods are shown in Figure 3. The machines used for the sequence of operations

are two Special purpose machines (SPM) and five CNC machines.


121

Figure 3: Flow chart for Process sequence

2.2 Time study

Time study is defined as a work measurement technique for recording the times and rates

of working for the elements of a specified job carried out under specified conditions, and for

analyzing the data so as to obtain the time necessary for carrying out the job at a defined level

of performance [6]. A time study has been carried out for all operations on the bearing cap in

the layout by direct observation method and the observed data is listed in Table 1.

Table 1 Details of Time study

Opn.

No.

M/C type No. of

operators

No. of

Components

Man working

time

in seconds

Man idle

time

İn seconds

M/C working

time

İn seconds

M/C idle

time

İn seconds

Cycle time

İn seconds

Cycle time per

Component

İin seconds

10 SPM 1 1 24 26 50 0 50 50

20 SPM 1 1 42 21 32 31 63 63

30-A VMC 1 2 95 0 95 0 95 48

30-B VMC 1 2 53 70 82 41 123 62

40-A VMC 1 2 63 15 25 53 78 39

40-B VMC 1 4 164 75 166 136 302 76

50 HMC 1 1 64 0 64 0 64 64

60 Deburring 2 1 60 0 0 0 60 60

70 Inspection 2 1 40 0 0 0 40 40

80 Washing &

Packing

2 1 10 4 4 10 14 14


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2.3 Takt time calculation

Takt time is the average time allowed to produce unit production to meet customer

demand and the process time should be less than or equal to the takt time. Takt time is

calculated based on machine available time and the required number of units. The procedure

followed to determine takt time for the current production of bearing cap is as follows:

Total available time = 3 Shifts / day /25 working days in a month

Customer Demand / day = 1167 pieces

Available Working time/shift

(Excluding lunch and tea break) = 420 minutes=25200 seconds.

Available time/day = 25200 x 3=75600 seconds.

Takt Time = Total available time /Customer demand

= 64.78 seconds/piece ≈ 65 seconds/piece

2.4 Cycle time calculation

From the time study on current layout (Table 1), it is identified that the operations 30A

and 30B are identical and so the cycle time of one component is found to be 55 seconds by

averaging the cycle times of these two machines. Similarly, operations 40A and 40B are

identical and by taking the average, one components’ cycle time is found to be 58 seconds.

For all the operations, cycle time for a single component is estimated and listed in Table 2.

Also, the same is depicted in the bar chart as shown in Figure 4.

Table 2 Processing time calculation

Opn

No. Process description

Cycle time

in seconds

10 Top and bottom face milling 50

20 Crank ,nut & joint face roughing 63

30 Nut face milling, drilling and processing dowel 55

40 Crank boring, Notch Milling 58

50 Joint face milling, Register width finishing 64

60 Deburring 60

70 Inspection 40

80 Washing and Packing 14


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Figure 4: Bar chart depicts the takt time for one day basis

2.5 Problems Identified in the current layout

The major issues faced in the current layout are explained as follows and the current

layout is depicted in Figure 5.

Figure 5: Current Layout

2.5.1 Idle time of the operator

Idle time of the operator is found from the time study and total idle time of all operators for three

shifts in one day is found and tabulated in Table 3.


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Table 3 Idle time of the operators for three shifts in one day

Sl.No Opn No.

Man idle time

for one cycle

in Seconds

Outputs/day

Idle time for 3

shifts in one

day in seconds

Idle time of

operator per

day inhours

1 20 21 1200 25200 7

2 30-

B 70 614 43024 11.95

3 40-

B 75 250 18775 5.22

Total 24.17

This idle time can be utilized in a better way by operating two machines simultaneously.

2.5.2 Waste in transportation by manual operation

In this layout material handling is done using wooden pallets which is transferred by trolley from one

station to another station. It consumes considerable time and results in increased manufacturing lead

time. The materials are moved from one station to another by operator causing motion waste and

transportation waste which is about 68.9 m for every batch of components as given in Table 4.

Table 4 Distance travelled by operator

Sl. No. Stations Distance travelled without MHS in

meters

1 RM- opn.10 1.53

2 opn.10-20 3.66

3 opn.20-30 12.2

4 opn.30-40 13.7

5 opn.40-50 10.7

6 opn.50-60 18.3

7 opn.60-70 4.88

8 opn.70-80 0.914

9 opn.80-FG 3.05

Total 68.9

The operator movement in the current layout is depicted in Figure 6. This has to be reduced with

suitable material handling device.


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Figure 6: Operator and material movement in Current layout

3. Proposed layout

Owing to the problems existing in the current layout, a new layout is proposed based on the study

and analysis of the current scenario.

The features of the new layout are listed as follows

1. All machines are connected with a new gravity feeder for carrying material from one station to

another which reduces transportation waste by the amount of 47.85 meters as shown in Table 5 and

feeder is shown in the Figure 7 as number 1.The transportation in the proposed layout is pictorially

depicted in Figure 8.

Table 5 Operator motion distance comparison

Sl. No. Stations

Current layout

without MHS in

meters

Proposed layout

with MHS in meters

Difference in

distance travelled

in meters

1 RM- opn.10 1.53 1.53

2 opn.10-20 3.66 0

3 opn.20-30 12.2 0

4 opn.30-40 13.7 0

5 opn.40-50 10.7 0

6 opn.50-60 18.3 16

7 opn.60-70 4.88 0

8 opn.70-80 0.91 0.5

9 opn.80-FG 3.05 3.05

Total (meter) 68.93 21.08 47.85

2. Two machines are interchanged because it is identified that the idle time of the operator is more

as mentioned in the Table 3. Operation 30B and 40B having high idle times 70s and 75s respectively. In

order to reduce the idle time of operators, the machines C and D are interchanged and the orientation of


126

C, E and F are altered as shown in the Figure 7 as number 2. In this new arrangement, Machines B and D

are operated by a single operator simultaneously. Similarly, machines E and F are operated by a single

operator simultaneously. This helps the operator to run two machines simultaneously by utilizing the idle

time.

3. A new window is included in the wall between deburring and inspection area to transfer the

material directly which reduces transportation and motion wastes. It is shown in the Figure 7 as number

3.

Figure 7: Proposed Layout

Figure 8: Material movements in the Proposed Layout


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4. Simulation study

The imitation of the operation of a real-world process or system over time is called simulation.

Simulation involves the generation of an artificial history of the system and the observation of that

artificial history to draw inferences concerning the operational characteristics of the real system that is

represented. The simulation is an indispensable problem-solving methodology for the solution of many

real-world problems. It is used to describe and analyze the behaviour of a system, ask what-if questions

about the real system, and aid in the design of real systems as explained by Adams et al.(1999), Jain and

Leong (2005). A simulation study is carried out using WITNESS as simulation software. Both current and

proposed systems are modelled and simulated in order to find out the optimum layout strategy.

4.1 Current layout simulation

A simulation run for 189000 seconds (one month machine available time) is done for the current

layout. The snap shot of the current layout arrangement with the operators and machines are shown in

Figure 9.

Figure 9: Simulation of Current layout

Data regarding number of activities required, uptime details of the machine, the number of operator

and their working time are given as input and the logic is based on the operation sequence and number

of workers available. The simulated results are shown in Table 6


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Table 6 Simulation of current layout

Opn.

No

Process

Current layout simulation

Machine

Labour

%idle %busy %setup %busy %idle

10 Top and bottom milling 0 100 0 100 0

20 Crank ,nut and joint face roughing 52.54 31.81 15.64 15.64 84.36

30-A Nut face milling, drilling and processing

dowel

67.19 32.80 0 32.81 67.19

30-B Nut face milling, drilling and processing

dowel

66.48 21.47 12.03 12.03 87.96

40-A Crank boring, Notch Milling 46.13 45.56 8.307 8.307 91.69

40-B Crank boring, Notch Milling 45.06 44.81 10.11 10.11 89.88

50 Joint face milling, Register width

finishing

0.70 99.29 0 99.29 0.70

60 Deburring 0.82 99.18 0 99.18 0.82

70 Inspection 33.9 66.1 0 66.1 33.9

80 Washing and Packing 60.34 23.14 16.52 16.52 83.48

Total Efficiency 37.32 56.42 6.26 46 54

4.2 Proposed layout simulation

In the proposed layout, a simulation run for 189000 seconds is done. The snap shot of the proposed

layout arrangement with the operators and machines are shown in Figure 10.

Figure 10: Simulation of proposed layout


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The simulated results are shown in Table 7.

Table 7 Simulation of proposed layout

Opn. No PROCESS

PROPOSED LAYOUT SIMUALTION

Machine Labour

%idle %busy %setup %busy %idle

10 Top and bottom milling 0 100 0 100 0

20 Crank ,nut and joint face roughing 52.52 31.83 15.65

33.23 66.77 30-B

Nut face milling, drilling and

processing dowel 66.48 21.47 12.03

30-A Nut face milling, drilling and

processing dowel 66.76 33.23 0 27.69 72.31

40-A Crank boring, Notch Milling 43.44 45.27 11.28 21.59 78.40

40-B Crank boring, Notch Milling 43.99 45.68 10.31

50 Joint face milling, Register width

finishing 0.70 99.29 0 99.29 0.70

60 Deburring 0.82 99.18 0 99.18 0.82

70 Inspection 33.9 66.1 0 66.1 33.9

80 Washing and Packing 60.34 23.14 16.52 16.52 83.48

Total Efficiency 36.9 56.52 6.58 57.95 42.05

From the Tables 6 and 7 the machine utilization is observed to be same in both the current and

proposed layout but the elimination of operators has increased the labour utilization. The performance

measures are Percentage of labour productivity and Percentage of machine utilization. The layout

efficiency values are given in Table 8.

Table 8 Comparison of layout results

Parameters Current Layout Proposed Layout Improvements

Machine utilization

%idle 37.32 36.90

No Change

%busy 56.42 56.52

%setup 6.26 6.58

Labour productivity

%busy 46.00 57.95 Increased by

11.95

%idle 54.00 42.05 Decreased by

11.95

Figure 11 shows the performance of current and proposed layouts. From this result, it is concluded

that there is no change in the machine utilization but the labor productivity is increased by 11.95%.


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Figure 11: Comparison of layout efficiency

5. Design of Gravity operated feeder for Bearing Cap

In this bearing cap layout, a gravity operated feeder has been designed for the material handling of

bearing cap. The main aim of this gravity feeder is to reduce material handling time and transportation in

between work stations.

The feeder is made up of 3 mm steel sheet which has four supports at its ends. The shape of the

feeder to accommodate the bearing cap is shown in Figure 12. This model can carry about 29 parts per

meter in the feeder.

Figure 12: Gravity feeder model


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5.1 Structural analysis of gravity feeder

A gravity feeder is modeled using solid edge v-19 and structurally analyzed using ANSYS 12.0 with

following data, the length of feeder is 1000mm and number of components it carries in one meter is 29.

The bearing cap weight varies from minimum 3kg to maximum of 4.25kg. So the maximum weight

4.25 kg is selected and the weight of 29 components is found to be 1209.08 N

According to the theory of pure bending [14], bending moment equation is expressed as follows

(M/I) = (σ/Y) = (E/R) (1)

Using the equation (1), bending stress is estimated to be 8.063N/mm2. The yield strength of steel

C15 with factor of safety 6 is found to be 39.24N/mm2 [13].

The model is completely meshed with solid brick 8 node 45 and it is shown in Figure 13.

Figure 13: Meshed model

5.2 Result and Inference

From the analysis, the maximum deformation is found to be 0.001426mm which is reasonably

negligible and it is displayed in Figure 14.


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Figure 14: ANSYS result

6. Summary and Conclusions

An attempt has been made to improve the productivity and profitability of the industry. The design

and development of the gravity feeder for the material handling have been done to reduce the motion

and transportation wastes. The simulation analysis of current layout is carried out to study the

performance in lean perspective and modifications in the layout have been made. A window opening is

made between the deburring and inspecting operation which saves time by 30 minutes for 100 parts .The

machines are replaced and their orientations are changed for easy transfer of material and for sharing the

idle time of the operators with other machines. The modifications in the layout will reduce two operators

and increase the utilization of the operators by 11.95%. It saves 640 rupees per shift from the operator’s

salary. Hence it saves Rs. 5, 99,040 per year which is a considerable savings in the total revenue. Also the

implementation of gravity feeder in between the workstations reduces the motion waste and monotonous

efforts of the labours which further enhances the labours’ job satisfaction and goodwill of the

organisation.

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improving productivity of manufacturing division using lean concepts