:: research report: - institut teknologi sepuluh...

Edwin Saferian 2510100006

:: BY

Industrial Engineering Department

Faculty of Industrial Technology

Institut Teknologi Sepuluh Nopember

Surabaya

WORKFORCE ALLOCATION BASED ON TIME STUDY AND WORK BALANCING

(CASE STUDY : PT MADUSARI MAS – RUNGKUT BRANCH)

Arief Rahman, S.T., MT

GUIDED AND

SUPERVISED BY

:: Research Report::

Research Background (1)

Source : PPM Riset Manajemen

Percentage of Industries Use Outsourced Worker


Office Support (Cleaning and maintenance)

Driver and transportation

Private Security

PT MADUSARI MAS

Rumahindahdanbersih.blogspot.com

Madusari Needs to Compete

Optimization of productivity can be conducted by distributing the workload in equal measure


“Equalized” workload doesn’t mean that every worker have the same scoring attribute,

considering the area in charge, hazard rate of area and other allowances

Productivity should be optimized

Problem Statement

“How to calculate the workers’ workload and work force allocation in order to increase productivity using work

study approach of stopwatch time study and work balancing. Optimization uses an integer programming to

equalize the level of workload.”

Research Objective

Determine the number of work force based on capacity need

Optimal workload and manpower in Madusari - Rungkut Branch can be

obtained

1 2

Research Benefits

Providing new assessment mechanism for workload

auditing for future reference

Research Scopes

Research object is PT Madusari Mas – Rungkut Branch

Field study and data gathering are conducted between March 2013 to May 2013

This research is focusing in main activities such as cleaning and facility maintenance

Observation is conducted only in two of three shift, the morning shift and the evening shift.

Research Scopes (2)

Job description will not be changed

during this research

Work elements that being identified are the main job (incidental work not included)

Chapter 1

Preface

Chapter 2

Literature Review

Chapter 3

Research Methodology

Chapter 4

Data Gathering and Processing

Chapter 5

Data Analysis and Interpretation

Chapter 6

Conclusion and Recommendation

Writing Systematic

Literature Review

Work Measurement

Stopwatch Time Study

Prod Leveling/Work Balancing

Operation Research

WORKFORCE ALLOCATION BASED ON TIME STUDY AND LINE BALANCING

Knapsack Theory

Literature Review : Stopwatch Time Study

time study is a direct and continuous observation of a task, using a timekeeping device

a work measurement technique consisting of careful time measurement of the task with a time measuring instrument, adjusted for any observed

variance from normal effort or pace and to allow adequate time for such items as foreign elements, unavoidable or machine delays, rest to overcome

fatigue, and personal needs

(Groover, 2007)

The Industrial Engineering Terminology Standard


Data Uniformity Test

Uniformity test is used to find out if the uniformity of sampled data

𝐿𝐶𝐿 = 𝑋 − 3ơ

𝑈𝐶𝐿 = 𝑋 + 3ơ

UCL / LCL = Upper/Lower Classified Level X = Mean of collected data Ơ = Standard deviation obtained

Data Adequacy Test

This test is conducted to find out whether the amount of collected data is adequate or not

2

.

.'

kX

SZN

N’ = Number of Observation should be conducted Z = Trust Index (95% ≈ index 2) s = Standard deviation of data = Mean of uniformed data k = level of error (5%) X


Determine the Performance Rating and Allowances

Unilever Allowances Standard

Calculating Normal Time

Normal time = Actual time x Performance Rating

Fixed Allowance

No Male Female

1 Personal Need 5 6

2 Fatigue 4 4

Total

Variable Allowance

Male Female

a. Standing 2 4

b. Work Postion

Rather Kneel

0 1

Kneeling

2 3

Lay down

7 7

c. Energy

2,5 kg 0 1

5 kg 1 2

7,5 kg 2 3

10 kg 3 4

12,5 kg 4 6

15 kg 6 9

17,5 kg 8 12

20 kg 10 15

22,5 kg 12 18

25 kg 14 -

30 kg 19 -

40 kg 33 -

50 kg 58 -

d. Lightning

Standard

0 0

Below Average

2 2

Above Average

5 5

e. Temperature

Fresh

0 0

Normal

5 5

Hot

5 15

f. Environment

Normal 0 0

Dusty 2 2

Hazardous 5 5

g. Noise

Repetitive

0 0

Randomly

2 2

High Ptched 5 5

Variable Allowance

Male Female

a. Standing 2 4

b. Work Postion

Rather Kneel

0 1

Kneeling

2 3

Lay down

7 7

c. Energy

2,5 kg 0 1

5 kg 1 2

7,5 kg 2 3

10 kg 3 4

12,5 kg 4 6

15 kg 6 9

17,5 kg 8 12

20 kg 10 15

22,5 kg 12 18

25 kg 14 -

30 kg 19 -

40 kg 33 -

50 kg 58 -

d. Lightning

Standard

0 0

Below Average

2 2

Above Average

5 5

e. Temperature

Fresh

0 0

Normal

5 5

Hot

5 15

f. Environment

Normal 0 0

Dusty 2 2

Hazardous 5 5

g. Noise

Repetitive

0 0

Randomly

2 2

High Ptched 5 5

Calculating Standard Time

St = 𝑁𝑜𝑟𝑚𝑎𝑙 𝑡𝑖𝑚𝑒𝑥 100%

100%−𝑎𝑙𝑙𝑜𝑤𝑎𝑛𝑐𝑒


Calculating Workload

WL = 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑡𝑖𝑚𝑒 𝑥 𝐹𝑟𝑒𝑞 𝑥 𝑁𝑜𝑈

𝑇𝑜𝑡𝑎𝑙 𝑊𝑜𝑟𝑘 𝑇𝑖𝑚𝑒

Literature Review : Work Balancing

Work Balancing, also known as production leveling or assembly line balancing, is a technique for reducing the work deviation. The assembly line balancing problem is a well-studied problem with many applications, including the automotive industry, consumer electronics, and household items

(Baybar, 1986)

Improvised by Genichi Taguchi, Imperial Navy Engineer and Toyota Corporation Consultan

Literature Review : Work Balancing

30

25

20

15

10

1 2 3

5

Takt (25 hrs)

15

30

17

1 2 3

25 25

12

Takt (25 hrs) 30

25

20

15

10

5

Improvised by Genichi Taguchi, Imperial Navy Engineer and Toyota Corporation Consultan

Literature Review : Linear Programming

Operations research, or management sciences, is a discipline that deals with the application of advanced analytical methods to help make better decisions. It is often considered to be a sub-field of mathematics

(Wetherbee, 1979)


The knapsack theory problem or rucksack problem is a problem in combinatorial optimization: Given a set of items, each item with a mass itself, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit

Knapsack Theory

Also known as burglar theory


Knapsack Theory

= Knapsack Capacity Item(s) Weight

Maximum-Filled Knapsack

=

Max Work hour Work(s) time completion

Optimal Workload Given


1


2

1

2


3

Takt level = 100%

Data Collecting and Processing

Data Collecting

Morning Shift

Area Number of Workforce

1 1

2 1

3 1

4 1

5 1

6 1

7 1

8 1

9 1

10 1

Total 10

Evening Shift

Area

Number of

Workforce

2 1

4 1

5 1

6 1

7 1

9 1

10 1

Total 7

Night Shift

Area

Number of

Workforce

2 1

4 1

5 1

6 1

7 1

9 1

10 1

Total 7

Work Shifts

First half Second half

Time Windows

Sub Area : Office Sub Area :Drum

Office Cleaning Second Sterilization

Colouring Room Drum Streilization

Liquid Puder Drum Packing

SLS Nedel Second Sterilization

Drum Box Wash

Clean Drum Packing

Garbaging

Hall Cleaning Waste Chem Treatment

Hall Cleaning Hall A Cleaning

Hall Cleaning Take Waste

Retrieve waste

Sub Area : Waste

Work Area 1

Data Collecting

Sub Area : Office Sub Area : Waste and Wash

Sweeping Teraso Conveyor

Mopping Teraso Chalk cleaning

Office Cleaning Waste Disposal

Washbay Teraso Pol ish

Tube Scrapping Shuttle Door

Layer Scraoping Windows Cleaning

Wal l ing

Piping

Washbay Pol ishing

Work Area 2

Sub Area : Relig Building Sub Area ; Office

Ablution Water Main Office PC

Stainless Fence TPM PC

Window List Meeting Room

Waste Disposal Packaging

Wooden Wall List Office Toilet

Bordet Floor PC Lobby

Dispensing Stair Trap

Musholla Vaccuum

Conveyor

Work Area 3

Sub Area : Hall A Sub Area : Engine

Sweeping Epoxy Seeping Bordest

Mopping Epoxy Sweeping Epoxy

Dusting Wall Mooping Bordest

Piping Tank Mopping Epoxy

Dusting Piping System Cabinet

Waste Disposal Office Desk

Sweeping Epoxy Piping Tank

Mopping Epoxy Dusting Piping System

Dusting Wall Waste Disposal

Sub Area : Hall B

Work Area 4

Module 1 Module 6

Sweeping Epoxy Sweeping Epoxy

Mopping Epoxy Mopping Epoxy

Sweeping Teraso Sweeping Teraso

Mopping Teraso Mopping Teraso

Machining Machining

Module 5 Mazanine





Machining Machining

Work Area 5

Sub Area : Packing Line Sub Area : Cabinet Roo

Dispensing Display Rack

Control Panel Cabinet filling

P3 Domino Waste Speaker Cleaning

Stair Bordes Sweeping Epoxy

Piping Mopping Epoxy

Machining Sweeping Teraso

Wall Dusting Mopping Teraso

Ceramic Wall

Bin Cleaning

Wall List Cleaning

Work Area 6

Data Collecting

Sub Area : Kugler Sub Area : D3





Machining Machining

Sub Area : Vega Sub Area : D4





Machining Machining

Sweeping Epoxy Mopping Teraso

Mopping Epoxy Machining

Sweeping Teraso

Mopping Teraso

Machining

Sub Area : D5

Work Area 7

Sub Area : Polishing

Sweeping Teraso

Sweeping Bordes

Polishing Teraso

Polishing Bordes

Work Area 8

SLS Area Chalking Zone



Dusting Wal Dusting Wal

Dusting Profi le Tank Dusting Profi le Tank

Dusting Piping System Dusting Piping System

Dusting Exi t Forkl i ft Dusting Exi t Forkl i ft

Waste Disposa l Waste Disposa l

Dusting Shuttle Door Dusting Shuttle Door

Seeping Bordest Dusting Fence

Sweeping Epoxy Steri l lan Cleaning

Mooping Bordesr Dusting Piping System

Mopping Epoxy Dusting Exi t Forkl i ft

Dusting Shuttle Door Waste Disposa l

Modul 1-7, Mezzanine

Work Area 9

Sub Area : Rest Room Sub Area : Entrance

Mopping Toilet BordaceWet Entrance

Equipment MaintenanceDry Entrance

Washtafel Inner Wash

Toilet Cleaning Sweep Entance

Urinoir Sub Area : Other

Cargo Procuring

Glass Cleaning

Work Area 10

Not Included in second half of

Morning Shift Evening Shift

Polishing Piping Hall A

Piping Hall A Piping Hall B

Data Collecting Work Sampling and Uniformity Test

Work Descriptiom

Sampling Time

1 2 3 4 5 6 7 8 9 10 11 12 13

Sweeping Teraso 398 347 348 401 565 334 378 342 403 328 385 400 388

Sweeping Bordes 295 275 299 255 256 222 310 298 288 289 298 301 290

Polishing Teraso 2759 2759 2716 2778 2743 2719 2702 2765 2770 2761 2760 2786 2782

Polishing Bordes 1993 1984 1967 1973 1931 2001 1920 1936 1908 1935 1936 1904 1975

Data Collecting

1 2 3 4 5 6 7 8 9 10 11 12 13

Sweeping Teraso 398 347 348 401 334 378 342 403 328 385 400 388 28,90738 371 9,3 10,0

Sweeping Bordes 295 275 299 255 256 310 298 288 289 298 301 290 17,34586 288 5,6 6,0

Polishing Teraso 2759 2759 2716 2778 2743 2719 2702 2765 2770 2761 2760 2786 2782 119,23 2754 2,9 3,0

Polishing Bordes 1993 1984 1967 1973 1931 2001 1920 1936 1908 1935 1936 1904 1975 144,23 1951 8,4 9,0

N'Sampling Time

stdev average

Adequacy Test

Basic Time (BT)

(minutes)

Standard Time

(minutes)

Speed

Area : Polishing

Sweeping Teraso 371 6,1833 0,7 4,328333333 5,150716667

Sweeping Bordes 288 4,7972 0,7 3,358055556 3,996086111

Polishing Teraso 2754 45,897 0,7 32,12820513 38,2325641

Polishing Bordes 1951 32,517 0,7 22,76166667 27,08638333

BT + (BT x %

Allowance)

OT

(sec)

OT

(min)Work Area 8

Rating Factor (RF)

(OT x %RF/100%)

Data Collecting-Standard Time

Data Collecting-Workload

Basic Time (BT)

(minutes)

Standard Time(ST)

(minutes)% Workload

Speed

Area : Polishing

Sweeping Teraso 6,1833 1 4 0,19 0,7 4,328333333 5,150716667 0,042922639

Sweeping Bordes 4,7972 1 4 0,19 0,7 3,358055556 3,996086111 0,033300718

Polishing Teraso 45,897 1 1 0,19 0,7 32,12820513 38,2325641 0,079651175

Polishing Bordes 32,517 1 1 0,19 0,7 22,76166667 27,08638333 0,056429965

21,23%

BT + (BT x %

Allowance)

(ST x NOU x

F)/Total

Work Time

Total

Allowanc

e

OT

(min)

Numbe

r of

Unit

(NOU)

Freq

(F)Work Area 8

Rating

Factor (RF)

(OT x %RF/100%)

8 Hours 480 Minutes

Total Workload

No Area Workload

1 1 124,38%

2 2 76,05%

3 3 75,48%

4 4 85,21%

5 5 68,90%

6 6 68,38%

7 7 73,74%

8 8 21,23%

9 9 82,91%

10 10 47,95%

72,42%

0,263

Average

Std Dev

Morning Shift

Data Collecting

Data Collecting

No Area Workload

1 2 53,44%

2 4 73,23%

3 5 58,66%

4 6 61,63%

5 7 65,25%

6 9 51,24%

7 10 46,96%

58,63%

0,0898

Evening Shift

Std Dev

Average

Optimization

Data Processing

Elemont No i Element Workload (/10000) w Element No i Element Workload (/10000) w

1 Afa_off 1598 15 Kug5 1518

2 Afa_waste 4592 16 kug4 1426

3 Afa_drum 6250 17 kug3 1494

4 Ent_rest 4456 18 Pack_PL 2520

5 Ent_ent 140 19 Pack_Kab 4250

6 Ent_ot 202 20 Mod165_1 1736

7 Slurs_SLS 5062 21 Mod165_5 1716

8 Slurs_chalk 2324 22 Mod165_6 1732

9 Slurs_mod 874 23 Mod165_M 1708

10 Sub_office 2564 24 Roompc_Off 3296

11 Subs_waste 5042 25 Roompc_rel 4254

12 Polish 2123 26 Pipe_A 2351

13 Kug_kug 1648 27 Pipe_B 2932

14 Kug_veg 4190 28 Pipe_E 888

Data Processing Summary of Major Elements Workload of Morning Shift

Data Processing SETS: ITEMS / AFACON_OFFICE, AFFACON_WASTE, AFFACON_DRUM, ENTRANCE_REST, ENTRANCE_ENTRANCE, ENTRANCE_OTHER, SLURRY_SLS, SLURRY_CHALK, SLURRY_MOD, SUBS_OFFICE, SUBS_WASTE, POLISHING, KUGGLER_KUGGLER, KUGGLER_VEGA, KUGGLER_5, KUGGLER_4, KUGGLER_3, PACKINGLINE_PL, PACKINGLINE_KABINET, MOD165_1, MOD165_5, MOD165_6, MOD165_M, ROOMPC_OFFICE, ROOMPC_REL, PIPING_A, PIPING_B, PIPING_ENGINE/ : INCLUDE, WEIGHT, RATING; ENDSETS DATA WEIGHT RATING = 799 1 !afacpnoffice; 2296 1 !afawaste; 3125 1 !adadrum; 2228 1 !entrest; 70 1 !entent; 101 1 !entoth; 2531 1 !slursls; 1162 1 !slurchalk; 437 1 !slurmod; 1282 1 !subofice; 2521 1 !subswaste; 2123 1 !polishing; !only once;

824 1 !kugkug; 2095 1 !kugveg; 759 1 !kug5; 713 1 !kug4; 747 1 !kug3; 1260 1 !packPL; 2125 1 !packKab; 868 1 !mod1651; 858 1 !mod1655; 866 1 !mod1656; 854 1 !mod165m; 1648 1 !roompcoff; 2127 1 !roompcrel; 2351 1 !pipeA; !only once; 1466 1 !pipeB; 444 1 !pipeEn; ; WORKER_CAPACITY = 5000; ENDDATA MAX = @SUM( ITEMS: RATING * INCLUDE); @SUM( ITEMS: WEIGHT * INCLUDE) <= WORKER_CAPACITY; @FOR( ITEMS: @BIN( INCLUDE));

1

2

3

Data Processing

“knapsack”/worker capacity

Major elements

Decision 1 = allocate

0 = not allocate

Translated as : Worker allocated to do elements/activites such as afacon_office, entrance_entrance, entrance_, kugler_kugler, kugler_5, kugler_4, kugler_3 and piping_engine

Data Processing-Morning Shift

First Half Second Half

Data Processing-Morning Shift

Total Workload : 728.34% Average : 91,04%

Optimized

Data Processing-Evening Shift

Total Workload : 204,99% Average : 51,24%

Total Workload : 172,19% Average : 43,047%

First Half

Second Half

Data Processing-Evening Shift

Optimized

Average : 94.30%

Analysis

Analysis-Pre Optimized

Pre Optimized Morning Shift Problem : Overload Work

Underload Work

Total Area : 10

Average Workload : 72,24%

Leads to

Very High Workload Deviation : 0,263

Analysis-Pre Optimized

Pre Optimized EveningShift Problem : Too many

underload Work

Total Area : 7 Average Workload : 58,63% Workload Deviation : 0,0898

Leads to

Very low workload given in total

Analysis-Optimized

No Area Workload No Area Workload

1 3 80,16% 1 1 90,62%

2 8 84,50% 2 2 90,90%

3 5 90,48% 3 3 96,96%

4 2 92,16% 4 4 98,70%

5 7 92,32% 94,30%

6 6 95,36% 0,041

7 4 95,48%

8 1 97,88%

91,04%

0,146

Average

Optimized Evening Shift

Average

Std Dev

Optimized Morning Shift

Std Dev


No Area Workload No Area Workload

1 3 80,16% 1 1 90,62%

2 8 84,50% 2 2 90,90%

3 5 90,48% 3 3 96,96%

4 2 92,16% 4 4 98,70%

5 7 92,32% 94,30%

6 6 95,36% 0,041

7 4 95,48%

8 1 97,88%

91,04%

0,146

Average


Average

Std Dev

Optimized Morning Shift

Std Dev




Analysis-Comparison

Total Worker 10

Average Workload 71,42%

Workload Deviation 0,263

Total Worker 7

Average Workload 58,63&


Pre Optimized

Morning

Evening

Total Worker 8

Average Workload 91,04


Total Worker 4

Average Workload 94,30%


Optimized

morning

Evening

Conclusion

Based on the analysis, the final conclusion to answer the research objective can be stated as below : 1. The morning shift has the initial total workload of 724,24% for 10 workers which the average

workload is 72,42% with the workload deviation of 0,263. The evening shift has the initial total workload of 410,41% for 7 workers which the average workload is 58,63% with the workload deviation of 0,0898.

2. The current work system is not an ideal work system, marked by the low average workload and significant workload deviation.

3. The proposed new work system gives the optimization of workload and work force. For the morning shift the initial average workload of 72,42% is increased to 91,04%, while the workload deviation reduced from 0,263 to 0,146, and the number of workforce is reduced from 10 workers to 8 workers. As for the evening shift the initial average workload of 58,63% is increased to 94,30%, while the workload deviation reduced from 0,0898 to 0,041, and the number of workforce is reduced from 7 workers to 4 workers.

Recommendation

As for the recommendation for this research can be stated as : 1. Madusari should revise the current workforce allocation to obtain the

optimal number of workload and the optimal number of workforce needed per shift.

2. This research would give more significant result if this research can capture the entire working shift, regarding this research is only capture morning shift and evening shift while the night shift can’t be captured due to permittal problem. If the night shift can be captured, all of the traits of each shift can be identified and analyzed, regarding the two shift that been captured has unique anomalies and problems on their own.

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References

Thank You

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