production capacity application
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A study applying Statistical tools for improving actual production line and evaluating possibilities for optimum efficiencyTRANSCRIPT
INCREASING THE CAPACITY OF THE NESTEA LEMON ICED TEA FILLING
SECTION OF DDC FOOD DEVELOPMENT CONTRACTING
SERVICES, INC. AT SAN FERNANDO, PAMPANGA
JAQUELYN MARGARET LAWAS MICIANO 2005-09921
A PRACTICUM STUDY PRESENTED TO THE FACULTY OF THE DEPARTMENT OF INDUSTRIAL ENGINEERING COLLEGE OF ENGINEERING AND AGRO-
INDUSTRIAL TECHNOLOGY UNIVERSITY OF THE PHILIPPINES LOS BAÑOS IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
BACHELOR OF SCIENCE IN INDUSTRIAL ENGINEERING
APRIL 2010
ii
ACCEPTANCE SHEET
The practicum study, attached hereto entitled “INCREASING THE CAPACITY OF THE
NESTEA LEMON ICED TEA FILLING SECTION OF DDC FOOD DEVELOPMENT
CONTRACTING SERVICES, INC. AT SAN FERNANDO, PAMPANGA”, prepared and
submitted by JAQUELYN MARGARET LAWAS MICIANO in partial fulfillment of the
requirements for the degree of Bachelor in Industrial Engineering is hereby accepted.
___________________________ ENGR. ROMARK O. CAYUBE
Adviser and Chairman, Guidance Committee
______________ Date Signed
_______________________________ _____________________________ ENGR. DIANA MARIE R. DE SILVA ENGR. MIKEL ANGELO B. YAP Member, Guidance Committee Member, Guidance Committee ______________ ______________ Date Signed Date Signed
__________________________________
DR. AURELIO A. DELOS REYES JR. Chairman, Industrial Engineering Department
______________
Date Signed
______________________________ DR. ARSENIO N. RESURRECCION
Dean, College of Engineering and Agro-Industrial Technology
______________ Date Signed
iii
ABOUT THE AUTHOR
The author was born in San Fernando, Pampanga on the 19th day of November,
1988. She is the youngest among the four children of Mr. Emmanuel R. Miciano and
Mrs. Thelma L. Miciano. Her siblings are Maximillian, Kathleen, and Jose Paolo. Her
parents and brother are currently residing in San Fernando, Pampanga while her sister
now is staying in Laredo, Texas, USA.
She attended her kindergarten at Asian Montessori Center in San Fernando,
Pampanga at the age of five where she was given a chance to be accelerated to primary
education. But then she continued her primary schooling at Morning Star Montessori
School Inc in Los Baños, Laguna and graduated with academic excellence. From her
hard efforts, she was given an opportunity to finish her secondary education at Philippine
Science High School Diliman where she embarked her journey towards achieving the
vision of becoming an engineer.
In the year 2005, the University of the Philippines Los Baños keenly accepted her
to attend under the Industrial Engineering program that was newly offered by the
academe. She became a volunteer in assisting freshmen to cope up with the college
environment and workload in cooperation with the Student Organization Administrative
Division. Also, a onetime honor roll student in the College of Engineering and Agro-
Industrial Technology.
iv
ACKNOWLEDGEMENT
This paper would not be possible without the Lord God’s words that served
as my STRENGTH, GUIDANCE and INSPIRATION. So I’d like to thank Him firstly
for filling me with compassion, pointing me my true directions and surrounding me
with wonderful people who accompanied me throughout my journey.
From the very beginning, my parents, Manny & Thelma, have greatly
extended their support financially, emotionally and spiritually to make my whole
practicum experience be substantial and unproblematic. Thank you for all the
endless prayers, powerful words of encouragement and especially the efforts of
critically understanding my study. I’m grateful for making my heart strong enough to
face the challenges before me.
To my supportive brother, Joseph, your patience on dealing with my feelings
of resentment has helped ease and lighten my mood. For all the errands you have
made for this paper that I have persistently requested, thank you. Those little things
are part of a great accomplishment.
To my ever-loving sister, Kathleen, your comforting words sufficed our
thousand-mile distance. You know me more than I know myself and for that you’ve
been saying the exact words I wanted to hear. Thank you for giving me courage and
understanding to confront the trials with dignity. You’re my twin, no one can top that!
To my cousins and relatives, I am grateful for putting more pressure in the
thought of me having to graduate on time. It encouraged me to push even more to
relieve your anxieties.
To my adviser, Sir Omak, thank you for all your efforts in helping me
construct a substantial work, for giving me conditional chances to finish my paper
and also for endowing upon me the principles of independence.
To my panelists, Sir Mikel and Ma’am Dianne, thank you for the docile
approach in criticizing my paper. Your suggestions greatly contributed into leading
my study towards a more technical portrayal.
To other members of the IED Faculty, Sir Arvin, Ma’am Kat and Sir Marc, I’d
like to extend my gratitude for effortlessly be a factor of significant concepts through
your different outlooks that made the interpretation of the paper more profound.
To my dear friend Ara, you have greatly influenced me through your simple
yet insightful technical advises in taking my brainstorming moments into the next
level. I deeply appreciate all the efforts you have made in assisting me finish my
paper not only in the technical areas but also for helping me get through it
emotionally without holding back the tears.
v
To Mafei, your conniving advices on dealing with the manuscript writings
were very entertaining and helpful in a way that it refreshes my thoughts once in a
while. For all the troubles you came across just to lend me hand, I value all your
efforts.
To Yani, May, Agbu and Pii, I’m very pleased to have friends like you who did
not hold back in helping out especially at the last minute. Your support no matter
how small or great it may seem is highly regarded.
To Seph, Ronald, Marvin, Cale and Co., for suddenly reaching out and
sending your concerns through your means, thank you. I greatly appreciate all the
energy you gave out in backing me up and for lifting my spirits up in the end.
To Rie, Wewikinns, Cagayat, Daryll, Teves and others that I may have failed
to mention, I’m thankful for letting me feel your deep concerns notwithstanding the
constraints of time and distance.
To Roy, I’m thankful for willingly extending a hand in any possible way even
with your hectic schedule and tons of responsibility. You have saved me for lending
me one of the most important things that made this paper possible, Thank you!
To my ever faithful friend, Marco, I do not know where to begin thanking you
for all your devoted efforts in alleviating my endeavors in putting together this
manuscript. For all the long nights of gravelling with crucial theories (which were
later dismissed), for all the sensible instructions and advices, for imparting the
biggest of efforts throughout my manuscript writing with your prudent perspectives,
and for simply always being there for me whenever I’m in need, you are my rescuer
amidst the burdens and hardship I’ve stumbled upon. For that, my heart is filled with
deep respect, compassion and gratitude to you.
Writing this manuscript made my college experience complete with all the
knowledge, emotions and energy put into one document. Herewith, I feel that I have
arrived at the finishing point of my college education. Without this, I would not be
able to recognize my potential of independently bringing up this whole document
that serves as a symbol of one of my greatest achievements. To God be the Glory!
vi
EXECUTIVE SUMMARY
DDC Food Development and Contracting Services Inc. (DDC-FDCSI) is a
subcontractual company of Nestle Philippines located at Block 14 Lot 26 Lazatin Blvd.,
Villa Victoria, Dolores, City of San Fernando, Pampanga. DDC-FDCSI handles food and
beverage powder in preformed pouches for the supplies of Nestle clients and not for
retailing.
DDC-FDCSI’s Nestea Lemon Iced Tea Production line, the foremost profit
contributor among DDC-FDCSI’s major products, has been experiencing a lot of
overtime workload in its Filling Section considering it is the resource constraint in the
line. It was found out based from the capacity requested by demand and the process
capacity of the Filling Section that the Nestea Lemon Iced Tea Production Line is
subjected to 116.59% implied utilization which further signifies that the demand exceed
the current process capacity by 16.59%. The additional capacity is being covered
through scheduled overtime hours in the Filling Section.
Harnessing the scientific method and Industrial Engineering concepts for
analysis, the root causes of the low capacity of the Nestea Lemon Iced Tea (NLIT) Filling
Section were carefully identified based on its association to Man, Method, Machine and
Materials. Experimental and controllable root causes were critically assessed and
evaluated in order to provide corrective and preventive solutions to achieve the main
objective of the study which is to increase the capacity of the NLIT Filling Section.
Factors contributing to the problem identified in the study were categorized
based on the insufficiency of the manpower capacity, the manual filling operations, and
the net weight discrepancies caused by the large capacity of the scoop. A controllable
factor identified was the occasional supervision within the manufacturing line which
contributed to the lax attitude of the workers. These factors were addressed from the
formulated alternatives of providing additional workers and parallel workstations,
automating the filling and sealing operations through a machine and reallocating
workers, customization of the scoop being utilized in the line and providing systematic
and meticulous guidelines for manufacturing line monitoring.
From the evaluation of alternatives, it was recognized that purchasing a filling
and sealing machine as well as reallocating workers to the next resource constraint
would return the highest profit that was estimated to accumulate up to 38,917,832 Php
annually. Implementing this alternative would increase the capacity of the whole
production line.
vii
TABLE OF CONTENTS
TITLE PAGE
TITLE PAGE i
ACCEPTANCE SHEET ii
ABOUT THE AUTHOR iii
ACKNOWLEDGEMENT iv
EXECUTIVE SUMMARY vi
TABLE OF CONTENTS vii
LIST OF FIGURES ix
LIST OF TABLES xi
LIST OF APPENDICES xiii
1.0 INTRODUCTION 1
1.1 Overview of the Company 2
1.2 Background and Significance of the Study 6
1.3 Statement of the Problem 11
1.4 Objectives of the Study 11
1.5 Scope and Limitations of the Study 11
1.6 Date and Place of the Study 12
1.7 Gantt Chart 12
2.0 METHODOLOGY 13
2.1 Procedures 16
2.2 Definition of Terms and Symbols 18
3.0 SYSTEMS DOCUMENTATION 19
3.1 General Processes 23
3.2 Product Under Study 24
viii
TITLE PAGE
3.3 Production Capacity 25
3.4 Production System 26
3.5 Process Documentation 26
3.6 Manpower Complement 32
3.7 Machine and Equipment 33
3.8 Plant Layout 35
3.9 Workstation Under Study 36
4.0 RESULTS AND DISCUSSSIONS 37
4.1 Problem Identification 38
4.2 Problem Analysis 39
5.0 SUMMARY AND CONCLUSION 72
6.0 RECOMMENDATIONS 75
7.0 AREAS FOR FURTHER STUDY 77
REFERENCES xiv
APPENDICES xv
ix
LIST OF FIGURES
FIGURE NO. TITLE PAGE
1-1 Sample Finished Goods of Dehydrated Culinary Products 4
1-2 The existing Organizational Chart followed by DDC-FDCSI 5
1-3 Percentages of Volume Distribution of major product in DDC-FDCSI
7
1-4 Production Output of NLIT based on monthly demand 9
1-5 Number of Scheduled Overtime Hours in the NLIT Filling Section 9
1-6 Exact Location of DDC Food Development and Contracting Services Inc.
12
1-7 Schedule of Activities Performed during the whole study period 12
3-1 Flow Process Chart of the General Processes in DDC-FDCSI 22
3-2 Actual Dimensions of 360g NLIT pouch and 12 x 360g case 23
3-3 Production System Framework of Nestea Lemon Iced Tea 25
3-4 Flow Process Chart of Batch Preparation of NLIT Production Line 27
3-5 Flow Process Chart of Dry Mixing of NLIT Production Line 29
3-6 Flow Process Chart of Filling of NLIT Production Line 31
3-7 Nestea Lemon Iced Tea Dry Mixer 33
3-8 Digital Balance (left), Horizontal Band Sealer (right) 34
3-9 Hot Foil Coder (left), Carton Coder (right) 34
3-10 Actual Layout of the Manufacturing Plant 35
3-11 Workstations in the NLIT Filling Section 36
4-1 Flow Rate of the system with a Resource Constraint 38
4-2 Total Overtime Hours in the Filling Section for June to Nov 2009 38
4-3 Summarized Causes of Low Capacity of the Filling Section 39
x
FIGURE NO. TITLE PAGE
4-4 Cause Factors Associated to Man 41
4-5 Cause Factors Associated to Method 42
4-6 Cause Factors Associated to Machine 44
4-7 Cause Factors Associated to Material 44
4-8 Production Monitoring Scheme for Production Line Inspection 47
4-9 Process Flow Diagram of Manufacturing Line Checks 50
4-10 Formula for computing % Improvement 53
4-11 Additional Parallel Workstations in the NLIT Filling Section 56
xi
LIST OF TABLES
TABLE NO. TITLE PAGE
1-1 List of DDC-FDCSI Products and their corresponding Package Sizes
3
1-2 Process Capacities for the three major Production Lines in DDC-FDCSI
7
1-3 Calculation of Implied Utilization of NLIT Production Line 8
1-4 Comparison of the Current from Expected Implied Utilization 10
2-1 List of Technical Terms used in the research and their Meanings 16
2-2 List of Symbols used in charts and their Meanings 17
3-1 List of Documents Handled by assigned Departments 19
3-2 Production Volume in kilograms of NLIT during June to Nov 2009 24
3-3 Sample Stopwatch Data of Filling Operations 30
3-4 Number of Workers in the Sections of the NLIT Production Line 32
4-1 Average Observed Time and Standard Time of Filling Elements 41
4-2 CNX Analysis of the Root Causes of Low Capacity of Filling Section
45
4-3 First Group of Experimental Factors 47
4-4 Second Group of Experimental Factors 48
4-5 Third Group of Experimental Factors 48
4-6 Alternative Solutions for Grouped Experimental Factors 52
4-7 Standard time of Operations in the Filling Section 53
4-8 Estimating the Number of Workers per Operation 54
4-9 Determining the Output Rate of Alternative A 55
4-10 Cost of Implementing Alternative A 57
4-11 Output Rate of Alternative B 58
xii
TABLE NO. TITLE PAGE
4-12 Cost of Implementing Alternative B 59
4-13 Output Rate for Alternative C 60
4-14 Cost of Implementing Alternative C 61
4-15 Comparison of Cost of Implementing Alternatives 62
4-16 Annual Cost of Alternative A 63
4-17 Annual Benefit of Alternative A 64
4-18 Annual Cost of Alternative B 65
4-19 Annual Benefit of Alternative B 66
4-20 Annual Cost of Alternative C 67
4-21 Annual Benefit of Alternative C 68
4-22 Weighted Rating of Importance 69
4-23 Rating for Net Benefit 69
4-24 Rating for Ease of Implementation 70
4-25 Rating of Alternatives 71
xiii
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Computation for the Goal Improvement
xv
B Nestea Lemon Iced Tea Conversion Units
xvi
C Time Study xvii
D Westinghouse System Ratings xxi
E Net Present Value of Alternative B xxii
F Certificate of Completion xxiii
1.0 INTRODUCTION
2
1.1 Overview of the Company
1.1.1 Company Profile
The DDC Food Development and Contracting Services, Inc. (DDC-
FDCSI)) was founded on March 1, 2004 by its General Manager, Danilo Dizon
Canlas, whose initials appear on the official business name of the company. He
was formerly committed with Nestle Philippines Employee who started as
Assistant Vice President– Technical Division, Service Department for culinary,
Products Specialist, Head of Food Application Group until becoming one of the
Vice Presidents. As a licensed Chemical Engineering who finished a Bachelor of
Science in Chemical Engineering in the Mapúa Institute of Technology, Engr.
Danilo D. Canlas broadened his knowledge on the food industry and attended
international seminars on food processing as well as management. After retiring
from Nestle Philippines, he decided to establish his own company. The first
commercial run of DDC FDCSI occurred on the March 6, 2004, five days after it
was established. The company was developed out of the need for contractual
services as anticipated by the founder. Originally, the company intended to be a
food development facility where new flavors of products and testing would be
handled. But their client, Nestle Philippines, decided to offer a bigger
responsibility in handling supplies for different products.
The major services offered by the company are Preparation of Flavor
Premixes, Batch Preparation, Dry Mixing and Filling of Powdered Products and
Keeping Quality Time Management for selected Powdered Products. The DDC
FDCSI serves as a third party subcontractor involved in the manufacturing and
packaging of designated products from their sole client, Nestle.
3
1.1.2 Products
The main products handled by DDC-FDCSI are classified into two family
groups: Dehydrated Culinary Products and Beverage Products. All the products
processed in their plant are powdered products. The list of the specific products
handled by the company is shown in Table 1-1.
DDC-FDCSI is entrusted by Nestlé to carry out the whole operation of
dehydrated culinary products as well as beverage products with the raw
materials provided by the Nestlé accredited suppliers. The dehydrated culinary
products consist of subfamily groups of Stocks and Gravies and Cream Soups
while the beverage service products comprise Milo Vending Mix and Nestea
lemon Iced Tea. These products are not available in retail stores but are provided
to other clients of Nestle such as fast food restaurants and malls.
Table 1-1 List of DDC-FDCSI Products and their corresponding Package Sizes
PRODUCTS Number of pouches per case
x net weight per pouch
Dehydrated Culinary
Stocks and Gravies
Chicken Stock 8x1kg Beef Stock 8x1kg Pork Stock 8x1kg Seafood Stock 8x1kg Chicken Gravy 8x1kg Basic Brown Sauce 8x1kg
Cream Soups
Cream of Chicken 8x1kg Cream of Corn 8x1kg Cream of Mushroom 8x1kg Cream of Asparagus 8x1kg Cream Soup Base 8x1kg
Beverage
Milo Vending Mix 8x1kg Nestea Lemon Iced Tea 12x360g *Source: DDC-FDCSI, 2009
4
Figure 1-1 Sample Finished Goods of Dehydrated Culinary Products
Individual sizes of products are indicated in Table 1-1 where large net
weights of the products are packed. This specification is primarily set by Nestle to
be distributed to their respective clients. Delivery of finished goods directly to the
clients of Nestle is not part of DDC-FDCSI services. Instead, finished goods are
primarily delivered straight to Nestle North Distribution Center in Bulacan.
5
1.1.3 Organizational Chart
The organizational chart of the company is shown in Figure 1-2 below
where the level of department involved in the study is indicated. Nestea Lemon
Iced Tea (NLIT) Production Line is the main focus as emphasized in the figure.
Three set of crews for every process are involved in the NLIT Production Line.
Figure 1-2 The existing Organizational Chart followed by DDC-FDCSI
6
1.2 Background and Significance of the Study
The food and beverage industry is composed of companies involved in
processing raw food materials and manufacturing, packaging, or distributing food or
drink. The exponential population growth brings about the continuous increases in
demand in the food and beverage industry. High levels of demand require high
volumes of production. Associated production costs become the critical factor that
determines the ability of the company to meet seasonal demands. It now appears
more practical and appropriate for a large company to decide to subcontract services
to third party companies in order to meet the level of seasonal demands.
DDC-FDCSI is considered a significant contributor in the food and beverage
industry as it handles the supply for Nestle. The services subcontracted by Nestle to
DDC-FDCSI are batch preparation, dry mixing and filling. Demand forecasts by
Nestle are not controlled by the company since they only offer services and not the
sales. Production volumes of DDC-FDSCI rely on the weekly supply outlook received
from Nestle. The goal of the company is to maximize their profit through cost-efficient
operations within the given factors that caused the problem identified in the study.
Recommendations for increasing the capacity of the Filling Section provide more
beneficial options instead of forcing them to increase their capacity through overtime.
From the list of products being handled by the company in Table 1-1, DDC-
FDCSI provided three separate production lines for the three major groups of
products being processed in the plant. These major production lines are manifested
in the organizational chart in Figure 1-2 and they are identified as the Dehydrated
Production Line, Milo Vending Mix Production Line and the Nestea Lemon Iced Tea
Production Line. Volume contribution of each product group is presented in Figure 1-
3 where the Nestea Lemon Iced Tea has the largest percentage of share compared
to the Dehydrated Culinary products and Milo Vending Mix. Focusing on cost-
efficient recommendations for Nestea Lemon Iced Tea would significantly have an
effect on the benefits gained by the company.
7
Figure 1-3 Percentages of Volume Distribution of major products in DDC-FDCSI
Major Product Families are designated to distinct production lines. Every
production line comprises of three sections, the Batch Preparation, Dry Mixing and
Filling which correspond to the services offered by the company. Process Capacities
of each section shown in Table 1-2 were provided by the Production Assistant based
on the historical records of actual performances. The highlighted values in Table 1-2
are the slowest production rates that incidentally determine the production rate of the
whole production line and the maximum capacity of the processes in series. The Dry
Mixing Section, Batch Preparation Section and Filling Section are the resource
constraints in the Dehydrated Culinary Products, Milo Vending Mix and Nestea Lemon
Iced Tea respectively. Capacity for Dehydrated Culinary Products and Milo Vending
Mix is one batch per hour determined by their corresponding resource constraints.
Table 1-2 Process Capacities for the three major Production Lines in DDC-FDCSI
Section Dehydrated
Culinary (batches/hour)
Milo Vending Mix (batches/hour)
Nestea Lemon Iced Tea (batches/hour)
Batch Preparation
1.25 1 1.125
Dry Mixing 1 1.5 1.5
Filling 1.875 1.875 0.625
*Source: DDC-FDCSI, 2009
20.27%
26.93%52.80%
Volume in kilogram Distribution of the Major Product Families in DDC-FDSCI
Dehydrated Culinary
Milo Vending Mix
Nestea Lemon Iced Tea
8
Based on the comparison of volume distribution, Nestea Lemon Iced Tea was
resolved as the product focus of the study. From the processes involved in Nestea
Lemon Iced Tea Production Line, the Filling Section has the lowest capacity of 0.625
batches per hour that determines the process capacity of the whole line. This further
narrows down the extent of the research to be mainly engaged in the NLIT Filling
Section. The low capacity of the Filling Section creates a predicament with the large
volume levels of Nestea Lemon Iced Tea handled by the company.
To further evaluate the demand levels and the process capacity of the NLIT
Production line, it would be necessary to calculate the implied utilization. In computing
for the implied utilization, the capacity requested by the demand is to be divided by the
available capacity of the production line. Capacity requested by demand is equivalent
to the total production output per month since DDC-FDCSI completes the monthly
demand received. The available process capacity was obtained from the rate of the
NLIT Filling Section and the actual production hours from June to November in the
year 2009. Table 1-3 shows the computed values of implied utilization. This
performance measure conveys how much demand exceeds the capacity of the
process where values exceeding 100% entails the deficiency in the capacity of the
production line. The recurrence of this scenario is apparent in the monthly production
of Nestea Lemon Iced Tea which verifies an imperative matter needed to be
addressed.
Table 1-3 Calculation of Implied Utilization of NLIT Production Line
NESTEA LEMON ICED TEA
June July Aug Sept Oct Nov
Capacity Requested by Demand
Batch per
month 144.755 124.485 129.842 121.392 140.037 143.960
Process Capacities
Batch per
month 120 115 120 115 125 115
Implied Utilization 120.63% 108.25% 108.20% 105.56% 112.03% 125.18%
9
Figure 1-4 shows the NLIT Production Line has a low capacity with respect to
the level of output being handled. DDC-FDCSI copes with the high levels of demand
by increasing the capacity through allowing scheduled overtime for the resource
constraint process.
Figure 1-4 Production Output of NLIT based on monthly demand
The actual number of overtime hours accumulated in meeting the high levels of
demand for the six-month data obtained is shown in Figure 1-5 below.
Figure 1-5 Number of Scheduled Overtime Hours in the NLIT Filling Section
June July Aug Sept Oct Nov
Actual Output 8377 7204 7514 7025 8104 8331
Actual Capacity 6944.44 6655.09 6944.44 6655.09 7233.80 6655.09
6000
6500
7000
7500
8000
8500
Pro
du
cti
on
Ou
tpu
t (i
n c
as
es
)
Production Output of NLIT for June to November 2009
June July Aug Sept Oct Nov
Overtime hours 115.80 99.59 103.87 97.11 112.03 115.17
85.00
90.00
95.00
100.00
105.00
110.00
115.00
120.00
Nu
mb
er
of
Ho
urs
Scheduled Overtime Hours in the NLIT Filling Section for the month of June to November 2009
10
The number of scheduled overtime hours shown in Figure 1-5 represents the
production hours of the Filling Crew which consists of seven workers. The standard
labor cost for contractual workers is 325.00 Php. Workers in the Production
Department are all contractual. Using the overtime rate of 130% primarily set by wage
policies of DDC-FDCSI incurs additional cost during scheduled overtime of seven
workers of the Filling Crew which is computed from the actual overtime hours. The
total additional overtime cost is equal to 475,832.18 Php for the period of June to
November 2009.
The length of the overtime hours is correlated with the increase of demand.
Overtime hours represent the amount of work required to accomplish demand above
the maximum capacity of the NLIT production line. The General Manager has
established average demand projections in the year 2010. The level of demand is
expected to increase up to 15,000 cases per month which is equivalent to 1.35
batches per hour.
Table 1-4 Comparison of the Current from Expected Implied Utilization
Parameter YEAR 2009 YEAR 2010
CAPACITY REQUESTED BY DEMAND
Batch per hour
0.72869 1.35
AVERAGE PROCESS CAPACITY
Batch per hour
0.625 0.625
IMPLIED UTILIZATION Percentage 116.59% 216%
The percentage of Implied Utilization calculated exceeded 100% as it signifies
the demand will further surpass the current process capacity of the NLIT Production
line by 16.59%. Continuing the scheduled overtime to cope with the capacity
requested by demand would double the monthly overtime cost. Thus, the need to
increase the capacity of the NLIT Production line is apparent.
11
1.3 Statement of the Problem
The capacity of Nestea Lemon Iced Tea Production Line cannot keep up with
the level of demand DDC-FDCSI is accepting. Having the lowest capacity, NLIT
Filling Section determines the production output rate. Relative to the 6-month
average capacity requested by demand, the process capacity is deficient by 16.59%.
As a result, it experienced a total of 4505 scheduled overtime hours of the Filling
Crew that incurred a total cost of 475,832.18 Php for the six-month period.
1.4 Objectives of the Study
The general objective of the study is to increase the capacity of the Nestea
Lemon Iced Tea Production Line by at least 44 % (see Appendix A for
computations).
The specific objectives of the study are to:
• illustrate the current flow of processes in the NLIT Production Line through
diagrams and flow process charts,
• identify the resource constraint that hinders the capacity of the system in
meeting the demand for NLIT Production through capacity profiles,
• determine the factors that caused the limitations of the lowest capacity,
• elevate the capacity of the resource constraint and generate alternatives,
• evaluate the associated costs and benefits of the alternative solutions, and
• Recommend the most cost-efficient alternative.
1.5 Scope and Limitations of the Study
The study opted for one product that contributes the largest percentage of the
total production volume of DDC-FDCSI which is Nestea Lemon Iced Tea. The focus
of the study is on the Filling Section of the NLIT Production Line since it has the
lowest capacity in the series of processes involved.
12
1.6 Date and Place of the Study
The plant and business office of DDC Foods Development and Contracting
Services, Inc. is currently situated at Block 14 Lot 26 Lazatin Blvd., Villa Victoria,
Dolores, City of San Fernando, Pampanga, Philippines. Figure 1-6 shows the road
map that indicates the exact location of the plant from the Jose Abad Santos Avenue
to Lazatin Blvd. The study began on April 13 and ended on June, year 2009.
Figure 1-6 Exact Location of DDC Food Development and Contracting Services Inc.
1.7 Gantt Chart
In laying out the activities throughout the study, Figure 1-7 shows the
procedures conducted and the duration of the completion of the tasks.
Figure 1-7 Schedule of Activities Performed during the whole study period
2.0 METHODOLOGY
14
2.1 Procedures
The identification of the possible areas of study was conducted during the
orientation of the basic processes being performed in the company. After the
comprehensive discussion and briefings regarding the policies, practices,
documentations, tests and responsibilities, the production facilities were observed.
The production supervisor provided indications of the possible areas where DDC-
FDCSI encountered problems. One of the possible areas that hold a critical
predicament was the Filling Section of the Nestea Lemon Iced Tea Production Line
since it has a lower production rate compared to the Batch Preparation and Dry
Mixing Section. In examining the weekly production schedule and the daily schedule
monitoring of production personnel, the filling process crew constantly experienced
scheduled overtime shifts to increase the capacity and keep up with the amount of
workload accepted. From this scenario, the need of increasing the capacity of the
NLIT production line was manifested. Data were gathered during the days when the
Filling Section was operating.
The seven processes in the filling process were thoroughly observed in the
succeeding days of production. Time Study was conducted during the Actual
Observation and Preliminary Data Gathering task in the schedule of activities as
shown in Figure 1-7. Snapback method was used to record the pace of the individual
processes. Stopwatch data is available in Appendix C where the recorded time is in
seconds. Rating factors of each observed time based on the current conditions
during the time of observation of the researcher which is supported by the
Westinghouse Rating System in Appendix D. Normal time was computed from the
observed time and performance rating where its average was used to obtain the
standard time of the system. Allowance Factors were included in the computation in
order to make the data more realistic. No outliers from the raw data were found thus
all recorded readings were used as samples for computations. From the statistical
data, the required number of samples was computed. All the computations are
shown in Appendix C according to the sequence of the time study procedure.
15
Further continuing the computations would bring about the actual number of
workers required for the whole operation in order to meet the target production rate
which was primarily set to the next resource constraint which is the Batch
Preparation Section with a rate of 1.085 cases per minute. Increasing the capacity
was limited to this rate to avoid formation of another bottleneck problem outside the
scope of the study.
From the initial data, analysis and documentation was made while
brainstorming for the main predicament. To further support the strength of the
problem identified, root causes were evaluated that contributed to the real problem.
Significantly knowing the source and generating solutions to small factors of cause
would in turn result to a greater improvement. Related production concepts were
deliberated to further illustrate the alternatives to be proposed. External research is
part of the evaluation where the applicable production concepts are selected to
appropriately quantify and exhibit the actual occurrences in the NLIT Filling Section
of DDC-FDCSI.
Generated alternatives were based on the root cause analysis and the
controllable, noise, and experimental analysis. Related causes were grouped
accordingly and each group has their corresponding alternatives based on the
identified root causes. Evaluating the alternatives was performed based on the
criteria relevant for the selection. At any alternative chosen, the other experimental
factors of the eliminated groups were as well solved with the chosen alternative. This
is true since the experimental factors are correlated relative to the main problem.
Cost-Benefit Analysis was conducted to assess the net benefits of the alternatives
using Factor Rating. The best alternative having the highest rating was to be
recommended. A summary and conclusion of the study was included and the areas
for further study are discussed in the latter part.
16
2.2 Definition of Terms and Symbols
The following tables serve as guide for technical terms used within the
company as well as the symbols used to describe certain processes.
Table 2-1 List of Technical Terms used in the research and their Meanings
Capacity Constraint Resource Any resource that is a potential bottleneck
if not addressed carefully.
Cycle Time
The amount of time each workstation has
to complete its set of tasks before the
product moves to the next station.
Fence 1 The lower limit for the acceptable range
Fence 3 The upper limit for the acceptable range
Flow Rate or Throughput Rate It is the average rate at which units flow
past a specific point in the process.
Implied Utilization Capacity requested by Demand divided
by the Available Capacity
Inter Quartile Range
Represents the length of the interval that
contains roughly the middle 50% of the
data.
NLIT Nestea Lemon Iced Tea
Non-bottleneck Any resource whose capacity is greater
than the demand placed on it.
North DC Distribution Center of Nestle in the
Northern part of Luzon
Polybags Polyethylene Bags with a maximum
capacity of 20 kilograms
Process Capacity the maximum output rate per unit time
Resource Constraint or Bottleneck
It is a resource that is so heavily loaded
that it cannot perform its entire assigned
task. It is the maximum process capacity.
Weekly Supply Outlook The weekly forecast of Nestle demand.
17
Table 2-2 List of Symbols used in charts and their Meanings
Symbol Meaning
Involve basic process and specific operations
Inspection of specified task and also used in specific
processes
Storage
Document involve in the process
Transport
Delay
Symbols were used to illustrate the flow of the processes involved in the
general procedure of the company as well as the detailed tasks in the production line
of Nestea Lemon Iced Tea which is further discussed in the next chapter.
3.0 SYSTEM DOCUMENTATION
19
3.1 General Processes
The three departments involved in the manufacturing process at DDC-FDCSI
are the Logistics, Production, and Quality Assurance. The specific tasks of each
department are illustrated in Figure 3-1 that serves as an overview of the whole
process. Manufacturing process starts with the procurement of raw materials based
from and packaging materials based from the weekly supply outlook up to the
dispatch of finished goods. Every operation in the process undergoes a standard
procedure and documentation. Checklists and forms are filled-out for the purpose of
recording the inventory and at the same time monitoring the every operation involved
in manufacturing for traceability purposes. The documents within the processes are
listed in Table 3-1 corresponding to the designated departments.
Table 3-1 List of Documents Handled by assigned Departments Department Document
Production
Start-up and Shutdown Checklist
Daily Schedule Monitoring of Personnel
Dehydrated Culinary Production Monitoring
Weekly Production Schedule
Checklist of recipe
Input/Output Net Weight Control
Production Report
Quality Assurance
Sensory Evaluation Tests
Truck Inspection Checklist (inbound/outbound)
Physico-chemical testing
Moisture Content Analysis
Input/Output Net Weight Control
Release Documents
Logistics
Weekly Supply Outlook
Requisition Slip
Sales Invoice
Inventory List
Packing Report
Production Output
20
The Logistics Department uses the SAP software of Nestle from the encoding
of raw materials to releasing them into their system. This software exclusively
ensures the safe transfer of significant data from DDC-FDCSI to Nestle. In acquiring
supplies from the warehouse, a requisition slip is sent containing the list of raw
materials and packaging materials needed in the manufacturing plant. Inventory
levels of raw materials are manually recorded through backward tracing of the
logistics from the acquired production reports. From the standard procedures,
inventory level monitoring is performed on a monthly basis.
The three main processes involve in Production of dry products are Batch
Preparation, Dry Mixing and Filling. Each process is further discussed in the
following sections of the paper. Furthermore, the track of production is carefully
monitored every step through a Production Monitoring Scheme followed as a
standard basis for operations. Monitoring between the manufacturing lines is
specifically performed by the production assistant.
The QA Department is responsible for the quality of the supplies received in
the plant and the finished goods. Release documents are taken by an authorized
person after comparing all relevant test results and process parameters with the
release criteria. This is both applicable for raw materials and finished goods. Raw
Materials have to undergo the internal release system before utilizing it for
production. A release of a finished product means that a given lot becomes free to be
handed over without restriction to a customer, for distribution or use. For that reason,
it is mandatory that each batch that is released meets all the release criteria before
being dispatched to Nestlé’s North Distribution Center. The Release System includes
all procedures related to release, from the way the release decision is taken, by
whom, on which basis, where the decision is recorded, as well as the consequences
of the release decision on the status and movement of the finished goods. DDC-
FDCSI is responsible for defining manufacturing parameters and criteria given in the
Product Specifications would be relevant as release criteria. Sensory evaluation, net
contents, and critical control points must all be included. Release criteria must be
met consistently during the whole production of a lot and evaluated accordingly.
21
Sensory evaluation holds an essential role in product development and
quality assurance. The taste, smell, texture and appearance of food and beverage
products are important when defining the final quality and product standard. Sensory
is a significant aspect of the over-all characteristics of the product. The sensory
characteristics are noticeable to the consumer and can either give them pleasure or
displeasure. It is of utmost importance to know what the sensory characteristics are
of a given product and be able to maintain these consistently. For this reason, DDC-
FDCSI regards sensory evaluation as an important release criterion for the Nestea
Lemon Iced Tea.
Nestle Quality System (NQS) requires sensory evaluation to be carried out
throughout the production, from raw materials to the products supplied to the
customer. The In/Out test has been chosen as the sensory quality control method for
Nestle Factories to guarantee consistent quality. A trained panel evaluates samples
as either “In” or “Out” of sensory specifications. For each product the sensory
specifications must comprise the list of key attributes, most of which should derive
from 60/40 consumer tests, and their acceptable range. Assessors are trained to
recognize what is “In” and what is “Out” by means of reference samples, pictures or,
in exceptional cases, descriptions illustrating the various “In”, “Borderline” and “Out”
cases for key attributes. The result of the evaluation is the percentage of assessor
rating the sample as “In”. This percentage determines which batches pass or fail,
based on a set of decision rules defined by Management. Product Standard is a
product which meets all analytical and sensory specifications and produced using
prescribed process parameters. The product sample must have been selected
approved and maintained using a standard procedure.
22
Figure 3-1 Flow Process Chart of the General Processes in DDC-FDCSI
23
3.2 Product under Study
From the products being handled in the company, the study chose to focus
on Nestea Lemon Iced Tea based from its large production volume contribution. As
seen previously in Figure 1-3, 52.80 % of the total production volume is contributed
by the Nestea Lemon Iced Tea Product alone.
Figure 3-2 Actual Dimensions of 360g NLIT pouch and 12 x 360g case
The actual image of the product is seen in Figure 3-2 above. The net weight
of the product is 360 grams per pouch. Conversion of units used within the
production is based from the batch size of the production line. For the NLIT, the
batch size is 250 kilograms per batch. Every case contains 12 pouches of NLI.
Further conversion of units is indicated in Appendix B. The product unit size of NLIT
is relatively lower than the other products having a standard weight of 1 kilogram.
Despite of this, Nestea Lemon Iced Tea produces the largest production volume.
The raw materials used in the production of NLIT are citric acid, tea extract,
gum Arabic, flavor lemon, sugar, sweetener aspartame, Vit C ascorbic acid, and
sweetener acesulfame. The raw materials are weighed according to their
specifications during batch preparation and are homogenously mixed in the Dry
Mixing Section before being filled into pre-formed pouches in the Filling Section.
24
3.3 Production Capacity
From the six-month data of production volumes shown in Table 3-2 below,
the Nestea Lemon Iced Tea has an average of 32186.16 kg per month. High
demands have been constantly met by the production capacity with scheduled
overtime of workers. The amount of scheduled overtime workload is relative to the
difference between the Production Volume and the Actual Capacity per month.
Overtime hours rely on the actual production hours available in the Filling Section.
The actual capacity is determined by the Filling Section having the lowest capacity in
the series of processes involved. Master Production Schedule dictates the allocation
of workload in a weekly basis. Filling Section only starts production as the preceding
Sections provide a complete set of mixed batches.
Table 3-2 Production Volume in kilograms of NLIT during June to Nov 2009 Month Production Volume (in kg) Actual Capacity (in kg)
June 36188.64 30000.00
July 31121.28 28750.00
August 32460.48 30000.00
September 30348.00 28750.00
October 35009.28 31250.00
November 35989.92 28750.00
25
3.4 Production System
The general production system of Nestea Lemon Iced Tea is illustrated in
Figure 3-3. The inputs to the system are the raw materials and packaging materials.
Raw materials for NLIT are sugar, tea extract, gum Arabic, flavor lemon, sweetener
aspartame, ascorbic acid, and sweetener acesulfame. Packaging materials would
include the pre-formed pouches, label paper adhesive, and case corrugated. In
undergoing the production process, the outputs of the process are NLIT cases with
the corresponding lot codes for traceability and the date of expiry. These labels are
relevant information required by food authorities.
Figure 3-3 Production System Framework of Nestea Lemon Iced Tea
Tracing a physical object is necessary when a need to retrieve information
about its history, application or location exists. Its usefulness is for tracking possible
defects if detected outside of the DDC-FDCSI system. Information regarding the
Date conducted, Personnel involved, Quantity produced, Batch Code, Analysis
(Passed or Failed), Manufacturing Date, Usage Decision Date, Usage Decision By,
Batch Code of bulk used, Inspection Lot Number of Finished Product, List of all raw
and packaging materials used with corresponding batch code, inspection lot number
and usage decision date, CCP monitoring, Date dispatched, and Destination plant
(North DC).
26
3.5 Process Documentation
3.5.1 Batch Preparation Section
In the Batch Preparation Section, the raw materials are obtained from the
storage area by pallets which are transferred with the use of a manual pallet jack.
Each raw material has its corresponding lot code which is used to monitor
material usage and inventory level. Lot codes used are exclusive only in the
DDC-FDCSI system which is manually recorded. This becomes relevant for
products having similar raw materials being used. Material usage recording uses
lot codes to hold information that would be relevant for tracing the raw materials
used for a specific batch in the NLIT production.
Weight specifications of raw materials in the Actual Production Area are
indicated on the NLIT Batch Preparation Checklist document as well as on
readable signs near the digital balance for referencing during the weighing
process. Workers assigned to weigh raw materials refer mostly on the signs
posted near them. Amounts are fixed based on the actual batch size used within
the production line which is 250 kilograms. Figure 3-4 shows the detailed
activities in the Batch Preparation Section with no significant delays occurring in
the process. Raw Materials are separately placed in polybags with their
corresponding weight specification and the capacity of the polybag upon
transferring to the Dry Mixing Section. Labeling format is based on the assigned
batch number indicated in the Master Production Schedule. Each Raw Material
occupy only one polybag except for the Sugar ingredient which requires ten
polybags. The labeling format for the sugar ingredients is as follows: “Raw
Material Symbol – Batch Number – A to J”. The remaining raw materials such as
the tea extract, citric acid, and other small components occupy only one polybag.
Labeling for the other raw materials would comprise of the raw material symbol
and batch number.
27
FLOW PROCESS CHART OPERATION: Batch Preparation of Beverage Production Line
LOCATION: DDC Plant
COMPANY: DDC Food Development and Contracting Services, Inc
SECTION: Beverage Line – Batch Preparation Process
ANALYST: Jaquelyn Margaret L. Miciano TYPE: Summarized METHOD: Present
ST
EP
DESCRIPTION
SYMBOL
REMARKS
OP
ER
AT
ION
TR
AN
SP
OR
T
DE
LA
Y
INS
PE
CT
ION
ST
OR
AG
E
1 Transport Raw Materials
to Batch Preparation Process
2 Scouring of dirt from bags of raw materials
Check presence of wood splinters
3 Labeling of Polyethylene
Bag
Uses marking
pen
4 Dragging the Raw
Material near the digital balance
5 Sieving of Sugar
Check presence
of foreign objects
6 Transferring Sugar to empty PE Bags
7 Weighing of Raw
Material according to recipe
Uses checklists for each recipe
8 Tying of Polyethylene
bag
9 Transferring weighed raw materials to pallet
10 Recording of Material
Usage
11 Stacking of Prepared
Batches on pallets
Figure 3-4 Flow Process Chart of Batch Preparation of NLIT Production Line
28
3.5.2 Dry Mixing Section
Prepared batches are transferred by plastic pallets using a manual pallet
jack to the Dry Mixing Section with batch numbers indicated on the polybags.
The labeled polybags are placed on pallets according to the sequence of
batches. One batch of sugar ingredient occupies one pallet. The remaining raw
materials are grouped according to ingredients and not by batch numbers. This is
to automatically distinguish the different ingredients. Tipping and loading the raw
materials to the dry mixer is done in a particular order for a preliminary mixing.
The order of placement in the dry mixer is five polybags of sugar, one polybag of
tea extract, citric acid and the small components and then the remaining five
polybags of sugar. Figure 3-5 demonstrates the operations concerning the Dry
Mixing Section of the production line.
From the conducted trials and tests, eight minutes is enough for the dry
mixer to attain the homogeneity of the bulk mixture. Sensory Evaluation tests are
carried out as part of the standard quality protocols of the QA Department where
sampling is made per batch as exemplified in the General Processes Section of
the chapter. Labeling format depends on the batch number being handled. There
would be a resulting 10 polybags of mixed powder for every batch and are
labeled as follows: “Batch Number – A to J”. A new set of polybags is used to
transfer the batches to the Filling section.
29
FLOW PROCESS CHART OPERATION: Dry Mixing of Beverage Production Line LOCATION: DDC Plant COMPANY: DDC Food Development and Contracting Services, Inc
SECTION: Dry Mixing Process
ANALYST: Jaquelyn Margaret L. Miciano TYPE: Machine METHOD: Present
ST
EP
DESCRIPTION
SYMBOL
REMARKS O
PE
RA
TIO
N
TR
AN
SP
OR
T
DE
LA
Y
INS
PE
CT
ION
ST
OR
AG
E
1 Setup of Machine
2 Transport Prepared
Batches to Dry Mixing Process
3 Label Polyethylene
Bags
according to
batch numbers
4 Pour raw materials
inside Dry Mixer
5 Run Dry Mixer
Set to 8 minutes for homogeneity
6 Pour bulk mixture into bins with labeled PE
Bags
7 Recording of Batch
Completion
8 Stacking of Prepared
bulk mixture on pallets
Figure 3-5 Flow Process Chart of Dry Mixing of NLIT Production Line
30
3.5.3 Filling Section
The batches handled in the Dry Mixing Section are transferred to the
Filling Section and are processed in sequence according to their batch numbers.
Operations involve are labeling, coding, scooping, weighing, sealing, wiping,
stacking and lot coding. A better illustration of the flow of the operations is
presented through a Flow Process Chart in Figure 3-6. The standard time was
calculated from the time study conducted within the NLIT Filling Section as
presented in Appendix C. Inspection is done every 15 minutes in the weighing
operation as part of the standard procedure for the net weight control. It is
determined whether the net weight of the particular pouch is within the allowable
weight range set by the company. The allowable range is ± 0.5 % of the 360-
gram net weight.
Time study was conducted to establish the standard time of the
operations in the Filling Section. This work measurement technique represents
an impartial production standard. Stopwatch data samples obtained are
presented in Table 9. Complete raw data samples are exhibited in Appendix C.
Elements chosen are the operations involved in the Filling Section as indicated
also in the Flow Process Chart in Figure 13. Standard time computations are also
located in Appendix C.
Table 3-3 Sample Stopwatch Data of Filling Operations Element
No. Process Name
Trial No. (Actual time in seconds) Ave
1 2 3 4 5 6 7 8
1 Labeling
of Pouches
72.34 56.04 47.86 59.73 74.85 83.91 60.32 83.16 67.38
2 Coding
of Pouches
35.77 47.72 64.07 84.42 59.75 60.28 54.27 76.34 60.3
3 Scoop Nestea Mixture
37.26 59.84 47.60 45.73 48.33 51.84 47.18 52.30 47.61
4 Weigh
Pouches 108.19 127.08 138.27 72.31 151.89 155.84 168.17 191.59 134.1
5 Seal
Pouches 123.57 160.72 126.78 106.94 145.06 113.87 157.28 175.17 134.8
6 Wipe off powder residue
59.67 71.54 55.78 57.85 72.36 59.89 89.24 62.74 65.75
7 Stack
and Lot coding
31.43 43.10 41.85 44.69 46.72 41.38 35.07 52.18 41.68
31
FLOW PROCESS CHART OPERATION: Filling of Beverage Production Line LOCATION: DDC Plant COMPANY: DDC Food Development and Contracting Services, Inc
SECTION: Filling Process
ANALYST: Jaquelyn Margaret L. Miciano TYPE: Summarized METHOD: Present
ST
EP
Tim
e (m
in)
DESCRIPTION
SYMBOL
REMARKS
OP
ER
AT
ION
TR
AN
SP
OR
T
DE
LA
Y
INS
PE
CT
ION
ST
OR
AG
E
1 Transport Packaging Materials to Labeling
Process
2 Transfer prepared bulk
mixture in filling process
3 0.9 Label Preformed
Pouches with stickers
4 0.7 Punch codes in
pouches
5 Place bulk mixture into
bin
6 0.6 Scoop bulk mixture into
pouches
7 1.7 Weigh pouches
According to specification
8 Input Output Weight
Content
Every 15 minutes
9 1.7 Seal pouches
10 0.9 Wipe off powder residues outside
pouches
11 0.5 Fill, Stack, Code Cases
and tag pallets
13 Transport pallets in
dispatch area
14
Dispatch
Figure 3-6 Flow Process Chart of Filling of NLIT Production Line
32
3.6 Manpower Complement
The three main processes in the company have their own designated crews.
Four workers are assigned to accomplish the tasks in the Batch Preparation Section
while the Dry Mixing Section requires only three. Presence of a dry mixer made tasks
under the Dry Mixing Section to become more inclined on the standard operating
procedure of the machine. Table 3-4 shows the tasks and the number of workers
assigned to it.
Table 3-4 Number of Workers in the Sections of the NLIT Production Line
SECTIONS TASKS NUMBER OF WORKERS
Batch Preparation
Siever 2
Weigher 2
TOTAL 4
Dry Mixing
Operator 2
Helper 1
TOTAL 3
Filling
Labeller 1
Coder 1
Scooper 1
Weigher 1
Sealer 1
Wiper 1
Stacker 1
TOTAL 7
*Source: DDC-FDCSI, 2009
Focusing on the Filling Section, its crew consists of seven operators in the
production line that includes the labeler, coder, scooper, weigher, sealer, wiper, and
stacker. Each operator is assigned to a single task within the production line. In
comparing the number of workers of each section, the Filling Section has the highest
number of workers involved in its operations.
33
3.7 Machine and Equipment
The Batch Preparation process has two equipments being used which is the
digital balance for the in acquiring the specifications of the raw materials. The 100-
kilogram capacity of the digital balance for Batch Preparation is larger than the one
used in the Filling Section.
The machine present in the production of Nestea Lemon is the dry mixer that
has a maximum capacity of 250 kilograms. The batch size in the production is
equivalent to the maximum capacity of the mixer. During operation, the whole cone
of the mixer rotates 360 degrees at the rate of 6 revolutions per minute. The
cylinders inside the cone rotate simultaneously with the cone ensuring the
homogeneity of the mixture.
Figure 3-7 Nestea Lemon Iced Tea Dry Mixer
In the Filling Section, there are four equipments being used within the
production line. These are the digital balance, horizontal band sealer, hot foil coder
and carton coder. Figure 3-8 shows the images of the digital balance and the
horizontal band sealer. Capacities of the two equipments are based from their
function. The digital balance has a capacity of 1200 grams. Transmission speed of
the horizontal band sealer is 3 feet per minute. Figure 3-9 displays the images for the
hot foil coder and carton coder. Its capacities are based on the maximum speed of
printing onto packaging materials. Printing speed of the hot foil coder is 20 to 50
prints per minute while the carton coder has a speed of 300 imprints per minute.
34
Figure 3-8 Digital Balance (left), Horizontal Band Sealer (right)
Figure 3-9 Hot Foil Coder (left), Carton Coder (right)
Given the capacities of the equipments used within the Filling Section, the
actual capacity is still determined by the production rate of the worker since these
equipments are manually used.
35
3.8 Plant Layout
An illustration of the actual floor layout of the plant is shown in Figure 3-10.
The three distinct production lines have their own area for Batch Preparation, Dry
Mixing and Filling. NLIT Filling Section is the focus of the study as stressed in the
figure.
Figure 3-10 Actual Layout of the Manufacturing Plant
36
3.9 Workstation under Study
Inside the NLIT Filling Production Area are the stations of the processes
executed in the whole Filling Section. The arrangement is presented in sequence
with the red arrows corresponding to its flow from the labeling to the stacking and lot
coding. Referring to the plant layout as basis, the bottom part of the workstation is
the partitioning wall from the NLIT Dry Mixing Area. Squares represent tables used
within the production line as processes are performed in standing position. Every
operation consists of only one worker to accomplish the task. The pallet at the side of
the scooper holds the mixed batches from the Dry Mixing Section while the pallet at
the end of the process would consist of the prepared NLIT cases in the Filling
Section. Proper palletizing is followed based on the required arrangement indicated
in the cases to ensure the locked position of the whole batch.
Figure 3-11 Workstations in the NLIT Filling Section
4.0 RESULTS AND DISCUSSIONS
38
4.1 Problem Identification
A resource constraint exists in the Nestea Lemon Iced Tea Production Line.
The actual capacity of each Section is based from their performances and production
output records. In comparing the capacities of the three production lines, the Filling
section has the lowest capacity indicating that it is the bottleneck of the system. The
production output rate is established by the bottleneck operation. As seen in Figure
4-1, the Filling Section limits the total production capacity to 0.625 batches per hour.
Figure 4-1 Flow Rate of the system with a Resource Constraint
In the effort of attaining the capacity requested by demand, the filling process
increases its capacity by regularly extending work hours of the Filling Crew. The
overall overtime hours accumulated to 4505 hours from the seven workers involved
in the filling process. Figure 4-2 shows the overtime production hours in the Filling
Section alone for the given monthly period as illustrated below.
Figure 4-2 Total Overtime Hours in the Filling Section for June to Nov 2009
Since the NLIT make up for 52.8 % of the total production volume, it would be
important to address capacity problems occurring in the NLIT Production Line. With
the high volumes of production, it is necessary to increase the capacity of the Filling
production line by dealing with the resource constraint.
June July Aug Sept Oct Nov
Overtime hours 115.80 99.59 103.87 97.11 112.03 115.17
50.00
70.00
90.00
110.00
130.00
Nu
mb
er
of
Ho
urs
Total Overtime Hours in the NLIT Filling Section for the month of June to November 2009
39
4.2 Problem Analysis
4.2.1 Identification of Root Causes of the Problem
From the three main processes in the NLIT Production Line, The Filling
section determines the average production rate since it has the lowest actual
capacity compared to the Batch Preparation and Dry Mixing Process. In
analyzing the factors that caused its low capacity, the factors were classified into
Man, Method, Machine and Material. The summarized first level factors of the
problem are presented through the fishbone diagram in Figure 4-3.
Figure 4-3 Summarized Causes of Low Capacity of the Filling Section
Each factors under of Man, Method, Machine and Material contributes to
the main predicament of having low capacity of the NLIT Filling Section. There
are no environmental conditions that significantly affect the capacity of the
process. Temperature, lighting and humidity in the production area are
maintained at constant levels without abrupt changes.
40
4.2.1.1 Man
The current production rate of the Filling Section with a crew
consisting of seven workers is 0.625 batches per hour. Insufficiency in the
number of workers was recognized from comparing the amount of
workload to the actual capacity of the process. Workload arrives from the
Dry Mixing Section with a rate of 1.5 batches per hour which is faster than
the process capacity of the Filling section thus workload is accumulated
through time. Part of the fast rate of the Dry Mixing Section is the
presence of a machine relative to the manual operations of the filling
process. The Filling Section lags in production by 0.875 batches per hour
which in turn is handled through allowing scheduled overtime of the Filling
Crew for every production. Production Department does not acknowledge
the need to increase the manpower capacity of the Filling Section and
instead chooses to tolerate overtime of the Filling Crew.
Workers of the Filling Crew are designated to distinct processes
involved in the production line which was illustrated in the layout of the
Filling Workstations in Figure 3-11. One of the processes involved is the
weighing of 360-gram pouches of Nestea Lemon Iced Tea powder. From
the time study conducted, the weighing operation takes the longest time
to execute. Referring to the average observed time and standard time of
the Filling operations in Table 4-1, the weighing of pouches has shown its
consistent time-consuming operation as it holds the longest time
recorded. Although the sealing operation does not linger far from the
values of the weighing operation, no problems contributed by the sealer
occur during this operation. For the weighing operation, adjustments have
to be made to ensure the 0.5 % allowable net weight difference of the
pouches. This has been caused by a factor associated to Man where the
scooper creates net weight discrepancies because of the scoop’s volume
capacity of 450 grams that exceeds the required amount of 360 grams
per pouch. For that, the weighing operation requires net weight
adjustments.
41
Table 4-1 Average Observed Time and Standard Time of Filling Elements
Element No. And Description
Average Observed Time (min/case)
Standard Time (min/case)
1. Labeling of Pouches 1.150 1.261
2. Coding of Pouches 0.957 1.049
3. Scoop Nestea mixture 0.818 0.896
4. Weigh pouches 2.274 2.493
5. Seal Pouches 2.252 2.469
6. Wipe off powder residue 1.146 1.257
7. Stack and Lot Coding 0.641 0.702
Another factor associated to Man is the occasional lax attitude of
workers which happens when there is no supervision. The production
assistant is responsible for supervising and overseeing the current status
of the production line. Part of this responsibility is ensuring the proper
execution of the tasks within the line with accordance to the expected
pace of production. This is compulsory to guarantee that the master
production schedule is followed. Failing to execute inspection between
shifts occur despite the existing production policy which contributes to the
further slowing down of the operations in the production line.
Figure 4-4 Cause Factors Associated to Man
42
4.2.1.2 Method
The factor associated with the Method used is the high processing
time of the Filling Section relative to the other processes in the NLIT
Production Line. Manual processes performed within the Filling
production line rely on the manpower capacity of the Seven-worker Crew.
The absence of a filling machine forces the Filling section to maximize
manpower capacity that is detrimental to the length of processing time. Its
significance is relative to the presence of a dry mixing machine in the
preceding process which is the Dry Mixing Section. Existence of a dry
mixer significantly increases the rate of production in the Dry Mixing
Section resulting to bottleneck processes within the NLIT production line
particularly the Filling process being next in line. Unbalanced capacities
within the line significantly contribute to additional production costs.
Manual net weight control is a contributing factor to the high
processing time. Adjustments have to be made during the weighing
process because of the high volume capacity of the scoop compared to
the required net weight of individual pouches. A smaller scoop is used to
make these adjustments. Since the NLIT product is the only product
handled at 360 grams per pouch and all the other products have a unit
size of one kilogram, the available 450-gram standard size of the scoop
within the plant has caused the weighing process of the NLIT Filling
Section to be a factor in the high process time. This is further supported
through the average observed time of the weighing operation as
presented in Table 4-1.
Figure 4-5 Cause Factors Associated to Method
43
4.2.1.3 Machine
Machine performances in the Filling section have not caused the
lowering of capacity of the production line. Manual operation of machines
causes it to depend on the worker’s handling speed. Efficiency of the
machines is relative to the performance of the worker as the equipments
used are semi-automated. Performance of the machines utilized in the
production line solely relies on the quality of the output being produced in
accordance to their functions. Sealing ability of the Horizontal Band
Sealer is measured through appropriate quality test for leaking of
pouches. Separate standard procedures are performed to measure these
performances. No significant defects have been recorded based on the
performance of the machines. Preventive Maintenance for these
machines that were illustrated in the previous chapter is regularly
performed upon start of production. This avoids considerable delays
during production resulting to no machine downtimes.
Initial number of machines was determined from the expected
level of production volume as well as from the existing Production lines of
the Dehydrated Culinary and Milo Vending Mix products. The Dehydrated
Culinary Production Line was design for small volumes of production with
respect to the levels of demand received for products under this group.
From the workstation designs of established production lines within the
plant, the design for the NLIT Production line originated on the existing
designs used by the company. Underestimation of the volumes of orders
has limited the capacity of the NLIT Filling Section to a single set of
machines. Quantity of the machines that were purchased by the company
for the NLIT Filling Section was based on the initial design of the
workstation that only has a single set for one production line. Decision on
the workstation design by the Management was resolved upon launching
the production of NLIT. Increase in the production volume of NLIT through
time was underestimated by Management that causes the low capacity of
the Filling Section.
44
Machines involved in the Filling section include the digital balance,
horizontal band sealer, hot foil coder and carton coder. One of each
machine is available in the NLIT Filling section. The number of machines
restricts the capacity of the process since increasing manpower is not
viable without additional machines. The layout of the workstation in Figure
3-11 supports the design of having a single operator for each machine.
Primary investment for the NLIT Filling Section was constricted to a single
set of machines. This further restricts the capacity of the Filling section as
the number of workers is relative to the number of machines available.
Additional manpower entails additional machines.
Figure 4-6 Cause Factors Associated to Machine
4.2.1.4 Material
The only contributing factor associated with the Material is the
small net weight of the product. Processing time is further lengthened by
the small breakdown of the large batch sizes. From the batch size of 250
kilograms, the process is required to break the large volume into 360
grams. The Filling Section performs this task to satisfy the requirements
of the client which is out of the control of the company.
Figure 4-7 Cause Factors Associated to Material
45
4.2.2 CNX Analysis
The associated root causes are further classified into C’s, N’s and X’s
which are controllable, noise and experimental factors respectively. Table 4-2
shows the summary of the root causes recognized and their corresponding
classification. Referring to Table 4-2, two noise factors, one controllable factor
and five experimental factors were identified. Factors classified under noise
entails that these causes would not be addressed by the study. Tackling the
controllable factor contributes to resolving the problem through adhering to the
standard practices within the manufacturing plant. Resolving experimental factors
by means of utilizing appropriate concepts related to the causes would in turn
result to determining alternative solutions to address the main problem.
Table 4-2 CNX Analysis of the Root Causes of Low Capacity of Filling Section
Root Causes Controllable
(C)
Noise
(N)
Experimental
(X)
Amount of work load exceeds the manpower capacity.
�
The scooper creates large net weight discrepancies.
�
The production assistant forgets to inspect the Filling Section. �
There is an absence of a filling machine.
�
The volume capacity of the scoop is 450 grams.
�
The workstation is initially designed for small volumes of production
�
There is a single set of machines available.
�
The net weight of the NLIT mixture to be filled is small.
�
46
Noises are root causes that are beyond the control of the company and
beyond the scope of the study. These are the problems that the study would not
be able to handle as the limitation of the scope applies. Noise is identified in the
Material cause factors where the net weight of the product was observed to be
small. Required weight specifications are provided by the client. DDC-FDCSI is
not involved in determining the appropriate net weight of the product that makes
the cause to be out of scope. This requires versatility in the production of DDC-
FDCSI to handle difference in weights of products. Another noise factor is
associated with Machine causes. Expecting small volumes of production has
limited the availability of machines to a single series. The General Manager and
the Project Engineer are the ones responsible for determining the number of
machines required. Demand levels for the product were underestimated thus
leading to a low capacity of the NLIT Filling Section relative to the capacity
requested by demand. These are the factors excluded in the scope of the study
because it is difficult to control.
Controllable root causes are merely problems due to lack of compliance
in the policies and standards set by DDC-FDCSI. To deal with this problem, strict
implementation of the rules should be manifested to the operators and
employees that directly affect the root cause of the problem. One controllable
factor associated with Man is identified through the CNX analysis which is
summarized in Table 4-2. Inspection of the Filling Section is a critical
responsibility of the production assistant as it affects the attitude and effort of
workers during production. Irregular production line monitoring occurs despite the
small facilities of the manufacturing plant. Recommended number of inspections
is at the minimum of twice per production shift based on the standard
procedures. The schedule of inspection has no fixed time slot to avoid the
workers’ anticipation of the production assistant’s arrival. A Production Monitoring
Scheme is currently followed by the production department. Details regarding the
standard procedure are presented in Figure 4-8. Examination process is
conducted by the production assistant during the first and last quarter of the
production shifts. Production output rate is to be checked by visually inspecting
the status of the production line.
47
DDCDDCDDCDDC Food Development Contracting Services,
Inc. San Fernando, Pampanga PRODUCTION
MONITORING SCHEME
ITEM: NESTEA LEMON ICED TEA
CHAPTER 3: MANUFACTURING LINE CHECKS
Area: FILLING
OBJECT PURPOSE
EXAMINATION
WHAT HOW WHEN BY WHOM
FILLING CREW
PRODUCTION LINE STATUS
CHECK
OUTPUT RATE
VISUAL INSPECTION
DURING THE FIRST AND LAST
QUARTER OF THE SHIFT
PRODUCTION ASSISTANT
Figure 4-8 Production Monitoring Scheme for Production Line Inspection
The experimental causes are the problems that can be altered in such a
way of achieving the objectives of the study. These are the ones that can have
quantitative solutions in order to improve the current system. To further address
each experimental factor, the initially identified experimental factors are to be
grouped.
Correlation exists between experimental cause factors and it would be
more comprehensive to further group the experimental factors according to their
causes. There are seven experimental causes identified. Cause factors
emphasizing the insufficient capacity are selected among the classifications of
Man, Machine and Method to determine the Experimental Factor Group A shown
in Table 4-3. The first would be the amount of workload exceeds the manpower
capacity. The workload entering the filling process is determined by the amount
of workload the Dry Mixing Section finishes. The last factors selected is
concerned with Machines that there is a single set of machines available in the
NLIT Filling Production Line. Machines identified in the series of processes
involved in the Filling Section are described in detail in the previous chapter. The
grouped factors are associated with the actual capacity of the NLIT Filling
Section in terms of the availability of the resources used in production.
Table 4-3 First Group of Experimental Factors
Group Experimental Factors
A
1. Amount of workload exceeds manpower capacity.
2. There is a single set of machines available.
48
The Experimental Factor Group B consists of only one cause factor as
shown in Table 4-4. Absence of a filling machine is recognized to have caused
the manual handling of the filling process. Because of this, the production output
rate is controlled and limited by the manpower capacity. Moreover, the
identification of this factor is as well relative to the preceding process in the Dry
Mixing Section where a dry mixer is present in the operation. This group
concerns the automation of the processes involved in the Filling Section through
introducing a filling machine. The need for a machine was developed from the
current rate of the preceding process. Attending to this need intends to resolve
the main problem as well as the other cause factors identified in the study.
Table 4-4 Second Group of Experimental Factors
Group Experimental Factors
B 1. There is an absence of a filling machine.
Lastly, Experimental Factor Group C involves the factor causes related to
the use of the inappropriate tool in the scooping area. From Table 4-5, the root
causes include the large volume capacity of the scoop and the necessity to make
weight adjustments. The following factors were identified in the cause factors
associated to Man and Method respectively. Group C particularly deals with the
high processing time contributed by the weighing operation in the filling process
which in turn is caused by the large capacity of the scoop.
Table 4-5 Third Group of Experimental Factors
Group Experimental Factors
C
1. The scooper creates large net weight discrepancies.
2. The volume capacity of the scoop is 450 grams.
The three experimental factor groups distinguished caused the main
problem of the Filling Section having a low capacity. Resolving any group of
factors would successively provide solutions for the other group of factors since
the causes are interrelated in such a way that they all address to the need to
increase in capacity of the Filling Section.
49
4.2.3 Presentation of Alternatives
4.2.3.1 Controllable Factor
From the CNX Analysis conducted in the previous section, the
single controllable factor identified is the inspection of the filling
production line by the production assistant. In attending to this cause,
proper regulation for controlling the cause is necessary to assure the
action to be consistently performed. The objective is to provide a
systematic procedure of inspecting production line status in order to avoid
the occasional lax attitude of the workers in between shifts. Illustration of
the flow of tasks for the procedure is presented in Figure 4-9.
The responsibility of the Manufacturing Line checks is designated
to the Production Assistant where production line monitoring would be
documented. From Figure 4-9, the Daily Production Schedule of the NLIT
Filling Section is to be checked whether the production line is scheduled
to operate on the specific day or not. The exact dates of production are as
well reflected on the Master Production Schedule of the Production
Department. Record keeping of inspection is to be made mandatory to
every scheduled production where the status and rate of production is
consistently monitored. During the days of operation, inspection is
conducted on the first four hours of the shift. Random Sampling for the
specific hour of inspection is carried out through the following steps:
i. Determine the current day of the month (1st to 31st).
ii. Divide the date to four, which is the duration of the initial
and final inspection.
iii. Get the remainder of the previous operation.
iv. Add the remainder to the start of the first four hours and
the last four hours of the shift.
v. Conduct the inspection within the computed hour.
50
Figure 4-9 Process Flow Diagram of Manufacturing Line Checks
51
The production area for the Filling Section is visited during the
hour of inspection as calculated from the random sampling method
outlined earlier. Specific minutes of the computed hour of inspection on
the production line would be subjectively determined by the Production
Assistant given that other responsibilities within the manufacturing plant
are still performed as well. The duration of the inspection estimated to last
20 minutes given that each processes in the Filling Section is overseen.
The Production Monitoring Scheme logbook is filled up with the
recorded relevant information. Exact time of inspection must be indicated
as well as the Batch Number being processed. Aside from the batch
numbers labeled on the polybags from the Dry Mixing Section, “A” to “J”
is labeled beside the corresponding batch number with respect to the ten
polybags of Nestea Lemon Iced Tea mixture. Similar format is to be used
in addressing the current batches being handled in the Filling Section
during the time of inspection. Recording these information would aid in
establishing the Master Production Schedule with the obtained actual
capacities of the Filling Section.
In any case of deviation of the current rate of production, the
assessment of the Production Assistant must be deliberated with the
Production Manager or any higher authority present in the manufacturing
plant. This is to ensure the proper plan of action before addressing it to
the problem. No fixed plan of action could readily address the problems
encountered in the manufacturing plant. Further analyzing the existing
problems would be required in any given scenario. The action to be
performed would be based on the strict product specifications and the
current target production rate. Existing plan of action being regularly
followed in the Filling Section is allowing scheduled overtime of workers
this is to address the slow production rate of the Filling Section relative to
the previous process. Other decisions made by the Production
Department are relative to the crisis being dealt with.
52
4.2.3.2 Experimental Factors
Experimental factors were initially grouped according to the
correlation of the causes. In generating possible alternatives,
corresponding solutions that specifically addressed each group of
experimental factors were established.
Table 4-6 Alternative Solutions for Grouped Experimental Factors
Experimental
Factor Alternative
A Additional workers and Parallel workstations
B Purchase Filling and Sealing Machine and
Reallocation of workers
C Customized scoop, Additional workers and
Parallel workstations
The objective is to elevate the capacity by 44% from the computed
Goal in Appendix A. Basis for this quantitative measure of improvement is
supported by the Six sigma concept. This percentage is acknowledged to
be the least possible % improvement that the study can accomplish.
Alternatives presented are expected to reach the 44 % increase in
capacity of the Filling Section.
Time study is the work measurement tool used to establish the
standard time of each operation. The standard times would become the
basis for identifying the filling operations that require increase in capacity.
As previously stated, the increase in capacity is further restricted by the
next resource constraint in the NLIT Production Line which is the Batch
Preparation Section. The rate of the Batch Preparation Process becomes
the target rate for improvement of the capacity of the Filling Section.
Computations for the standard time are indicated in Appendix C. Table 4-
7 shows the result of the computed standard times.
53
Table 4-7 Standard time of Operations in the Filling Section
Element No. And Description
Standard Time (min/case)
1. Labeling of Pouches 1.261
2. Coding of Pouches 1.049
3. Scoop Nestea mixture 0.896
4. Weigh pouches 2.493
5. Seal Pouches 2.469
6. Wipe off powder residue 1.257
7. Stack and Lot Coding 0.702
Total 10.127
To determine the adequacy of the new production rate of the
proposed alternatives, a comparison between the rates should be made
using the formula from Figure 4-10. The current output rate of the Filling
Section to be used in the formula would be entirely fixed for all
alternatives which is equivalent to 0.625 batches per hour. From the
longest standard time to be computed from the proposed alternatives, the
unit measurement of minutes per case would be translated into batches
per hour based on the conversion of units in Appendix B. This would
result to the new output rate of the proposed alternative to be entered in
the formula. Consistent unit measurement between elements is required
upon applying the formula presented in Figure 4-10. The calculated %
improvement would be applied to all the alternatives as a tool for
evaluation. (Bautista,2008)
Figure 4-10 Formula for computing % Improvement
54
Alternative A: Additional workers and Parallel workstations
Alternative A would deal with the Group A experimental factors by
determining the required number of additional workers and their
corresponding workstations. Estimation of the required number of workers
is shown in Table 4-8. Standard minutes values is derived from the ideal
target output rate of 1.125 batches per hour which is equivalent to 0.9216
minutes per case and setting this as the divisor for the established
standard times. Values in the standard minutes’ column were rounded off
to estimate the number of operators required in the production line.
Table 4-8 Estimating the Number of Workers per Operation
Element No. And Description
Standard Time
(min/case)
Standard minutes
(min/case)
Number of operators
1. Labeling of Pouches 1.261 1.368 2
2. Coding of Pouches 1.049 1.138 1
3. Scoop Nestea mixture 0.896 0.973 1
4. Weigh pouches 2.493 2.705 3
5. Seal Pouches 2.469 2.679 3
6. Wipe off powder residue 1.257 1.364 2
7. Stack and Lot Coding 0.702 0.762 1
Total 10.127
13
To identify the slowest operation, the estimated number of
operators is to be divided into the standard minutes for each of the seven
workers. This would be necessary in order to determine the new flow rate
of production of the filling process. The slowest operation would entail the
production rate of the line with the additional workers allocated
accordingly to the operations with the longest standard times as done
earlier. Table 4-8 shows the result of identifying the new output rate of the
production line as proposed by Alternative A.
55
Table 4-9 Determining the Output Rate of Alternative A
Element No.
And Description
Standard Time
(min/case)
Number of Workers
Standard Time/
Number of Workers
1. Labeling of Pouches 1.26 2 0.631
2. Coding of Pouches 1.05 1 1.049
3. Scoop Nestea mixture 0.90 1 0.896
4. Weigh pouches 2.49 3 0.831
5. Seal Pouches 2.47 3 0.823
6. Wipe off powder residue 1.26 2 0.629
7. Stack and Lot Coding 0.70 1 0.702
Total 10.13 13 6.191
From the computations done in Table 18 above, the longest
operation is the coding of pouches. This operation determines the output
from the line which is, 1.049 minutes per case or 0.988 batches per hour.
The output rate of Alternative A is expected to increase by 0.363 batches
per hour which is equivalent to the additional capacity offered by the
proposed alternative.
Parallel workstations are to be added relative to the estimated
number of workers for each operation. The proposed layout of the added
weighing and sealing workstations is shown in Figure 28. Flow of
operations is indicated by the arrows. For the labeling and wiping of
pouches, no additional workstations were introduced since the operation
does not require any machines to be added. Extra workers for these
operations maximize the space of the workstations and double the
amount of work that can be performed in the operation.
56
STACKING AND
LOT CODING
Figure 4-11 Additional Parallel Workstations in the NLIT Filling Section
In comparing the output rate of alternative A to the current rate,
the formula in Figure 4-10 was utilized to determine the percent
improvement of the proposed alternative. From the new output rate of
alternative A, the current output rate was improved by 58.08% which
exceeded the goal primarily set in the study. The intention of additional
workers is to level with the current rate of production and not exceed it.
The initial cost of hiring additional workers is 400 Php each. Additional six
workers would result to a total of 2,400 Php hiring cost. Parallel
workstations are to be established relative to the additional workers
required. Referring to Table 4-9, an additional of two workers is to be
added in the weighing and sealing operations. The cost of digital balance
is 6,500 Php each thus an additional of two units would add up to 13,000
Php. Horizontal band sealers cost 13,000 Php each. Adding two
additional sealers costs 26,000 Php. The total cost of the additional
parallel workstations is 41,000 Php. Table 4-10 shows the breakdown of
the costs of implementing Alternative A from the unit cost of every
additional resource proposed in the alternative.
57
Table 4-10 Cost of Implementing Alternative A
Additional Resources Cost Total
People 6 Hiring cost
per worker
400 Php 2,400 Php
Horizontal Band
Sealer
2 Unit Price 13,000 Php 26,000 Php
Digital Balance 2 Unit Price 6,500 Php 13,000 Php
TOTAL 41,400 Php
Alternative B: Purchase Filling and Sealing machine and
Reallocation of workers
Alternative B requires the company to invest on an Automated
Filling and Sealing machine. The machine is expected to perform the
labeling, coding, filling and sealing operations involved in the Filling
Section. An automated filling and sealing machine has an estimated cost
of 300,000 Php given by the accredited machine suppliers. These
suppliers located in the country usually obtain assembly parts from other
countries as well. Further customizing the filling and sealing machine to
completely cover the whole operation for the Filling Section would entail
additional cost. Availability of the machine was considered as a factor in
determining the viable option to pursue. The filling and sealing machine
appropriate for the filling operations has a capacity of 30 pouches per
minute which is equivalent to 2.5 cases per minute. The rate was
obtained from the maximum weight capacity the machine can fill and the
design capacity of the machine. The filling machine is expected to run at
a rate of 0.4 minutes per case. Presence of the machine does not entirely
hold the output rate of the entire filling operation.
58
Based on Table 4-11, the output rate of Alternative B is
determined by the longest operation which is the stack and lot coding.
Introducing a Filling and Sealing Machine increases the capacity of the
Filling Section to 0.702 minutes per case which is equivalent to 1.477
batches per hour. An additional capacity of 0.852 batches per hour is
made available by Alternative B.
Table 4-11 Output Rate of Alternative B
Element No. And Description
Standard Time (min)
Number of
Workers
Machine Capacity
(min)
Standard Time/ Number of Workers or
Machines
1. Labeling of Pouches
1.26 0
0.4 0.4
2. Coding of Pouches
1.05 0
3. Scoop NLIT mixture
0.90 0
4. Weigh pouches 2.49 0
5. Seal Pouches 2.47 0
6. Wipe off powder residue
1.26 2 - 0.628
7. Stack and Lot
Coding 0.70 1 - 0.702
Total 10.13 3 - 1.73
Referring again to Table 4-11, the number of workers required for
the whole operation is reduced to three. The remaining four workers is to
be reallocated to the NLIT Batch Preparation Section. The NLIT Batch
Preparation Section currently has four workers in the production area and
adding the four workers from the Filling Section would double its current
capacity of 1.125 batches per hour to 1.6875 batches per hour. In doing
so, the longest process in the NLIT Production Line would still be the
Filling Section having the capacity of 1.477 batches per hour.
59
Reallocation of the workers from the Filling Section to the Batch
Preparation Section would require one day training on the procedures
performed. Tasks of the four workers to be reallocated are sieving and
weighing. The Production Assistant will be the one responsible for training
the workers. A day’s training is sufficient enough since the task follows a
standard procedure. Breakdown of costs for Alternative B is presented in
Table 4-12.
Table 4-12 Cost of Implementing Alternative B
Modification of
Resources
Cost Total
Worker
Retraining
4 Cost per
worker
325 Php 1,300 Php
Trainer 1 Cost per
trainer
400 Php 400 Php
Filling and
Sealing Machine
1 Unit Price 300,000 Php 300,000 Php
TOTAL 301,700 Php
The % improvement of Alternative B relative to the current output
rate of the system is equivalent to 136.32% based on the formula shown
in Figure 4-10 using the increased capacity of 1.477 batches per hour.
Alternative B increased the capacity of the whole NLIT Production Line by
also addressing the capacity constraint resource which is the Batch
Preparation Section. Although the total cost of implementing Alternative B
is higher, it is paid back by the high percentage of improvement. Further
evaluation is made in the succeeding section to compare improvement
results among the alternatives.
60
Alternative C: Customized scoop, Additional workers and Parallel
workstations
Alternative C focuses on the customizing a failsafe scoop to
reduce weight adjustments in the production line. The 450-gram scoop is
to be replaced with a 360-gram scoop. Customization of the scoop from a
supplier would cost the company 1,500 Php each. Introducing the
customized scoop is expected to contribute at least 44% improvement
based from the computation of the goal thus the weighing process is
expected to reduce processing time by 44%. Computing for the improved
rate of the weighing operation is as follows:
Improved rate of weighing process = 44% of 2.4925 minutes per case
= 1.0967 minutes per case
Processing time of the weighing of pouches is expected to reduce
by 1.0967 minutes per case which would result to the expected standard
time of the process to be 1.3958 minutes per case. This was reflected on
the standard time used for the weighing operation in Table 4-13 below.
Table 4-13 Output Rate for Alternative C
Element No. And Description
Standard Time (min)
Number of
Workers
Standard Time/ Number of Workers
1. Labeling of Pouches 1.26 2 0.631
2. Coding of Pouches 1.05 1 1.049
3. Scoop NLIT mixture 0.90 1 0.896
4. Weigh pouches 1.40 2 0.698
5. Seal Pouches 2.47 3 0.823
6. Wipe off powder residue 1.26 2 0.628
7. Stack and Lot Coding 0.70 1 0.702
Total 10.13 12 5.427
61
Output Rate of Alternative C is dictated by the longest operations
in its series of processes which is the coding of pouches. This can be
compared with the output rate of Alternative A since the same operation
turned out to be the longest. Thus, the output rate of Alternative C is
similar to that of Alternative A which is 1.049 minutes per case or 0.988
batches per hour. Conveying the % improvement of its increase in
capacity, it is also 58.08% relative to the current output rate. Five workers
are added as estimated in Table 4-13 with their respective tasks. Since
additional workers are placed to operations requiring machines, additional
parallel workstations are as well needed to implement this modification.
Corresponding costs of additional resources is presented in Table 4-14.
Table 4-14 Cost of Implementing Alternative C
Additional Resources Cost Total
People 5 Hiring cost
per worker
400 Php 2,000 Php
Horizontal Band
Sealer
2 Unit Price 13,000 Php 26,000 Php
Digital Balance 1 Unit Price 6,500 Php 6,500 Php
Customized Scoop 1 Unit Price 1,500 Php 1,500 Php
TOTAL 36,000 Php
The alternatives presented addresses to each of the group of
experimental factors identified in the root cause analysis. Although the
alternatives do not deal with all the experimental factors, each is expected
to resolve the root causes through directly providing a solution to the main
problem. The uniform objective of the three alternatives of increasing the
capacity of the Filling Section supports this approach.
62
4.2.4 Cost-Benefit Analysis
Costs and Benefits from the maintenance, opportunity and additional
capacity can be translated to its monetary value to determine the Net Benefit
of the three alternatives. This would be used as criteria in the evaluation in
the next subchapter. Table 4-15 suggests the cost of implementing each of
the alternatives to be chosen. From Table 4-15 below, Alternative B requires
the largest investment compared to the other alternatives.
Table 4-15 Comparison of Cost of Implementing Alternatives
Alternative Total Cost of Implementation
A 41,400 Php
B 301,700 Php
C 36,000 Php
Costs of maintenance as well as the opportunity loss for each
alternative is accounted and translated in a yearly basis. Benefits from the
additional capacity and overtime savings were also projected annually in
order to obtain the Net Benefit of the alternatives. Formulas used were
obtained from Park 2002.
Alternative A: Additional workers and Parallel workstations
Parameters used were projected annually based from the average
production and overtime hours per year as well as the annual frequency of
repairs for the machines. The frequency of repairs is based from the
maintenance performed by the company in its current system. Cost of
maintenance for the machines involved was provided by the Production
Assistant.
63
Table 4-16 Annual Cost of Alternative A
Maintenance Cost Php Time Total
Manpower 6 Regular rate per
hour
40.625
Average production hours per
year
2304 561,600
Manpower 6 Overtime rate per
hour
52.8125
Average expected OT hours per year
1287.12
407,856.15
Horizontal Band Sealer
2
Replace-ment of Sealing Belt and Cleaning
750
Frequency of replace-ment per
year
12 18,000
Digital Balance
2 Repairs 500 Frequency of repair per year
2 2,000
Opportunity Cases per
hour
Cost
per
case
Time
Opportunity loss in capacity
7.928 450
Average production hours per
year
2304
8,220,000
TOTAL 9,209,456.15 Php
Opportunity cost was computed relative to the ideal rate which is the
capacity of the next resource constraint, the Batch Preparation Section. This
was set as the Capacity Resource Constraint provided that it is the next
bottleneck in the system and exceeding this capacity would create further
problems in balancing the NLIT Production Line. Batch Preparation Section
has a capacity of 1.125 batches per hour. Comparing this to the proposed
rate of 0.988 batches per hour, the difference is 0.137 batches per hour
which is equivalent to 7.928 cases per hour. Refer to Appendix A for
conversion of parameters. The cost per case is equivalent to the net profit per
case given by the company.
64
Table 4-17 Annual Benefit of Alternative A
% Improvement Overtime Benefit
58.08%
Average overtime
man-hours per year
OT Cost per
hour (Php) Savings
9009.84 52.8125 276,363.33
Increased Capacity
Additional cases per
year
Service Rate
Per case (Php) Additional Profit
48,400 450 21,780,000
TOTAL 22,056,363.33 Php
Computing for the Net Benefit of Alternative A is as follows:
Net Annual Benefit = Annual Benefits – Annual Cost
= 22,056,363.33 – 9,209,456.15
= 12,846,907.18 Php
65
Alternative B: Purchase Filling and Sealing Machine and Reallocation of
workers
Table 4-18 Annual Cost of Alternative B
Maintenance Cost Php Time Total
Filling and Sealing Machine
1
Repair and
Mainte-nance
1,500 Frequency of repair per year
12 18,000
Miscella-neous (Parts
replace-ment and others)
7,500 Frequency of repair per year
4 30,000
Opportunity Cases
per hour
Cost
per
case
Time
Opportunity loss in capacity
12.182 450
Average production hours per
year
2304 12,630,000
TOTAL 12,678,000 Php
Cost of maintaining the filling and sealing machine was estimated by
the Project Engineer of the company as well as the occurrence of repairs per
year. These estimations were as well based from the expected number of
repairs based on the projected levels of production and the cost of obtaining
parts from the supplier of the machine. The costs were projected annually
based on the frequency provided. Opportunity costs were computed relative
to the doubled capacity of the Batch Preparation after reallocating workers
from the Filling Section which is 1.6875 batches per hour. From these, the
Annual Cost of Alternative B was computed to have accumulated to
12,678,000 Php.
66
Table 4-19 Annual Benefit of Alternative B % Improvement Benefit
136.32%
Overtime
Average
overtime man-
hours per year
OT Cost per
hour Savings
9009.84 52.8125 475,832.18
Increased Capacity
Additional
Capacity in
cases
Service Rate
Per case Additional Profit
113,600 450 51,120,000
TOTAL 51,595,832.18 Php
Computing for the Net Benefit of Alternative B is as follows:
Net Annual Benefit = Annual Benefits – Annual Cost
= 51,159,652.68 – 12,678,000
= 38,917,832.18 Php
Net Present Value at 13% bank interest rate = Php70,134,756.49
67
Alternative C: Customized scoop, Additional workers and Parallel
workstations
Parameters used were projected annually based from the average
production and overtime hours per year as well as the annual frequency of
repairs for the machines. The frequency of repairs is based from the
maintenance performed by the company in its current system. Cost of
maintenance for the machines involved was provided by the Production
Assistant. The unit costs used for this alternative are similar to the costs used
in alternative A since the resources added were the same. Resources differ in
terms of quantity as compared to Alternative A.
Table 4-20 Annual Cost of Alternative C
Maintenance Cost Php Time Total
Manpower 5 Regular rate per
hour
40.625
Average production hours per
year
2304 468,600
Manpower 5 Overtime rate per
hour
52.8125
Average expected OT hours per year
1287.12 339,880.13
Horizontal
Band
Sealer
2
Replace-
ment of
Sealing
Belt and
Cleaning
750
Frequency
of replace-
ment per
year
12 18,000
Digital Ba-lance
1 Repairs 500 Frequency of repair per year
2 1000
Opportunity Cases
per hour
Cost
per
case
Time
Opportunity loss in capacity
7.928 450
Average production hours per
year
2304 8,220,000
TOTAL 9,047,480.13 Php
68
Loss in opportunity was derived from the ideal rate which is the Batch
Preparation. The ideal rate was set to avoid creation of new problems in the
system. Increase in capacity is limited by the Batch Preparation Section since
it is the next bottleneck in the production line. Batch Preparation Section has
a capacity of 1.125 batches per hour. Comparing this to the proposed rate of
0.988 batches per hour, the difference is 0.137 batches per hour which is
equivalent to 7.928 cases per hour. Refer to Appendix A for conversion of
parameters. The cost per case is equivalent to the net profit per case given
by the company.
Table 4-21 Annual Benefit of Alternative C
% Improvement Benefit
58.08%
Overtime
Average overtime hours per year
OT Cost per hour (Php)
Savings
9009.84 52.8125 276,363.33
Increased Capacity
Additional cases per year
Service Rate Per case (Php)
Additional Profit
48,400 450 21,780,000
TOTAL 22,056,363.33 Php
Computing for the Net Benefit of Alternative C is as follows:
Net Annual Benefit = Annual Benefits – Annual Cost
= 22,056,363.33 – 8,707,600
= 13,008,883.18.20 Php
69
4.2.5 Evaluation of Alternatives
Alternatives are evaluated according to the ease of implementation as
well as their corresponding net benefits. Criteria selected are further weighed
based on its importance. The level of importance is assessed relative to the
measure of the criteria with respect to the given objective of the study.
Table 4-22 Weighted Rating of Importance
Description Rating
Very Important 5
Important 4
Fairly Important 3
Not That Important 2
Irrelevant 1
Assessing the net benefits includes the consideration of additional
investment the company needs to pursue the given alternative. The rating of
importance of this criterion is very important. Net benefits define the
projected payback of investments entered in the proposed alternatives which
is very useful for long-term strategy. Using this as a measure for evaluation
clearly helps on the degree of the return on investments.
Table 4-23 Rating for Net Benefit
Description (values are in Php) Rating
32,000,000 – 40,000,000 5
24,000,000 – 32,000,000 4
16,000,000 – 24,000,000 3
8,000,000 – 16,000,000 2
0 – 8,000,000 1
70
The importance for ease of implementation is weighed based on the
ability of the company to execute and handle new developments. This
measure is considered important given the existing constraints in the
company that could cause additional problems. From the established rating
factors, each alternative were individually assessed based on the selected
criteria.
Table 4-24 Rating for Ease of Implementation
Description Detailed Description Rating
Very Easy to Implement
Technology of resources used is already acknowledged and available.
5
Easy to Implement Technology of new resources used is already acknowledged.
4
Moderately Easy to Implement
Technology of new resources used is recognizable
3
Difficult to Implement Technology of new resources used is advanced.
2
Very Difficult to Implement
Advance technology of new resources used requires expertise.
1
Alternative A received a rating of “2” under the Net Benefit factor
based on the range of values presented in Table 4-25. Its ease of
implementation is relatively easy to pursue since similar worker tasks and
machines would only be added to the existing work design. No new
technology is introduced for this alternative.
Alternative B was given a rating of “5” and “3” for the net benefit and
ease of implementation factor respectively. High returns were projected
through the cost-benefit analysis which entered the highest range for the net
benefit factor giving the alternative rate of “5”. Introducing a new machine
comes with necessary trainings for the new technology and the
implementation of a different set of standard procedures for the proper
handling of the Filling and Sealing Machine.
71
Alternative C has the same range of annual benefits as of the first
alternative for the reason that the two alternatives resulted to the same %
improvement as well as having close costs of implementation. Rating for this
alternative was affected by the computed Net Benefit value relative to the
high payback of Alternative B. Introduction of a customized design of the
scoop does not entail a new level of technology since the tasks were not
drastically altered in this alternative. Because of this, Alternative C is
considered easy to implement since only additional workers and machines
were modified with similar operations performed.
Table 4-25 Rating of Alternatives
Alternative
Rating Factor
Rating Net Benefits
(5)
Ease of Implementation
(4)
A 2 4 36
B 5 3 37
C 2 4 36
Do Nothing 1 5 25
The alternatives are evaluated based on the criteria chosen. The
ratings are shown in Table 4-25. Alternative B has the highest rating which is
to purchase a filling and sealing machine and reallocate workers.
5.0 SUMMARY AND CONCLUSION
73
DDC Food Development and Contracting Services, Inc manufactures food and
beverage powdered products of Nestle Philippines. The Production Department consists
of three major production lines: Dehydrated Culinary Products, Milo Vending Mix and the
Nestea Lemon Iced Tea. The study dealt with the Nestea Lemon Iced Tea Production
Line which comprises of three processes: Batch Preparation, Dry Mixing and Filling.
Further focus was given to the Nestea Lemon Iced Tea Filling Section through the
recognition of its low capacity relative to the other processes.
From the concepts and tools for analysis used throughout the study, the results
pointed to the problem of the Filling Section having low capacity. It was determined that
the capacity of the current system lags 16.59% from the capacity requested by demand.
Analyzing the problems presented a number of root causes classified into Man,
Machine, Method and Material. These were further examined through CNX Analysis
where the experimental factors were identified as follows: 1. Amount of workload
exceeds manpower capacity, 2. The number of machines is insufficient, 3. There is an
absence of a filling machine, 4. The scooper creates large net weight discrepancies and
5. The volume capacity of the scoop is 450 grams. These factors were grouped
according to the aspect being addressed as well as the alternatives to be provided.
A set of alternatives were proposed to address the root causes identified in the
analysis. Alternative A provided solutions of additional workers and parallel workstations.
Alternative B deals with the absence of a filling machine through recommending an
investment on acquiring a filling machine and reallocating existing workers to the next
resource constraint of the production line. Alternative C concentrated on the remaining
the cause factors through customizing the volume capacity of the scoop. The %
improvements of the alternatives were determined from the increase in capacity
presented relative to the current production rate of the NLIT Filling Section. Calculations
resulted to 58.08 % increase in capacity for Alternative A and C while an increase in
capacity of 136.32 % is expected in the implementation of Alternative B. Large
differences in % improvement is apparent given that Alternative B automates the whole
process. To further filter the alternatives, a cost-benefit analysis was conducted to
project the net benefits of each alternative.
74
Costs of opportunity loss, modification of resources proposed, and maintenance
were considered upon implementing each alternative. Estimated costs of implementation
were 41,400 Php, 301,700 Php and 36,000 Php for Alternatives A, B and C respectively.
To further assess how the % improvement of the alternatives would reflect on monetary
terms, savings from overtime and expected additional profit from the increased capacity
were accounted and translated as annual benefits for each alternative. Projected annual
benefits were 12,846,907.18 Php, 38,917,832.18 Php and 22,056,363.33 Php for
Alternative A, B and C respectively.
The alternatives were evaluated with the selected criteria for assessment, net
benefits and the ease of implementation. Weighted rating was provided for each
criterion. Rates for each alternative were outlined from the given description of every
level presented in the criteria. Costs were determined from the additional investment
required by the alternatives presented. The ease of implementation was weighed
according to how the company is expected to acknowledge the alternatives presented.
Based from the evaluation, Alternative B has the highest rating.
Initial investment is required for the implementation of the chosen alternative
which amounts to 301,700 Php that is reflected from the purchase of a brand new filling
and sealing machine as well as the cost of retraining reallocated workers of the Filling
Section to the Batch Preparation Section of the NLIT Production Line. This investment is
paid back with an annual net benefit of 38,917,832.18 Php accounting the overtime
savings and opportunity costs.
6.0 RECOMMENDATIONS
76
Analyzing the operations of the company resulted to addressing major problems
encountered in the manufacturing line such as having a low capacity of the Nestea
Lemon Iced Tea Filling Section. Through comprehensively scrutinizing the predicaments
recognized, the study has provided recommendations.
The selected recommendation for increasing the capacity of the Filling Section is
to purchase an automated filling and sealing machine and reallocate workers in the
production line. This option would result to higher gains as compared to the other
alternatives. Automating the filling operation increases the capacity by 136.32 % relative
to the existing output rate of the Filling Section. Benefits corresponding to the increase in
capacity cover the initial investment required to implement this recommendations.
Addressing the causes of the factors classified to Machine, the other root causes is
expected to be solved at the same time given that all root causes attends to the main
problem identified in the study.
Providing a systematic and meticulous Production Monitoring Scheme for
manufacturing line checking is recommended in addressing the controllable factor
identified in the study. Inspection of the production lines is necessary to avoid the lax
attitude of workers during production. Random sampling of the specific time of inspection
prevents the workers from anticipating the arrival of the inspector. Documentation is
made from the inspection as monthly reports are filed and necessary actions are
executed to possible problems in production. It is mandatory that the inspection is to be
regularly imposed to stabilize the performance of the production line.
7.0 AREAS FOR FURTHER STUDY
78
The study focused on addressing the capacities of the Nestea Lemon Iced Tea
Production Line and particularly dealing with the Filling Section. Attending to the basis of
the master production schedule is a main interest for future studies as it can provide
great opportunities for improvement. Design of the production lines is currently in series
which creates problems of transfer and buffer batch sizes. Tackling this problem is a
different perspective where inventory system would be involved.
Two other main product groups, Milo Vending Mix and the Dehydrated Culinary
Products, can be further examined through individual studies. From the capacities
presented by the three production lines, the Dry Mixer of Milo Vending Mix is observed
to be underutilized. Since Milo contributes 27% of the total production volume, further
examining its status can contribute to the company as well.
The usage of polyethylene bags within the production line is not restricted as
seen in the Flow Process Charts of the NLIT Production Line. This is apparent in all the
three production lines. No control is imposed on the use of polyethylene bags and the
scraps are not accounted by the company. Further investigating this scenario would
promote green manufacturing in the company.
Lastly, the cleaning procedure of the tools and equipments used in all production
lines require the use of Oxonia solution. This is to keep all products pathogen-free by
constantly monitoring the area and the equipment being used. All the equipments are
cleaned with the Oxonia solution and its use is not regulated by the company. Effects of
the oxonia solution when released to the environment are not concerned by the
company. DENR has already questioned this issue of release of the chemical. Further
analyzing this and the possibility of introducing a water treatment facility within the
manufacturing plant would assure the company’s compliance to the environmental
standards.
xiv
REFERENCES
ANUPINDI R., S. CHOPRA, S. DESHMUKH, J.A. VAN MIEGHEM & E. ZEMEL. 2008.
Managing Business Process Flows: Principles of Operations Management.
International Edition. New Jersey: Pearson Education Inc.
ARNOLD, J.C. & J.S. MILTON. 2004. Introduction to Probability and Statistics: Principles
and Applications for Engineering and the Computing Sciences. 4th edition. New
York: McGraw-Hill Inc.
BAUTISTA, J.L.A. 2008. Minimizing Idle Times at the Powder Condiments Line of
Consolidated Packaging Enterprises, San Francisco Del Monte, Quezon City.
Undergraduate manuscript. UP Los Baños.
HEIZER, J. & B. RENDER. 2005. General principles in operations management. 7th
edition. New Jersey: Pearson Education South Asia
NIEBEL, B.W. 1993. Motion and time study. 9th ed. USA: Richard Irwin, Inc.
PARK, C.S. 2002. Contemporary engineering economics 3rd edition. New Jersey:
Pearson Education, Inc.
STEVENSON W.J. 2007. Operations Management. 9th edition. New York: McGraw-
Hill/Irwin Education Inc.
xv
Appendix A: Computation for the Goal Improvement
For the purpose of having a definite objective of the study, the Six sigma concept
on Goal computation is used. (Bautista, 2008) The formula below was used to determine
the Goal.
GOAL = Baseline – 0.7(Baseline – Entitlement)
Baseline = Lowest Process Capacity in the NLIT Production Line (Filling Section)
= 0.625 batches per hour
Entitlement = Capacity Constraint Resource (CCR) in the NLIT Production Line
(Batch Preparation Section)
= 1.125 batches per hour
GOAL = 0.625 – 0.7(1.125-0.625)
= 0.275
% IMPROVEMENT = 0.275/ 0.625
= 44 %
The quantitative goal of the study is to increase the current process capacity of
the NLIT Production Line by 44 %.
The capacity of the Filling Section is the current baseline as it establishes the
average performance of the process. Using the capacity of the Batch Preparation as the
entitlement and the target performance, the difference in the capacities multiplied by
0.70 and subtracting this from the baseline would lead to the percentage of improvement
that the study aims to achieve.
xvi
Appendix B: Nestea Lemon Iced Tea Conversion of Units
1 pouch = 360 grams
1 case = 12 pouches
1 pallet = 96 cases
1 batch = 250 kilograms
NLIT Parameters June July Aug Sept Oct Nov
In Cases 8377 7204 7514 7025 8104 8331 In Kilograms 36188.64 31121.28 32460.48 30348 35009.28 35989.92 IN Batches 144.7546 124.4851 129.8419 121.392 140.0371 143.9597
xvii
Appendix C: Time Study
The time study conducted was based from Niebel, 1993 as well as all formulas
used. The table below shows the observed time recorded with 10 initial samples:
No. Process Name
Trial No. (Actual time in seconds)
1 2 3 4 5 6 7 8 9 10
1 Labeling
of Pouches
72.34 56.04 47.86 59.73 74.85 83.91 60.32 83.16 63.72 71.85
2 Coding
of Pouches
35.77 47.72 64.07 84.42 59.75 60.28 54.27 76.34 71.93 48.43
3 Scoop Nestea Mixture
37.26 59.84 47.60 45.73 48.33 51.84 47.18 52.30 36.75 49.26
4 Weigh
Pouches 108.19 127.08 138.27 72.31 151.89 155.84 168.17 191.59 119.79 107.91
5 Seal
Pouches 123.57 160.72 126.78 106.94 145.06 113.87 157.28 175.17 133.79 104.86
6 Wipe off powder residue
59.67 71.54 55.78 57.85 72.36 59.89 89.24 62.74 68.09 60.37
7 Stack
and Lot coding
31.43 43.10 41.85 44.69 46.72 41.38 35.07 52.18 47.13 33.26
In computing for outliers within the observed data, the next table below shows
the computed range of acceptance using IQR.
Observation Trial No.
Process No. And Description
1. Labelling of Pouches
2. Coding of Pouches
3. Scoop Nestea mixture
4. Weigh pouches
5. Seal Pouches
6. Wipe off powder residue
7. Stack and Lot Coding
1 47.86 35.77 36.75 72.31 104.86 55.78 31.43
2 56.04 47.72 37.26 107.91 106.94 57.85 33.26
3 59.73 48.43 45.73 108.19 113.87 59.67 35.07
4 60.32 54.27 47.18 119.79 123.57 59.89 41.38
5 63.72 59.75 47.6 127.08 126.78 60.37 41.85
6 71.85 60.28 48.33 138.27 133.79 62.74 43.1
7 72.34 64.07 49.26 151.89 145.06 68.09 44.69
8 74.85 71.93 51.84 155.84 157.28 71.54 46.72
9 83.16 76.34 52.3 168.17 160.72 72.36 47.13
10 83.91 84.42 59.84 191.59 175.17 89.24 52.18
AVERAGE 67.38 60.30 47.61 134.10 134.80 65.75 41.68
STD. DEV. 11.80 14.63 6.84 34.48 24.07 10.03 6.63
MEDIAN 59.73 48.43 45.73 108.19 113.87 59.67 35.07
Q1 74.85 71.93 51.84 155.84 157.28 71.54 46.72
Q3 15.12 23.50 6.11 47.65 43.41 11.87 11.65
1.5IQR 22.68 35.25 9.17 71.48 65.12 17.81 17.48
FENCE1 37.05 13.18 36.57 36.72 48.76 41.87 17.60
FENCE3 97.53 107.18 61.01 227.32 222.40 89.35 64.20
NUMBER OF
SAMPLES 5.87 11.26 3.95 12.65 6.10 4.45 4.85
xviii
Comparing the range from fence 1 to fence 3 of each operation, the observed
times in the first table have fallen within the range. There are no significant outliers in the
observations made. The number of samples made was also enough to support the
establishment of standard times.
Additional Trials were conducted to meet required number of samples. In the
computations made, an additional of at least three samples was needed to make the
observations significant. The table below presents the additional five observations:
Process No.
Process Name
Trial No. (Actual time in seconds) Average
Standard Deviation 11 12 13 14 15
1 Labeling
of Pouches
77.54 84.12 59.78 68.50 71.64 72.32 9.20
2 Coding
of Pouches
59.16 36.15 47.88 72.01 43.06 51.65 14.13
3 Scoop Nestea Mixture
53.25 47.54 59.72 51.09 48.29 51.98 4.89
4 Weigh
Pouches 133.68 152.63 107.72 131.79 179.75 141.11 26.85
5 Seal
Pouches 159.30 164.28 105.43 104.26 145.78 135.81 29.07
6 Wipe off powder residue
75.27 93.25 58.13 83.91 63.74 74.86 14.35
7 Stack
and Lot coding
33.87 30.32 29.16 31.79 34.59 31.95 2.30
The performance factor to be utilized in calculating the standard time was
obtained in accordance with the Westinghouse rating system. Appendix D holds the
complete ratings for each category. Ratings for the whole observation of each operation
are presented below. The sum of the ratings is subtracted from the perfect performance
factor leaving a 97% performance factor for the whole production line relative to its
actual execution of operations.
Skill C2 +0.03
Effort E1 - 0.04
Conditions D +0.00
Consistency E - 0.02
Algebraic Sum - 0.03
Performance Factor 0.97
xix
In computing for the Normal Time, the following formula is used:
NORMAL TIME = OBSERVED TIME * PERFORMANCE FACTOR (Neibel,1993)
The table below shows the computed Normal times based from the observed time:
Element No. And Description
Normal Time (min/case)
1. Labeling of Pouches 1.116
2. Coding of Pouches 0.928
3. Scoop Nestea mixture 0.793
4. Weigh pouches 2.206
5. Seal Pouches 2.185
6. Wipe off powder residue 1.112
7. Stack and Lot Coding 0.621
Total 8.962
Allowance factors were derived from the observations of the researcher during
production. These allowances are presented in the table below.
ALLOWANCE FACTORS
PERSONAL NEEDS 5 0.05
BASIC FATIGUE 4 0.04
STANDING 2 0.02
MONOTONY 2 0.02
0.13
xx
Using total of the allowances identified, the value is used in computing for the standard time of the operations in the Filling Section with the formula below:
STANDARD TIME = NORMAL TIME * (1 + ALLOWANCE FACTOR) (Neibel,1993)
The table below shows the computed Standard times based from the observed time:
Element No. And Description
Standard Time (min/case)
1. Labeling of Pouches 1.261
2. Coding of Pouches 1.049
3. Scoop Nestea mixture 0.896
4. Weigh pouches 2.493
5. Seal Pouches 2.469
6. Wipe off powder residue 1.257
7. Stack and Lot Coding 0.702
Total 10.127
xxi
Appendix D: Westinghouse System Ratings
Westinghouse System Skill Ratings +0.15 A1 Super skill +0.13 A2 Super skill +0.11 B1 Excellent +0.08 B2 Excellent +0.06 C1 Good +0.03 C2 Good 0.00 D Average - 0.05 E1 Fair - 0.10 E2 Fair - 0.16 F1 Poor - 0.22 F2 Poor Westinghouse System Effort Ratings +0.13 A1 Excessive +0.12 A2 Excessive +0.10 B1 Excellent +0.08 B2 Excellent +0.05 C1 Good +0.02 C2 Good 0.00 D Average - 0.04 E1 Fair - 0.08 E2 Fair - 0.12 F1 Poor - 0.17 F2 Poor Westinghouse System Condition Ratings +0.06 A Ideal +0.04 B Excellent +0.02 C Good 0.00 D Average - 0.03 E Fair - 0.07 F Poor Westinghouse System Consistency Ratings +0.04 A Perfect +0.03 B Excellent +0.01 C Good 0.00 D Average - 0.02 E Fair - 0.04 F Poor
xxii
Appendix E: Net Present Value of Alternative B
The Net Present Value of Alternative B would determine the equivalent worth of inflows to outflows based from the costs and benefits identified in the alternative. Assumptions were made upon projecting the net present worth of the annual cash flows. Service life used was based on the useful life of the Filling and Sealing Machine which is 10 years. The interest rate was obtained from the current bank interest available, 13%. The net cash flow was determined from the costs and benefits of the alternative for each year. Present values for every year of the given useful life of the machine were computed. Formulas from Park, 2004 were used to project those values by dividing the net cash flow by 1 plus the bank interest rate raised to the equivalent service life.
Year Costs Benefits Net Cash flow Present Value at
year 2010
2010 -301,700
-301,700 -301,700
2011 12,678,000 51,159,652.68 38,917,832.18 11,487,348.04
2012 12,678,000 51,159,652.68 38,917,832.18 10,165,794.72
2013 12,678,000 51,159,652.68 38,917,832.18 8,996,278.515
2014 12,678,000 51,159,652.68 38,917,832.18 7,961,308.42
2015 12,678,000 51,159,652.68 38,917,832.18 7,045,405.681
2016 12,678,000 51,159,652.68 38,917,832.18 6,234,872.284
2017 12,678,000 51,159,652.68 38,917,832.18 5,517,586.092
2018 12,678,000 51,159,652.68 38,917,832.18 4,882,819.551
2019 12,678,000 51,159,652.68 38,917,832.18 4,321,079.249
2020 12,678,000 51,159,652.68 38,917,832.18 3,823,963.937
Net Present Value 70,134,756.49 Php
The recommended solution is expected to return a positive Net Present Value and return profit as the worth of inflows is greater than the equivalent worth of outflows.
DDC DDC DDC DDC 14-26 Lazatin Blvd.,Villa Victoria, Dolores, City of San Fernando, Pampanga
20 March 2010
To whom it may concern:
This is to certify that
Engineering Student of the University of the Philippines Los Baños underwent on
training (OJT) at DDC Food Development Contracting Services, Inc., from 20 April 2009
to 30 May 2009. She successfully completed the two hun
requirement of the academic program by observing processes, methods and operations,
and doing hands-
Materials Warehouse.
This certification is being iss
whatever lawful intent it may serve.
Issued this 20th day of March 2010 at City of San Fernando, Pampanga, Philippines
DANILO D. CANLAS
General Manager
Appendix F: Certificate of Completion
DDC DDC DDC DDC FOOD DEVELOPMENT AND CONTRACTING SERVICES, INC
26 Lazatin Blvd.,Villa Victoria, Dolores, City of San Fernando, PampangaTel No. (045) 961-8220; Telefax No. (045) 961-4636
To whom it may concern:
This is to certify that MS. JAQUELYN MARGARET L. MICIANO
Engineering Student of the University of the Philippines Los Baños underwent on
training (OJT) at DDC Food Development Contracting Services, Inc., from 20 April 2009
to 30 May 2009. She successfully completed the two hundred (200) hours of actual work
requirement of the academic program by observing processes, methods and operations,
-on work in the Production Department, Logistics, Quality Assurance, and
Materials Warehouse.
This certification is being issued upon request of Ms. Jaquelyn Margaret L. Miciano for
whatever lawful intent it may serve.
day of March 2010 at City of San Fernando, Pampanga, Philippines
DANILO D. CANLAS
General Manager
xxiii
DEVELOPMENT AND CONTRACTING SERVICES, INC
26 Lazatin Blvd.,Villa Victoria, Dolores, City of San Fernando, Pampanga
L. MICIANO, a BS Industrial
Engineering Student of the University of the Philippines Los Baños underwent on-the-job
training (OJT) at DDC Food Development Contracting Services, Inc., from 20 April 2009
dred (200) hours of actual work
requirement of the academic program by observing processes, methods and operations,
on work in the Production Department, Logistics, Quality Assurance, and
ued upon request of Ms. Jaquelyn Margaret L. Miciano for
day of March 2010 at City of San Fernando, Pampanga, Philippines