ATENEO STUDENT BUSINESS REVIEW

2ATENEO STUDENT BUSINESS REVIEW

ATENEO STUDENT BUSINESS REVIEW3

Message from the Dean

Congratulations once again to the Operations Cluster for this back-to-back Techne 3 and Techne 4 publication.

Once more, the students, faculty, and Cluster Chair have proven their capability and commitment to produce a series of student papers that reflect their applied learning in quantitative and operations management issues.

The variety of topics in Techne 3 and 4 has again demonstrated the usefulness and applicability of topics in the Operations Management (Opeman) cluster courses to any industry or any small, medium, or large company/institution.

The inclusion of some green technology-related topics is a welcome development because they are very relevant to the University’s thrust on

environment and development, AGSB’s research agenda, and our Mulat Diwa’s Cura Kalikasan project.

May the Operations Cluster, with its talented and committed faculty and leaders, continue to lead in the publication of student papers. Please continue to teach and inspire the students so they can learn more, do more, publish more, and do better.

Alberto L. BuenviajeDean

Once more, the students, faculty, and Cluster Chair have proven their capability and commitment to produce a series of student papers that reflect their applied learning in quantitative and operations management issues.


Message from the Operations Cluster HeadCongratulations to the AGSB Operations Cluster for coming up with the back-to-back issues, Techne 3 and Techne 4. Also, a warm welcome to our readers and friends to the 3rd and 4th editions of our magazine!

These two issues were the output of 45 MBA student-authors whose contributions focused on the “Green” theme, which is aligned with our school’s emphasis on nation-building. As in the first two issues, we compiled our MBA students’ projects in Applied Management Science and Operations Management. The potentials of the Applied Management Science and Operations Management tools in improving our workplaces and daily lives are limitless. So in this issue, side-by-side articles on the power crisis in Mindanao and the waste management system in the Payatas dumpsite, are equally interesting write-ups on tikoy, shipping costs of computers to public schools, and even laundry concerns.

As always, I convey my heartfelt gratitude to my colleagues in the Operations Cluster for their efforts to encourage innovation, and to continuously motivate and guide our MBA students.

Thank you to all and happy reading!

Ralph AnteProfessor and Operations Cluster ChairAteneo Graduate School of Business

As always, I convey my heartfelt gratitude to my colleagues in the Operations Cluster for their efforts to encourage innovation, and to continuously motivate and guide our MBA students.


Message from EditorTechne is a magazine about numbers, making it distinct from other management publications. Additionally, you will find this particular issue unique. Let me count the ways – by way of numbers, of course:

2 Means this is a double issue. The front cover is Techne 3. Flip the page around and, surprise, the front cover becomes Techne 4.

2 The Operations Cluster manages a number of AGSB technically-oriented courses. Organized by the Cluster, this magazine focuses on two courses: Management Science and Operations Management.

2 The number of types of numbers presented: constants and variables. Of course, the variables are identified with the “let x =” expression.

3 Articles are trilingual. Although mostly in English, you will find a smattering of Greek letters such as mu, lambda, rho, sigma in a few places, plus a dash of Chinese.

13 The number of articles in this publication, sufficiently large to cover a wide range of technical applications for large corporations, government, schools, small and medium enterprises (SMEs), entrepreneurs, and for corporate social responsibility (CSR) initiatives. Ideas are aplenty: how to best move people and things from point A to point B, how to justify green initiatives, how to reduce time, and how to optimize resources. My personal favorite is the remarkable application of technical tools in the life of Carmina. Hopefully you will find at least one article that will match your interest.

25 The number of applications of mathematical tools (models, processes, concepts, formulas, diagrams, Excel templates) the students had to learn for the articles. Specifically, these consist of six applications of Monte Carlo Simulation, five of Linear Programming, four of Linear Regression, three each of Queuing Models and Project Management, and one each of Inventory Management, Integer Programming, Process Improvement, and Quality Management.

45 The number of AGSB students who collaborated to write the articles. Many are engineers, accountants, and IT graduates, but you will be surprised to know how many lawyers, doctors, and politicians were involved as well.

510 Nanometers, the wavelength of visible green light in the color spectrum, right below the blue light. Management of the interaction and impact of humans on the environment is signified by green, the theme of Techne 3.

0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, … Fibonacci sequence is an integer sequence where each subsequent number is the sum of the previous two. We do not have this yet in the current issue but with the way things are moving, you may be able to read about its application, perhaps in the field of decision theory, in a future Techne article.

Ed LegaspiEditorTechne: Managing through NumbersAteneo Graduate School of Business


Power Crisis in Mindanao:

Dr. Jhanssen Castillo

Ma Katrina Mae Garcia-Dalusong

Rafi Delica

Atty. Anita C. Mapalo

Irwin Tieng

Mary Ann M. Viloria

A Quantitative Analysis of Options andImpacts

P

P

PGG

C

H

HH

HH

H

HH

D

DD

SURIGAODEL NORTE

SURIGAODEL SUR

CAMIGUINA GUSANDEL NORTE

MISAMIS ORIENTALMISAMIS

OCCIDENTALZAMBOANGA

DEL NORTEBUKIDNON

AGUSANDEL SUR

DAVAODEL NORTE

DAVAOORIENTAL

DAVAODEL SUR

NORTHCOTABATO

LANAODEL SUR

LANAODEL NORTE

SOUTHCOTABATO

SULTAN KUDARAT

MAGUINDANAO

ZAMBOANGADEL SUR

BASILAN

SULU

TAWI-TAWI


Introduction

rownouts in Mindanao occurred more frequently and lasted longer by mid-February 2012. Not long after, the power outage turned into

a crisis. Crisis of this kind is not the first one that Mindanao has experienced. In 2010, almost during the same period, Mindanao suffered from a debilitating shortage of electricity that even threatened to derail the automation of

elections that year on the island. Officials in charge of the control power supply were constrained to resort to load shedding to meet demand and fill the required level of reserves to maintain the stability of the electricity grid.

In both years, the cause of the crises is the reduced dependability of Mindanao’s power-generating plants, the biggest of which are hydroelectric. The base load of the region is no longer sustainable, considering the many variables that affect

water supply—from rainfall, natural calamities, and even seasonal variations such as the El Niño phenomenon, to the depredations of man.

Mindanao power plants are still owned by the government through the National Power Corporation (Napocor). The latter is up for sale under the Electric Power Industry Reform Act (EPIRA); however,

Bthus far, no significant takers have expressed interest due to the staggering debts incurred. Several projects have already been approved and are awaiting investment: 2 X 13.75 MW Bunker-fired Power Plant; 15 MW Diesel Power Plant; 15 MW HFO Peaking Plant; 2 X 4 MW Cabulig Mini-Hydroelectric Power Plant; and the Southern Mindanao Coal-fired Power Plant.

This paper examines the various sources of power in Mindanao, their impact on consumers in terms of the associated generation, environmental, and health costs, and the risks related to investing in such alternative energy sources. The ultimate goal is to identify a solution to the Mindanao power crisis.

Forecasting: Linear Regression

Forecasting electricity consumption is applied using several theoretical methods including growth curves, multiple linear regression methods that use economic, social, geographic, and demographic factors, and autoregressive integrated moving average. The objective is to forecast demand from 2015 to 2020 using simple linear regression.

Different types of energy forecasts exist due to the shifts in consumption patterns that occur over time. Electricity consumption varies according to the season and the time of day. Energy consumption varies from season to season—more electricity is used on special holidays and summer months than during the rainy season when the temperatures are usually moderate. Residential energy consumption varies from day to day—more electricity is used on weekdays than on weekends. Electricity consumption also varies from

The base load of the region is no longer sustainable, considering the many variables that affect water supply—from rainfall, natural calamities, and even seasonal variations such as the El Niño phenomenon, to the depredations of man.


hour to hour, as more electricity is used during the morning hours, and then electricity consumption declines during the late morning and afternoon hours, and increases again during the evening hours.

Table 1 presents the maximum use of electricity consumption data for 2001 to 2011 from the Department of Energy (DOE). Electricity demand from 2012 to 2020 can be estimated using simple linear regression.

Maximum Use of Electricity (Y)

Period (X1) Year

954 1 2001

995 2 2002

1,131 3 2003

1,177 4 2004

1,149 5 2005

1,228 6 2006

1,241 7 2007

1,204 8 2008

1,303 9 2009

1,288 10 2010

1,346 11 2011

1,390 12 2012

1,425 13 2013

1,459 14 2014

1,494 15 2015

1,528 16 2016

1,563 17 2017

1,597 18 2018

1,632 19 2019

1,666 20 2020

Table 1. Electricity Demand (MW)

Formula Y=a+b(x1)

Where a 976.3272727

b 34.49090909


Optimization: Linear Programming

Linear programming is used to determine the areas where electricity would be optimally sourced while taking into account generation charges, environmental costs, and health costs.

Generation charges are indicated in the monthly electric bills of consumers. The charges pertain to the cost for the supply of electricity that was transmitted to their homes. Table 2 illustrates the generation charges in pesos per MW per hour. The data were derived from the 2011 post-EPIRA rates provided by the DOE.

Type of Plant Generation Charges [GC(i)]

Oil 9,940

Geothermal 2,886

Hydroelectric 529

Solar 2,886

Coal 5,164

Biomass 5,500

Table 2. Generation Charges per Type of Plant (Php/MW-hr)

Summary Output

Regression Statistics

Multiple R 0.93556575

R Square 0.87528327

Adjusted R Square 0.86142586

Standard Error 45.5163862

Observations 11

ANOVA

df SS MS F Significance F

Regression 1 130858.5091 130858.5091 63.16353 2.33E-05

Residual 9 18645.67273 2071.741414

Total 10 149504.1818

Coefficients Standard Error t Statistics P-Value Lower 95% Upper 95%

Intercept 976.327273 29.43407195 33.16996964 1.01E-10 909.7427762 1042.742776

X Variable 1 34.4909091 4.339817145 7.947548925 2.33E-05 24.67356067 44.30825751

t Statistics are good

Model is good


The combustion of fossil fuels to generate electricity is a known source for carbon dioxide (CO2) emissions. This gas poses environmental concerns, and this aspect was considered in this study [(1)(2)(3)]. Table 3 illustrates the amount of carbon dioxide (in kilograms) released per type of plant. The basis for deriving the peso equivalent was the widely used cost estimate of a ton of CO2, which amounts to $100 and is equivalent to Php4.3 per kilogram per MW/hour.

Type of Plant Kg of CO2 equivalent/MW-hr Environmental Costs [EC(i)]

Oil 893 3,840

Geothermal 58 250

Hydroelectric 15 65

Solar 30 130

Coal 941 4,046

Biomass 893 3,840

Table 3. Environmental Costs per Type of Plant (Php/MW-hr)

Cadmium and mercury emissions from each plant were also determined to obtain the health costs, which are illustrated in Table 4. The peso equivalent was determined based on the costs (in pesos) for each of these elements, that is, 3,023 per mg of mercury and 307 per mg of cadmium.

This paper covers two linear programming formulations—in one formulation, all costs are involved, and in the other, generation charges are solely considered. Table 5 summarizes Tables 2 to 4.

Table 4. Health Costs of Mercuryand Cadmium per Type of Plant (P/MW-hr)

Type of Plant Cadmium Mercury Health Costs [HC(i)]

Oil 13,003 2,766 15,769

Geothermal 100 0 100

Hydroelectric 91 0 91

Solar 100 0 100

Coal 18,748 18,748 37,496

Biomass 0 2,766 2,766


Table 5. Generation Charges, Environmental Costs, and Health Costs (P/MW-hr)

The variables and constraints used in the study are defined in Tables 6 and 7.

Table 6. Variables

Generation Cost= X(i,n) * GC (i)

Environmental Cost=

X(i,n) * EC(i)

Health Cost= X(i,n) * HC(i)

Total Cost= Generation Cost+Environmental Cost+Health Cost, where k represents the number of plants per category as summarized in Table 7.

Table 7. Number of Plants per Type of Source

The constraints in the formulation are limited to demands and capacity only. Table 8 presents the constraints used for demands from 2015 to 2020. The figures for demand are derived from the linear regression output with an allowance of an additional 20 percent to serve as reserve electricity.

Table 8. Peak Demand per Year

Type of Plant Generation Charges [GC(i)]

Environmental Costs [EC(i)]

Health Costs [HC(i)]

Oil 9,940 3,840 15,769

Geothermal 2,886 250 100

Hydroelectric 529 65 91

Solar 2,886 130 100

Coal 5,164 4,046 37,496

Biomass 5,500 3,840 2,766

X(i,n) Power plant n of type/category i [DECISION VARIABLE]

C(i,n) Capacity of each plant under Category i (MW-hr/month)

GC(i) Generation Charges (Peso per MW-hr)

EC(i) Environmental Costs (Peso per MW-hr)

HC(i) Health Costs (Peso per MW-hr)

Type of Plant Number of Plants (k)

Oil 14

Geothermal 1

Hydroelectric 12

Solar 1

Coal 2

Biomass 1

Limits (MW)

2015 demand 1,792

2016 demand 1,834

2017 demand 1,875

2018 demand 1,917

2019 demand 1,958

2020 demand 1,999

Σ6

i=1Σ

k

n=1

Σ6

i=1Σ

k

n=1

Σ6

i=1Σ

k

n=1


The limits on capacity are summarized in Table 9, in which the power taken from each plant must not be greater than its dependable capacity, generally less than

installed capacity. The italicized plant names are the proposals that have been granted permit as of 2012. The rest are existing power plants.

Table 9. A Summary of the Dependable Capacity of Each Power Plant (MW)

Power Plant Installed Capacity

Dependable Capacity

Mindanao Energy System 1 18.9 18.0

Mindanao Energy System 2 27.5 27.0

Cotabato Light 10.0 9.9

Bajada DPP 58.7 48.0

SPPC 59.0 50.0

PB 104 32.0 16.0

TMI 2 100.0 100.0

TMI 1 100.0 100.0

WMPC 113.0 100.0

Iligan Diesel 1 62.7 56.4

Iligan Diesel 2 40.0 36.0

2 X 13.75 MW Bunker-fired Power Plant

27.5 24.7

15 MW Diesel Power Plant 15.0 13.5

15 MW HFO Peaking Plant 15.0 13.5

Crystal Sugar 21.0 7.0

Solar PV 1.0 1.0

Power Plant Installed Capacity

Dependable Capacity

Mt. Apo 1037.0 827.0

Agus 1 80.0 52.0

Agus 2 180.0 135.0

Agus 4 158.1 156.0

Agus 5 55.0 55.0

Agus 6 200.0 155.0

Agus 7 54.0 27.0

Pulangi 4 255.0 200.0

Sibulan Hydro 42.6 36.0

Agusan 1.6 1.6

Bubunawan 7.0 4.9

Talomo Hydro 4.5 4.5

2 X 4 MW Cabulig Mini Hydro Power Plant

8.0 7.2

Mindanao Coal 232.0 210.0

Southern Mindanao Coal-fired Power

200.0 180.0


Risk Analysis: Monte Carlo Simulation

The actual daily and monthly demand fluctuates throughout the year. Historically, the variation is at a certain percentage of the peak value for the year. Using the monthly peak and monthly demand from 2001 to 2011 as shown in Appendix A, a probability list was created (Table 10).

Table 10. Cumulative Probabilities for the Monthly Demand

The monthly demand for 2015 to 2020 was simulated using a uniform random number generator in MS Excel and the probabilities in Table 10. Based on the linear programming results on the prioritization of power plant to use, the power demand in excess of the existing capacity is distributed to the new power plants. Applying the Monte Carlo simulation, a total of 10,000 trials were conducted for each year. The average utilization, generation costs, and selling prices were calculated based on the demand scenario. The generation costs used are the same as those discussed in Table 2, whereas the selling prices are shown in Table 11. A screenshot of the Monte Carlo worksheet is presented in Appendix B. The expected payback period for the investment is also calculated based on generation costs and selling prices.

The average utilization, generation costs, and selling prices were calculated based on the demand scenario.

Cumulative Frequency

Monthly Demand as Percentage of

the Year’s Peak (%)

0.000 78

0.015 80

0.023 82

0.030 84

0.030 86

0.114 88

0.212 90

0.348 92

0.545 94

0.705 96

0.841 98

1.000 100


Max Use of Electricity

Period Year

1,390 12 2012

1,425 13 2013

1,459 14 2014

1,494 15 2015

1,528 16 2016

1,563 17 2017

1,597 18 2018

1,632 19 2019

1,666 20 2020

Proposed Plant Cost (P/MW-hr)Selling Price

(P/MW-hr)Investments

(in million pesos)

8 MW Cabulig Hydro 528 2,000 814

15 MW HFO Peaking 9,940 12,000 379

15 MW Diesel 9,940 12,000 550

13.75 MW Bunker 9,940 12,000 500

13.75 MW Bunker 9,940 12,000 500

Coal Plant 5,164 6,000 18,000

Table 11. Costs and Prices of Each Proposed Plant

Results and Discussion

From the forecast results in Table 12, demand will reach 1,666 MW in 2020 from 1,390 MW in 2012, indicating a

Table 12. Electricity Demand (MW)

17% increase. Hence, the gap between demand and capacity (1,280MW) is expected to widen further.

Period in Different Years

1,8001,6001,4001,2001,000

800600

2000 2005 2010Predicted Actual

2015 2020


Separate linear programming optimization outputs were derived for each year, from 2012 to 2020. However, only the outputs of 2015 and 2020 are presented in Table 13.

Table 13. Power Plant Optimal Mix (MW)

For 2015, the total costs would amount to Php7.0 million broken down into the following: generation charges, Php4.1 million; environmental costs, Php0.8 million; and health costs, Php2.1 million.

Only five of 14 oil plants and no coal plant are utilized; all other power plants are fully utilized. Notably, the choice among the diesel-powered plants is solely decided by the computer. The demands from oil-fueled plants can be transferred to other

generators of the same type. Looking at the case where we ignore the health and environmental factors, the coal-powered plants will be used instead of the oil-powered generators.

Power Plant/Generation CompanyMinimize

Generation Cost Only

Minimize Overall Cost (including health and

environment)2015 2020 2015 2020

Mindanao Energy System 1 - - - -Mindanao Energy System 2 - - - -Cotabato Light - - - -Bajada DPP - - - -SPPC - - - -PB 104 - - - -TMI 2 - - - -TMI 1 - - - 86WMPC - - - 100Iligan Diesel 1 - - 35 56Iligan Diesel 2 - - 36 362 X 13.75 MW Bunker Fired Power Plant - - 25 2515 MW Diesel Power Plant - - 14 1415 MW HFO Peaking Plant - - 14 14Mt. Apo 827 827 827 827Agus 1 52 52 52 52Agus 2 135 135 135 135Agus 4 156 156 156 156Agus 5 55 55 55 55Agus 6 155 155 155 155Agus 7 27 27 27 27Pulangi 4 200 200 200 200Sibulan Help 36 36 36 36Agusan 2 2 2 2Bubunawan 5 5 5 5Talomo HEP 5 5 5 52 X 4 MW Cabulig Mini Hydro Power Plant 7 7 7 7Solar PV 1 1 1 1Mindanao Coal 130 210 - -Southern Mindanao Coal Fired Power - 127 - -Crystal Sugar - - 7 7


For 2020, the total costs would amount to Php13.1 million broken down into the following: generation charges, Php6.1 million; environmental costs, Php1.6 million; and health costs, Php5.4 million. Only five of 14 oil plants and no coal plant are utilized; all other power plants are fully utilized. Similar to the case in 2015, the coal-powered plants are utilized instead of the oil-powered generators when health and environment are not considered.

Although coal may be the fourth most affordable option for generation charges, it yields the highest health cost. The electricity source that would yield the lowest cost to the consumer and the

environment would be hydroelectric, followed by solar, geothermal, oil, and coal. For the succeeding years (2015 to 2020), the effective generation costs given the two options are calculated and presented in Figure 1. The gap increases with time because an increasing number of oil-powered plants are used instead of the cheaper coal. Hydro and geothermal plants are always running at full capacity. To the common Juan with a monthly electrical energy consumption of 450 kw-hr, demonstrating responsibility toward the environment and fellow Filipinos would amount to Php0.33/kw-hr in 2015 (or approximately Php150/month) and increase to Php0.79/kw-hr in 2020.

Figure 1. Yearly Generation Cost (P/Kw-Hr)

3.5

3

2.5

2

1.5

12014 2015 2016

Do Not consider H&E

Effec

t Gen

erat

ion

cost

(Php

/ Kw

-hr)

Consider H&E

2017 2018 2019 2020 2021


The yearly demand, revenue, and gross margin were calculated based on the Monte Carlo simulation of 1,000 trials (months). Table 14 illustrates an example for 2015. For 2011, the cheapest hydroelectric plant is only 65% utilized for the year; the other plants have a much lower utilization.

The same calculations were implemented for each year from 2015 to 2020. The

resulting utilization is summarized in Figure 2. The coal-powered plant is no longer included due to its high cost to health and environment. Although the utilization in 2015 is below 65% for hydro and roughly 25% for oil-powered plants, the scenario changes by 2020. In this scenario, all the plants will run at their full dependable capacity and with 100% utilization.

Figure 2. Utilization per Plant per Year

120%

100%

80%

60%

40%

20%

0%2015

8MW Cabulig Hydro

13.75 Bunker

15MW HFO PeakingHydro

13.75 Bunker

15MW Diesel

2016 2017 2018 2019 2020

8MW Cabulig Hydro

15MW HFO Peaking 15MW Diesel 13.75 Bunker 13.75 Bunker

Monthly Average 1378.6

Number of Times Demand Exceeds Existing Capacity 65% 65% 46% 46% 30% 30%

Average Monthly Power Taken from New Players (MW) 34.6 4.7 7.0 6.3 4.8 3.7

Energy for 1 year (MW-hr) 26812 39673 35621 27228 21021

Cost per MW-hr PHP 528 PHP 9,940 PHP 9,940 PHP 9,940 PHP 9,940

Generation Cost (in Million) PHP 14.2 PHP 20.9 PHP 18.8 PHP 14.4 PHP 11.1

Selling Price per MW-hr PHP 2,000 PHP 12,000 PHP 12,000 PHP 12,000 PHP 12,000

Revenue (in Million) PHP 53.62 PHP 476.08 PHP 427.45 PHP 326.73 PHP 252.15

Gross Margin (in Million) PHP 39 PHP 455 PHP 409 PHP 312 PHP 241

Investment (in Million) PHP 814 PHP 379 PHP 550

Table 14. Year 2015 Power Demand and Costs


Having different utilization and different capacities implies different revenues per year. The gross margin per plant (gross margin = selling price – generation cost) is shown in Figure 3, along with the investment for each plant. Although the cost for hydroelectric power plant is low, the low margin and capacity of the hydro-plant cannot sell the adequate required power to recover the investment, which is the second highest among the alternatives. The expected

payback period is a minimum of 12 years without even taking into account the operating costs into the equation. Conversely, the investment for oil-powered plants will be recovered quickly due to the costly selling price despite the high generation cost. In fact, the margin per MW-hr of the oil-powered plant is twice that of the hydroelectric power plant. Recovery on investment will be realized within a few months (operating cost is excluded from the calculation).

Figure 3. Utilization per Plant per Year

5000

4000

3000

2000

1000

0

-1000

-2000

INVESTMENT

2020

2019

2018

2017

2016

2015

8MW Cabulig Hydro

15 MW HF0Peaking

15MW Diesel

13.75 Bunker

13.75 Bunker

Annual Gross Margin


Conclusion and Recommendations

The peak power demand in Mindanao is expected to increase to 1,666 MW by 2020. This demand is to be met by creating new power plants. The cheapest overall cost would initially involve the use of hydro and oil-powered plants before using coal-fired plants. The direct impact on the bill of the consumer of using this human and environment-friendly power

mix is an increase in generation cost by P0.33/kw-hr in 2015. This situation is the cost of responsible citizenship in this aspect of power distribution.

Based on the results of risk analysis, an investor seeking to earn profit will need to invest in oil-powered plants. The government, however, might still support the funding for the small hydroelectric power plants as its participation in producing low-cost clean energy. The results

imply that hydropower has the least overall cost to consumers, but with a payback period of 12.8 years to investors. The coal-fired power plant is the most expensive option, both to investors and consumers, with a very long payback period because it will be seldom utilized; meanwhile, the oil-powered plant is in the middle for both consumers and investors. Given the overall cost to consumers and financial risk to investors, the recommendation is to fund the Heavy

Fuel Oil (HFO) peaking plant in Davao. In consideration of environmental effects, part of the profit can be used for ensuring top-of-the-line solutions to minimize harmful emissions and/or capture CO2.

The ethical framework affecting the decision has likewise been considered. Thus, the assessment provides social benefits, minimizes social injuries, and is consistent with the rights of those affected. The action sought to be advanced also exhibits concern for the well-being of others.

The recommended action reflects social responsibility to alleviate the poor conditions of fellow Filipinos in Mindanao. The avoidance of brownouts and the lower cost of electricity are the positive effects of the recommendation. Moreover, environmental issues have been considered and addressed.

References

1. Climate Risks and Carbon Prices: Revising the Social Cost of Carbon. Economics for Equity and the Environment. [Online] http://www.e3network.org/social_cost_ carbon.html.

2. Environmental Impact of Electricity Generation. [Online] http://en.wikipedia. org/wiki/Environmental_impact_of_ electricity_generation.

3. Full cost accounting for the life cycle of coal. Epstein, Paul et al. 1, s.l. : Ann. N.Y. Acad. Sci, 2011, Vol. 1219.

4. http://www.manilatimes.net/index.php/ opinion/110-editorials/20581-napocor- ngcp-should-honestly-accept-blame. [Online]

Given the overall cost to consumers and financial risk to investors, the recommendationis to fund the Heavy Fuel Oil (HFO) peaking plant in Davao.


Appendix A. Monthly Demand Variation from 2001 to 2011 (Source: Department of Energy)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Jan 887 911 946 1058 1082 1062 1108 1158 1143 1240 1250

Feb 900 913 918 1044 1082 1077 1141 1146 1163 1180 1231

Mar 903 900 978 1027 1082 1088 1177 1162 1158 1026 1219

Apr 887 927 985 1051 1087 1090 1167 1190 1186 1009 1253

May 901 920 1001 1099 1055 1118 1109 1159 1194 1211 1253

Jun 897 931 1021 1035 1072 1100 1145 1188 1220 1238 1264

Jul 894 924 976 1025 1045 1093 1141 1163 1198 1214 1298

Aug 926 941 980 1086 1061 1119 1141 1147 1208 1221 1290

Sep 947 927 995 11092 1078 1090 1203 1163 1217 1235 1288

Oct 954 950 1019 1134 1122 1130 1193 1156 1292 1263 1290

Nov 938 965 1051 1155 1149 1147 1158 1204 1303 1286 1326

Dec 933 995 1131 1177 1105 1228 1241 1175 1282 1288 1346

PEAK 954 995 1131 1177 1149 1228 1241 1204 1303 1288 1346


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Payatas:

Maximizing the Profits of Dump Site

Waste PickersRosalie B. Formento

Gladys OlanoZosimo Richard Carlos III

Margaret Samson Jhie Camacho

Collection of Non-

Marketable Post

Consumer Waste

Plastics and Used

Cooking Oil

Densification/Melting (up

to 200C) and molding of

melted plastic by casting

into the metal moulds

Shredding/Grinding of Styropor,

Plastic Bags and

metalized plastics/

Aluminum foil packs

Weighing, Loading

and Pre-heating of cooking

oil, Feeding of shredded

plastics into the

Densifier

Finished Sample

Products


Introduction

he strategies for solving the problem on solid waste in Metro Manila and other urban centers in the country have been the subject of extensive

research and debate. However, the problem persists and worsens every day. Based on the studies conducted by the National Solid Waste Management Commission Secretariat of the Environmental Management Bureau (EMB), an average of 0.56 kg of waste is produced per person living in the metropolis per day. This finding implies that with a population of 12 million, total waste generated in Metro Manila alone could run up to 6,720 metric tons per day or 201,600 metric tons per month. According to waste analysis and characterization study conducted by the DOST-ITDI, approximately 56% of this waste is biodegradable and 44% is non-biodegradable and mostly, recyclable.

At the rate we are producing waste, finding ourselves in the midst of a large number of man-made mountains of garbage is highly probable. The tragedy that has befallen the residents of the Payatas dump site in Quezon City – in

which it’s a mountain of garbage slid down, in the process burying several garbage pickers – should strengthen our resolve to practice solid waste management.

In reality, recycling and composting account for only 720 tons per day, or roughly 11% of solid waste generated in Metro Manila due to non-segregation, unavailable recycling technologies, and lack of markets for recyclables.

Hence, the DOST has developed technologies for solid waste management, which are simple, affordable, environmentally friendly, and capable of generating livelihood. One of these technologies is the DOST plastic densifier, a plastic recycling technology that converts waste styropor/plastics, especially foamed Polystyrene and plastic sando bags and laminates, into useful products, such as tables, chairs, cat walk tiles, plastic planters, pails, boards, bricks, and synthetic timber plank. One unit of the plastic densifier and other accessories were donated by LGU Quezon City as grant to provide a livelihood opportunity for the the residents of Payatas, where more than 2,000 scavengers work as waste pickers of recyclable materials from the dump site as an income-generating activity.

Based on the Waste Analysis and Characterization Study (WACS) conducted by the DOST in 2009, an average of 411 trips using 10-wheeler, six-wheeler, and mini-dump trucks are being received by the Payatas controlled dump site per day. The average total volume of solid waste is approximately 5,800 m3 a day or 1,200 tons a day. The classification of wastes is presented in Table 1.

Based on the studies conducted by the National Solid Waste Management Commission Secretariat of the Environmental Management Bureau (EMB), an average of 0.56 kg of waste is produced per person living in the metropolis per day.

T


Table 1. Classification of Solid Waste

Monte Carlo Simulation

From the given WACS data of actual trips for nine days, the probability of occurrence of trips was calculated based on the frequency of occurrence in the nine-day trip period. Each number of trips for each source is assigned a probability % of occurrence, and this factor was used as reference on randomly picked number. An average per day trip was derived from the average of 100 randomly picked numbers. The resulting average daily trip is multiplied by 30 to arrive at the monthly equivalent.

The WACS data yielded the daily average ton of waste per trip. The total trip of

30 days was multiplied by the ton per trip to generate the total tons for the 30-day period, which is then broken down into how much of the waste was biodegradable, plastic, glass, paper, metal, inert, special waste, and residual. The breakdown was also part of the WACS data. Each source has a different breakdown of waste; the data calculated were based on per-source breakdown to achieve more accuracy.

Tables 2 to 5 describe the process of using the WACS trip data, converting these to probability distribution, determining the tons of waste at source using simulation, and breaking these down by type of waste.

Classification Particulars

Biodegradable Fruit and vegetable peelings/scraps, bones, leftovers, grass, wood, parts of plants and trees, tissue, dead animals, animal waste, manure, and other biodegradable materials

Recyclable

Plastics PET bottles, PVC pipes, shampoo bottles, PP white, PE white, PE black, cups, blowing, and other recyclable plastics

Glass/Glass Bottles Glass bottles, broken glasses, and other recyclable glass

Paper White paper, newspaper, cartons, folders, cardboard, yellow paper, and other recyclable paper

Metals Aluminum, aluminum cans, tin cans, GI sheet/yero, brass, and other recyclable metals

Residual

Styrofoam Styrofoam

Sando Bags Sando bags

Laminates Candy wrappers, foil packs, sachets, pouches

Others Rags, cloth, rubber slippers, foam, tires, shoes made of leather, leather bags, and other residuals

Inert Ceramics, unrecyclable glass, bricks, stones, pebbles, ash, dirt, broken plates/bowls, and other inert

Special Wastes Injections, tissue and cloth with blood, diapers, napkins, battery, paint, fluorescent lamps, and other special wastes


Table 2. Actual Source Data from WACS

Table 3. Probability Distribution from the Frequency of Occurrence

Number of Trips

Commercial Hospital Residential Institution Market

Day 1 27 2 364 10 23

Day 2 37 6 395 5 11

Day 3 33 1 381 2 14

Day 4 33 3 377 2 18

Day 5 33 4 384 5 13

Day 6 35 5 371 7 13

Day 7 25 3 167 - 11

Day 8 27 2 348 7 20

Day 9 36 6 377 3 9

Commercial

No. of Trips Occurence Probability

25 1 11%

27 2 22%

33 3 33%

35 1 11%

36 1 11%

37 1 11%

Total 9 100%

Hospital


1 1 11%

2 2 22%

3 2 22%

4 1 11%

5 1 11%

6 2 22%

Total 9 100%

Residential


167 1 11%

348 1 11%

364 1 11%

371 1 11%

377 2 22%

381 1 11%

384 1 11%

395 1 11%

Total 9 100%

Commercial Lower Limit Upper Limit No. of Trips

11% - 10% 25

22% 11% 32% 27

33% 33% 66% 33

11% 67% 77% 35

11% 78% 88% 36

11% 89 99% 37

Hospital Lower Limit Upper Limit No. of Trips

11% - 10% 1

22% 11% 32% 2

22% 33% 55% 3

11% 56% 66% 4

11% 67% 77% 5

22% 78% 99% 6

Residential Lower Limit Upper Limit No. of Trips

11% - 0.10 167

11% 0.11 0.21 348

11% 0.22 0.32 364

11% 0.33 0.43 371

22% 0.44 0.66 377

11% 0.67 0.77 381

11% 0.78 0.88 384

11% 0.89 0.99 395


Table 4. Simulation for 100 Days

Institution


- 1 11%

2 2 22%

3 1 11%

5 2 22%

7 2 22%

10 1 11%

Total 9 100%

Institution Lower Limit Upper Limit No. of Trips

11% - 0.10 -

22% 0.11 0.32 2

11% 0.33 0.43 3

22% 0.44 0.66 5

22% 0.67 0.88 7

11% 0.89 0.99 10

Market


9 1 11%

11 2 22%

13 2 22%

14 1 11%

18 1 11%

20 1 11%

23 1 11%

Total 9 100%

Market Lower Limit Upper Limit No. of Trips

11% - 0.10 9

22% 0.11 0.32 11

22% 0.33 0.55 13

11% 0.56 0.66 14

11% 0.67 0.77 18

11% 0.78 0.88 20

11% 0.89 0.99 23

Commercial

DAY RNs No. of Trips

1 0.90 37

100 0.93 37

Average per Day 31

30 Days 944

Hospital


1 0.84 6

100 0.47 3

Average per Day 4

30 Days 113

Residential


1 0.72 381

100 0.64 377

Average per Day 345

30 Days 10,346

Institution


1 0.90 10

100 0.71 7

Average per Day 5

30 Days 143

Market


1 0.64 14

100 0.79 20

Average per Day 15

30 Days 441

No. of Trips (Simulated Daily Ave. x 30 days)

Tons per Trip(WACS) Total Tons

Commercial 944 3.24 3,053

Hospital 113 2.80 317

Residential 10,346 2.83 29,288

Insitution 143 2.880 399

Market 441 2.70 1,189


Types of Waste Commercial Hospital Residential Institution Market

Biodegradable 44% 52% 49% 52% 56%

Plastics 5% 4% 6% 4% 5%

Glass/Glass Bottles 2% 3% 2% 3% 1%

Paper 20% 14% 6% 14% 10%

Metal 1% 0% 1% 0% 1%

Inert 2% 1% 1% 1% 5%

Special Wastes 4% 3% 12% 3% 4%

Residual-Styrofoam 1% 2% 1% 2% 1%

Residual-Sando Bags 12% 14% 15% 14% 12%

Residual-Foil Packs 2% 1% 1% 1% 1%

Residual-Others 8% 6% 7% 6% 4%

In Tons Commercial Hospital Residential Institution Market

Biodegradable 1,332 164 14,289 206 666

Plastics 147 14 1,721 18 62

Glass/Glass Bottles 59 9 686 11 13

Paper 607 43 1,753 54 114

Metal 27 1 282 2 10

Inert 68 4 148 4 61

Special Wastes 112 11 3,611 13 49

Residual-Styrofoam 25 6 183 8 9

Residual-Sando Bags 382 43 4,250 54 148

Residual-Foil Packs 46 4 392 5 8

Residual-Others 248 19 1,971 24 49

TOTAL 3,053 317 29,288 399 1,189

In Kg Commercial Hospital Residential Institution Market

Biodegradable 1,332,364 163,625 14,289,346 205,614 665,805

Plastics 146,932 13,953 1,721,118 17,534 61,547

Glass/GlassBottles 58,954 8,636 686,121 10,852 12,813

Paper 607,379 43,181 1,753,098 54,261 113,599

Metal 27,210 1,322 282,007 1,661 10,296

Inert 67,722 3,534 148,272 4,441 61,318

Special Wastes 112,164 10,634 3,610,858 13,363 48,849

Residual-Styrofoam 25,093 6,208 183,159 7,801 9,152

Residual-Sando Bags 381,539 42,965 4,250,463 53,991 148,376

Residual-Foil Packs 45,954 4,210 392,485 5,291 8,466

Residual-Others 247,608 18,932 1,971,145 23,790 48,505

Table 5. Breakdown of Types of Waste

The composition of waste from different sources (in terms of weight) derived from the Monte Carlo simulation and WACS data is presented in Table 6.


Total kg% Composition

Residential Market Commercial Institution Total per Month

Biodegradable 14,289,346 665,805 1,332,364 369,239 16,656,755 49%

Plastics 1,721,118 61,547 146,932 31,487 1,961,083 6%

Glass/Glass Bottles 686,121 12,813 58,954 19,488 777,377 2%

Paper 1,753,098 113,599 607,379 97,442 2,571,513 8%

Metal 282,007 10,296 27,210 2,982 322,495 1%

Inert 148,272 61,318 67,722 7,976 285,287 1%

Special Wastes 3,610,858 48,849 112,164 23,996 3,795,867 11%

Residuals:

Styrofoam 183,159 9,152 25,093 14,009 231,414 1%

Sando Bags 4,250,463 148,376 381,539 96,956 4,877,335 14%

Foil Packs 392,485 8,466 45,954 9,501 456,406 1%

Others 1,971,145 48,505 247,608 42,722 2,309,980 7%

TOTAL 29,288,072 1,188,725 3,052,920 715,799 34,345,516 100%

Table 6. Total Quantity for One Month

Small junk shops in the Payatas area buy recyclable materials [i.e., plastic bottles (PET), glass/glass bottles, papers, and metals] from waste pickers, which they in turn sell to recycling facilities. However, the recycling facilities do not accept residual wastes (styropor, sando bags, and laminates) that are wet and contaminated with dirt and oil. These materials are processed through the plastic densifier that converts them into useful products. Through this project, the residual wastes are bought from scavengers for Php1 per kg, thus augmenting the latter’s income.

Linear Programming

This study aims to determine how the Payatas Controlled Disposal Facility can maximize its profit using the styropor/plastic densifier. As part of the agreement with the DOST, the LGU Quezon City has allocated a space with shelter for the operation of the densifier. The DOST trained at least three operators to run

the densifier. Other machineries, such as grinder, molder, and other auxilliaries, which are required by the densifier were also donated by the DOST.

Through linear programming (LP), the study aims to determine the optimum output of the densifier to maximize profits, and how much of each product should be produced by the densifier. The optimum outputs will help draw recommendations to maximize the benefit of the plastic densifier not only to the LGU, but also to the residents of Payatas.

Based on the results of the Monte Carlo simulation through which the amount of residual wastes brought to Payatas dump site per month was simulated, the assumption is that 60% of these residual wastes can be processed in the plastic densifier (Table 7). Another assumption is that fuel/electricity, which comes from the biogas generated from the disposal facility, will remain free at Payatas.


Total (in kg)

Residual Wastes for the Densifier Total per Month 60% of Weight

Styrofoam 231,414 138,849

Sando Bags 4,877,335 2,926,401

Foil packs 456,406 273,843

Table 7. Potential Input to the Plastic Densifier

The decision variables used for the optimization model are as follows:

Ts = number of small tables to make per month

Tb = number of large tables to make per month

C = number of chairs to make per month

P = number of planters to make per month

Cw = number of catwalk tiles to make per month

The identified constraints are as follows:

Table 8. Resource Data

Numerous establishments in Quezon City, such as fast food chains and restaurants, generate large volumes of used cooking oil. However, other backyard businesses buy this quantity from these establishments. The backyard businesses recycle and filter used cooking

oil, and then sell the filtered cooking oil to street vendors or carinderias that use the filtered oil for further cooking. Given this competition, the Payatas group could only source out 2,400 kg of used cooking oil per month.

Material Cost per Kg Kg Available per Month

Waste styropor 1.00 138,849

Waste plastics/sando bags 1.00 2,926,401

Waste laminates 1.00 273,843

Used oil 18.00 2,400


Ratio P/kg Type of Product

Table (Small)

Table (Large)

Chairs Planters Catwalk Tiles

Unit Weight (kg) 6.0 12.0 4.5 2.5 1.0

Waste Styropor 0.5 1 3.0 6.0 2.25 1.25 0.5

Waste Laminates 0.13 1 0.75 1.5 0.56 0.31 0.13

Waste Plastic/Sando Bags

0.13 1 0.75 1.5 0.56 0.31 0.13

Used Oil 0.25 18 1.5 3.0 1.13 0.63 0.25

Total 6.0 12.0 4.5 2.5 1.0

Raw Material Cost, Php/unit

31.50 63.00 23.60 13.10 5.30

Labor Cost, Php/unit 43.80 87.50 35.00 18.40 7.00

Other Material Cost, Php/unit

100.00 150.00 120.00

Total Production Cost, Php/unit

175.30 300.50 178.60 31.50 12.30

Selling Price, Php/unit 300.00 600.00 250.00 45.00 15.00

Profit, Php/unit 124.70 299.50 71.40 13.50 2.70

Table 9. Product Data

The monthly minimums are based on the current quantities that the Payatas group is making and are bought by the LGU for its own use or for giveaways. The monthly maximums are based on the maximum capacity of the densifier using three batches as determined through the PERT analysis (Pls refer to the next case).

The material requirements and all relevant costs for each potentialproduct that can be made from the densifier are presented in Table 10.

Table 10. Material Requirements and Costs

Product Data

Small Tables Large Tables Chairs Planters Catwalk Tiles

Monthly Minimum 32 16 40 76 200

Monthly Maximum 480 240 600 1140 3000


Putting together all of the above, one possible LP model is as follows:

Objective: Maximize Profit = Selling price – total production cost

Maximum Profit = 124.8Ts + 299.5Tb + 71.4C + 13.5P + 2.8Cw

Constraints:a. Material Limitations (in kg)

3Ts + 6Tb + 2.25C + 1.25P + 0.50Cw <= 138,849 (waste styropor)

0.75Ts + 1.50Tb + 0.56C + 0.31P + 0.13Cw <= 2,926,401 (waste laminates)

0.75Ts + 1.50Tb + 0.56C + 0.31P + 0.13Cw <= 273,843 (waste plastic sando bags)

1.50Ts + 3.00Tb + 1.13C + 0.63P + 0.25Cw <= 2,400 (used cooking oil)

b. Minimum and Maximum Number of Products to Make per month

32 <= Ts <= 480 (Small tables) 16 <= Tb <= 240 (Large tables) 40 <= C <= 600 (Chairs) 76 <= P <= 1,140 (Planters) 200 <= Cw <= 3,000 (Catwalk Tiles)

c. Non-negativity factors

Ts, Tb, C, P, Cw >= 0

Running Excel’s Solver, the optimal production for the five products are as follows: Small Tables = 480 units/month, Large Tables = 240 units/month, Chairs = 600 units/month, Planters = 368 units/month, and Catwalk Chairs = 200 units/month. The resulting optimal profit is Php180,155/month.

This result implies that to maximize profits in operating the plastic densifier, the production of small tables, large tables, and chairs should be maximized. The production of catwalk tiles and planters should be kept at the minimum of 200 units and 368 units per month, respectively.

The quantity of used cooking oil available is binding, that is, the Payatas group should source more used cooking oil from other sources. An increase or decrease of 1 kg of cooking oil would indicate an increase or decrease in profit by Php21.40.

The quantities for large tables, small tables, and chairs are also binding. A decrease or increase in the unit of large tables would imply a decrease or increase in profit of Php235.21. Similarly, a decrease or increase in the unit of small tables would indicate a decrease or increase of Php92.65 in profit. An increase or decrease in the unit of chairs would also imply an increase or decrease in profit by Php47.18.

Moreover, an increase or decrease in the unit of catwalk tiles would indicate a decrease or increase in profit by Php2.56. This premise suggests that the Payatas group can opt not to produce catwalk tiles at all due to the small profit that can be generated from it.

The quantity of used cooking

oil available is binding, that

is, the Payatas group should

source more used cooking oil

from other sources.


Start Shredding

30

Pre-heatingof oil

30

Weighing

15

Melting

90Filing

30Curing

60

Finish

Removingfrom molders

15u u

u uu

u uu

u

Figure 1. Network Diagram for the Operation of One Batch per Day

PERT

Currently, the Payatas group operates only one batch per day, with a daily capacity of 50 kg. The group takes four hours to complete the processing of one batch. Given the lunch break, the Payatas group opted not to process a second batch due to time constraints. The group performed a PERT analysis to determine the optimum number of batches that can be produced in one day.

The current production schedule for a day’s operation is shown in Figure 1. The critical path (in red) takes a four-hour operation. At least three people are involved in the processing of the residual wastes; thus, maximizing the capacity of the plastic densifier may be achieved by introducing a second and a third production batch, as illustrated in Figure 2.

Task Name Duration Start Finish Predecessors

1 Start 0 mins 8:00 AM 8:00 AM

2 Shredding 30 mins 8:00 AM 8:30 AM 1

3 Weighing 15 mins 8:30 AM 8:45 AM 2

4 Melting 90 mins 8:45 AM 10:15 AM 3,5

5 Pre-heating of oil 30 mins 8:00 AM 8:30 AM 1

6 Filling 30 mins 10:15 AM 10:45 AM 4

7 Curing 60 mins 10:45 AM 11:45 AM 6

8 Removing from Molders 15 mins 11:45 AM 12:00 PM 7

Finish


Task Name Duration Start Finish Predecessors

1 Start 0 mins 8:00 AM 8:00 AM

2 Shredding 1 30 mins 8:00 AM 8:30 AM 1

3 Weighing 1 15 mins 8:30 AM 8:45 AM 2

4 Melting 1 90 mins 8:45 AM 10:15 AM 3,5

5 Pre-heating of oil 1 30 mins 8:00 AM 8:30 AM 1

6 Filling 1 30 mins 10:15 AM 10:45 AM 4

7 Curing 1 60 mins 10:45 AM 11:45 AM 6

8 Removing from Molders 1 15 mins 11:45 AM 12:00 PM 7


10 Weighing 2 15 mins 9:00 AM 9:15 AM 3,9

11 Pre-heating of oil 2 20 mins 10:45 AM 11:05 AM 6

12 Melting 2 90 mins 11:05 AM 12:35 PM 11,10

13 Filling 2 30 mins 12:35 PM 1:05 PM 12

14 Curing 2 60 mins 1:05 PM 2:05 PM 13

15 Removing from Molders 2 15 mins 2:05 PM 2:20 PM 14


17 Weighing 3 15 mins 9:30 AM 9:45 AM 10, 16

18 Pre-heating of oil 3 20 mins 1:05 PM 1:25 PM 13

19 Melting 3 90 mins 1:25 PM 2:55 PM 17, 18

20 Filling 3 30 mins 2:55 PM 3:25 PM 19

21 Curing 3 60 mins 3:25 PM 4:25 PM 20

22 Removing from molders 3 15 mins 4:25 PM 4:40 PM 21

Finishu

u u u u u u

u u

u u u

u uu

uu

u

u

uu

u

u

uu

u

Start

S130

W115

W215

M290

F230

C260

R215

O320

M390

F330

C360

M190

F130

C160

R115

O130

O220

R315

Fin3

S230

S330

W315

1 = 1st batch 2 = 2nd batch3 = 3rd batch

F = FillingC = CuringR = Removal from MoldsFin = Finish

S = Shredding O = Oil preheatingW = WeighingM = Melting

u Fin2

u Fin1

Figure 2. Network Diagram for the Operation of Three Batches per Day


As depicted in the foregoing figure, several processes can be conducted simultaneously, and preheating for the second and third batches of oil can be crashed to 20 min from the original 30 min because the densifier is already hot on the second and third batches. If the production starts at exactly 8:00 am, all three batches can be finished at 4:40 pm. This process can be crashed further by 15 min on the next day if the shredding and weighing of the first batch were done on the previous day. Therefore, if production starts at 8:00 am, all three batches can be completed by 4:25 pm the next day.

Conclusion/Recommendations

The quantity of tables and chairs produced are binding due to the low capacity of the plastic densifier. The Payatas group can opt to add more units of plastic densifier to accommodate the allowable increase in the volume of tables and chairs, thereby increasing profits. However, more used cooking oil should be available as well. The sourcing of used cooking oil by the Payatas group is one of the problems encountered due to competition with other businesses; hence, the LGU Quezon City in collaboration with the Department of Health should create an ordinance that prohibits business establishments that generate used cooking oil from

selling their used cooking oil to people who recycle it for further cooking as this procedure would cause health risks to consumers.

The Payatas group should operate for three batches a day to maximize the usage of the plastic densifier. This approach will also optimize profits for the Payatas group.

The LGU Quezon City could help in the marketing of the products by encouraging green consumerism in the city. The LGU may create an ordinance requiring government offices to patronize recycled products to promote environment-friendly or so-called “green” products. Product development could also be done in which these products could be laminated or covered (as in office cubicle tables) to enhance product value.Assuming that market demand for the product is twice the maximum volume that can be produced by one densifier and the availability of used cooking oil is not

The LGU may create an ordinance requiring government offices to patronize recycled products to promote environment-friendly or so-called “green” products.


limited, then an additional unit of plastic densifier would produce an optimal profit of Php180,155.

The investment of Php150,000 for an additional unit of densifier could be recovered in a month. However, an additional densifier would also need additional auxiliary equipment such as molders. Each set of molders would cost Php50,000 to Php80,000 depending on the type of product. Large tables would require four sets of molders, whereas small tables would require eight sets. Assuming that a bigger quantity of the large tables are to be produced, four sets of molders would cost Php240,000.

Considering that the shredder is undercapacity and can still accommodate twice the quantity of raw materials required, acquiring an additional shredder is unnecessary. The total additional investment for a densifier and the molders would be Php390,000. The payback period is 2.16 months (Php390,000/Php180,155), assuming that the densifier is used solely for the large tables, which would produce additional 240 units per month.

Another LP formulation can be done to calculate optimal profit if data will be amended (e.g., increase in the quantity of used cooking oil available).

References

Casabar, C., et al., 2009. “Waste Analysis Characterization Study in Payatas Controlled Dump Site.” A Project of the Department of Science and Technology in collaboration with the Payatas Operations Group.


Establishing a Green & Sustainability Course Program

Ateneo CCE:

Atty. Anton Carlo E. EspinoEdwin J. FalcoteloEvangeline P. Y. HoDeanna Marie A. LansaganArleen M. PachecoMayelle C. Wycoco

DISCLAIMER:Dates and number of course runs of some of the financial management trianing programme presented in this paper do not represent the actual course runs of the center.Green finance training course is not yet offered by the center.


Introduction

he Center for Continuing Education (CCE) is Ateneo’s dynamic link to the world of business, finance, and technology. It also addresses issue-specific

industry concerns that require immediate, purposeful, and focused responses.

Ateneo CCE continuously serves the development needs of people and organizations from the private and public sectors, dutifully reflects the breadth and depth of the Ateneo Graduate School of Business vision and mission, and consistently champions the Ateneo de Manila University’s ideals of Excellence, Service, and Ethics.

Ateneo CCE is a training provider with the following core characteristics:

1. Workplace-based courses with immediate take-away value Ateneo CCE courses are immersed in the workplace. Participants are equipped with competencies and values based on the current realities of their respective organizations.

2. Interplay of workplace and classroom dynamics Ateneo CCE classrooms are microcosms of the workplace; the workplace is a classroom of practical learning. The interplay of workplace and classroom dynamics provides immense opportunities for learning, collaboration, and innovation.

3. Industry specialists as resource persons Programs and courses are designed, developed, and delivered by industry practitioners who are renowned experts in their respective fields.

4. Practical, experiential, and highly interactive Methodologies are based on business simulations, practical exercises, case analyses, practical applications, and focused discussions.

5. Integration of ethics, information management, and research Business decision process considers ethics; Information Management is applied on content and delivery; and Research renders courses relevant to specific industries.

6. Synergy with the academe and industry The unique pool of expertise and experience from the academe and from the roster of seasoned industry professionals enriches the courses. Successful participants earn units toward a graduate degree. Their competencies are certified by Ateneo CCE and distinguished industry partners. As part of the Ateneo, CCE maintains the university’s tradition of excellence that is exemplified in the following dimensions:

T

Programs and courses are designed, developed, and delivered by industry practitioners who are renowned experts in their respective fields.


1. Emphasis on the basics Basic skills that endure time, as well as fundamental competencies that form the foundation of sound management education enable leaders to cope with rapid changes in the environment.

2. Premium on critical thinking The hallmark of Jesuit education, which Ateneo CCE embodies, is a mindset that constantly questions rather than simply accepts, and analyzes causes and effects before formulating strategies, thereby making the practice of management a dynamic exercise.

3. Drive for excellence The Jesuit “Magis” is the driving force behind an individual’s efforts to give his best, always do better

CERTIFICATIONThe Ateneo CCE, the country’s premier institution for professional training, maintains and strengthens its market position through internationally recognized certification programs.

DIPLOMAThe Ateneo CCE’s unique and competitive advantage is the expertise in customizing courses to effectively address the needs of specific disciplines and industries.

Business ExcellenceThe Ateneo CCE systematically enhances the quality and productivity of professionals through continuously evolving, workplace-based, and performance-driven education programs, namely:

• Marketing and Sales Management• Human Resources Management• Financial Management• Entrepreneurship• Technology, Quality, and Operations Management• Leadership and Management• Lifelong Learning

each time, and avoid complacency and mediocrity with fierce determination.

4. Commitment to serve Education is not merely for an individual’s sole benefit, but also for the service of others. This powerful paradigm inspires Ateneo CCE to get things done the Ateneo way and to do so for the greater glory of God, Ad majorem Dei gloriam.

Ateneo CCE’s program classifications are diploma, certification, and business excellence. Ateneo CCE further customizes training programs based on distinct client needs.


Linear Programming

Linear programming (LP) could be a useful tool for planning the courses Ateneo CCE would offer. To illustrate this premise, the LP application is focused on the Financial Management courses currently offered but classified under Business Excellence category, specifically, Accounting for Non-Accountants I (ANA I), Accounting for Non-Accountants II (ANA II), Accounting for Management Decision Making (AMDM), Techniques of Financial Analysis (TFA), Credit Analysis (CA), and Finance 101. The six courses were chosen as these are the Center’s top-selling (i.e., most

in demand) courses under the Financial Management classification. The process presented here is repeatable across all the other courses of the CCE.

The data show the courses and the number of participants per run. The course fees, despite inflation, remained unchanged. The gross revenue was obtained from the number of participants. The Center offers an Early Eagle Rate, which is 10% off the published or regular rate.

The application of LP to Ateneo CCE starts with the decision variables. The six Financial Management courses are represented by the following variables: X1 = ANA I, X2 = ANA II, X3 = AMDM, X4 = TFA, X5 = CA, and X6 = Finance 101. The variables represent the number of times each course will be offered in a year.

The objective is to maximize profits for the six financial management courses. An example of the template used for the computation of profit for each of the financial management courses is presented in Table 1. The assumption is that total number of students per class is 25, with the Early Eagle Rate applied to 15 of them.

Certification

Diploma

Marketing and salesManagement

Human Resource Management

Financial Management

Entrepreneurship

Technology, Quality andOperations Management

Leadership and Management

Lifelong Learning


Table 1. Example of a Course Profit Template

The objective function can be stated as follows:

Maximize Profit = 64,100×1 + 29,400×2 + 64,100×3 + 224,800×4 + 224,800×5 + 224,800×6

The aforementioned objective is influenced by the following constraints: 1. Days Allocated Each of the six financial management courses lasts for the duration listed in Table 2. Moreover, the Center’s Management allocated 90 days for Financial Management courses in a year’s time.

Therefore, 3×1+ 2×2 + 3×3 + 3.5×4 + 3.5×5 + 3.5×6 <= 90 days

Table 2. Duration per Course

2. Maximum Demand Based on the historical data, the demand at 25 students for each course run for Financial Manage- ment courses is shown in Table 3.

Therefore, ×1<= 12, ×2<= 8, ×3<= 7, ×4<= 9, ×5<= 5, ×6<= 3

Table 3. Maximum Demand for Each Course

3. Minimum Demand The Financial Management courses are part of the Center’s core training programs. Thus, running the said courses at least once a year constitutes the Center’s policy.

Therefore, ×1 >= 1, ×2 >= 1, ×3 >= 1, ×4 >= 1, ×5 >= 1, ×6 >= 1

Course Title ANA I

Number of Days 3

Number of Students 25

Course Fee PhP

Early Eagle Rate (60%) 14,580 15 218,700

Regular Rate (40%) 16,200 10 162,000

Total Revenue 25 380,700

RP Fee 24,000 72,000

Marketing 20,000

Room 10,000 30,000

Equipment 400 9,600

Food Service 600 45,000

Training Kits 800 20,000

Total Direct Expenses 196,600

Overhead 40,000 120,000

Total Expenses 316,600

Profit 64,100

Course Duration

ANA I 3.0 days

ANA II 2.0 days

AMDM 3.0 days

TFA 3.5 days

CA 3.5 days

Finance 101 3.5 days

Course Runs/Year

ANA I 12 times

ANA II 8 times

AMDM 7 times

TFA 9 times

CA 5 times

Finance 101 3 times


The optimal solution generated by Solver (LP’s optimizer in Excel) is presented in Table 4. To maximize profit, Ateneo CCE should offer nine runs of ANA I, one run of ANA II, one run of AMDM, nine runs of TFA, five runs of CA, and three runs of Finance 101 in one year. Implementing the optimal solution will enable Ateneo CCE to generate profits totaling PhP4,455,350 every year on only these six courses. Furthermore, Solver indicates that each increase in runs for TFA, CA, or Finance 101 will further improve profits by PhP150,000. Conversely, each reduction in runs of any of these three courses will correspondingly reduce profits by PhP150,000.

Ateneo CCE must regularly offer the Financial Management courses as the demand increases. Despite the small class size in some courses, Ateneo CCE should perhaps consider the participants who register for the class and build on the value of the client, not solely on profit. In such instances, revising the parameters

Table 4. Solver’s Optimal Solution

of the LP model should generate new optimal course mixes that can guide the decision process.

Green Finance Course

As part of its business commitment to promote Green and Sustainability awareness, Ateneo de Manila University hosted a management and research conference in 2012 titled, “The Nature of Business: Green Innovations and Competitive Advantage,” to shed light on several complex issues on green innovations facing business.

Establishing the first Green and Sustainability course program at Ateneo CCE is an appropriate – and timely – response to the rising demand for green initiatives. This program is designed to cater to employees in businesses adopting sustainable practices, as well as government agencies and NGOs.

ANA I ANA II AMDM TFA CA FINANCE 101

9 1 1 9 5 3

Objective Function (Max P) 64,100.00 24,800.00 64,100.00 224,800.00 224,800.0 224,800.00 4,455,350.00

Constraints

Days Alloted 3 2 3 3.5 3.5 3.5 90 < = 90

Demand ANA I 1 9 < = 12

Demand ANA II 1 1 < = 8

Demand AMDM 1 1 < = 7

Demand TFA 1 9 < = 9

Demand CA 1 5 < = 5

Demand FINANCE 101 1 3 < = 3

Min ANA I 1 9 > = 1

Min ANA II 1 1 > = 1

Min AMDM 1 1 > = 1

Min TFA 1 9 > = 1

Min CA 1 5 > = 1

Min FINANCE 101 1 3 > = 1


In addition, the program is a strategic means of contributing to nation building while earning profit for the CCE.

The possible green and sustainability courses that can be offered to the public are as follows:1. Green Finance2. Green Building/Systems/Materials3. Green Certifications and Standards4. Green Cleaning Practices and Food Service5. Green Supply Chain Management6. Renewable Energy7. Social Responsibility8. Sustainability Planning9. Transportation Program/Green Fleet Management10. Waste Management11. Water Resource Conservation and Management

Following the results of the LP sample case on Finance previously discussed, Ateneo CCE should develop Green Finance as its first Green and Sustainability course offering.

Green Finance (or Environmental Finance) is a course designed to educate people about Environmental Management System (EMS). It is focused on the elements of environmental accounting, the associated eco-efficiency metrics, and the financial elements of sustainability planning.

With Green Finance, opportunities can be identified and action taken by implementing environmental/ carbon accounting as part of a sustainability plan. Plans should prioritize actions based on the return on investment, as well as meet energy, water, and waste-reduction targets. An organization’s plan to cut emissions may include making infrastructure improvements, reducing

fossil fuel use, and implementing better maintenance practices. This plan can also be incorporated into procurement programs to help improve the supply chain of products used. Renewable energy systems and energy conservation programs are other initiatives that are included in environmental/carbon Finance plans.

With a budget of PhP130,000, Ateneo CCE will roll out the Green Finance course program by August 5-6, 2013. However, CCE intends to advance the roll-out to July 15-16, which is 15 working days sooner. The CCE goal is to meet the new roll-out date with the least possible cost increase. This goal may be achieved using the program evaluation and review technique (PERT).

PERT

PERT was used to determine the estimated duration time of each activity and the likelihood (probability) that the project could be finished on or ahead of time. The project tasks and dependencies are listed in Table 5. The expected time per task is shown in Table 6. The PERT diagram is illustrated in Figure 1. The Critical Path is identified in Table 7 as path ABDEGHIJKMN.

Tables 8 and 9 demonstrate that the expected completion time of the project is 62 working days, and less than 1.8% chance that the project can be finished within 49 days is noted. The variance (σ2) is dominated by activities K and L, having values of 25 and 30.25, respectively. This variance reduces the probability of completing the project more quickly because variance is the divisor to compute for the level z. To increase the chances of completing the project more quickly,


the gap in both activities K and L must be narrowed by streamlining the process, thereby increasing efficiency. The other approach to reduce project time is by allocating manpower and resources, which will result to crashing.

Table 5. Work Breakdown and Sequence of Activities

Table 10 shows the crashing information, and Table 11 details the crashing process. Crashing the project by 15 days will incur an additional cost of PhP16,300, and will exceed the original budget of PhP130,000 by PhP34,450.

Table 6. Using Historical Data to Compute Expected Time and Variance (In Days)

Task Name Activity Predecessor

Assess the Needs A -

Source the Subject Matter Expert (SME) B A

Select the Training Method C B

Design the Course D B

Develop the Course Content E D

Source the Resource Person (Instructor) F C

Prepare the Training Materials G E, F

Conduct a Pilot Test: Info Session H G

Evaluate, Revise, and Finalize the Course I H

Set the Training Date (and Conduct Product Orientation) J I

Promote the Program Course K J

Activate the Sales Operation L J

Handle Last-minute Details (Go or No Go?/Room and Food Reservation) M K, L

Conduct the Training N M

Activity Optimistic Most Likely Pessimistic Expected Time (t) Variance (σ2 )

A 0.5 1.5 2.0 1.42 0.06

B 1.0 2.0 3.0 2.00 0.11

C 0.5 1.0 2.0 1.08 0.06

D 1.0 2.0 5.0 2.33 0.44

E 8.0 15.0 22.0 15.00 5.44

F 1.5 3.0 4.0 2.92 0.17

G 1.0 2.0 6.0 2.50 0.69

H 1.0 1.0 1.0 1.00 0.00

I 2.0 3.0 5.0 3.17 0.25

J 0.5 1.0 2.0 1.08 0.06

K 15.0 30.0 45.0 30.00 25.00

L 12.0 27.0 45.0 27.50 30.25

M 0.5 1.5 2.0 1.42 0.06

N 2.0 2.0 2.0 2.00 0.00


Figure 1. Network Diagram

Table 7. Critical Path

Table 8. Expected Time of the Critical Path (In Days)

Table 9. Probability that the Project will be Finished in X Days

Table 10. Duration and Costs of Project Task

Start A B

C F

D E

G H I J M N

K

L

Finishg ggg

gg

gg

g g gg

g

gg

g g

Task Name Task Duration (Days)

Normal Cost (PhP)

Crashed Time (Days)

Crashing Cost (PhP/Day)

Assess the Needs A 1.5 750 0.5 400

Source the SME B 2.0 1,000 0.5 400

Select the Training Method C 1.0 500 0.5 400

Design the Course D 2.5 5,000 1.0 1,000

Develop the Course Content* E 15.0 36,000 6.0 1,500

Source the Resource Person (Instructor) F 3.0 1,500 2.0 500

Prepare the Training Materials G 2.5 2,250 1.0 800

Conduct a Pilot Test H 1.0 8,000 0.0 0

Evaluate, Revise, and Finalize the Course I 3.5 7,000 1.0 1,000

Set the Training Date (and Product Orientation) J 1.0 2,000 0.0 0

Promote the Program Course K 30.0 20,000 5.0 1,400

Activate the Sales Operation L 27.5 10,000 5.0 800

Handle Last-minute Details (Go or No Go?/Room and Food Reservation)

M 1.5 750 1.0 600

Conduct the Training N 2.0 53,400 0.0 0

X days z F (z) % Probability, z < F(z)

60 -0.34 0.6331 36.69

55 -1.22 0.8888 11.12

50 -2.10 0.9821 1.79

45 -2.98 0.9986 0.14

Critical Path Expected Time (t) Sum of Variance (σ2) Standard Deviation (σ)

ABDEGHIJKMN 61.92 32.13 5.67

Path Duration (Working Days)

ABCFGHIJKMN 48.58

ABCFGHIJLMN 46.08

ABDEGHIJKMN 61.92

ABDEGHIJLMN 59.42


Table 11. Crashing Process

Monte Carlo Simulation

In the present case, determining the risks associated with the development of a new training program (i.e., Green Finance) for Ateneo CCE is essential. The best means to attain this aim is through simulation, particularly, the Monte Carlo simulation. Considering that Green Finance is a new training program, historical data that can be used for simulation are unavailable. Thus, the next best approach is to look into the historical data of another training program similar to Green Finance. In this particular case, the historical data of Techniques in Financial Analysis (TFA) were chosen to simulate the risks involved in developing the Green Finance training program.

The cost of creating this program is PhP148,150; the cost could possibly increase to PhP164,450 if the same program were to be crashed. Hence, determining whether the time, resources, and efforts to be invested in the creation of this training program will be

worthwhile is critically important. The key questions that need to be answered are as follows:

1. Will Ateneo CCE be able to recoup its investment in creating the Green Finance training program? If so, how long will it take Ateneo CCE to recoup the investment?

2. What is the probability that CCE will not generate any profit?

PathsNormal

CompletionCrash A

1Crash B

1.5Crash M

.5Crash G 1.5

Crash D1.5

Crash I2.5

Crash K2.5

Crash E4

ABCFGHIJKMN 49.0 48.0 46.5 46.0 44.5 44.5 42.0 39.5 39.5

ABCFGHIJLMN 46.5 45.5 44.0 43.5 42.0 42.0 39.5 39.5 39.5

ABDEGHIJKMN 62.5 61.5 60.0 59.5 58.0 56.5 54.0 51.5 47.5

ABDEGHIJLMN 60.0 59.0 57.5 57.0 55.5 54.0 51.5 51.5 47.5

Completion Cost 148,150 148,150 148,550 149,150 149,750 150,950 152,450 154,950 158,450

Additional Cost 0 400 600 600 1,200 1,500 2,500 3,500 6,000

Total 148,150 148,550 149,150 149,750 150,950 152,450 154,950 158,450 164,450


Table 12. TFA Three-year Data

Based on Table 12, it appears that to compute revenue; however, the number of enrollees for the training module is uncertain. Moreover, taking into account the number of enrollees who pay the Early Eagle Rate vis-à-vis those who pay the Regular Rate is important. These factors determine the total revenue of Ateneo CCE for this particular training program.

With regard to expenses, a fixed cost of PhP150,200 represents the RP fee, marketing, and room and equipment rental. The variable costs, which change depending on the number of enrollees at a given period, cover food service and training kits.

Courses Data Point Year Date

No. of

Pax

Early Eagle Pax

Regular Rate

Early Eagle Rate

Revenue

Regular Rate

Revenue

Total Rev-enue

Revenue/Student Total Profit Profit/

Student

Techniques of Financial Analysis 1 2012 January 9-12 22 15 7 246,500.00 165,000.00 511,500.00 23,250.00 158,500.00 7,204.55

Regular Rate: Php 25,000.00 2 February 6-9 10 7 3 157,500.00 75,000.00 232,500.00 23,250.00 (86,700.00) (8,670.00)

Early Eagle Rate: Php 22,500.00 3 April 9-12 28 8 20 180,000.00 500,000.00 680,00.00 24,285.71 308,600.00 11,021.43

3.5 days 4 May 7-10 22 20 2 450,000.00 50,000.00 500,000.00 22,727.27 146,000.00 6,636.36

5 June 4-7 17 10 7 225,000.00 175,000.00 400,000.00 23,529.41 60,500.00 3,558.82

6 July 9-12 14 7 7 157,500.00 175,000.00 332,500.00 23,750.00 138,900.00 9,921.43

7 September 3-6 16 8 8 180,000.00 200,000.00 380,000.00 23,750.00 43,400.00 2,712.50

8 October 8-11 19 9 10 202,500.00 250,000.00 452,500.00 23,815.79 107,200.00 5,642.11

9 November 5-8 28 8 20 180,000.00 500,000.00 680,000.00 24,285.71 308,600.00 11,021.43

10 2011 December 11-15 25 21 4 472,500.00 100,00.00 572,500.00 22,900.00 209,800.00 8,392.00

11 April 11-14 26 16 10 360,000.00 250,000.00 610,000.00 23,461.54 244,400.00 9,400.00

12 June 13-16 29 8 21 180,000.00 525,000.00 705,000.00 24,310.34 330,700.00 11,403.45

13 August 8-11 33 13 20 292,500.00 500,000.00 792,500.00 24,015.15 406,600.00 12,321.21

14 October 10-13 10 6 4 135,000.00 100,000.00 235,000.00 23,500.00 (84,200.00) (8,420.00)

15 November 7-10 24 14 10 315,000.00 250,000.00 565,000.00 23,541.67 205,200.00 8,550.00

16 2010 February 15-18 26 18 8 409,500.00 195,000.00 604,500.00 23,250.00 239,400.00 9,207.69

17 April 12-15 8 6 2 126,000.00 60,000.00 186,000.00 23,250.00 (128,400.00) (16,050.00)

18 June 14-17 25 10 15 225,000.00 375,000.00 600,000.00 24,000.00 237,300.00 9,492.00

19 August 16-19 18 13 5 283,500.00 135,000.00 418,500.00 23,250.00 75,100.00 4,172.22

20 November 15-18 27 19 8 425,250.00 202,500.00 627,750.00 23,250.00 259,000.00 9,592,59

Total 427 299 128 5,303,250.00 4,782,500.00 10,085,750.00 3,179,900.00


Table 13. Techniques for the Financial Analysis of Income Statement

A simple income statement was developed for the course (Table 13). The income statement presupposes that 25 students are enrolled in the TFA training module. It also presupposes that 60% of the enrollees paid the Early Eagle Rate, whereas 40% paid the Regular Rate. In this scenario, Ateneo CCE is expected to earn profits amounting to PhP224,800.

The foregoing illustration is only one of the numerous scenarios should Ateneo CCE decide to pursue and offer the new training module called Green Finance. Thus, conducting simulations is imperative to more accurately determine the possible financial outcome of this undertaking.

A number of software products that facilitate the Monte Carlo simulation are currently available. One of these products is the Oracle Crystal Ball, considered as the leading spreadsheet-based application for predictive modeling, forecasting, simulation, and optimization. The Oracle Crystal Ball provides unparalleled insights into the critical factors affecting risk. The Crystal Ball enables tactical decision making, thereby achieving objectives and gaining a competitive edge under even the most uncertain market conditions.

Course Title Techniques of Financial Analysis Students Eagle

Faculty Min Max Probability Min Max Probability

Number of Days 3.5 0 4 0.00 0 9 0

5 9 0.05 10 19 0

Income 10 14 0.15 20 29 0.15

Number of Pax 25 15 19 0.20 30 39 0.05

Course Fee 20 24 0.15 40 49 0.1

Early Eagle Rate 22,500.00 337,500.00 60% 25 29 0.40 50 59 0.2

Regular Rate 25,000.00 250,000.00 40% 30 34 0.05 60 69 0.2

587,500.00 35 39 0.00 70 79 0.2

1.00 80 89 0.05

Direct Expense 90 99 0.05

RP Fee 24,000.00 84,000.00

Marketing 20,000.00

Room 10,000.00 35,000.00

Equipment 400.00 11,200.00

Food Service 600.00 52,500.00

Training Kits 800.00 20,000.00

Overhead 40,000.00 20,000.00

TOTAL EXPENSES 362,700.00

PROFIT 224,800.00


The results of the simulation (i.e., 100,000 attempts using the Oracle Crystal Ball) of the offering of the Green Finance training program are shown in Chart 1. The simulation yielded a mean profit of PhP149,086. This result implies that every offering of this training program is likely to generate such a profit for Ateneo CCE.

Despite the projection of a high probability for earning profit from the Green Finance training module, the Oracle Crystal Ball warns against the possibility of earning no profit or even losing money (Chart 2). Accordingly, an 18.65% chance that Ateneo CCE will have no profit or even lose money once it offers the training module is established.

Chart 3 shows that Ateneo CCE has a 41.15% chance of earning a profit of PhP224,800—the same profit indicated in the income statement presented in Table 13. Finally, Chart 4 highlights a 50% chance that Ateneo CCE will earn a profit ranging from PhP35,400 to PhP267,500 per run of Green Finance.

After performing the simulations, reverting to the original questions raised by CCE is imperative:

1. Will Ateneo CCE be able to recoup its investment in creating the Green Finance training program? If so, how long will it take Ateneo CCE to recoup the investment?

2. What is the probability that Ateneo CCE will not generate any profit?

The answer to the first question is yes—Ateneo CCE will be able to recoup its investment. The surprising aspect, however, is the time it will take the Center to recoup the investment. The simulation reveals that Ateneo CCE would only need to offer the Green Finance training module once, and its investment amounting to PhP148,150 will already be covered. The reason is that the simulation, after 100,000 attempts, revealed a mean profit of PhP149,086.

Moreover, even if the Center is forced to crash the time to create the Green Finance training module, the corresponding additional amount will be so negligible that Ateneo CCE’s decision will not be affected. The difference in the crashed amount and the mean profit Ateneo CCE will make is only PhP15,364.

Given the justification for the high probability of earning a profit, Ateneo CCE could already risk the 18.65% chance of not making any money. Despite the possibility, the opportunity is extremely significant to ignore.

Overall, based on the simulation, creating the Green Finance training program will not only enable the Center for Continuing Education to adapt to changing times, but will also substantially contribute to its profits.


Chart 1. Crystal Ball Simulation Results for the Green Finance Training Program

Chart 2. Crystal Ball Indication of the Likelihood of Incurring a Loss

Overall, based on the simulation, creating the Green Finance training program will not only enable the Center for Continuing Education to adapt to changing times, but will also substantially contribute to its profits.

100.000 Trials

0.04

0.03

0.02

0.01

0.00(200,000.00)

-Infinity InfinityCertainty: 100%

200,000.00 300,000.00 400,000.00(100,000.00) 100,000.000.00

4,400

4,000

3,600

3,200

2,800

2,400

2,000

1,600

1,200

800

400

0

Frequency View

Pro

babi

lity Frequency

PROFIT

100.000 Displayed

100.000 Trials

0.04

0.03

0.02

0.01

0.00(200,000.00)

-Infinity 0Certainty: 18.646%

200,000.00 300,000.00 400,000.00(100,000.00) 100,000.000.00

4,400

4,000

3,600

3,200

2,800

2,400

2,000

1,600

1,200

800

400

0

Frequency View

Pro

babi

lity Frequency

PROFIT

100.000 Displayed

Mean = 149,086.44


Chart 3. Crystal Ball Indication of the Likelihood of Realizing Significant Profits

Chart 4. Crystal Ball Indication of the Likelihood of Realizing Profits Around the Mean

. . . the key challenge is to nurture a culture that has the discipline, patience, and courage to look beyond short-term solutions and opt for business practices that can offer the greatest positive impact and longevity.

100.000 Trials

0.04

0.03

0.02

0.01

0.00

(200,000.00)

224,800.00 267,500.00Certainty: 50%

200,000.00 300,000.00 400,000.00(100,000.00) 100,000.000.00

4,400

4,000

3,600

3,200

2,800

2,400

2,000

1,600

1,200

800

400

0

Frequency View

Pro

babi

lity Frequency

PROFIT

100.000 Displayed

Mean = 149,086.44

100.000 Trials

0.04

0.03

0.02

0.01

0.00

(200,000.00)

224,800.00 InfinityCertainty: 41.646%

200,000.00 300,000.00 400,000.00(100,000.00) 100,000.000.00

4,400

4,000

3,600

3,200

2,800

2,400

2,000

1,600

1,200

800

400

0

Frequency View

Pro

babi

lity Frequency

PROFIT

100.000 Displayed

Mean = 149,086.44


• The Monte Carlo simulation was performed to assess the risk of offering Green Finance course.

A number of alternatives to promote green and sustainability efforts are available, including the application of quantitative analysis. However, the key challenge is to nurture a culture that has the discipline, patience, and courage to look beyond short-term solutions and opt for business practices that can offer the greatest positive impact and longevity. This challenge can be resolved through the Center for Continuing Education in a tried and tested fashion: the Ateneo way.

References

1 http://www.oracle.com/us/products/applica-tions/crystalball/crystal-ball-product/overview/index.html

2The list of possible green and sustainability courses and the Abstract for Green Finance are quoted from ecochamber.com.

Conclusion

Quantitative analysis provided value to this paper. The concept or the process of changing the corporate mindset and culture to embrace green and sustainable undertakings can be daunting. Nevertheless, introducing Green Finance course is an initiative that is a step closer toward creating a greener and more sustainable planet and a responsible citizenry, as well as achieving sound profits. Yes, profits are attainable; this paper ascertained the possibility of making money out of green innovations.

• LP was applied to maximize the profit of existing Finance courses.

• PERT was employed to identify the variance of activities in the development of new courses before applying crashing to maximize cost savings.


Ensuring Continuous

ISO 9001 CertificationImprovement through

Ivy Joy AlagarDaniel Leon S. BerroyaAtty. James S. BironDong HaiyanEliza T. GuadesIrene Palad


ISO 9001 Certification

Introduction

owadays, most manufacturing companies aim to improve their operational efficiency and effectiveness. Obtaining a certification to ISO

Management System Standards helps companies to attain their desired outcome. To ensure operational safety, large companies often invest in ISO certification. In some industries, ISO certification is a regulatory requirement. Aside from ensuring the manufacture of high-quality products, the benefits of ISO certification include cost savings in optimized processes, improved customer satisfaction, opportunities for catering to new markets, increased market share, guaranteed food safety, and even minimized negative impacts on the environment.

Aligning company operations with ISO 9001 Quality Management System and achieving certification can help solve problems in customer dissatisfaction with linkage to outsourced processes and streamlining of established workflows. ISO 9001 Quality Management System certification can address current problems in delivery, storage, and customer complaints. Such certification is a means to manage product and service challenges. Moreover, processes are established and put in place. Controls are also identified and prevent the recurrence of non-conformance.

N

ISO 9001 Quality Management System Certification

A company may have in place a process for receiving incoming raw materials. However, the criteria for selecting, evaluating, and continuously monitoring suppliers must be established. Existing purchasing policy must also be reviewed to include a provision for outsourced services. ISO 9001 elements 7.4.1 to 7.4.3 will help companies to ensure that the

ISO 9001 Quality Management System certification can address current problems in delivery, storage, and customer complaints.

• Target of Company to be known in the global market as an exporter like that of KLT and Profood which can aid in increasing market share domestically

• Operational Efficiency• Customer Intimacy

T

A

R

G

E

T

Structured 3PL processes

ISOcertified

High customerdeliverycomplaint

High ratingvendorperformance

No structured3PL processes

Non-ISOcertified

Solution

• Customer Complaints on product quality upon delivery, mishandling, equipment not calibrated

VOC

Goals

IPO

Current & Target Situation

C

U

R

R

E

N

T


purchased product or service conforms to the specified purchase requirements. The standard will lay down the guidelines that help the organization to evaluate and select suppliers based on their ability to supply products in accordance with the organization’s requirements. The criteria for selection, evaluation, and re-evaluation shall be established to include site inspection and audit of supplier premises to verify the purchased product or service.

A manufacturing process or system must be in place as this is the heart of service realization. A company may also conform to Good Manufacturing Practice (GMP) and gear toward Hazards Analysis and Critical Control Point (HACCP) to efficiently structure its food processing system and safeguard food against unsafe elements and practices. Critical control points (CCPs), simultaneously with HACCP alignment, may also be

established alongside verification and validation processes that complement product research and development (R&D).

ISO 9001 Quality Management System certification will likewise facilitate the review and improvement of all the processes of each department aimed toward customer satisfaction.

The following diagram illustrates a sample flow of operations from batching to storage and delivery of finished products. When purchase orders are received from clients, the outsourced delivery company transports the products from the cold storage provider (also outsourced) to the clients’ premises. As presented in the diagram, the process demonstrates a lack of control over its frozen products; it only covers receiving to cooling. The challenge now lies in the cold storage and delivery of frozen products.

Processing

Critical Control Point

Prepare by 50kgPrepare chemicals and other materials needed

1-4 HRKettle is by 50kg capacity

1-4 HRMachine Retort is by 50kg capacity

30 MINIndustrial Packaging (primary)

15 MINLAB checking if within standards

1 HRIndustrial Packaging (secondary)

5-7 Days (freezing)3rd Party Cold Storage (nearby)

will depend on the client3rd party Reefer Van



Batching

Cook

Sterilization

Check Parameters

Pack & Seal

Cool

Store (-18 to -23)

Delivery


Production processes had been aligned with GMP and HACCP. Other processes, such as those of HR, Sales, delivery, and control and monitoring of its outsourced processes and production, need to be aligned with ISO 9001 elements to achieve customer satisfaction in all aspects of the organization. Customer satisfaction is focused both on external clients and internal clients (i.e., employees). Streamlining processes to efficiently deliver services to internal and external clients is vital. The same approach must be applied to all stages, from receiving to delivery. Although storage and delivery are outsourced, the standard specifies that the organization should have control over its outsourced processes as these could affect the quality of the product and service being rendered.

Following are some ISO elements that a company may use to align production process with the Quality Management System (QMS):

ISO Procedure 7.3.1. Design and Development Planning. The organization shall plan and design the product development according to customer requirements. This step ensures that the work flow to be established is in accordance with the output agreed with the customer. The step also enables the organization to control product or service quality at any stage of the production process. Review and verification shall be established at each stage to ensure conformity to requirements.

ISO Procedure 7.3.2, 7.3.3. Design and Development Planning Input and Output. The organization should identify the process inputs that affect product design and development. Inputs can be in the

form of customer input, international standards, industry practices, technological experiences, records, data, market research, and feedback information from past experiences. These inputs will help identify the processes that are critical to product or service quality. The output should include information that can be verified and validated to determine if the design and development efficiently meet product or service requirements. This aspect is evident in the above workflow – checking of parameters, cooling temperature, cooking temperature, sterilization, and packing and seal specifications.

ISO Procedure 7.3.4. Design and Development Review. Design such as R&D and workflow must be continuously and regularly reviewed to determine if the current process still meets customer requirements. Through this step, the company can ascertain if its practices are at par with industry practices and trends. This factor will enable the company to identify any problems in the process and recommend the necessary solutions.

ISO Procedure 7.3.5, 7.3.6. Design and Development Verification and Validation. Verification should be performed to check if the design and development outputs have met the design and input requirements. Validation should be performed to determine if the resulting product is capable of meeting the specified requirements. This step should be undertaken prior to product delivery or, in this case, prior to delivery to third-party logistics. The company may perform laboratory results and product quality monitoring as a form of verification and validation at a specific stage in the production process and prior to delivery to its cold storage provider.

5-7 Days (freezing)3rd Party Cold Storage (nearby)

will depend on the client3rd party Reefer Van


Customer delivery complaints may be anchored in products that do not meet the agreed temperature. An investigation of the company’s supply chain and the amount of control/monitoring it has applied to ensure product or service quality indicated that the company lacked established processes and control at each stage of the delivery and storage processes. Hence, a proposed flow is shown below:

Delivery and Storage toThird Party Supplier


Finishes Goods from Production Plant

Logging items in the stock card

Delivery Schedule

Observation of the FIFO Policy

Reefer Van/Truck

Storing of product in Cooling Warehouse(Company Owned)

Trip TicketDelivery Slip

Delivery at Third Party ColdStorage/Unloading

Inspection of itemsChecklist

Product TemperatureProduct Integrity PackagingTruck GMPVehicle TemperatureProper Stacking

Holding Time

Checklist

Warehouse GMPFIFO PolicyProper StackingWarehouse TemperatureObservation of the 7 dayIncubation

Checklist

Truck GMPTruck TemperaturePackaging IntegrityProduct TemperatureDistribution Staff’s Personal HygieneCOA (Certificate of Analysis)Holding TimeProper Stacking/NoMixture of othergoods

Checklist


Distribution Staff’s Personal Hygiene





The CCPs are strategically placed in areas where the quality and safety of goods may be altered due to factors such as environment, man, and equipment. This step ensures that stringent controls are maintained at these critical stages. Finished goods need to be inspected before leaving the manufacturing plant. Checklists are used as a guide for ensuring the items are in good condition prior to release, transit, and unloading. These include product temperature, packaging, proper stacking of goods in the truck, and cleanliness. The goods must be transported to the cooling warehouse (company-owned) to prepare the goods for a frozen state. Inside the cooling warehouse, temperature and FIFO (First-In-First-Out) policy must be observed to ensure proper stock rotation and product quality.

Transferring goods from the cooling warehouse to reefer vans is critical as it may negatively affect product quality. Delivery (in transit) to the third-party cold storage supplier is also crucial. Delivery and transfer from cold storage to reefer vans can break the cold chain that makes these stages critical. If the cold chain is not sustained at all stages, harmful microorganisms can proliferate due to an increase in temperature. Therefore, temperature in cold storage must be well monitored to ensure that goods are kept within -18°C to -23°C. The company can check the log books or monitoring sheets of the supplier during internal audit activities as objective evidence to ensure that the equipment is performing on standard. The data could also be used as preventive maintenance inputs.

Delivery to Customer



Loading bay @ Cold Storage

Warehouse staff to checktrip ticket

Storage

Reefer Van/Truck (Delivery to cold client)



Delivery to Client

Reefer Van/ Truck (IN TRANSIT)

Checklist

Truck GMPTemperature


Items to be delivered

Checklist

Storage Temperature

Stacking Height

Warehouse GMP

Checklist

Loading Bay GMPTruck/Storage GMPTemperature Distribution Staff’sPersonal HygieneHolding Time

Checklist



Finished Goods from Third Party Cold Storage

Checklist

Truck GMPTruck TemperaturePackaging IntegrityProduct TemperatureDistribution Staff’s Personal HygieneItems to be deliveredCOA




Once the orders from clients are made, goods are transferred to reefer vans for delivery. The CCPs reflect the areas to be monitored that could affect product quality. Proper laboratory reports must be presented upon delivery to the client as proof that goods and services have met acceptable criteria. The proposed flow results in the hiring of additional quality assurance (QA) staff members to perform inspections and audit of CCPs for proper monitoring.

The company should perform an internal audit on the suppliers to monitor the decline and improvements in performance against its performance standards. This measure will also help the company to ensure that product or service quality is similar to what had been agreed with its customers. Internal quality audit (IQA) should identify process gaps that provide opportunities for refining workflows.

ISO Procedure 7.5.5. Preservation of Product. The organization shall preserve the product during internal processing and delivery to the intended destination to maintain conformity to requirements. Although the company uses a third-party supplier for cold storage and delivery, established processes and controls must be put in place. The owner is responsible for ensuring that the goods meet the agreed specifications as they reach the client’s premises. As applicable, preservation shall include several activities, such as identification (labelling), handling, packaging, storage (at the proper temperature), and protection.

Following are the ISO elements that should be aligned in all stages of the supply chain:

ISO Procedure 8.2.4. Monitoring and Measurement of Product/Service. The organization should be able to monitor and measure product or service characteristics to verify that product or service requirements have been met. This verification can be in the form of logging sheets or receiving checklist to verify if the product has met the agreed criteria when it reached the client’s premises.

ISO Procedure 8.3. Control of Non-Conforming Product. The organization should establish ways to identify the products that do not conform to product requirements. Such products should be identified and prevented from being delivered or used inadvertently. This guideline is related to the next one.

ISO Procedures 8.5.2 and 8.5.3. Corrective and Preventive Action. Once a deviation from product requirements has been identified, the organization must eliminate the cause of non-conformity and prevent its recurrence. The organization should also eliminate the potential causes of non-conformities. Corrective action is an immediate solution to the challenge at hand. Meanwhile, preventive action includes root cause analysis to eliminate the real cause of the problem and prevent any recurrence.

ISO Procedure 4.2.4. Control of Records. Records shall be established and maintained to provide evidence of conformity to requirements. Records are objective evidence during internal audits and certification audits. ISO Procedure 4.2.3 Control of Documents complements this procedure. Control of documents ensures the use of documents by authorized individuals, establishes document retention, and identifies the appropriate authority required for


the document. This step ensures that despite the absence or unavailability of the approver, an alternate signs off on the document, thereby preventing unauthorized use.

ISO Procedure 6.2.2.2. Awareness and Training. The organization shall provide the appropriate education and training to employees/suppliers to emphasize the importance of meeting the needs and expectations of customers and other interested parties. Such education and training should include increasing the awareness of the consequences of failing to meet customer expectations. Employees should be continuously informed about the trends and best practices in the industry and those in their fields of expertise to provide better service to the company’s internal and external clients.

ISO 9001 Quality Management System is aimed at customer satisfaction and continuous improvement; thus, the process flow of soliciting customer feedback and internal audit must be developed. Incorporating customer feedback with the corrective and preventive action procedures effectively measures organizational performance in terms of meeting and exceeding customer requirements. Management should be involved in this process as their commitment is vital in ensuring that complaints and process gaps are resolved. Informing customers about the corrective and preventive action that the company has undertaken is also vital. This approach enables clients to feel that they are important. Moreover, the approach is an affirmation that the company intends to improve.

Internal audit must be conducted to determine whether the QMS conforms

to the International Standard and to the QMS requirements established by the organization.

Process owners of the area being audited must ensure that the necessary corrective and preventive actions are undertaken. Results of internal audits must be discussed during management review. The management review will function as an avenue for evaluating the efficiency and effectiveness of the system. The review, headed by the leadership, will open the discussion and evaluation of inputs and the exchange of new ideas, as well as infuse performance improvement in product and service quality that extends to the entire organization.

Workflow for rejected product delivery is also reviewed to incorporate changes originating from inputs of customer feedback. Temperature is added as one of the reasons for product rejection. Appropriate steps are likewise added to control the unintended use of the rejected product.

ISO 9001 certification acts as guideline for resolving and improving operation management challenges; at the same time, it is also a holistic approach to achieving customer satisfaction and continual improvement in all aspects of the business. Moreover, ISO 9001 certification provides linkages between departmental processes, and ensures that all functions are value adding, efficient, and effective.

Impact of ISO 9001 Quality Management

ISO 9001 certification provides numerous benefits, including the following:


• Reduces the cost of rejected products or lost sales; eliminates wastage and spoilage due to excess production and rejection (see the sample of Cost–Benefit Analysis)• Avoids errors and bad orders• Increases revenue; boosts the availability of products for sale as product rejection decreases • Promotes efficiency in production and service; eliminates idle time and rework resulting from unsure processes and workflow; increases revenue as more products are available for sale and more customers are satisfied• Enhances brand image due to increased loyalty and the prioritization of product or service quality and customer satisfaction• Increases employee morale due to regular training; enhances the brand image

• Develops and reinforces mutual supplier relationships (i.e., suppliers are also the partners of the organization)• Increases customer loyalty and confidence due to product and service improvement• Maintains product and service consistency as standardized processes are strictly implemented to ensure the delivery of safe, high quality products• Promotes a factual approach to decision making as it entails the analysis of customer feedback and records; the company has a basis for its decisions and need not simply rely on gut feel that can lead to losses; impacts can be identified and controlled

Cost–Benefit Analysis(Annual estimates, in pesos)

Year 1 Year 2 Year 3 TOTAL

Benefit

Rejection cost 6,000,000 4,800,000 2,880,000 13,680,000

% of Rejection 80% 60% 30%

Adjusted rejection cost 4,800,000 2,880,000 864,000 8,544,000

Cost Avoidance 1,200,000 1,920,000 2,016,000 5,136,000

Cost

ISO Certification Costs 200,000 75,000 75,000 350,000

Training costs

One-time QA and process owners 300,000 300,000

Yearly training 200,000 200,000 400,000

Machines

Metal detector (goods receiving and inspection) 200,000 200,000 200,000 600,000

Manpower

3 FTEs for extra audit inspections 356,400 356,400 356,400 1,069,200

Method

ISO-certified facility rental 600,000 600,000 600,000 1,800,000

Delivery 100,000 100,000 100,000 300,000

Total Cost 1,756,400 1,531,400 1,531,400 4,819,200

Total Savings -556,400 388,600 484,600 316,800


The estimated annual loss due to non-sales of rejected items is Php6 million. The implementation of ISO 9001 is expected to decrease by 20% annually. The aforementioned ISO certification costs are three-year estimates and are valid for only one site. In three years, the estimated benefit for this project is Php5.136 million. Additional costs that will be incurred include ISO certification, training and manpower, machine/device acquisition, and other operation costs involving storage and delivery. ISO certification for three years in one site costs Php350,000. The bulk of the cost is usually incurred during the first year. Training costs involve one-time training for process owners and QA employees. Refresher training will also be conducted annually for the employees. Such training will help employees to become familiar with the new procedures, thereby ensuring product quality and safety.

A metal detector that usually costs Php1 million will be required for receiving and inspection of goods; its estimated useful life is five years. Additional depreciation cost using a straight-line method will be Php200,000. ISO 9001 would require a new QA team that will handle extra audit inspections. The team will involve three additional staff members with an estimated annual cost of Php356,400. Seeking a new, ISO-certified storage facility for keeping products at the required temperature is also encouraged. Moreover, extra cost to improve the delivery processes is expected. The improvement of these processes is estimated to cost Php700,000 annually.

The net benefit of this three-year project is Php316,800. Although the net benefit may not be considerable, the impact on corporate reputation and customer

satisfaction will be significant. Given that company procedures will be audited and improved, lesser costs on spoilage and wastage will be expected. Keeping these procedures in place will facilitate the company’s application for other certifications on food safety and quality, which are often required in the global markets.

Recommendations

ISO 9001 certification is an achievable step toward continuous improvement. This organization should ensure the control of its rejected products to help minimize costs on wastage and lost sales. It needs to discuss the results of audit during management reviews so that management is informed and engaged in resolving gaps and challenges resulting from an inefficient system. The company achieves continuous improvement only through these reviews.

The company’s purchasing policy should be reviewed and modified to cover outsourced services such as storage and delivery. It should establish policies on the control of rejected products, purchasing, and supplier policies. Performing a second-party audit on suppliers to conduct a check and balance on the processes of its suppliers is also advisable to ensure that they are aligned with the company’s storage and delivery requirements. Compliance with and certification to ISO 22000 Food Safety Management System – a combination of ISO 9001 (QMS), HACCP and GMP – is highly encouraged to meet the quality, food safety, and marketing goals of the company. Finally, the company must perform benchmarking on ISO-certified clients, suppliers, and competitors to enable it to imbibe best practices.


Finger Printing Face Capturing

Database Server where both biometrics and logical data are stored.

Workstation

Contains here common information

asked by government

agencies

Application Forms

Streamlining the GovernmentID Processing SystemAna Teodisio Arthur Villones Gladys Estandian Ricky Lascano


Background

hen applying for an identification (ID) number or a change status request from government

agencies, employees, especially new entrants to the workforce, have to go to the offices of these agencies and suffer from long queues to fill in forms requiring the same information to prove one’s identity. Examples of such information are Name, Address, Telephone Number, Gender, Citizenship, Date of Birth, Place of Birth, Civil Status, Beneficiaries, Employment Classification, and Signature. These agencies typically employ one to two windows to accommodate applicants who would have to wait a half day or even an entire day simply to obtain an ID.

The amount of effort to fill in the forms and the amount of space required to archive the collected information in databases of the different agencies can be a good reference for study, thereby providing an improvement opportunity through data integration and possibly service delivery consolidation. This paper proposes to integrate and simplify the process for obtaining the IDs from

four government agencies, namely, Social Security System (SSS), Bureau of Internal Revenue (BIR), Pag-IBIG, and PhilHealth.

SSS ID Application

The Applicant fills in SSS Form E-1 (Personal Record) and submits it to an SSS branch together with the following documents: Birth Certificate, Baptismal Certificate, Driver’s License, Passport, and Professional Regulation Commission (PRC) Card, or Seaman’s Book. If the Applicant does not have any of these

documents, any two supplementary ID cards, such as school ID and ATM card, are acceptable.

BIR ID Application

The Applicant fills in BIR Form 1902 (Application for Registration for Individuals Earning Purely Compensation Income, Non-Resident Citizens, and Resident Alien Employee). The documentary requirements are

Birth Certificate of the Applicant, Birth Certificate(s) of the declared dependent(s), waiver of the husband on his right to claim additional exemptions if his wife claims exemption; and Marriage Contract if married. The employer accomplishes the applicable sections of the application form. The Applicant submits all the documentary requirements to the BIR Revenue District Office that has jurisdiction over the

W

This paper proposes to integrate and

simplify the process for obtaining the IDs from four government

agencies, namely, Social Security

System (SSS), Bureau of Internal Revenue (BIR), Pag-IBIG, and

PhilHealth.


location of the employer’s office where the Applicant is expected to report for work.

Pag-IBIG ID Application

The Applicant goes to the nearest Pag-IBIG branch. For branches provided with an Online Membership Registration System, the Applicant encodes the required information. Otherwise, the Applicant fills in and submits the Pag-IBIG form (i.e., Member’s Data Form).

PhilHealth ID Application

The Applicant fills in the PhilHealth Membership Registration Form and submits it with all the supporting documents to the HR Department. The practice whereby employers handle their employees’ application with PhilHealth is commonplace.

Research Design

An investigative type of research design was applied through actual visits to the different government branches under study within the vicinity of Makati City, as well as interviews with guards-on-duty, clerks, employees, and officers-in-charge. This kind of research design is best suited for this study, enabling the researchers to collect data for the Queuing Theory through direct interaction with the staff

who have first-hand information. The data collected are validated by observing and extracting the arrival and service times’ information of each agency through a discrete time and motion study, and even participating in actual queues.

To determine the number of new employees joining the workforce, the regression data were extracted from the SSS statistical information. The simple linear regression forecasting method was used to determine the number of new entrants to the workforce consisting of new college graduates seeking employment, including college under-graduates, working students, and out-of-school youth within and over the legal age of 18.

Population and Sampling Procedure

Given the sheer size of the scope of study, the sample frames used for the research are the government branches in Makati (i.e., the SSS branches in Gil Puyat, Ayala, and J.P. Rizal, the BIR branches in Atrium and Gil Puyat, and the Pag-IBIG branch in Gil Puyat. PhilHealth was excluded because employers register their new employees in batches, a process that avoids employees queuing up at the PhilHealth branch.

The aim of sampling within the Makati area is to provide benchmark information

An investigative type of research design was applied through actual visits to the different government branches under study within the vicinity of Makati City, as well as interviews with guards-on-duty, clerks, employees, and officers-in-charge.


on the primary attributes of the queuing model, such as arrival times and service times, and the overall throughput time, which may be extrapolated to any location across the country if, in the future, a similar study is to be made on these government agencies.

Limitations and Assumptions

Given the short duration of the study, the averages resulting from the primary data are assumed to apply to the entire day. In actuality, the visits did not take a whole day, and the averages were based on the information provided by the researchers’ contacts inside these agencies.

The selected sample is assumed to represent the actual population (i.e., the agencies outside Makati will have similar outcomes regardless of geographical locations). Moreover, the application data obtained from SSS are extrapolated and assumed to be similar for the other three government agencies.

The researchers were unable to obtain information on costs, such as ID processing cost and forms cost, required to effectively perform a quantitative

Table 1. Linear Regression Data

benefit-cost analysis. Consequently, the researchers provided qualitative evaluation based on perceived benefits obtained from interviews with colleagues, and classmates, as well as random discussion with personal contacts and among the researchers themselves.

Linear Regression Model

The linear regression model was used to forecast the annual increase in SSS membership for the Employee category. Thus, the number of individuals who will benefit should an Integrated ID Processing System be adopted by the government can be established.

The linear regression data show the total number of registered members from 1999 to 2008 (Table 1). The data include workers classified into three categories, namely, Employees, Self-Employed, and Voluntary Members. Results of the regression analysis are presented in Figures 1 and 2.

Year Period (X) Number of Workers (Y)

1999 1 21,325,966

2000 2 22,630,832

2001 3 23,532,666

2002 4 24,308,033

2003 5 25,051,234

2004 6 25,666,386

2005 7 26,227,636

2006 8 26,739,282

2007 9 27,241,220

2008 10 27,759,568


Figure 2. Linear Model

From the Summary Output generated from the regression analysis solver of Excel, the R2 nears 1, which implies that the data model is a good fit. Further analysis suggests that the model results

also passed the significance F and t-Stat requirements, thus providing a high level of confidence that the values generated from the linear equation are acceptable. Given these results, the number of

Regression Statistics

Multiple R 0.9881225

R Square 0.97638607

Adjusted R Square 0.97343432

Standard Error 340754.076

Observations 10

ANOVA

df SS MS F Significance F

Regression 1 3.84E+13 3.84E+13 330.783 8.58E-08

Residual 8 9.29E+11 1.16E+11

Total 9 3.93E+13

Coefficients Standard Error t Statistics P-Value Lower 95%

Intercept 21295543.1 232779.3 91.48382 2.27E-13 20758752.99

X Variable 1 682316.212 37515.79 18.18744 8.58E-08 595804.6475

Figure 1. Summary Output of Regression Analysis

X Variable 1 Line Fit Plot

30,000,000

y = 682316x + 2E+07R2 = 1

Y

Predicted Y

Linear [Predicted Y]

X Variable 1

25,000,000

20,000,000

15,000,000

05 100 15

10,000,000

5,000,000


Table 2. Forecast Data

However, the forecast annual increase includes the other two categories (Self-Employed and Voluntary Members). To obtain the portion applicable only to the number of Employees, the researchers used the data provided by the SSS Head Office, which show the Total Registration as of June 2011 per category (Table 3).

The total number of individuals who will benefit should the Integrated ID Processing System be adopted by the government is 682,316 per year. Of this number, 69% or 470,798 are Employees; 20% or 136,463 are Self-Employed; and 11% or 75,055 are Voluntary Members.

Queuing Analysis

The queuing technique was used to quantify the benefit in terms of time savings for each individual should the proposed Integrated ID Processing System be adopted. For this purpose, the Makati area and the category Employees were used as sample population. The researchers went to the different government agencies in Makati to observe and conduct interviews. The data collected are shown in Table 4, and the queuing results are presented in Table 5.

Source: http://sssintranet/MIS/html/factmemjdec10.htm

Cluster Employee Self-Employed Voluntary Total

North Luzon 724,314 590,296 137,834 1,452,444

Central Luzon 1,179,164 914,433 185,392 2,278,989

NCR 11,059,427 1,043,845 1,996,920 14,100,192

South Luzon 1,232,429 494,825 169,636 1,896,890

Bicol Region 283,100 321,886 78,106 683,092

Western Visayas 1,485,555 559,381 170,458 2,215,394

Central Visayas 1,494,646 344,088 172,500 2,011,234

Northern Mindanao 910,982 619,748 101,067 1,631,797

Western Mindanao 353,071 282,234 46,848 682,153

Southern Mindanao 1,287,202 610,262 183,297 2,080,761

Total 20,009,890 5,780,998 3,242,058 29,032,946

Percentage 69 20 11 100

memberships can be forecast for 2009 to 2011, annually increasing by 682,316 (Table 2).

Year Period (X)Number of

Workers (Y)

2009 11 28,801,021

2010 12 29,483,338

2011 13 30,165,654

Table 3. Registration of New SSS Members


Table 4. Queue Parameters

Table 5. Queuing Results for the Current Setup (in minutes)

Based on the foregoing computation, an individual spends an average of almost an hour in applying for ID numbers with the three Government Agencies. This

Data Gathered

Number of Applicants (for the month of August)

Monthly Daily Mean Service Time [in minutes (µS)]

Mean Arrival Time [in minutes (µA)]

SSS

Gil Puyat 798 38 4.5 10.26

Ayala 600 29 4.5 13.44

J.P. Rizal 1,251 60 4.5 6.50

BIR

Atrium 1,473 70 5 5.57

Gil Puyat 1,152 55 5 7.09

HDMF 2,340 111 6 3.51

System Load Factor (r)

WTM [r/(1-r)]

µW µL Throughput(µS+µW)

Average

SSS 9.8019

Gil Puyat 0.4386 0.7813 3.515 0.34 8.0159

Ayala 0.3348 0.5033 2.264 0.16 6.7649

J.P. Rizal 0.6923 2.2500 10.12 1.55 14.6250

BIR 32.9185

Atrium 0.8977 8.7752 43.87 7.87 48.8760

Gil Puyat 0.7052 2.3922 11.96 1.68 16.9610

HDMF 1.7094 2.4097 14.45 4.11 20.4582 20.4582Total Time Spent in ID Processing for the Three Agencies 63.1786

does not include yet their travelling time, time spent in traffic, and waiting for transportation.


Table 6. SSS Applicants

Table 7. Queue System Recommendation (in minutes)

From the resulting computation, the following premises are established: individuals applying for government-issued ID numbers will spend 24 minutes

Monthly Daily

Gil Puyat 798 38

Ayala 600 29

J.P. Rizal 1,251 60

Total 2,649 127

Mean Service

Time

Mean Arrival Time

System Load

FactorWTM µW µL Throughput

Integrated (1 processor/server)

6 4 1.5000 3.0000 18.0000 4.5000 24.0000


6 4 0.7500 1.4147 8.4900 2.1200 14.4900


6 4 0.5000 0.1579 0.9500 0.2400 6.9500

Queuing for the ProposedIntegrated ID ProcessingSystem

Using the data from SSS as shown in Table 6 (with the assumption that new employees applying for SSS ID number will also obtain BIR and HDMF numbers), the recommended queue system is presented in Table 7.

if the setup will have a single server; 14.5 minutes if at least two servers are used; and 7 minutes if three servers are used. Each setup will give applicants time savings of between 39 minutes and 56 minutes.

Findings and Recommendations

The annual increase in new applicants is 682,316 nationwide, and the average throughput time experienced by an applicant is 63 minutes. The longest stay-put time is determined at the BIR at 33 minutes, followed by Pag-IBIG at 20 minutes, and SSS at 10 minutes. The current situation demonstrates that each of the four agencies serves its constituents individually. Using an integrated ID processing mechanism can dramatically reduce the overall throughput time to 7 minutes using three servers.


The integration of the ID processing of the four government agencies under study can provide beneficial effects to both government and its people, specifically the entrants to the labor market. This study can be the beginning of something new in improving government structures – an innovation that is scalable as the need for government services increases. Looking into alternative forms of providing better services while lowering costs is a viable notion for the government.

This study recommends the adoption of a shared service model for ID processing in these four agencies. Shared services primarily aim to reduce duplication of effort, lower incidental times at queues, enable government staff to focus on the most valuable work in the agency, and provide more services given the limited resources. The costs on the following qualitative factors can be minimized: processing of registration forms into a singular information sheet, integration of staff services, savings on office space, and fraud deterrence.

Shared services can provide the means for these agencies to put more emphasis on their core functions – social services for SSS, tax collection for BIR, housing requirements for Pag-IBIG, and adequate provision of health services to Filipino workers for PhilHealth. Shared services can de-couple non-core activities that are viewed as “support” and secondary to their core processes.

Although not explicitly quantified, the business case of a shared service center can demonstrate that the intended benefits would accrue to the new employees, the service provider, and the government as a whole. However, a shared service approach requires collaboration among the four agencies, with the possibility of establishing a separate office for the purpose.

References

BIR Website: Application for Taxpayer Identification Number (TIN)http://www.bir.gov.ph/reginfo/regtin.htm

SSS Website: How to Registerhttps://www.sss.gov.ph/sss/index2.jsp?secid=109&cat=2&pg=null

SSS New Members Registrationhttp://sssintranet/MIS/html/factmemjdec10.htm

PhilHealth Website: PhilHealth Membershttp://www.philhealth.gov.ph/members/index.htm

Pag-IBIG Fund Website: Members Serviceshttp://www.pagibigfund.gov.ph/memserv/


Everyone in the list is taking up or has completed his/her Master’s Degree in Business Administration at the Ateneo Graduate School of Business.

Ivy Joy Alagar graduated from the University of the Philippines-Los Baños in 2008. She works at The Hong Kong and Shanghai Banking Corporation Manila (HSBC) as Accounts Payable Assistant II. She has been with the Back Office–Operations team for more than four years.

Daniel Leon S. Berroya graduated with a BS degree in Marketing and Corporate Communication at San Beda College in 2006. He worked at Globe Telecommunication, Inc. in Sales and Customer Service. In 2009, he moved to Food Customs, a food manufacturing business for processed fruits and vegetables. He is currently the Sales Manager engaged in local and export transactions.

Atty. James S. Biron graduated cum laude from the University of the Philippines-Diliman with a degree in B.A. Political Science in 2004. He then took up Law at the Ateneo de Manila School of Law and graduated in 2008. He successfully passed the Bar Exams given in the same year. He is currently a Legal Manager in a New York-based firm that has an office in Makati City.

Jejie Junio Camacho graduated from the Technological University of the Philippines with a degree in BS Engineering. He works with Analog Devices Phils, where he is responsible for process improvements in test manufacturing such as error proofing, standardization, yield improvement, process simplification, and process qualifications.

Zosimo Richard S. Carlos III obtained his B.S. in Computer Science major in Software Technology degree from De La Salle University. He has a Project Management Professional® certification from the Project Management Institute. He worked at ABS-CBN Interactive–Multi-media, HanbitSoft, and IP e-Games Ventures. He is currently a Project Manager at IBM Solutions Delivery.

Dr. Jhanssen Castillo is an Ophthalmology Consultant at Tagaytay Hospital and Medical Center, Sta. Rosa Hospital and Medical Center, Westlake Medical Center, and iHealth Multispecialist Clinic. He completed an observership at Jules Stein Eye Institute UCLA, Retina Division, under the tutelage of Dr. Anurag Gupta, and at Stanford University Medical Center, Cornea and External Disease Division, under Dr. Christopher Ta. Dr. Castillo obtained his certificate in aesthetic medicine in Singapore under the American Academy of Aesthetic Medicine. He completed his residency training in Ophthalmology at Manila Doctors Hospital where he graduated as the “Most Outstanding Resident”. He earned his medical degree, bene meritus, at the University of Santo Tomas and Bachelor of Science in Biology, cum laude, at De La Salle University-Dasmariñas.

Contributors


Ma. Katrina Mae Garcia-Dalusong is the Director of Sales for the Inquirer Group of Companies-Freezine and Commercial Magazine Business Unit. Prior to this position, she headed the publishing and business development, and marketing and distribution departments. She graduated with honors from Assumption College, San Lorenzo, with a degree in Bachelor of Communications, Major in Advertising and Public Relations. She received exemplary recognition for Best Thesis.

Rafi Delica is a Senior R&D Engineer at Excelitas Technologies. He graduated with a degree of BS in Applied Physics from UP Diliman, and obtained his MS and PhD in Physics from the same university after a research stint at Osaka University.

Atty. Anton Carlo E. Espino is a Senior Partner of the Delloro Espino & Saulog Law Offices. He holds a degree in A.B. Economics from the University of Santo Tomas and a Bachelor of Laws from San Beda College. He has a diploma in Real Estate Management from the Consortium of De La Salle and Chamber of Real Estate & Builders’ Association, Inc. He is a Licensed Real Estate Broker and Real Estate Appraiser (sixth placer) both under the Professional Regulation Commission. He has been a consultant to the Legal Department of the Quezon City Government for more than six years. Anton specializes in Criminal and Civil Litigation as well as Labor and Real Estate Laws.

Gladys Estandian took up BS Accountancy at St. Louis University in Baguio. She is an intercompany accountant in Chevron Holdings Inc., focusing on accounting and billing.

Edwin J. Falcotelo is a network specialist with 12 years of experience in the telecommunication industry. He now works for Verizon as Lead Engineer under the Enterprise Solution Organization. He is a certified Project Management Professional. Moreover, Edwin has completed two graduate diploma courses from the Ateneo Graduate School of Business-Center for Continuing Education, Leadership and Management Development Program (Mini-MBA) and Applied Project Management.

Engr. Rosalie Battung-Formento earned her degree in Bachelor of Science in Chemical Engineering from the University of the Philippines-Los Baños in 1999. She recently obtained her Master of Energy degree at the University of Auckland, New Zealand, with First Class Honors. She completed her academic units on her MS in Materials Science and Engineering at UP Diliman. She joined DOST in 2002; in the past three years, she was the Assistant Regional Director for Technology Operations of DOST-NCR. Rosalie manages various energy- and environment-related projects and the programs of the Metropolitan Manila Industry and Energy Research and Development Consortium–Energy sector, and leads the technology transfer of various DOST-generated waste management technologies.

Eliza T. Guades is an Accounting Officer III at the Central Accounting Office of Ateneo de Manila University. She obtained her Bachelor of Science in Accountancy degree from Polytechnic University of the Philippines and passed the CPA Licensure Exam.


Dong “Kimberley” Haiyan graduated from Henan University of Science and Technology in China. She majored in International Economics and Trade and received her Bachelor degree in Economics in 2007. She worked at Yinlu Bicol Mining Corp. from 2007 to 2011 as Executive Secretary to the President.

Evangeline P.Y. Ho graduated with a degree in B.S. in Information Systems from Deakin University Melbourne, Australia. A Manila-based Singaporean citizen, Evangeline has eight years of experience in Sales & Marketing and Event Management. She worked for four years at Koelnmesse Singapore, which is one of the world’s largest trade fair companies, organizing annually more than 100 trade fairs, conferences, and events worldwide. She was the Regional Sales Manager in charge of sales and developments with project management responsibilities in Asia. She previously worked at Moevenpick Hotels & Resorts in Dubai in the areas of Training and Quality. She is fluent in English and Mandarin, conversant in Bahasa, Hokkien, and Teochew, and has a fair understanding of German and French.

Deanna Marie A. Lansangan is the Head of the Training Services Group of the Ateneo Graduate School of Business-Center for Continuing Education. A Registered Nutritionist-Dietician (RND), Deanna graduated with a BS degree in Nutrition and Dietetics from the University of Santo Tomas. She has units in MA in Public Administration from UP Diliman. She worked as a Training Manager at the Advocate of Philippine Fair Trade, Inc., a non-government organization funded by the European Union, the UK Government, and CORDAID of the Netherlands that seeks to enhance the entrepreneurship of the food and handicraft producers in the country.

Ricky Lascano has a BS Accounting degree from Guagua National Colleges and is currently working as a fixed assets analyst at Chevron Holdings Inc.

Atty. Anita C. Mapalo graduated cum laude from UP Diliman, with a degree in B.A. Philosophy. She proceeded to take up Law in San Beda College and completed the course at the Ateneo de Cagayan-Xavier University. She has considerable experience in both litigation and contract management, having served the Department of Justice through the Public Attorney’s Office for seven years and the Procurement Unit of San Miguel Pure Foods Company (SMPFC). She currently handles the procurement of local and domestic freight and logistics services, and manages the Import Services Group of SMPFC.

Gladys Maliwat-Olano completed Accountancy at the Pamantasan ng Lungsod ng Maynila. She had 12 years of experience in Finance with Dao Heng Bank, Cosmos Bottling, Coca-Cola Bottling, and JG Summit Holdings. She is a Senior Manager for Finance at ACS of the Philippines, a Xerox Company.

Arleen M. Pacheco heads the Trade Marketing and Sales Administration Group of Fly Ace Corporation, makers of category-leading food brands, such as Jolly Canola Oil, Claro Palm Oil, Goodlife Noodles, Dona Elena Olive Oil, and Jolly Cow Fresh Milk. She has held various Sales and Brand Management positions in her past companies, Splash Corporation and Big E Food Corporation (makers of Lemon Square brand).


Irene Palad graduated from the University of the Philippines-Diliman in 2005. Irene is a Business Development Executive at SGS (Société Générale de Surveillance) Philippines, Inc. She is tasked to expand SGS services for the Philippines and Guam through the identification of client needs, and packaging SGS services that would best fulfill customer requirements, objectives, and program. She is concurrently a trainor and second-party auditor on food safety.

Margaret P. Samson graduated from Sta. Isabel College, Manila, with a degree in Bachelor of Science in Accountancy. She has 12 years’ work experience with Andersen Consulting, EVA Airways Corporation, Deutsche Bank Knowledge Services, and currently with Accenture, Inc.

Analyn Teodosio is a Certified Public Accountant. She graduated with a degree in BS in Accountancy, magna cum laude, from Colegio de San Agustin, Bacolod City, and was named The Most Outstanding Accounting Graduate by PICPA and Pricewaterhouse. Ana works at Century Pacific Group of Companies–Meat Business, manufacturer of the Argentina corned meat brands.

Cong. Irwin Tieng is on his second term as a Representative of Buhay Party-List. His legislations focus on the promotion of life and the welfare of persons with disabilities. Among his famous legislations is the Anti-Photo and Video Voyeurism Act, otherwise known as the Cyber-Boso Law. He has been awarded as the most consistent outstanding congressman for 2010. In addition to his sterling record is his commissionship as reserve officer of the Philippine Marines, Navy, and AFP with a rank of Lieutenant Colonel. Aside from his dedication to government service, Irwin is also engaged in running various family-owned companies. He completed his Bachelor Degree in Business Administration at De La Salle-College of Saint Benilde.

Arthur Roy Villones has more than 17 years of diverse industry experience in engineering, information technology, banking services, and the academe. He has worked with a number of multinational organizations, such as Intel, IBM, J.P. Morgan Chase & Co., and Citibank, as well as local institutions, such as J.G. Summit Holdings and the University of San Carlos. His latest position was VP for Solution and Support at Citibank, N.A. (Philippines). A certified Project Management Professional, Art teaches Project Management. He has a degree in Mechanical Engineering from the University of San Carlos, Cebu City, and a graduate certificate for MS in Mechanical Engineering from the University of the Philippines-Diliman.

Mary Ann M. Viloria is the Country Marketing Manager of EMC Philippines. With more than 10 years of marketing experience, Ann has an extensive product marketing background from Microsoft Philippines where she led the marketing business of Microsoft Dynamics ERP and CRM while encompassing product management, channels, PR, and events management. Prior to joining Microsoft, Ann established her own events company, V and M Creativemedia Marketing. She earned her Bachelor of Science in Business Administration, Major in Marketing from the Pamantasan ng Lungsod ng Maynila.

Mayelle C. Wycoco is an entrepreneur. She is the owner of Sona Spa, a wellness center located in Tanauan, Batangas. She likewise works as Area Sales Manager for Alveo Land, a subsidiary company of Ayala Land. She has a degree in B.S. Food Technology from the University of Santo Tomas.
