Posts by OCDex Administrator

Uncovering the Most Vulnerable in Times of Crisis: Analyzing Procurement Capacity Index with Multi-Criteria Decision-Analysis

Featuring a published paper by one of OCDex’ fellows, Engr. John Raymond Barajas of Bicol University College of Engineering


This paper presents a multi-criterion decision analysis approach to developing a procurement capacity index for local government units (LGUs) in the Philippines. The index serves to assess the resilience of LGUs in times of crisis, particularly in the context of the COVID-19 pandemic. This study utilized two open datasets published by the Philippine government from January to June 2020, and identified five criteria for the procurement capacity index: total approved budget of the contract, internal revenue allotment, number of awarded tenders, number of tenders posted, and fund utilization rate. This study then employed the criterion impact loss (CILOS) method to determine the weight vectors of the identified set of criteria, and calculate the index as a weighted sum based on these vectors. This study found that the fund utilization rate and internal revenue allotment are the two most important criteria for determining the capacity of an LGU to secure goods or services during a crisis such as the pandemic. This insight is consistent with observations drawn from use cases in the US, UK, and Canada as revealed in reviewed literature. Results also revealed that LGUs can be categorized into three clusters based on their procurement capacities: low, medium, and high. Moreover, the developed index facilitated the ranking of LGUs according to their procurement capacity, revealing that LGUs located in Regions II, III, VI, VII, VIII, and X have insufficient budget allocation, thus strongly suggesting urgent intervention from the national government. Overall, the developed index can serve as a valuable decision aid tool to assist the government in identifying LGUs that need additional support to procure resources or services required to mitigate the consequences of a crisis.

Reach out to the author (or to us) for the full paper:

Public Data Analytics: Community Problem Solving

VOLUME 2 of the OCDex Public Data Analytics Series

Data science and analytics has demonstrated its power in informing decision-making and problem-solving. Data can reveal trends and insights that would have otherwise been obscured. It can give decision-makers key information needed to craft effective and optimal solutions to organizational problems. It can help predict potential bottlenecks and challenges, so that organizations may come prepared when it happens. Data science and analytics is a sought out
skill in the digital age.

The Covid-19 pandemic and its resulting limitations on mobility has forced many transactions and communications to migrate from the physical space to the digital space. This sudden global digitalization resulted in an increase in data produced and a subsequent increase in the potential game-changing insights that these data may be hiding.

While many in the private sector have been seen leveraging the power of data for business insights and maximization of revenue, the public sector is yet to catch up in terms of digitalization and data utilization, especially in developing countries. The power of data would especially help communities and local governments in coming up with efficient, effective, and inclusive policies and solutions to problems.

The aim of the 2022 OCDex project run is to bring data scientists and analysts together, and demonstrate how analysis of government data can be used to help solve problems in local communities. The project aims to demonstrate how it can help inform local policymaking and project planning, and how citizens and researchers can participate and help their respective local government units in overcoming community challenges hand-in-hand. This handbook hopes to convince local governments and authorities to invest in good data housekeeping and integrate data science and analytics into their decision-making.

This handbook features how academics and data enthusiasts used public data to help inform solutions to various community problems such as healthcare, inclusivity, and accessibility for persons with disabilities, fairness, and transparency in public procurement, and ensuring enough supply of utilities. Lastly, this handbook presents a replicable model of cooperation between local governments and their local researchers and data enthusiasts toward the effective use of data science and analytics for community building.

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Reflections: How analysis of government data can help LGUs, NGAs, and local communities

Author: Roben A. Juanatas (CCIT – National University)

Analysis of government data, in my opinion, has the ability to bring about considerable change for the LGU or community. Local governments are particularly enthusiastic about promoting this new approach to community engagement. Many local governments have begun to see the value of analyzing government data for more open and transparent operations. The study of government data can assist the LGU in staying connected, informed, and up-to-date with their community’s day-to-day operations. 

Furthermore, this trend may aid the LGU in continuing to deliver and make decisions that are in the best interests of the community. It also opens up new possibilities for local government applications, boosts community interest, and can lay the groundwork for new technology innovation and economic progress. Furthermore, data analysis can provide relevant facts, which can serve to bring a community together and empower them to create the future.  

Finally, it is not simply the simplification of the community’s operations and proving progress toward strategic priorities that fosters community confidence through data analysis. It is important in a variety of government areas, including education, employment, manufacturing, agriculture, and criminality, to name a few. The information gathered in LGU can be used to develop strategic policies and provide insights to make better judgments. 

This article is the author’s reflection on the insight gained from the recently concluded OCDex 2022 Public Data Analytics Fellowship Trainings.

For more information about the article, please reach out to the author: or Layertech labs support at

Data Analytics a way to solve the crisis in education: A local setting approach

Author: Ramon L. Rodriguez (National University – Manila)

The current pandemic highlights further the problem in the educational sector in the country. Even before the pandemic, the World Bank conducted an assessment of the country’s education system and found that a crisis in education existed. As we know, education is one of the foundations for the success of every community. Can we develop strategies to address this crisis using data analytics in the local settings? The LGU should explore this strategy to develop a concrete solution based on data. Decisions of developing new programs should be data-driven, especially in the local government settings.

If data-driven decisions are highly utilized, many improvements can be made to every LGU, including fiscal adequacy. Suppose the LGU has updated data on the profile of students, households, and problems encountered by every student. In that case, it can be a basis for a new strategy for addressing issues in education. Say, for instance, what support is needed by certain schools and what resources. The data may be available, but it is important to structure it and become ready for analysis. At this point, data analytics plays a significant role.

On the other hand, data analytics can also be used to match the needs of every LGU in terms of job availability on a local and global scale. The matching gap in skills needed in the industry has been highlighted for years, but actions to address this are still not concrete. However, different agencies have undertaken several initiatives but not enough to solve the problem. Almost every LGU in the Philippines has community colleges where locals can enroll. The intention of establishing community colleges is anchored on the principle that education is one of the backbones of community development. Having a community college is a great idea, but we have to make sure the programs offered are sustainable and benefit the family. We also want to avoid underemployment scenarios wherein graduates are employed not in their respective specialization. If we have data on the graduates and the skills needed in the local, national and global, then it is easy for us to match and decide what programs can be offered by schools. The budget for education is allocated per LGU. At the national level, almost 16% of the national budget is for education, but still, we have a crisis in education. The need to use data analytics in addressing issues in the education sector is a must.

This article is the author’s reflection on the insight gained from the recently concluded OCDex 2022 Public Data Analytics Fellowship Trainings.

For more information about the article, please reach out to the author: or Layertech labs support at

Reflections: Data to help Cities and Municipalities

Author: Nico O. Aspra (Bicol University)

In our generation today, technology has rapidly consumed everyone. It has rooted itself in the core foundation of modern society. With various age groups, even toddlers, access to technology is easy. As such, it has enabled agencies or companies to gather data from their users. With this accessible data, inevitably, advertisers specifically target consumers by their liking through the use of available information to gain more profit. With this in mind, this has revolutionized the advertising world from its traditional delivery to tailor-fitted advertisements. Policymakers can also utilize this strategy to tailor-fit the community’s specific needs. Local government units can benefit through data analytics by collecting and analyzing municipal- or city-wide information regarding a certain topic and thereby utilize this data to address problems or issues accordingly. Before the data even reaches the regional or national level, it can already be organized at the local level. Consequently, data will be more systematic and available at all levels of government, and if the data permits, challenges, and problems can be more easily addressed.

This article is the author’s reflection on the insight gained from the recently concluded OCDex 2022 Public Data Analytics Fellowship Trainings.

For more information about the article, please reach out to the author: or Layertech labs support at

e-Participation: A Road to Strategic Decision-Making Using Data-Driven Approach

Author: Lany L. Maceda (Bicol University)

Data has a very powerful role to play. Analyzing data is very important nowadays. We are no longer in the era of simply digitization, it now allows any government agencies both at the local and national levels to get more data to have an evidence-gathering basis. A data-driven approach could mean that the organization uses facts and metrics as its basis in forecasting, in aid of legislation or policymaking. A strategic decision based on data analysis could result in a decision that is in consonance with the organizational initiatives and objectives with an end goal of better serving the community.

When the Local Government Units/National Government Agencies realize the importance of data, this denotes that they empowered the community members for better decision making, every day. A data-driven decision is objective and rational since it undergoes systematic and scientific analysis of data.

e-Participation fosters civic engagement and open participatory governance with the use of Information and Communication Technologies (ICTs). This means that data-driven citizen feedback could be greatly needed improve the basic services of the government agencies. The said approach could address issues like Anti Red-Tape Authority (ARTA), transparency, and corruption in the bureaucracy, local and national levels.

his article is the author’s reflection on the insight gained from the recently concluded OCDex 2022 Public Data Analytics Fellowship Trainings.

For more information about the article, please reach out to the author: or Layertech labs support at

Reflections: How do you think data and analytics can help your LGU, NGAs, and/or community and how do you propose ways to do it?

Author: Gabriel Avelino Sampedro (National University)

In the age of digitalization, everything is done online. Most of the data collected from people are stored in the cloud entities, and the amount of data stored there exponentially increases daily. It can be challenging to organize, manage, and obtain insights from various sizes and varieties of data. One solution for managing massive amounts of data is implementing self-serving, automated solutions to daily operations such as data input and reporting, which can improve efficiency and increase the efficacy of the mission. Data analytics plays a significant role in obtaining insights from our data in managing data. Data analysis is the process of studying data by using analytical or statistical methods in order to discover important information. The goal of this process is to conclude the data. The data analytics process helps us develop a clearer picture of the current situation for decision-makers to provide data-driven solutions to problems. The conclusive findings may come in a summary or an image, like a chart or graph. Showing data visually, which makes the job simpler, is known as data visualization. Data analysis may be done using various methods, including data mining, text analytics, and business intelligence. Data analysts use charts and graphs rather than tables or databases to convey data. It simplifies complex facts and makes it easier for the eyes to take in the information. Furthermore, data analytics may even assist in preventing fraud, waste, and abuse and helping with staffing shortages.

Using the knowledge obtained from the OCDex training, one possible application of using data analytics is for predicting arrival times of mass transportation systems. One of the significant issues in the country is the poor transportation system we have. Train stations encounter very long lines, and not everyone in line will immediately be served. One of the main issues in the system is the uncertainty of arrival times. People would go to train stations randomly, hoping to ride the train. The long wait can be pretty frustrating, and plans made throughout the day can be ruined. Although it will not entirely solve the whole issue, one way to solve this is to develop a system for predicting the arrival times of trains. In addition, providing people with data on the number of people on the platform (through the data from gate entries) can help people make data-driven decisions about their travel. The purpose of the system is to decongest some modes of transport and allow people to consider other options. The solution would use data from the station platform and logs of train operators to develop a machine learning-based model to improve this process.

This article is the author’s reflection on the insight gained from the recently concluded OCDex 2022 Public Data Analytics Fellowship Trainings.

For more information about the article, please reach out to the author: or Layertech labs support at

Predicting Public Procurement Irregularities in the COVID-19 Response of Local Government Units (LGUs) in the Philippines

Authors: Barajas, J.R., Aspra, N., Gealone, P.J., Lucero, A., Padua, O., Ramos, M.

Motivated by ensuring transparency, fairness, and efficiency in public procurement at the time of the COVID-19 pandemic, a team of engineers and faculty from Bicol Region (Region 5) in the Philippines collected, digitized, and analyzed public procurement data to inform the COVID-19 response of select Local Governments in the country.

Highlights of the Report:

• On average, only 2 out of 10 LGU contracts have been awarded in

• For every Php1 spent, approximately Php1 remains unspent in
the procurement of goods and services made by LGUs.

• A total of Php481 billion were distributed across all LGUs in the country
for 2020 but only 10% of this budget was allocated for the procurement
of drugs and medicines. 40% of this budget went to construction

• Excluding LGU contracts not posted in the PhilGEPS website, only Php10
billion (2.16% of the total LGU budget) was allocated for COVID-
19 related contracts.

• An equivalent amount of Php720 million was potentially lost from 786
LGU contracts flagged as irregular.

• Audit findings for LGUs were primarily centered on directing accountable
officers to comply to documentary requirements mandated by existing
circulars, memorandums, and Philippine laws.

• A logistic regression model with an accuracy of 91.29% was developed
to identify contracts that are potentially irregular.

Short Summary of the Report:

From examination of 296,220 local government unit contracts, this project was able to develop a logistic regression model capable of predicting potentially irregular LGU contracts posted on the PhilGEPS website for the fiscal year 2020 at an accuracy of 91.29%. Validation of the model using metrics derived from the confusion matrix revealed that the developed model had a recall score of 1.0 and a precision score of 0.029. While the precision of the model may be
low, the high recall score is deemed more important in this use-case since it would be more costly for an LGU to miss out on irregular contracts. Overall, the developed prediction model is seen to be highly beneficial as a decision
support tool for LGUs since this could potentially narrow down the number of awarded LGU contracts to be legally reviewed resulting in a faster turnover of review cycles conducted within a given fiscal year.

The team’s collected datasets are available for download in the OCDex open data portal, attribution to the authors and contributors is required for use.


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Are you interested in this report? Do reach out to us at so that we can directly connect you with the authors, as well as the documentations they submitted.

Using Government Procurement Data to Influence Bidding Strategy of Local Contractors

Using Philippine Government E-Procurement System (PhilGEPS) data of 19 local government units from years 2016-2018, four out of six key information requirements of local contractors and bidders were answered. Reducing the cost of acquiring information led to increase in bidding confidence, and a more concrete bidding and logistics strategy.

The paper was presented in the 2020 International Symposium on Technology and Society, hosted by the University of Arizona, Tempe, AZ, USA.

This paper explores how open government data can be used to influence suppliers and contractors to participate in public bidding, by minimizing the cost of information acquisition that influence bidding decision and strategy. The researcher conducted data scraping, pre-processing, and exploratory techniques on official procurement data of nineteen (19) Local Government Units (LGUs) in Albay, Philippines, and triangulated results with local government actors, contractors, and businessmen.

The study showed that the existing procurement data mandated by the Philippine procurement law to be released can answer 4 out of 6 key information requirements of bidders, thereby increasing overall bidding confidence. Using dashboards that regularly collate, preprocess, process, and visualize these data can help local procurement competitivenes.

For more information, the paper’s DOI is: 10.1109/ISTAS50296.2020.9462199

Automated Process Mining in Philippine Procurement Event Logs – Automated Process Flow Detection

“Process mining applies data science to discover, validate and improve workflows . By combining data mining and process analytics, organizations can mine log data from their information systems to understand the performance of their processes, revealing bottlenecks and other areas of improvement. “

(IBM, 2021).

A relatively new technology, process mining, is getting more and more popular due to increases and improvements in data production and management, and cloud technology. Companies and organization that use process mining can “see” the “current state” of their process workflow, and rapidly evaluate compliance to the “target” flows.

“Companies that adopt an incremental improvement approach, on the other hand, tend to spend too much time on analyzing the “as-is.” In addition, their current process analysis is frequently based on interviews and sticky notes, which executives sometimes regard as overly subjective and treat with justifiable skepticism. “

(Davenport and Spanyi, 2019)

Public procurement is a business process that is prone to corruption and administrative inefficiency, affecting the quality of service delivery to the public.  Using state university’s three-year procurement data as a sample, a paper published and presented by Layertech Labs in the 7th International Conference on Behavioural and Social Computing (BESC) – Bournemouth, United Kingdom, explores the use of process mining on publicly available procurement data to discover the underlying structure of procurement processes of government entities in the Philippines, check for conformance with the prescribed process in the procurement law, and identify potentially problematic nodes. 

In the paper, event logs were generated from official public procurement data and mined with heuristics-based process mining algorithm, using free, open-sourced tools. The discovered processes revealed a concept drift in publication of contract award, a point for inspection and improvement for the agencies involved.

Using the process miner, analysts were able to immediately detect and visualize drifts in the prescribed process, and pinpoint the specific process points (and actors) involved. Not only this technology will help speed up analysis, but the system will also allow LGUs, auditors, and even stakeholders to constantly monitor the state of their public procurement

To read the FULL PAPER (DOI: 10.1109/BESC51023.2020.9348306), go to IEEEExplore:

Interested to apply this tool in your business operations? Shoot us a message!

Follow OCDEX for more updates about this miner!


IBM (2021) What is Process Mining? Available at: (Accessed: 25 May 2021).

Davenport and Spanyi (2019) What is Process Mining and Why Should Companies do it? Available at: (Accessed: 25 May 2021).