SPARTA’s SPCapstone001: Data Analyst Capstone Course: CONCEPT PAPER for Covid-19 Business Impact

 TRUSTING IN DIGITAL PIVOT FOR BUSINESS RECOVERY

INTRODUCTION

Background

Doctors are more concerned with COVID-19. Unlike Spanish Flu, there are asymptomatic carriers of the disease. That one detail makes it harder to mitigate the spread of the virus by simply taking temperatures, (Amenabar, 2020). On that note, it is hard to foretell when we can live without the fear of exposing ourselves to COVID-19 virus and its variants. This concept paper builds upon insights from various firms who commenced surveys in the businesses and to strategically focus on the economic problem caused by this pandemic.

Need for this Study

Time is a luxury that all country leaders don’t have. Making the right decisions not only to health factors but also the economic risks will be vital in the recovery of the Philippines and its people. The concept will identify the relationship between demand and supply factor in job security, effective rollout of the vaccine and digital solutions across businesses.

Problem Statement

Does pivoting digitally alone can help to recover business during pandemic?

Objective

The research paper seeks to verify the relationship of economic shock and consumer’s behavior in spending. The second objective is to determine the influence of upskilling and digitalization of firms in gaining competitiveness in this time of pandemic.

LITERATURE REVIEW

COVID-19 and Its Unprecedented Crisis

From 1918’s Spanish Flu pandemic, the American Public Health Association (APHA) stated that mixing of bodies and sharing of breath in crowded rooms, was dangerous (Billings, 1997). The lockdown aims to reduce the transmission of the virus by limiting contact between people. Thus, officials all over the world strictly implement different kinds of confinement and mitigation measures like social distancing. However, this measure implicated a profound impact not only socially but also created an unprecedented economic impact.

GDP plunged to negative 9.5 which is the lowest since 1980 and unemployment rate doubled from 5.1 of 2019 to 10.4%.

Figure 1 — Source: International Monetray Fund, 2021

The main contributors to the decline were: Construction: -24.2 percent
Other Services: -38.0 percent
Real Estate and Ownership of Dwellings: -13.2 percent

Figure 2 — Source: Philippine Statistics Authority, 2021

Economic Shock of Lockdown

Economy works when money moves and during lockdown people stopped moving and so is the money. Philippine Government borrows, allotted and spend trillions of pesos to mitigate the supply- demand shocks of COVID-19. A supply shock is when public health authorities and employers prevent service workers from doing their jobs and a demand shock, on the other hand, is something that reduces consumers’ ability or willingness to purchases goods and services at given prices (Brinca, Duarte, & e Castro, 2020).

More than Php200-billion were allotted by the government to cover, sustain and fund the declines as seen above in figure 2, procurement, trainings and projects in response of the COVID-19 pandemic under Bayanihan II project.

Figure 3 — Source: Department of Budget and Management, 2021

Vaccine. Job. New Reality.

Vaccine hesitancy is not only an arising problem in Philippines but also declared one of the ten threats to global health by WHO (World Health Organization, 2019). Spreading correct information such as efficacy, side effects and the benefit of the vaccine to face the new normal helped the Philippine Government in doubling the people vaccinated on May 2021.

Figure 4 — Source: World Health Organization, 2021

Successfully educating the citizens that vaccine is not the 100% solution in fight against actively mutating COVID virus and due to uncertainty when this pandemic will end, Philippine consumers became very conservative with their buying habits despite government injected funds for citizens to spend (Figure 2).

Cautious spending resulted a demand and supply shock which causes job loss and insecurity. Based on Inter-Agency Task Force for the Management of Emerging Infectious Diseases, 2020 report, 50% of private companies’ workers experienced decline in income and 80% of which is due to job loss.

Figure 5 — Source: Inter-Agency Task Force for the Management of Emerging Infectious Disease, 2020

Due to left and right retrenchment, Filipinos are looking for ways to acquire extra income and/or serve their clients contactless. And the outcome is digitalization. This is the latest competition now in the upcoming new reality of the world. Consumers are already aware of food delivery services and shopping online since pre-pandemic but how about the telecommuting or working from home?

ASIAN DEVELOPMENT BANK SURVEY, 2020 resulted that 57.3% of micro, 12% of small and 11.1% of medium firms’ workers are not possible to work from home. The same survey shows that financial assistance and tax incentives may help their businesses to adopt the new normal.

Figure 6 — Source: Asian Development Bank, 2020

Digitalisation to Solve the Problem

Consumer confidence is expected to remain low even after the ECQ is lifted. Expectation of a worse family income situation is especially pronounced among the low-income group (Inter-Agency Task Force for the Management of Emerging Infectious Diseases, 2020). And that is a significant economic problem.

Digitalisation for businesses will not only attract consumers to buy their goods or services but it will generate a well of data that can help them dive and perform the analytics. Analytics will solve in managing of inventories and will also help to know more about erratic decision changes of Filipinos due to this never-ending battle cause of pandemic.

METHODOLOGY

Quantitative

In cross-sectional design, we will use descriptive and categorical analysis and prescriptive analysis will be utilized to determine the next policies to be developed by the government.

Qualitative

For qualitative data it will undergo transcriptions and reported on themes.

REFERENCES

1. Amenabar, T. (4 September, 2020). The Washington Post. Retrieved from The Washington Post:

https://www.washingtonpost.com/history/2020/09/01/1918-flu-pandemic-end/

2. Billings, M. (June, 1997). The Influenza Pandemic of 1918. Retrieved from Stanford University: https://virus.stanford.edu/uda/fluresponse.html

3. Brinca, B., Duarte, J. B., & e Castro, M. (17 June, 2020). Decomposing demand and supply shocks during COVID-19. Retrieved from VoxEU.org : https://voxeu.org/article/decomposing-demand-and-supply-shocks- during-covid-19

4. Department of Budget and Management. (31 May, 2021). COVID-19 Budget Utilization Reports as of May 31, 2021. Retrieved from Department of Budget and Management: https://www.dbm.gov.ph/index.php/programs-projects/status-of-covid-19-releases#bayanihan-2

5. International Monetary Fund. (April, 2021). International Monetary Fund. Retrieved from World Economic Outlook (April 2021): https://www.imf.org/external/datamapper/datasets

6. Inter-Agency Task Force for the Management of Emerging Infectious Diseases. (2020). WE RECOVER AS ONE. Manila: National Economic and Development Authority (NEDA).

7. World Health Organization. (2019). Top Ten Threats to Global Health in 2019. Retrieved from World Health Organization: https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019

8. World Health Organization. (2021). COVID-19 Explorer. Retrieved from World Health Organization: https://worldhealthorg.shinyapps.io/covid/

9. ASIAN DEVELOPMENT BANK. (17 July, 2020). ADB Philippine Enterprise Survey on COVID-19 Impact. Retrieved from ADB Data Library: https://data.adb.org/dataset/adb-philippine-enterprise-survey-covid-19-impact

10. Philippine Statistics Authority. (11 May, 2021). National Accounts. Retrieved from Republic of the Philippines — Philippine Statistics Authority: https://psa.gov.ph/national-accounts/base-2018/estimates

Evolution of Logistics Analyst

Antoine-Henri, Baron Jomini a French officer defined logistics (1838; Summary of the Art of War, 1868) as “the practical art of moving armies,” The word clearly describes the movement of services of people and supplies to provide requirements in winning a war. Now, the word logistics is widely use by companies in defining their process of managing and planning goods and resources to serve its purpose.

Evolution of Logistics Analyst by Wilma Lapuz

The Progressing Role in Logistics:

1. Logistics Customer Service — this first line of defence in logistics. They are the one handling calls and emails. This role not only supports import/export/local department but also ad hoc jobs required by accounts or sales. They are the ones also responsible in general filing or other administrative duties.

2. Logistics Officer — this position ensures to support international/local order fulfilment and deliveries in a timely and efficiently manner. Sounds easy? Think again. They are the ones responsible in stock inventory and data entry, sometimes not only their own companies but some of the clients too.

3.Logistics Analyst — due to automation and rapid change of technology, you may notice some of the online job postings of companies are combining the two above roles and adding up analytics expertise on it.

Company Benefits Having a Logistics Analyst
Data is the New GOLD. In that case, logistics companies even freight forwarding businesses are seating unknowingly in a pot of gold.

So how logistics or freight forwarding companies can take advantage and stay afloat in the Fourth Industrial Revolution? If you will observe companies like DHL or Bollore they are actively hiring analysts now. Why? Because companies do not want to run their companies through gut-feel or crystal ball. Data companies like Google proved that many business will be more productive if decisions will be made using data.

Imagine, using the historical data of your clients you can anticipate the volume of materials they needed, from which supplier and what mode of transport. You can provide them quotations even if they are not asking. Wink-wink! That’s a customised customer service.

What else? Do you know why you keep on losing in bidding to get the project? Do you remember why are you winning? In aggregating your data you can anticipate the moves of your competitors.

Remember building relationship with your client and suddenly no more business calls from them? As a salesperson, will you meet them for lunch just to know what went wrong? Well, have you taken care of the KPIs of their stakeholder and provided customer experience? Remember customer experience now is valuable as getting your service on a lower price.

Of course, logistics analysts monitors not only the KPIs but also the procedures and practices to identify developments in planning and execution.

Do you need a Logistics Analyst even you are a Small/Local Company?
I want you to answer this question, are you competing with multinational company by giving quotations to your perspective importer/exporter? How can you compete if they their logistics analysts research the expectations of the clients in a certain project? Remember, some companies are willing to pay the price as long as you can meet their deadlines and KPIs.

Another thing to think of.. do you know that many start-ups that’s armed with data are now entering the logistics industry? They are providing quotes more quickly and offers an agile bid on clients. You know that in logistics/freight forwarding time is of the essence.

The line of difference in Logistics/Freight Forwarding industry is getting thinner and thinner. Competition is evolving fundamentally and companies should always examine whether they have the capacity and capabilities to change and compete.

Will you emerge and develop solutions to move forward and upward or you will just stay where you are because.. well I know, it is tough to change.

Exploring Supply Chain Dataset

As I have written on my past posts, supply chain / logistics companies are one of the wells of data and most of them are not aware of it.

For this post, I will scratch some parts of data cleaning and apply some descriptive analysis on the data I have found in Kaggle.

Here are the usual steps I am taking in cleaning the data.

As practised, these are the main libraries that I usually use in Python and let’s import these now.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Now, let’s call in my downloaded dataset titled: DataCoSupplyChainDataset.csv. It is very important to know too the description of every column name we have in the csv and it is very helpful that we found a dataset that also has this feature.

df = pd.read_csv('DataCoSupplyChainDataset.csv')
info = pd.read_csv('DescriptionDataCoSupplyChain.csv')
pd.set_option('max_colwidth', 1)
info

Now let’s explore our data.

pd.set_option('display.max_columns', None)
df.head()
df.info()

https://github.com/WilmaLapuz/Portfolio/blob/main/SUPPLYCHAIN.ipynb

Have you noticed what is wrong on the datatype on above column? Shipping date (DateOrders) is in object. Let’s convert it to a proper data type.

df['shipping date (DateOrders)'] = pd.to_datetime(df['shipping date (DateOrders)'], format='%m/%d/%Y %H:%M')
df['order date (DateOrders)'] = pd.to_datetime(df['shipping date (DateOrders)'], format='%m/%d/%Y %H:%M')
df.info()

Let’s further simplify this data.

If you will check the values on each row, you will find that there are somewhat duplicates of columns. To see what other columns we need to drop, let’s check what columns consist equal values.

df['Customer Id'].equals(df['Order Customer Id'])
df['Benefit per order'].equals(df['Order Profit Per Order'])
df['Order Item Cardprod Id'].equals(df['Product Card Id'])

Drop all sensitive and duplicate/redundant variables to clean our data.

df.drop([
    'Benefit per order',
    'Customer Email', 
    'Customer Password',
    'Product Image',
    'Order Zipcode',
    'Product Description',
    'Order Item Cardprod Id',
    'Order Customer Id'
], axis = True, inplace = True)

df.head()

These are the few variables that caught my eyes:

1. Type
2. Late_delivery_risk
3. Customer State
4. Order Country
5. Order Region
6. Order Status
7. Product Name
8. Shipping Mode

Using the above list of variables and describe function, here are the questions that may a supply chain want to be known.

1. What type of payment that will be likely to be fraud? From what country? What product?

fraud=df[df['Order Status']=='SUSPECTED_FRAUD']
fraud_payment=fraud['Type'].value_counts().nlargest().plot.bar(figsize=(20,8), title="Payment Type With Suspected Fraud Cases")fraud_ordercountry=fraud['Order Country'].value_counts().nlargest().sort_values(ascending=False).plot.bar(figsize=(20,8), title="Top 5 Countries With Suspected Fraud Case")fraud_ordercountry=fraud['Product Name'].value_counts().nlargest().sort_values(ascending=True).plot.barh(figsize=(20,8), title="Top 5 Products With Suspected Fraud Case"

https://github.com/WilmaLapuz/Portfolio/blob/main/SUPPLYCHAIN.ipynb

2. What year has the most oder shipment from the state of Illinois?

df['year'] = pd.DatetimeIndex(df['order date (DateOrders)']).year
IL=df[df['Customer State']=='IL']
IL['year'].value_counts().plot.bar(figsize=(20,8), title="Illinois Record of Shipments")

3. What shipping mode and region that has a higher delivery risk?

LATE=df[df['Delivery Status'] == 'Late delivery']
LATE['Shipping Mode'].value_counts().plot.bar(figsize=(20,8), title="Shiping Mode with Risk of Late Delivery")

https://github.com/WilmaLapuz/Portfolio/blob/main/SUPPLYCHAIN.ipynb

This is only the start of many things what we can uncover using this supply chain dataset. I will do my best to use this dataset for my other upcoming projects.