All India Petrol Pump Demand Assessment Study for Petrol and Diesel Consumption

Client

A leading infrastructure advisory organization wanted to understand the all-India sectoral demand for diesel and petrol across fuel retail outlets. The study was conducted for the Ministry of Petroleum and required large-scale observation across petrol pumps operated by major oil marketing companies, including BPCL, HPCL, and IOCL. The research aimed to evaluate current consumption patterns and support demand estimation for the next five years. This fuel demand assessment helped generate structured fuel usage insights for national-level planning.

Objective of the Study

The primary objective of the study was to understand all-India sectoral demand for diesel and petrol across selected petrol pumps.

Key objectives included:

• Measuring petrol and diesel consumption across different outlet categories through petrol and diesel consumption research in India

• Understanding vehicle-wise fuel refilling behaviour and overall vehicle fuel behaviour

• Capturing fuel quantity by vehicle type through vehicle wise petrol consumption analysis in India

• Evaluating demand patterns across metros, Tier 1, Tier 2, Tier 3, Tier 4, small towns, and highways

• Assessing petrol and diesel demand for the next five years through petrol demand forecasting and diesel demand estimation

• Gathering feedback from petrol pump managers and owners

• Supporting weekly data delivery for demand analysis and monitoring

Research Methodology

Market Xcel conducted a large-scale petrol pump observation study across selected BPCL, HPCL, and IOCL outlets. The client provided a list of 3,000 petrol pumps per quarter, categorized into A, B, C, and D outlet types based on location and traffic characteristics.

Research Approach:
Petrol pump observation and fuel demand assessment

Methodology:
Continuous outlet-level observation with vehicle-type and fuel-quantity recording as part of a large-scale petrol pump observation survey in India

Sample Size:
3,000 petrol pumps per quarter
12,000 outlets covered over one year

Fieldwork Duration:
October 2020 to October 2021

Outlet Categorization:

• Category A: Metros and Tier 1 cities

• Category B: Tier 2 and Tier 3 cities

• Category C: Tier 4 cities and small towns

• Category D: Highway-based petrol pumps

Observation Duration:

• Category A, B, and C outlets: 12-hour observation from 8:00 AM to 8:00 PM

• Category D outlets: 24-hour observation from 8:00 AM to 8:00 AM the next day

Key Information Captured:

• Type of vehicle visiting the pump
• Quantity of fuel refilled
• Petrol and diesel consumption pattern
• Morning and evening totalizer readings
• Daily outlet-level fuel movement
• Manager / owner feedback
• Weekly data delivery outputs

Planning and Monitoring

The study required strong manpower planning because each petrol pump had to be observed continuously for 7 consecutive days. For 12-hour observation outlets, one interviewer was deployed. For 24-hour highway outlets, one interviewer was deployed for the day shift and another for the night shift.

In high-traffic petrol pumps or outlets with multiple totalizers, Market Xcel deployed 2–3 interviewers even for 12-hour observation to ensure accurate capture of all refilling activity.

The 3,000 quarterly outlets were distributed evenly across 12 weeks, with around 250 outlets covered each week. Category A to D outlets were also distributed evenly across the weeks to maintain balanced coverage. The team tried to ensure that very few outlets were left for the last two weeks of each quarter.

Local district-wise teams were arranged to reduce travel time and cost. Local teams also brought stronger area familiarity, which supported smoother field execution. Interviewers and supervisors with their own conveyance were preferred, especially for better mobility during field monitoring.

Supervisor-led interviews were conducted with petrol pump managers and owners to capture qualitative feedback. A centralized backend team with strong Microsoft Excel knowledge was deployed for data checking, cleaning, and quality monitoring. Given the intensity of the study, the centralized team’s working hours were extended from 8:00 AM to 9:30 PM.

Research Outcome

The study delivered a large-scale view of fuel demand patterns across petrol pumps in India. It helped the client understand vehicle-wise refilling behaviour, outlet-category differences, highway versus city-level demand patterns, and petrol-diesel consumption trends across different regions.

Key outcomes included:

• Coverage of 12,000 petrol pumps over one year

• Vehicle-wise petrol and diesel refilling data

• Outlet-category-wise demand visibility across metros, towns, small cities, and highways

• Daily and weekly fuel consumption analysis

• Totalizer-based validation of observed sales

• Manager and owner feedback for contextual understanding

• Improved data accuracy through DSR validation, attendance checks, GPS checks, and photo-based monitoring

• Structured inputs to support fuel demand forecasting study for petrol pumps and petrol and diesel demand estimation for the next five years

Challenges and Actions Taken

Interviewer Attendance and Timeliness

During client field visits, it was reported that interviewers were sometimes not present at the petrol pump or were not reaching on time.

Action Taken:

Market Xcel introduced a selfie-based attendance mechanism. Interviewers shared selfies with the petrol pump in the background every morning before starting work and every evening before closing fieldwork. Additional selfies were also collected during the day. These photo links were shared with the client during weekly delivery to demonstrate field presence and quality control.

Observed Sales Not Matching Actual Sales

The client raised concerns that captured sales were not matching the actual sales reported by the petrol pump during the 7-day fieldwork period.

Action Taken:

Supervisors were asked to collect photos of Daily Sales Reports maintained by petrol pump managers. The team managed to obtain DSRs for more than 70% of outlets and compared them with observed data before final submission. Where discrepancies were found, the data was corrected or fieldwork was cancelled in cases of large variation.

Initial Delivery Delays

Since this was a new type of study, some decisions had to be taken during fieldwork. Initial deliveries were delayed because data cleaning, photo checking, and deliverable preparation took more time than expected.

Action Taken:

The centralized team started working on weekends as well. All levels of the backend team supported weekend work, with breaks only between the previous week’s delivery and the start of the next week’s fieldwork.

Weekly State-Level Coordination

Handling multiple states every week created coordination challenges between the field and centralized teams.

Action Taken:

Each executive was assigned a maximum of 2–3 states for data analysis. The same executive continued handling the same states throughout the study, which improved coordination and understanding between centralized and field teams.

High Data Volume and Server Issues

The flow of data was much higher than expected. From the second day onward, the server crashed and data synchronization became a major issue.

Action Taken:

The team immediately shifted data from the server to a common drive to ensure smoother uploading and better data movement.

Data Security

The client raised concerns about data security because a large number of people were involved in data handling and cleaning.

Action Taken:

A common drive was created to store and share clean data files. Access was restricted within office premises, helping address data security concerns while preventing system issues caused by heavy datasets.

Task Familiarity and Efficiency

Different tasks were being assigned every week, which made it difficult for team members to build task-specific expertise.

Action Taken:

Specific tasks such as GPS accuracy checks, photo checking, and totalizer reading validation were assigned to separate executives. Repeatedly handling the same task helped executives improve speed, accuracy, and understanding.

Absurd Refilling Figures

In the beginning, some entries showed unrealistic refilling quantities, such as 23 litres for a two-wheeler.

Action Taken:

Maximum capping was introduced for each vehicle type based on practical tank capacities. For example, two-wheeler fuel entry was capped at 17 litres, preventing unrealistic fuel quantities from being entered.

Highway Outlet Vehicle Mix Issues

The client questioned why two-wheelers and cars/taxis appeared in high numbers at highway-based petrol pumps.

Action Taken:

Basic logic checks were introduced by outlet category. City-based Category A and B outlets were expected to show higher numbers of regular vehicles, while highway-based Category D outlets were expected to show higher movement of buses and trucks.

Low Tractor Count in Agriculture-Driven States

The number of tractors reported for refilling was lower than expected in agriculture-rich states.

Action Taken:
From the next delivery onward, state-specific logic checks were introduced. For example, states such as Punjab and Haryana were expected to show higher tractor movement due to their agriculture-driven profile.

Loose Petrol Sale Entries

In the early phase, loose petrol sale entries were received, which the client flagged as problematic because loose petrol sale is not legally permitted.

Action Taken:
The team began closely monitoring such entries from the second week onward. Any reported loose petrol sale was cross-verified with the field team and supported with photos or videos wherever required.

Excel-Based Cleaning Difficulty

Each petrol pump generated a huge number of vehicle-level rows, making Excel-based cleaning difficult and time-consuming.

Action Taken:
Market Xcel created a dedicated Python program exclusively for this study. This reduced the data cleaning time significantly and improved accuracy.

Variation in Sales Across Quarters

There were large variations in sales for the same petrol pump from one quarter to another, without clear explanations initially.

Action Taken:
The team began comparing current round data with previous round data for each outlet in subsequent quarters. Qualitative ground feedback was also included, such as low sales caused by fuel non-availability. This helped reduce client queries.

Frequent Requests for Previous Reports

The client frequently requested earlier reports and datasets, even during later quarters.

Action Taken:
Market Xcel maintained a detailed record of all deliverables throughout the project, ensuring that older reports and datasets could be retrieved and shared whenever required.

Business Impact

The study helped the client build a structured understanding of petrol and diesel demand across India’s fuel retail network. By capturing vehicle-wise refilling behaviour, totalizer readings, outlet category patterns, and weekly demand movement, Market Xcel enabled stronger visibility into fuel consumption across metros, towns, small cities, highways, and agriculture-driven regions.

The research supported demand forecasting for the next five years and strengthened the client’s ability to understand how petrol and diesel usage varies by outlet type, region, vehicle category, and location context. The project also improved field and backend systems through selfie-based attendance, DSR validation, data capping, logic checks, Python-enabled data cleaning, and centralized delivery management.

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