Financial Analysis

SRG Financial & Operational Control Analysis

Reconstructed full financial statements from 153,000+ raw transactions for a multi-stream convenience retail business — then delivered a data-driven expand-or-sell recommendation with 64% signal strength to expand, backed by a DSCR of 2.65× and an asset-based net worth of $473,425.

Python Pandas NumPy Matplotlib Jupyter Notebook HTML / CSS Excel

Skills Demonstrated

Financial Statement Construction Revenue Cross-Validation Inventory Reconciliation Balance Sheet Restatement Business Valuation Expansion Feasibility Analysis Data Pipeline Design Executive Dashboard Design Business Storytelling

March 2026

7 weeks

153,000+ transactions · 17 data sources

English

Executive Summary

A 19-month financial and operational deep-dive into Sola Retail Group, a multi-stream convenience retail business generating $1.9M in revenue across hotspot Wi-Fi, retail merchandise, lottery, and ATM lines. Working from raw POS exports, bank statements, payroll records, and invoices — with no pre-formatted financials — I reconstructed a full Income Statement, Cash Flow Statement, and Balance Sheet from scratch. A physical inventory count uncovered $22,415 in shrinkage across hotspot vouchers and lottery tickets, triggering a formal balance sheet restatement. The engagement concluded with a strategic expand-or-sell recommendation: Expand, supported by a DSCR of 2.65×, near-zero debt of $5,852, and a growing hotspot revenue trend of +$1,397/month.

1 Business Problem

The owner of Sola Retail Group — a single-location convenience retail store operating since August 2024 — needed a data-driven answer to a high-stakes strategic question: should the business be sold at its current value, or expanded to a second location? No formal financial statements existed. The business ran on POS system exports, bank records, and paper invoices — all unstructured and unreconciled.

As the engaged analyst, I was responsible for building the complete financial picture from raw data and turning it into an actionable decision framework the owner could present to lenders, brokers, or potential buyers.

The Challenge

Without verified financials, the owner had no reliable basis for a sale price, no way to assess loan eligibility for expansion, and no visibility into whether reported profits were real — or inflated by an unrecognized inventory accounting gap. This analysis aimed to (1) verify every dollar of revenue and expense, (2) produce auditable financial statements, and (3) build a scoring model that weighed 12 strategic signals across profitability, debt serviceability, revenue trends, and operational health.

2 Key Questions

This analysis was structured around answering the following business questions:

Is reported revenue accurate? Do POS totals reconcile with daily cash deposits and bank credits across all 577 operating days?
Are the financials reliable? Can a complete, auditable Income Statement, Cash Flow Statement, and Balance Sheet be built purely from raw transactional data?
Is reported inventory correct? Do book inventory balances match physical counts at the SKU level — and if not, what is the shrinkage exposure?
What is the business actually worth? Using multiple valuation methods (SDE multiples, asset-based, revenue multiple), what is a defensible asking price range?
Can the business support a second location? Does cash flow, DSCR, and growth trajectory support a $200K loan for expansion?
Sell or expand? Which path produces better long-term financial outcomes for the owner?

3 Data Overview

The analysis was built entirely from 17 raw operational data sources provided by the client — no aggregated reports, no pre-formatted statements. Every figure in the final financials traces back to a source document.

Attribute	Details
Data Source	Client-provided raw exports: POS systems (Hotspot, Retail, Lottery), bank statements, daily deposit logs, purchase invoices, payroll records, rent, insurance, utilities, tax filings, and physical inventory counts
Time Period	May 2024 – February 2026 (22 months; 19 operational)
Records	153,099 POS transactions · 577 deposit log entries · 1,293 bank entries · 2,487 payroll records · 89 SKU physical count
Key Fields	transaction_date, sku, qty_sold, unit_price, unit_cost, deposit_amount, bank_credit, payroll_amount, invoice_total, physical_count_qty
Limitations	No accrual-basis accounting system existed; financial statements were reconstructed from cash-basis operational records, and inventory balances were derived using transaction data and a single physical inventory count due to the absence of periodic inventory tracking.

4 Methodology

Nine Jupyter notebooks were executed in sequence — each building on the verified outputs of the previous. No hardcoded values: every figure is computed dynamically from source data.

Financial Statement Construction (NB 01–03): Built the Income Statement, Cash Flow Statement, and Balance Sheet from scratch. Revenue aggregated from three POS exports; COGS computed via purchase-invoice methodology; all operating expenses loaded from individual source files. Balance Sheet derived from cash (CFS), purchase-based running inventory totals, prepaid schedules, and equity from the accounting base.
Revenue Cross-Validation (NB 04): Three-layer reconciliation across all 577 operating days: POS daily totals → Daily Deposit Log → Bank Statement. Daily over/short computed for each of the 577 days. Result: −$36.65 cumulative over/short, confirming 93.9% cash handling accuracy and zero end-to-end revenue gap.
Physical Inventory Reconciliation (NB 05–07): NB 05 computes expected-on-hand at the SKU level from cumulative purchases minus POS-consumed units and compares it against the physical count across retail merchandise, hotspot vouchers, and lottery tickets. Findings: 415 hotspot units missing ($10,356 at cost); 320 lottery tickets missing ($5,155 face value); 89 retail SKUs — zero variance. NB 07 presents the inventory reconciliation model, building a month-by-month inventory ledger to demonstrate how regular physical counts detect small discrepancies early before losses accumulate, with hotspot vouchers used as the sample category to illustrate the framework.
Balance Sheet Restatement (NB 06): Applied inventory findings as point-in-time adjustments to the February 2026 balance sheet. Hotspot inventory written down by $10,356; Deferred Lottery Commission liability increased by $5,155. Retained Earnings restated using direct bank-cash RE formula rather than the accounting identity — matching the methodology of the original BS construction.
Valuation & Decision Modeling (NB 08): Computed four valuation methods: SDE multiples (2×/2.5×/3×), asset-based (Total Assets − Liabilities), and Revenue 0.5×. Built full expansion feasibility model: DSCR, loan serviceability, self-fund gap, Location 2 revenue projections, and break-even timeline. Applied a 12-signal weighted scoring model across profitability, cash, debt, revenue trends, and operational health.
Executive Dashboard (NB 09): Consolidated all outputs into a single self-contained HTML dashboard. All charts embedded as base64 — no external dependencies. Designed for non-technical stakeholder review with narrative context alongside every metric.

5 Key Insights

The analysis revealed several actionable insights:

Critical

⚠️ $22,415 in Undetected Inventory Shrinkage

Physical inventory count identified $22,415 in missing stock at selling price — 415 hotspot vouchers ($17,260) and 320 lottery tickets ($5,155). All 89 retail SKUs were fully accounted for. The shrinkage is concentrated in the highest-value, lowest-oversight product lines and had no corresponding record in the books.

So What? The pattern points to a staff access and oversight gap rather than a system error. Without corrective controls, this loss scales proportionally with each new location — reaching ~$44,830/year at two sites.

Critical

📊 Verified Net Worth of $473,425 — Near Zero Debt

After full financial reconciliation, the business holds $231,407 in cash, carries only $5,852 in total liabilities, and has a verified asset-based net worth of $473,425. Cash conversion rate of 90.3% confirms that reported profit is translating into real cash — not just accounting entries.

So What? A near-debt-free business with strong cash reserves and a verified balance sheet is in a strong negotiating position — whether the decision is to sell or to approach a lender for expansion financing.

Important

🚀 Hotspot Revenue Growing at +$1,397/Month — But Profitability Is Declining

Hotspot Wi-Fi vouchers account for 65% of TTM revenue ($790,410) and show a consistent upward trend at +$1,397/month. Other streams are stable but flat. Net income, however, has been declining over the last 3 months — with February 2026 posting a slight loss — creating a tension between revenue growth and bottom-line performance.

So What? Hotspot is the primary growth driver and should anchor any expansion strategy. The declining profitability trend warrants monitoring and stabilisation before capital is committed to a second location.

The Verdict

✅ Data-Driven Recommendation: Expand

A 12-signal weighted scoring model across profitability, cash position, debt serviceability, revenue trends, and operational health returned a 64% signal to Expand. A $200K loan at 7% over 5 years produces a DSCR of 2.65× — well above the 1.25× lender minimum — confirming that current cash flow can comfortably service the debt.

So What? The financial case for expansion is sound. The one prerequisite: inventory controls must be resolved before scaling — otherwise the shrinkage risk compounds across locations and erodes the projected returns.

6 Recommendations

Based on the analysis, here are the actionable recommendations:

Expand — Apply for a $200K Loan Before Self-Funding

With a DSCR of 2.65× — well above the 1.25× lender minimum — the business can comfortably service a $200,000 loan at $3,960/month. This preserves $164,449 in available cash as a safety buffer rather than depleting reserves. A loan is the financially superior expansion path; attempting to self-fund adds 22+ months of unnecessary delay.

Fix Inventory Controls Before Opening Location 2

Implement daily physical count verification for hotspot vouchers and lottery tickets — the two lines with confirmed shrinkage. Introduce access controls and a dual-custody procedure for high-value denominations. This is a non-negotiable prerequisite: the $22,415 shrinkage at one location doubles to ~$44,830/year across two locations without intervention.

If Selling Instead — Price at $444,292–$533,150 and Disclose Proactively

The recommended asking range anchors to SDE multiples (2.5×–3.0×), with the asset-based value of $473,425 as an independent reference. Present the inventory restatement proactively in the sale package — buyers who discover it in due diligence use it to renegotiate aggressively; buyers who receive it upfront treat it as a sign of well-managed, transparent books.

Monitor the 3-Month Net Income Decline Before Committing Capital

Average net income over the last 3 months was $5,741 — declining, with February 2026 posting a −$579 loss. Before signing a lease for Location 2, confirm this is a temporary dip and not the start of a structural margin compression. Set a trigger: if 3 of the next 4 months are profitable, proceed with loan application.

Expected Business Impact

Valuation Clarity: Established a defensible asking range of $444,292–$533,150 vs. no baseline — enabling a confident, evidence-backed sale or financing conversation
Shrinkage Recovery: Identifying and controlling $22,415/year in inventory loss prevents a doubling to ~$44,830 across two locations if unchecked
Loan Readiness: DSCR of 2.65× confirms $200,000 loan eligibility — translating to an expansion that generates an estimated $102,171 in combined annual net income vs. single-location trajectory
Revenue Verified: $1.9M in revenue confirmed end-to-end with −$36.65 total variance — giving any buyer or lender full confidence in top-line figures

7 Dashboard & Visuals

A self-contained HTML executive dashboard was built as the capstone deliverable — consolidating all 9 notebook outputs into a single, shareable file with no external dependencies. All charts are embedded as base64 images. Designed for non-technical stakeholder review.

SRG Executive Dashboard — Executive Overview

📊 Open Executive Dashboard View Code on GitHub

Business Health — Revenue by Stream, Net Income Trend, Cash Position

Inventory & Shrinkage Discovery — Reconciliation by Category

8 Process Documentation

A detailed methodology document covering the full analytical pipeline — from data ingestion and financial statement construction to inventory reconciliation, restatement, and the final expand-or-sell recommendation.

View Full Project Documentation

9 Data Dictionary / KPI Definitions

Key metrics and variables used throughout this analysis:

Metric / Variable	Definition
TTM Revenue	Trailing 12-Month total revenue across all four streams (Hotspot, Retail, Lottery, ATM). Used as the primary revenue baseline for valuation and DSCR. TTM = $1,223,329.
SDE (Seller's Discretionary Earnings)	Net Income + Owner Withdrawals added back. Represents total economic benefit to a working owner-operator. Used as the basis for earnings-multiple valuation. SDE = $177,717.
DSCR (Debt Service Coverage Ratio)	Annual Net Income ÷ Annual Debt Service (total loan payments). Measures ability to cover loan obligations from operating income. Lender minimum: 1.25×. SRG DSCR: 2.65×.
Gross Margin	Gross Profit ÷ Revenue. Measures the percentage of revenue retained after direct cost of goods sold. TTM Gross Margin: 34.0%.
Cash Conversion Rate	Net Cash from Operating Activities ÷ Net Income. Measures how much reported profit converted to actual cash. 19-month rate: 90.3%. A rate below 100% indicates timing differences (prepaid items, accruals).
Over/Short	Daily Deposit Amount minus POS Daily Sales Total. A positive value means more cash was deposited than POS recorded; negative means a shortage. Cumulative over/short = −$36.65 across 577 days.
Qty Variance (Inventory)	Expected-on-hand (cumulative purchases − POS-consumed units) minus Physical Count quantity. A positive variance indicates missing units (shrinkage). Hotspot: 415 units missing. Retail: 0 units missing.
Loss at Cost vs. Loss at SP	Loss at Cost = missing units × unit cost (balance sheet impact). Loss at SP = missing units × selling price (revenue/economic impact). Hotspot: $10,356 at cost, $17,260 at SP. Lottery: $5,155 at face value (consignment — no balance sheet asset).
Prepaid Lottery Tickets	Cumulative ACH payments made to the state lottery authority minus cumulative lottery ticket sales revenue recognized. Represents prepaid lottery obligation carried as an asset on the balance sheet. Feb 2026: $12,822.
Deferred Lottery Commission	Cumulative commission credits received from the state minus commission earned per POS. Represents commission received in advance that has not yet been recognized as income. Restated Feb 2026: $5,852.
Safety Reserve	3 months of average operating expenses held in cash as a buffer before committing to expansion capital deployment. SRG Safety Reserve: $66,958. Available cash after reserve: $164,449.
Asset-Based Valuation	Total Assets − Total Liabilities = Net Worth. Reflects the liquidation value of the business; most appropriate when the balance sheet is strong relative to earnings. SRG Asset-Based: $473,425.

10 Next Steps

Future improvements and extensions planned for this analysis:

Automate Monthly Reporting Pipeline

Convert the 9-notebook batch into a scheduled pipeline that auto-generates updated financials each month from a POS export drop. This would give the owner a self-refreshing dashboard without requiring manual re-runs — reducing reporting time from hours to minutes.

Build Location 2 Forecast Model

Extend the expand/sell model with a month-by-month Location 2 projection — incorporating ramp-up curves, seasonal patterns from Location 1, and sensitivity analysis on revenue assumptions. This would sharpen the 75-month break-even estimate and surface risk scenarios.

Implement XmR Control Charts for Cash Handling

Replace the static over/short summary with Statistical Process Control (XmR) charts that signal when daily variances are outside normal limits. This creates an early warning system for cash handling issues rather than relying on cumulative hindsight reviews.

Migrate to POS-Consumption-Based COGS for Hotspot

Work with the owner to update the accounting methodology from purchase-invoice COGS to POS-consumption COGS for hotspot vouchers. This eliminates the $34,431 overstatement gap structurally — so future financials are accurate at the source rather than requiring post-period restatements.

Conduct Quarterly Physical Counts as Ongoing Control

Design a lightweight quarterly inventory count protocol for hotspot and lottery lines — with a standardized reconciliation template that flags SKU-level variances automatically. The first follow-up count would confirm whether the current shrinkage was a one-time event or a recurring issue.

View Full Project Code

9 Jupyter notebooks · 17 data sources · 21 output files · Full project documentation

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Note: This case study uses synthetic datasets designed to reflect realistic retail business patterns. All financial figures are computed dynamically from raw transactional data. Recommendations are analytical in nature and should be considered alongside the owner's risk tolerance, market conditions, and operational factors not captured in the data.