Case Discussion: Toby Biotech & Tableau

Using Data Visualization to Explore Receivables Risk

SMM635 – Data Visualization

Module A

The Context of This Case

A1 · Who Is Toby Biotech?

• Toby Biotech Inc. is a fictional global life sciences equipment firm.
• Main product lines:
  • Instruments
    
  • Consumables
    
  • Software
    
  • Services
• Main customers:
  • Research labs
    
  • Hospitals and clinics
    
  • Universities and public-sector organisations
• Toby sells high-value products on credit to professional customers,
  so receivables are a natural and important part of the business.

A2 · Economic Background

• The case takes place during a period of global economic slowdown:
  • Major markets are in or near recession.
  • Customers’ cash flows are under pressure.
• Typical effects in this environment:
  • More sales are done on credit instead of cash.
  • Customers take longer to pay.
  • Companies see more product returns and stronger pressure for price concessions.
• In this setting, receivables become a key place where financial risks can build up,
  and some reported numbers may become less reliable.

A3 · Toby’s 20X3 Financial Snapshot (Unaudited)

• Net sales revenue (20X3): about $1.132 billion
• Pre-tax income (20X3): about $218 million
• Year-end accounts receivable (AR):
  • $177 million at 31 Dec 20X3
    
  • Increase of $19 million (≈12%) over the prior year
• Sales vs. AR growth
  • Sales have grown by roughly 10% year-on-year.
    
  • AR has grown by roughly 12%, so receivables are increasing faster than sales.
• In a weak economy, this pattern raises questions about
  how quickly customers are paying and how realistic the reported AR balance is.

Module B

Core Accounting Ideas (for Non-Accounting Students)

B1 · Accounts Receivable (AR)

• Accounts Receivable (AR)
  • Money that customers owe Toby for goods or services already delivered
    
  • Reported as an asset on the balance sheet
• Key idea:
  • AR is a good asset only if customers actually pay.
    
  • If they do not pay, AR turns into credit losses.
• In this case, we will use Tableau to understand where this AR sits:
  • Which countries and cities
    
  • Which sales offices
    
  • Which customers

B2 · Adjusting AR for Risk: ACL & RRA

• Allowance for Credit Losses (ACL)
  • Management’s estimate of receivables that will not be collected
    
  • Higher ACL → more bad-debt expense → lower profit and lower net AR
• Reserve for Returns and Allowances (RRA)
  • Expected future product returns and post-sale price concessions
    
  • Higher RRA → lower revenue → lower profit and lower net AR

Net AR = Gross AR – ACL – RRA

• If management is too optimistic, ACL and RRA may be too low,
  which makes AR and profit look better than they really are.

B3 · Why CECL Was Introduced

• Historically, many firms used an “incurred loss” model:
  • Losses were recognised mainly when there was clear evidence of trouble
    (missed payments, defaults, downgrades, etc.).
  • This often made loss recognition too slow and too small,
    especially before and during the 2008 financial crisis.
• Regulators wanted a more forward-looking approach:
  • One that forces firms to consider not just past problems,
    but also what is likely to go wrong in the future.

B4 · The CECL Model

• Under the Current Expected Credit Loss (CECL) model, firms must:
  • Estimate lifetime expected credit losses on receivables.
  • Use:
    • Past experience
      
    • Current conditions
      
    • Reasonable and supportable forecasts
• This means:
  • The allowance reflects not only what has already gone bad,
    but also what is expected to go bad over the life of the receivable.
• In our case:
  • In a weakening economy, CECL pushes Toby to raise ACL earlier and more aggressively, rather than waiting for customers to actually default.
  • If AR is growing faster than sales but ACL does not respond, the reported AR and profit may be too optimistic.

B5 · Revenue Recognition & Timing

• Revenue recognition determines:
  • When and how much revenue is recorded
• Aggressive revenue recognition:
  • Records revenue earlier in the process
    
  • Can temporarily inflate revenue, AR, and EPS
• Under CECL, even if revenue is recognised early:
  • Companies are expected to record expected credit losses on those receivables at the same time.
  • If revenue is recognised aggressively without adjusting ACL,
    the financial statements can become misleading.
• In a downturn, pressure to “hit the numbers” can still lead to:
  • Looser credit policies
    
  • Delayed recognition of losses in practice
    
  • Heavy use of returns and allowances to quietly adjust results

Module C

Stakeholders & Their Incentives

C1 · Toby Management’s Perspective

• Key players: CFO, Controller, CRO
• Main objectives:
  • Meet or beat analyst EPS expectations
    
  • Maintain a strong credit rating for a planned bond issue
• Under pressure, management may:
  • Stretch credit terms to keep sales volume up
    
  • Delay recognising credit losses and returns
    
  • Underestimate ACL and RRA to support reported profit

C2 · TRG Audit Team’s Perspective

• TRG LLP is Toby’s external auditor.
• Main responsibility:
  • Provide reasonable assurance that the financial statements are fairly presented
• In this engagement, TRG:
  • Sets overall materiality at about 4% of pre-tax income
    
  • Focuses on risk of overstatement in:
    • Net AR
      
    • Revenue
      
    • ACL and RRA
• The audit team must decide:
  • Where to concentrate audit effort
    
  • What extra evidence to collect

C3 · Investors and Lenders

• Investors and bondholders care about:
  • Quality of earnings, not just the earnings level
    
  • Ability to generate cash and repay debt
• Small changes around EPS targets or leverage ratios can:
  • Move Toby’s share price
    
  • Affect the interest rate demanded by lenders
• Because of this, the way Toby estimates ACL and RRA,
  and how realistic its revenue is, matters directly to capital markets.

Module D

From Data Tables to Risk Patterns

In this section, we will:

• Dive into Toby’s receivables data
• See what kinds of risks and patterns are hiding in the ledgers
• Use a few simple visualisations to understand the data

D1 · What Is in This Dataset?

D1 · What Is in This Dataset?

This case dataset brings together four types of information:

• Client information
  
• Invoice information
  
• Return information
  
• Allowance information

D1a · Client Information

• Which organisations are Toby’s customers
  
• Where they are located
  • Customer city
    
  • Customer country
    
• Which sales office is responsible for each customer
  • Sales office city
    
  • Sales office country

D1b · Invoice Information

• Individual sales transactions:
  • Invoice ID / invoice number
    
  • Invoice date
    
  • Invoice amount
    
• These invoices are what build up the receivables balance:
  • More invoices → higher AR
    
  • Larger invoices → more exposure in a single transaction

D1c · Return Information

• When customers send products back:
  • Return date
    
  • Days between invoice and return
    
• How much revenue is reversed:
  • Return amount
    
  • Invoice net of return (what is left after returns)

D1d · Allowance Information

• Post-sale price concessions and credits:
  • Allowance amount
    
  • Allowance date
    
  • Days between invoice and allowance
    
• How large the concession is compared with the invoice:
  • Allowance percentage
    
  • Invoice net of allowance

D2 · Four Lenses on the Same Story

We will explore Toby’s receivables through four blocks:

1. Block 1 – Customer balance
   • Where is the money? (countries, cities, sales offices, customers)
2. Block 2 – Invoices
   • How big are the invoices, and how old are the receivables?
3. Block 3 – Returns
   • Are products coming back? When, and from whom?
4. Block 4 – Allowances
   • Where is Toby “cutting price after the fact”?

For each block we will:

• Start with clear business questions
  
• Build a few simple Tableau visualisations
  
• Discuss the insights from the visualisations

D3 · Block 1 – Customer Balance Questions

When you explore the customer balance data, think about:

• Which countries or cities show the largest AR balances?
• Which sales offices are responsible for the biggest AR balances?
• Are there a few customers that account for a large share of total AR?
• If one country / city / sales office / customer ran into trouble, how much of Toby’s receivables would be at risk?

D5 · Block 2 – Invoices Questions

When you explore the invoice data, think about:

• Are most invoices small and similar,
  or do a few very large invoices dominate the total?
• How are invoices distributed over time
  (by month or by quarter)? Do you see any spikes?
• Do you see signs of older, slow-moving invoices
  that have not been collected for a long time?

D6 · Block 3 – Returns Questions

When you explore the returns data, think about:

• Do most returns happen soon after the sale,
  or do many returns happen a long time later?
• Which customers have high total return amounts
  or very frequent returns?
• Are there invoices where returns remove most or all of the original sale?

D7 · Block 4 – Allowances Questions

When you explore the allowances data, think about:

• When are allowances granted:
  mostly shortly after invoicing, or often long after the sale?
• How large are allowances as a percentage of the original invoice amount?
• Are there customers who regularly receive large concessions?
• Do you see any unusual patterns:
  for example, allowances that almost wipe out the original invoice?

D8 · Module D – Reflection Questions

After you have explored all four blocks, think about:

• Where does the receivables risk seem most concentrated
  (by region, office, customer, or invoice type)?
• Which patterns would make you doubt whether ACL or RRA are high enough?
• If you were the audit team,
  which customers, regions, or time periods would you want to investigate further,
  based on the patterns you saw in the data?