2. Monte Carlo Simulation with @Risk
Risk Assessment to capture uncertainty due to variability in business.
Input derived from
distribution fitted
from historical data
Or Experience.
Average(“expected”)
outcome does not capture
the uncertainty in outcome.
Output
Distribution
3. • JarirBookstore buys 2017 Calendars for SAR 7.5 and sells them @ SAR
10 and gets refunded SAR 2.5 for all unsold calendar.
• Scenario 1: Demand follows a triangular distribution:
Minimum: 100; Most Likely: 175; Maximum: 300
• Decision: What is the optimal quantity of calendar to order in Dec’16.
News Vendor Problem
4. • Two years of historical data of any new model introduced in market:-
i. Actual Demand
ii. Early application ( Guests who booked),
iii. Delivered to Guests who booked.
Fitting Distribution from Historical Data
5. Cash Balance Models
• All companies track their cash balance over time.
• As specific payments come due, companies sometimes need to take out
short-term loans to keep a minimal cash balance.
6. • Objective: To simulate Entson’s cash flows and the loans the company must take out to
meet a minimum cash balance.
• Solution: Entson Company believes that its monthly sales from November of the current
year to July of next year are normally distributed with the means and standard deviations
given in the table below.
• Each month Entson incurs fixed costs of $250,000.
• In March, taxes of $150,000 and in June taxes of $50,000 must be paid.
• Dividends of $50,000 must also be paid in June.
• Entson estimates that its receipts in a given month are a weighted sum of sales from the
current month, the previous month, and two months ago, with weights 0.2, 0.6, and 0.2:
• Materials and labor needed to produce a month’s sales must be purchased one month in
advance, and the cost of these averages to 80% of the product’s sales.
Cash Balance.xlsx (slide 1 of 3)
7. • At the beginning of January, Entson has $250,000 in cash, and the
company wants to ensure that each month’s ending cash balance never
falls below $250,000.
• This means that Entson might have to take out short-term (one-month)
loans. The interest rate on a short-term loan is 1% per month.
• At the beginning of each month, Entson earns interest of 0.5% on its
cash balance.
• The completed simulation model is shown to the right.
Cash Balance.xlsx (slide 2 of 3)
8. • Set the number of iterations to 1000 and the number of simulations to
1. Then run the simulation in the usual way.
• After running the simulation, obtain the summary results and the
summary trend chart shown below.
Cash Balance.xlsx (slide 3 of 3)
9. Case: Maintaining Minimum Cash Balance
Known:
1. Monthly sales during the period from November’16 to July’17 is normally distributed with known means and SD.
2. Fixed cost estimated at $250,000 monthly.
3. Taxes-> March: $150,000 and June:$50,000.
4. Dividend-> June: $50,000
5. Raw Material Cost = 80 % of Product Sale and incurred one month lead before sales.
6. Receipts in a given month(Rt) = Weighted Sum of Sales from the current and last two months ( St, St-1, St-2)
Current month: Weight(Wt) = 0.2; previous month : Weight(Wt-1) = 0.6; two months ago : Weight(Wt-2) = 0.2
Rt = Wt * St + Wt-1 * St-1 + Wt-2* St-2
7. The company must maintain minimum Cash Balance of $250,000.
8. If cash balance falls below minimum cash threshold, the company resorts to a cash revolver for deficit amount and returned
with in the following month with interest @ 1% per month.
9. The company earns interest of 0.5% on its cash balance.
Estimate:
i. Maximum Loan the company need to take out to meet its desired minimum cash balance.
ii. How the loan will vary over time.
iii. Total interest paid on the loan.
10. 21%
22%
18%
17%
23%
Payment Week Distribution
0: Didn’t Pay within the month 1: Paid In First Week
2: Paid In Second Week 3: Paid In Third Week
4: Paid In Fourth Week
Payment Week Distribution
W1
W2
W3
W4 0
Payment week distribution can help in organizing our collection effort if we have
a model to predict each Guests’ payment behavior.
11. Actionable insights from predicted Advance payer behavior.
Strategic Collection Effort helps us to identify advance Guests and are handled accordingly
to maintain their payment behavior and avoid alienating these profitable customers.
Treatment of Advance Guests
العزيز عميلنا
سد خدمة طريق عن أو فروعنا من بأي السيارة إيجار بسداد نذكركماد
االئتماني سجلكم جودة على ًاحفاظ
للتمويل جميل عبداللطيف
(ii) After waiting for 2/3 days time to react on SMS,
second SMS as reminder.
(iii) After waiting for 2/3 days time to react on SMS, if
still not paid, collector calls to remind.
At end of each week, we check Guest’s payment
status whose maturity time to pay within that
week has arrived. If not paid then (i) send SMS.
12. Actionable insights from predicted Due Guest’s payment behavior.
Strategic Collection Effort helps us to identify and intensify collection efforts early
on, and call Guests only when necessary: Optimize calling effort.
العزيز عميلنا
خدمة طريق عن أو فروعنا من بأي المستحق السيارة إيجار سداد سرعة نأمل
االئتماني سجلكم جودة على ًاحفاظ سداد
للتمويل جميل عبداللطيف
Right Guest to call, at the Right Time, with Right Intensity.
• Priority list of calling: Overdue Customers from previous month.
Guests are treated by calls coupled with SMS:-
• At end of each week, we check Guest’s payment status whose
maturity time to pay within that week has arrived. If not paid
then initiate collection effort.
• Guest are ranked in the calling list according to their paying
behavior so that the collection agent can adapt their call
tone/message.
13. If Guests are repeated delinquent, then following SMS is
used instead of wasted calls, and delegate to legal.
Actionable insights from predicted Overdue Guest’s payment behavior.
Strategic Collection Effort helps us to identify fruitless calls and replace with other
effective treatment strategy like starting with SMS and followed by actual legal handling.
المحترم عميلنا
بالرياض التنفيذ محكمة أمام لمطالبتكم القانونية للشؤون ملفكم بتحويل نفيدكمبموجب
منكم الموقعة ألمر سندات
للتمويل جميل عبداللطيف
Week1
onwards
Treatment of Potential Overdue Guests
Since most Guest pay their due in later part
of the month, the first week is utilized by
collectors to call those who are predicted to
default within the month window ( potential
overdue).
14. • Guest likelihood to “survive” steadily
decreases, till it reaches 0, implying, the Guest
should have paid by then.
• Potential Delinquent customers’ probability to
“survive” will not reach 0, within an identified
window (beyond which Guest is Overdue).
Survival Analytics to predict Guest payment behavior.
Survival Model helps us to predicts:-
i. Which Guest will pay; ii. When will they pay; iii. Ranking of bad defaulter
15. Guest Mari
talSt
atus
Id
Nati
onali
tyId
WorkPl
aceId JobId
GuestG
overnm
entId Age
NoOfDe
penden
ts Income
GuestG
enderId
NoOfCo
ntracts
Ope
ning
Due
SAR
Openi
ngAdv
ance
U/C* Predicted
1008783530 1 1 600 130 2 38 0 19000 1 1
…. ….
0.854 0
How two extreme Guests are predicted( Defaulter):-
Best rated Guest in August 2016.
*U/C=Used Period/Contract Period
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
4 4 4 3 4 4 4 4 3 3 0 2 3 3 3 2 3 2 2 2 1 2 1 1 1 1 1 0 0 0 0 0 0 0 0
Used Period
Paid in Week
16. Guest Mari
talSt
atus
Id
Nati
onali
tyId
WorkPl
aceId JobId
GuestG
overnm
entId Age
NoOfDe
penden
ts Income
GuestG
enderId
NoOfCo
ntracts
Ope
ning
Due
SAR
Openi
ngAdv
ance
U/C* Predicted
2344026428 1 40 430 105 1 52 0 50000 1 1
…. ….
0.812 1
How two extreme Guests are predicted( Week 1 Payer):-
Best rated Guest in August 2016.
*U/C=Used Period/Contract Period
Used Period
Paid in Week
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
1 0 4 1 3 4 4 2 2 2 1 1 0 4 1 3 1 4 1 4 1 1 4 4 1 3 1 1 1 1 1 1 1 2 1 1 1 1 1
17. Results Oct 2015.
661 Guests
Total
Paid 508 19 527
Not Paid 40 94 134
Total 548 113 661
Pay Not PayActual
Predicted
Model Accuracy= (508+94)/661=
Model Misclassification Rate= (40+19)/661=
96.39%Paid Prediction Rate=508/527=
91.07%
70.15%
8.92%
Unpaid Prediction Rate=94/134=
Total Due reported = SAR 159,517
Average Due of Predicted cases who indeed did not pay
= SAR 1,697.
Assuming 30% chances of recovery from these cases,
Overdue recovery= 0.3*1697*94=47,855
Overdue reduction = 45,855 SAR
18. Month Year Week1 Week2 Week3 Week4 till end Defaulter
October 2015 66.26% 69.14% 85.02% 59.91% 91.07%
November 2015 84.37% 66.28% 73.08% 73.95% 93.78%
December 2015 75.64% 67.28% 65.16% 75.21% 93.48%
January 2016 62.92% 79.25% 88.35% 73.49% 91.57%
February 2016 61.51% 78.80% 87.91% 76.20% 81.14%
March 2016 60.12% 69.19% 89.56% 76.89% 70.81%
April 2016 60.62% 70.62% 88.12% 80.75% 92.62%
May 2016 60.12% 69.19% 89.56% 76.89% 70.80%
June 2016 69.86% 63.59% 79.55% 80.85% 87.94%
July 2016 64.50% 83.25% 86.20% 77.95% 86.44%
August 2016 68.08% 64.43% 78.33% 71.73% 89.28%
September 2016 75.21% 74.15% 84.61% 68.74% 70.86%
October 2016 72.59% 88.08% 89.51%
Collection Optimization Model Accuracy Results.
The accuracy matters most in the defaulter prediction, where average accuracy is 85%.
Weekly Prediction helps in load balancing calls for collectors.
19. Month Year
Paid
Prediction
Rate
UnPaid
Prediction
Rate Quality
October 2015 96.39% 70.15%
November 2015 97.61% 79.45%
December 2015 98.59% 72.46%
January 2016 97.27% 75.63%
February 2016 77.80% 92.94%
March 2016 93.12% 71.64%
April 2016 97.83% 70.39%
May 2016 85.05% 22.40%*
June 2016 89.91% 81.68%
July 2016 85.65% 87.40%
August 2016 92.15% 10.38%*
September 2016 62.95% 89.96%
Prediction Accuracy Affecting Revenue from Collection Effort
The blips in Unpaid Prediction rate in May’16 and Aug’16 is explained by the sudden
change in trend of overdue. However as the trend restores, the accuracy is restored.
20. 1. This results is under testing at Amir Soultan Branch with two collectors
level for November.
2. Starting December’2016, the solution should be extended to the entire
Amir Soultan Branch to halt the rising overdue.
Above steps can be implemented with “0” cost.
3. After gaining the confidence and improvement in the model, the scope
will be extended to allJan’2017 onwards this solution will be extended
to sub-region with 1 SAS license and 1 server, dispatching weekly lists
to each collector.
4. Mar’2017, after review of result, SAS consultants will be required to
architect this solution for entire region. License cost will remain same,
server cost will increase.
Way Forward:-
